geometryintersects
1 if intersects
dist
3
false
geometryintersects
second geometry
geom2
false
2
false
rectangle
rectangular geometry variable
5
<p style="margin-top: 0">
<br>
<u><b>Description</b></u><br><br><b>rect</b> defines a rectangular
region from user-defined coordinates within an input variable, creating
a geometry variable "<i>rectangle</i>" as a result. To define the
rectangle, the user provides four arguments in the following order: <i>xlo</i>,
<i>ylo</i>, <i>xhi</i>, and <i>yhi.</i><br><br>
</p>
false
ylo
2
low value of second coordinate
defines a rectangle geometry
rect
defines a rectangle geometry
xhi
false
high value of first coordinate
3
1
xlo
low value of first coordinate
false
4
yhi
false
high value of second coordinate
returns 1 if geometries intersect
returns 1 if geometries intersect
returns 1 if geometries intersect
1
first geometry
false
geom1
2
asind
false
arc sine in degrees
x
1
sine argument
false
arc sine function
asind
asind
arc sine function
arc sine function
6
false
geometry clipped to given rectangle
geom'
clips geometry to given rectangle
3
false
y0
south
x0
2
west
false
geometryclipbyrect
geometry
geom
1
false
5
false
y1
north
clips geometry to given rectangle
geometryclipbyrect
false
x1
east
4
clips geometry to given rectangle
first geometry
1
geom1
false
computes distance between geometries
2
geom2
false
second geometry
geometrydistance
geometrydistance
computes distance between geometries
3
false
dist
distance between geometries
computes distance between geometries
1
BofA=C1
A
Variable to be converted
false
medianover3
var'
false
median over the given grids
3
returns difference
false
2
second geometry
geom2
geometrydifference
returns difference
false
difference of geom1 and geom2
difference
3
false
first geometry
1
geom1
returns difference
geometrydifference
envelope
false
2
geom2
geom1
false
first geometry
1
returns envelope g2 of g1
geometryenvelope
geometryenvelope
returns envelope g2 of g1
returns envelope g2 of g1
geometrycrosses
returns 1 if geometries cross
returns 1 if geometries cross
returns 1 if geometries cross
geom1
false
1
first geometry
2
geom2
second geometry
false
geometrycrosses
false
1 if crosses
3
dist
returns 1 if g1 is within g2
false
geom2
second geometry
2
geometrywithin
3
1 if within
dist
false
returns 1 if g1 is within g2
geometrywithin
1
geom1
false
first geometry
returns 1 if g1 is within g2
false
intersection
3
intersection of geom1 and geom2
returns intersection
returns intersection
returns intersection
geometryintersection
geometryintersection
false
2
geom2
second geometry
first geometry
1
false
geom1
atan2
two argument arc-tangent function
arc tangent function
3
arc tangent in radians
atan
false
atan2
arc tangent function
1
Y
false
numerator
false
2
X
denominator
returns boundary g2 of g1
returns boundary g2 of g1
geometryboundary
geometryboundary
2
boundary
geom2
false
false
first geometry
1
geom1
returns boundary g2 of g1
1 if g1 covers g2
false
dist
3
geometrycovers
returns 1 if g1 covers g2
returns 1 if g1 coversg2
second geometry
2
geom2
false
geometrycovers
returns 1 if g1 covers g2
false
first geometry
1
geom1
Geometry A contains Geometry B if and only if no points of B lie in the exterior of A, and at least one point of the interior of B lies in the interior of A. An important subtlety of this definition is that A does not contain its boundary, but A does contain itself. Contrast that to ST_ContainsProperly where geometry A does not Contain Properly itself.
Returns TRUE if geometry B is completely inside geometry A. For this function to make sense, the source geometries must both be of the same coordinate projection, having the same SRID. ST_Contains is the inverse of ST_Within. So ST_Contains(A,B) implies ST_Within(B,A) except in the case of invalid geometries where the result is always false regardless or not defined.
geometrycontains
1 if contains
3
dist
false
1
geom1
false
first geometry
returns 1 if g1 contains g2
Returns true if and only if no points of B lie in the exterior of A, and at least one point of the interior of B lies in the interior of A.
Returns
geometrycontains
false
second geometry
geom2
2
computes area of geometries
geometryarea
computes area of geometries
computes area of geometries
geometryarea
area of geometries
area
false
2
false
geom
geometry
1
3
false
geom'
geometry simplified according to tolerance
geo1
1
false
geometry
2
tolerance to use to simplify geometry
tolerance
false
geometrytoposimplify
geometrytoposimplify
simplifies geometry by given tolerance
simplifies geometry by given tolerance
simplifies a geometry, ensuring that the result is valid geometry having the same dimension and number of components as the input.
The simplification uses a maximum distance difference algorithm similar to the one used in the Douglas-Peucker algorithm.
In particular, if the input is an areal geometry ( Polygon or MultiPolygon ),
the result has the same number of shells and holes (rings) as the input, in the same order, and
the result rings touch at no more than the number of touching points in the input (although they may touch at fewer points)
geometrylength
computes length of geometries
geometrylength
computes length of geometries
2
length of geometries
length
false
geometry
geom
false
1
computes length of geometries
arc tangent function
arc tangent function
2
atand
false
arc tan in degrees
false
1
x
tangent argument
atand
atand
arc tangent function
geometrydisjoint
returns 1 if geometries are disjoint
1 if disjoint
3
dist
false
returns 1 if geometries are disjoint
geometrydisjoint
1
geom1
false
first geometry
geom2
false
2
second geometry
returns 1 if geometries are disjoint
returns 1 if g1 covered by g2
geometrycoveredby
false
first geometry
1
geom1
geometrycoveredby
3
1 if g1 covered by g2
dist
false
false
second geometry
2
geom2
returns 1 if g1 covered by g2
returns 1 if g1 covered by g2
arc cosine function
arc cosine function
false
acosd
arc cosine in degrees
2
x
cosine argument
false
1
arc cosine function
acosd
acosd
2
false
readgrib2
int
false
3
atan
arc tangent in degrees
arc tangent function
numerator
Y
1
false
arc tangent function
two argument arc-tangent function
atan2d
X
2
false
denominator
atan2d
returns union
returns union
geometryunion
geometryunion
1
geom1
first geometry
false
false
union
union of geom1 and geom2
3
geom2
false
second geometry
2
returns union
filters geometry by flag
filters geometry stream by flag: geometries corresponding to zero values of the flag are removed by being marked as missing
flag
2
false
flag to control filter
flagfiltergeom
filters geometry by flag
3
geometry filtered by flag
false
geom'
flagfiltergeom
false
1
geometry to be filtered
geom
returns 1 if geometries touch
returns 1 if geometries touch
geometrytouches
geom1
first geometry
false
1
returns 1 if geometries touch
geom2
false
2
second geometry
3
1 if touches
false
dist
geometrytouches
Computes the geometric center of a geometry, or equivalently, the center of mass of the geometry as a POINT. For [MULTI]POINTs, this is computed as the arithmetic mean of the input coordinates. For [MULTI]LINESTRINGs, this is computed as the weighted length of each line segment. For [MULTI]POLYGONs, "weight" is thought in terms of area. If an empty geometry is supplied, an empty GEOMETRYCOLLECTION is returned. If NULL is supplied, NULL is returned.
The centroid is equal to the centroid of the set of component Geometries of highest dimension (since the lower-dimension geometries contribute zero "weight" to the centroid).
This method implements the OpenGIS Simple Features Implementation Specification for SQL 1.1.
This method implements the SQL/MM specification. SQL-MM 3: 8.1.4, 9.5.5
returns centroid g2 of g1
geometrycentroid
geometrycentroid
returns centroid g2 of g1
false
geom2
centroid
2
first geometry
1
false
geom1
geom1
false
1
first geometry
1 if equal
false
dist
3
returns 1 if g1 equals g2
false
second geometry
geom2
2
returns 1 if g1 equals g2
geometryequalsexact
geometryequalsexact
returns 1 if g1 equals g2
false
Grids over which median will be derived
2
grids
medianover2
returns 1 if g1 equals g2
false
geom1
1
first geometry
returns 1 if g1 equals g2
1 if equal
3
false
dist
2
second geometry
false
geom2
geometryequals
geometryequals
returns 1 if g1 equals g2
var
false
Variable from which median will be derived
1
medianover1
returns convex hull g2 of g1
geom2
false
2
convex hull
first geometry
1
false
geom1
returns convex hull g2 of g1
returns convex hull g2 of g1
geometryconvexhull
geometryconvexhull
geom1
first geometry
false
1
3
1 if overlaps
dist
false
geometryoverlaps
geometryoverlaps
returns 1 if g1 overlaps g2
returns 1 if g1 overlaps g2
returns 1 if g1 overlaps g2
second geometry
geom2
false
2
simplifies geometry by given tolerance
simplifies geometry by given tolerance
geometrysimplify
2
tolerance
false
tolerance to use to simplify geometry
1
geo1
false
geometry
simplifies a geometry, ensuring that the result is valid geometry having the same dimension and number of components as the input.
The simplification uses a maximum distance difference algorithm similar to the one used in the Douglas-Peucker algorithm.
In particular, if the input is an areal geometry ( Polygon or MultiPolygon ),
the result has the same number of shells and holes (rings) as the input, in the same order, and
the result rings touch at no more than the number of touching points in the input (although they may touch at fewer points)
3
geometry simplified according to tolerance
geom'
false
geometrysimplify
116
the number of station data points that must be included within the influence radius of a gridpoint for an analysis value to be calculated for that gridpoint. The default value is 3.
minstns
true
minimum longitude for output grid
xmin
107
true
115
RANGEEDGESTEP
true
Performs a cressman objective analysis to create gridded data from station data
data values at the stations
data
103
minimum latitude for regridding
112
true
ymin
station ids
104
IWMO
the factor by which the average minimum station separation distance (calculated within the routine) is multiplied on the first pass of the cressman scheme to establish the influence radius for the pass. Default is 4.
true
pass1
117
ymax
true
113
maximum latitude for output regridding
110
true
RANGEEDGESTEP
griddeddata
120
returned gridded data
xmax
maximum longitude for output regridding
true
108
cressman:
105
start of cressman scheme parameters
longitude
101
longitude coordinates for stations used in the analysis
118
the factor by which the average minimum station separation distance (calculated within the routine) is multiplied on the second pass of the cressman scheme to establish the influence radius for the pass. Default is 2.5
true
pass2
ystp
true
114
latitude step for output regridding
106
X
true
102
latitude
latitude coordinates for stations used in the analysis
111
Y
true
true
109
xstp
longitude step for output regridding
<br>
<u><b>Detailed Syntax</b></u><br><br><i>longitudes</i><br><i>latitudes</i><br><i>data</i><br><b>[</b><i>IWMO</i><b>]</b>
<b>cressman:</b><br><i>n</i> <b>minstns</b><br><b>X</b> <i>xmin</i> <i>xmax</i>
<i>xstep</i> <b>RANGEEDGESTEP</b><br><b>Y</b> <i>ymin</i> <i>ymax</i> <i>ystep</i>
<b>RANGEEDGESTEP</b><br><i>n1</i> <b>pass1</b><br><i>n2</i> <b>pass2</b><br><i>n3</i>
<b>pass3</b><br><b>:cressman</b><br><br><u><b>Input</b></u><br><br><i>longitudes</i>
is a stream of longitude coordinates for stations used in the analysis<br><i>latitudes</i>
is a stream of latitude coordinates for stations used in the analysis<br><i>data</i>
is a stream of data values at the stations<br><i>IWMO</i> is the name of
the grid of station identifiers<br><br><u><b>Parameters</b></u><br><br><i>n
</i><b>minstns</b> : <i>n</i> is the number of station data points that
must be included within the influence radius of a gridpoint for an
analysis value to be calculated for that gridpoint. The default value is 3.<br><br><b>X</b>
<i>xmin</i> <i>xmax</i> <i>xstep</i> <b>RANGEEDGESTEP</b> : specification
of the east-west domain of the analysis to be produced, in terms of
minimum and maximum longitude values and the grid step, in degrees. The
default values for <i>xmin</i>, <i>xmax</i>, and <i>xstep</i> are -180.,
180., and 2., respectively.<br><br><b>Y</b> <i>ymin</i> <i>ymax</i> <i>ystep</i>
<b>RANGEEDGESTEP</b> : specification of the north-south domain of the
analysis to be produced, in terms of minimum and maximum latitude values
and the grid step, in degrees. The default values for <i>ymin</i>, <i>ymax</i>,
and <i>ystep</i> are -90., 90., and 2., respectively.<br><br><i>n1</i> <b>pass1</b>,
<i>n2</i> <b>pass2</b>, <i>n3</i> <b>pass3</b> : <i>n1</i>, <i>n2</i>, and <i>n3</i>
are the factors by which the average minimum station separation distance
(calculated within the routine) is multiplied on each pass of the cressman
scheme (3 passes are automatically done), to establish the influence
radius for each pass. The default values for the first, second, and third
passes are 4, 2.5, and 1.5, respectively.<br><br><u><b>Description</b></u><b>
</b><br><br><b>cressman</b> performs a Cressman (1959) objective analysis
of input station data onto a user-defined latitude-longitude grid. A
three-pass successive correction scheme is used here, allowing for the
influence radius to be tightened on each pass. The influence radius (R)
for each pass is calculated as the product of a user-defined constant and
the average distance between each station and its nearest neighboring
station. On each pass of the analysis, for each gridpoint in the analysis
domain, an isotropic distance-based weight is calculated for each station
within the influence radius of the gridpoint. This weight is calculated as
follows: W = (R<sup>2</sup> - r<sup>2</sup>)/(R<sup>2</sup> + r<sup>2</sup>)
where R = influence radius and r = station-to-gridpoint distance.
Distances are calculated along a great circle path. The weighting function
is illustrated in the following graph:<br><br><img alt="Image of
cressman weighting function" src="http://iri.columbia.edu/~mbell/cress_weight.jpg"><br><br>The
analysis value on the third and final pass at each gridpoint is calculated
as:<br><br>Z<sub>3</sub> = Z<sub>2</sub> + sum[W*(zo<sub>j</sub> - zb<sub>j</sub>)]/sum[W]<br><br>where
Z<sub>2</sub> is the value of the analysis on the previous pass, W is the
distance-based weight for each station within the influence radius of the
gridpoint, zo<sub>j</sub> is the observed value at station j, zb<sub>j</sub>
is the background value at the station location j calculated via bilinear
interpolation from the analysis on the previous pass, and the sums are
calculated over the number of stations within the influence radius of each
gridpoint.<br><br>If the minimum station number requirement specified in
the <b>minstns</b> parameter is not met or exceeded, a missing value will
be assigned to the gridpoint. This allows the user to restrict the region
to which valid analysis values are assigned based upon a simple measure of
station density.<br><br><u><b>References</b></u><br><br>Cressman, G. P.,
1959: An operational objective analysis system. <i>Mon. Wea. Rev.</i>, <b>87</b>,
367-374.<br><br><u><b>Example</b></u><br><br><font color="#008000">SOURCES
.NOAA .NCEP .CPC .CAMS .station .temperature </font><br><font color="#008000">lon
lat temp [IWMO] cressman: </font><br><font color="#008000">3 minstns </font><br><font color="#008000">X
-180. 180. 2. RANGEEDGESTEP </font><br><font color="#008000">Y -90. 90. 2.
RANGEEDGESTEP </font><br><font color="#008000">4. pass1 </font><br><font color="#008000">2.5
pass2 </font><br><font color="#008000">1.5 pass3 </font><br><font color="#008000">:cressman</font><br><br><a href="
http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.CPC/.CAMS/.station/.temperature/lon/lat/temp%5BIWMO%5Dcressman:/3/minstns/X/-180./180./2./RANGEEDGESTEP/Y/-90./90./2./RANGEEDGESTEP/4./pass1/2.5/pass2/1.5/pass3/:cressman/">Live
Example Link</a><br>
Performs a cressman objective analysis to create gridded data from station data
:cressman
the factor by which the average minimum station separation distance (calculated within the routine) is multiplied on the third pass of the cressman scheme to establish the influence radius for the pass. Default is 1.5.
pass3
119
true
2
variable to be rank correlated with <i>var1</i>
<p> Note that <i>var1</i> and <i>var2</i> should have similarly-defined <i>grids</i>. Regridding one variable to match the other may be necessary (see example below).
false
var2
SOURCES .NOAA .NCEP .CPC .GSOD .MONTHLY .DATA .mean .pan .evaportransporation
<br>SOURCES .NOAA .NCEP .CPC .GSOD .MONTHLY .DATA .maximum .temp
<br>IWMO (105130) VALUE
<br>[T]rankcorrelate
<p>
OR
<p>
SOURCES .NOAA .NCEP .CPC .CAMS_OPI .v0208 .anomaly .prcp
<br>T (Jan 1980) (Dec 2003) RANGE
<br>X (-150) (-80) RANGE
<br>Y (-10) (10) RANGE
<br>SOURCES .NOAA .NCEP .EMC .CMB .GLOBAL .Reyn_SmithOIv2 .monthly .sst
<br>T (Jan 1980) (Dec 2003) RANGE
<br>X (-150) (-80) RANGE
<br>Y (-10) (10) RANGE
<br>[X Y]regridAverage
<br>[X Y]rankcorrelate
var1
false
1
variable to be rank correlated with <i>var2</i>
4
minfrac
Minimum fraction of data that must be present (i.e., fraction not indicated as missing) within the selected domain in order for the rank correlation to be calculated. If minfrac is not present, then a missing value is returned. If minfrac is not given, then the rank correlation is calculated regardless of the amount of data present in the domain.
true
grid(s) (i.e., independent variables) over which rank correlation coefficient is to be calculated
3
grids
false
rankcorrelate
Calculates the Spearman rank correlation coefficient of two variables over specified grids (i.e., independent variables)
Computes the 96 percentile widths
computes the 96 percentile widths
width96
width96
96 percentile widths
false
width962
var'
2
var
1
width961
false
Variable the computation will be derived from
Tgrid
false
monthly time grid of precip
monthly time grid of <b>precip</b>
2
true
minimum fraction of non missing data not to return missing
minfrac
6
minimum fraction of non missing data not to return missing
wasp
First year of climatological period
range_low
First year of climatological period
3
false
Last year of climatological period
4
false
Last year of the climatological period
range_high
Computes the Weighted Anomaly Standardized Precipitation index
wasp
WASP index
7
WASP index
1
monthly precipitation
false
monthly precipitation
precip
false
interval
5
width (in units of <b>Tgrid</b>) of the overlapping running window
<p style="margin-top: 0">
The WASP index consists in monthly precipitation departures from a
long-term (<b>year1</b> to <b>year2</b>) average that are standardized
by dividing by the standard deviation of monthly precipitation (across
years). The standardized monthly anomalies are then weighted by
multiplying by the fraction of the climatological annual precipitation
for a given month. These weighted anomalies are then sum over <b>N</b>-month
running window.
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
The minimum fraction of available data <b>minfrac</b> is applied to that
last running sum. For each <b>N</b>-month window, if there a portion of
missing months greater than <b>minfrac</b>, <b>wasp</b> will return
missing.
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
The climatological weighting factor is used to dampen large standardized
anomalies that result from small precipitation amounts occuring near the
start or end of dry seasons and to emphasize anomalies during the heart
of the rainy seasons. However you may still want to apply a dry mask on
very dry parts of your data.
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
<u><b>References</b></u>
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
Lyon, B. <a href="http://journals.ametsoc.org/doi/abs/10.1175/JCLI3598.1">The
strenght of El Niño and the spatial extent of tropical drought.</a> <i>Geophys.
Res. Lett.</i>, <b>31</b>, 2004, L21204
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
<u><b>Example </b></u>
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
The following computes WASP-12 on a monthly precipitation dataset from
NOAA, for the full period of time available but relative to the base
period 1981-2010.
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
<a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.CPC/.Merged_Analysis/.monthly/.latest/.ver2/.prcp_est/T/(1981)/(2010)/12/wasp/">Live
Example</a>
</p>
Computes the Weighted Anomaly Standardized Precipitation index
Calculates the Spearman rank correlation coefficient of two variables over specified grids (i.e., independent variables)
coefficient
false
Spearman rank correlation coefficient of <i>var1</i> and <i>var2</i> over <i>grids</i>.
<p>
<i>coefficient</i> is not dependent on <i>grids</i>, but is dependent on any other grids that <i>var1</i> or <i>var2</i> depended on (if any).
5
performs varimax rotation on the results of svd
1
dataset of svd results
false
svddataset
false
dataset containing the rotated eofs
rotated_eofs
3
varimax
false
var
0
data to be analyzed
1
true
wghts
weights
time grids over which svd is to be found
3
false
time
svd
Computes singular value decomposition
weighted-average
<p style="margin-top: 0">
seasonalAverage can be used to pick out a season from data that depends
on time.
</p>
<p style="margin-top: 0">
For example, consider
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
SOURCES .NOAA .NCEP .EMC .CMB .GLOBAL .Reyn_SmithOIv2 .weekly .ssta
</p>
<p style="margin-top: 0">
T (Nov-Jan) seasonalAverage
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
This is also accessible <a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.EMC/.CMB/.GLOBAL/.Reyn_SmithOIv2/.weekly/.ssta/T/%28Nov-Jan%29seasonalAverage/">here</a>.
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
It can also be used to pick out seasons that start and end within
months, with a slightly different syntax, e.g.
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
SOURCES .NOAA .NCEP .EMC .CMB .GLOBAL .Reyn_SmithOIv2 .weekly .ssta
</p>
<p style="margin-top: 0">
T (6 Nov - 8 Jan) seasonalAverage
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
which is also accessible <a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.EMC/.CMB/.GLOBAL/.Reyn_SmithOIv2/.weekly/.ssta/T/%286%20Nov%20-%208%20Jan%29seasonalAverage/">here</a>
</p>
seasonalAverage
false
1
input dataset or variable
data
computes seasonal Average over time
computes seasonal Average over time
independent variable that represents time
false
2
seasonally averaged data
5
false
dataset or variable that has been seasonally averaged
season length
3
false
4
true
minfrac
Computes weighted average
Computes weighted average
false
grids
grids to be averaged over
3
var
variable to be averaged
false
1
true
4
grids to be renamed
grids_renamed
Minimum fraction of data that must be present (i.e., fraction not indicated as missing) within the selected domain in order for the weighted-average to be calculated. If <i>minfrac</i> is not present, then a missing value is returned. If <i>minfrac</i> is not given, then the weighted-average is calculated regardless of the amount of data present in domain.
minfrac
5
true
2
Wghts
weights to be used in averaging. If a geometry, will be rasterized to match <b>var</b>. If a lat/lon geometry, then a cosine latitude weighing is also applied.
false
<u><b>Description</b></u>
<p>
<b><u>Examples</u></b>
</p>
<h4>
Global average
</h4>
SOURCES .NOAA .NCDC .ERSST .version2 .SST<br>{ Y cosd } [ X Y ]
weighted-average
<h4>
NINO3.4 region
</h4>
SOURCES .NOAA .NCDC .ERSST .version2 .SST<br>190 -5 240 5 georect<br>[X
Y]weighted-average
<h4>
Averaging over a shape defined by an administrative boundary
</h4>
<table align=right>
<tr><td align=center><a href="http://iridl.ldeo.columbia.edu/expert/ds:/SOURCES/.Features/.Political/.Niger/.Districts/.the_geom/SOURCES/.UEA/.CRU/.TS2p1/.monthly/.prcp/T/%28Jan%201980%29%28Dec%202002%29RANGE/Y/11/24/RANGEEDGES/X/-1/17/RANGEEDGES/SOURCES/.Features/.Political/.Niger/.Districts/.the_geom%5BX/Y%5Dweighted-average/:ds/figviewer.html?my.help=more+options&map.T.plotvalue=Jan+1980&map.Y.units=degree_north&map.Y.plotlast=25.41783N&map.url=a-+.SOURCES+.WORLDBATH+.bath+-1+min+-a-+.the_geom+-a-+.prcp+-a+X+Y+fig:+white+mask+blue+fillby+black+countries+:fig&map.domain=+%7B+/prcp+0.0+1.4+plotrange+/T+240.5+plotvalue+X+0.04162598+18.708309+plotrange+Y+9.0844936+25.41783+plotrange+%7D&map.domainparam=+/plotaxislength+300+psdef+/plotborder+72+psdef+/XOVY+null+psdef&map.zoom=Zoom&map.Y.plotfirst=9.084494N&map.X.plotfirst=0.04162598E&map.X.units=degree_east&map.X.modulus=360&map.X.plotlast=18.70831E&map.prcp.plotfirst=0.0&map.prcp.units=mm/month&map.prcp.plotlast=1.4&map.plotaxislength=432&map.plotborder=72&map.fnt=Helvetica&map.fntsze=12&map.XOVY=auto&map.color_smoothing=1&map.framelbl=framelabelstart&map.framelabeltext=&map.iftime=25&map.mftime=25&map.fftime=200" OnMouseOver="self.status='Click here to explore the data';return true"><img src="http://iridl.ldeo.columbia.edu/expert/ds:/SOURCES/.Features/.Political/.Niger/.Districts/.the_geom/SOURCES/.UEA/.CRU/.TS2p1/.monthly/.prcp/T/%28Jan%201980%29%28Dec%202002%29RANGE/Y/11/24/RANGEEDGES/X/-1/17/RANGEEDGES/SOURCES/.Features/.Political/.Niger/.Districts/.the_geom%5BX/Y%5Dweighted-average/:ds/a-+.SOURCES+.WORLDBATH+.bath+-1+min+-a-+.the_geom+-a-+.prcp+-a+X+Y+fig:+white+mask+blue+fillby+black+countries+:fig+//prcp/0.0/1.4/plotrange//T/240.5/plotvalue/X/0.04162598/18.70831/plotrange/Y/9.084494/25.41783/plotrange+//plotaxislength+300+psdef//XOVY+null+psdef//plotborder+72+psdef+.gif" NOSAVE border=0 ALT="Click for options and more information" title="Click for options and more information" width=372 height=334></a>
<br><img src="http://iridl.ldeo.columbia.edu/expert/ds:/SOURCES/.Features/.Political/.Niger/.Districts/.the_geom/SOURCES/.UEA/.CRU/.TS2p1/.monthly/.prcp/T/%28Jan%201980%29%28Dec%202002%29RANGE/Y/11/24/RANGEEDGES/X/-1/17/RANGEEDGES/SOURCES/.Features/.Political/.Niger/.Districts/.the_geom%5BX/Y%5Dweighted-average/:ds/a-+.SOURCES+.WORLDBATH+.bath+-1+min+-a-+.the_geom+-a-+.prcp+-a+X+Y+fig:+white+mask+blue+fillby+black+countries+:fig+.auxfig+//prcp/0.0/1.4/plotrange//T/240.5/plotvalue/X/0.04162598/18.70831/plotrange/Y/9.084494/25.41783/plotrange+//plotaxislength+300+psdef//XOVY+null+psdef//plotborder+72+psdef+.gif" NOSAVE border=0 width=322 height=56></td></tr>
</table>
The following example averages precipitation values gridded at 0.5 deg.
lat./lon. resolution from the University of East Anglia TS2.1 data set
over districts in the country of Niger. The shapes of the districts in
Niger are located in a data set in the "Features" section of the Data
Library data hierarchy.
<p>
Note that the spatial domain of the TS2.1 data set has been restricted
to just include the nation of Niger and its immediate vicinity (1°W -
17°E lon., 11°N - 24°N lat.). Restricting the selection of data to only
those data that are needed will speed the calculation and reduce
resource use in the Data Library.
</p>
<p>
The TS2.1 data set depends upon time (T), longitude (X), and latitude
(Y). The district geometries depend upon a single grid named "idr_id"
that simply indexes the districts. After the application of the <b>weighted-average</b>
command, the result of the calculation will be a set of
district-averaged precipitation values that depend only upon time (T)
and the district index (idr_id).
</p>
<p>
<font color="#008000">expert<br>SOURCES .UEA .CRU .TS2p1 .monthly .prcp<br>T
(Jan 1980) (Dec 2002) RANGE<br>Y 11 24 RANGEEDGES<br>X -1 17 RANGEEDGES<br>SOURCES
.Features .Political .Niger .Districts .the_geom<br>[X Y]weighted-average</font><br>
</p>
<p>
<a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.UEA/.CRU/.TS2p1/.monthly/.prcp/T/(Jan%201980)(Dec%202002)RANGE/Y/11/24/RANGEEDGES/X/-1/17/RANGEEDGES/SOURCES/.Features/.Political/.Niger/.Districts/.the_geom%5BX/Y%5Dweighted-average/">Live
Example Link</a>
</p>
<i>var </i> averaged over <i>grids</i> weighted by <i>Wghts</i>.
<p><i>wtavgvar</i> no longer depends on <i>grids</i>, but it does depend on any other grids that <i>var</i> and/or <i>Wghts</i> depend on.
6
wtavgvar
false
Independent variable over which variables will be multiplied and summed
mulsum3
ivar
3
returns A*B multiplied and summed along independent variable. If independent variable is an array of independent variables, sums over all of them
mulsum
Variable to be multiplied and summed
mulsum1
A
1
false
4
ivar_renamed
Independent variables that will be renamed to match the independent variables summed over (Ivar)
true
Returns A*B multiplied and summed along independent variables. If
independent variable is an array of independent variables, sums over all
of them
mulsum
5
C
false
mulsum4
A*B multiplied and summed along grid(s) ivar. If grid is an array of grids, sums over all of them
Variable to be multiplied and summed
false
2
B
mulsum2
<p>
mulsum is the equivalent of matrix multiplication -- one simply names the
independent variables that are to be summed over. For example,
</p>
<pre>
SOURCES .CAC .ssta
T
/test unitmatrix
[T]mulsum
</pre>
<p>
multiples ssta data by a unit matrix (T,T_out), which is merely an
inefficient way of changing the time variable from T to T_out. While
having both T and T_out are important in defining the transformation
matrix test, in practice we would just as soon keep the time variable
called T after the transformation. mulsum has an optional argument
which enables that to be done easily. We can change our example to
</p>
<pre>
SOURCES .CAC .ssta
T /test unitmatrix
[T][T_out]mulsum
</pre>
<p>
so that T_lag is renamed to T after the summation has removed the
original T grid.
</p>
<font color="#800000"><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.CAC/.ssta/T//test/unitmatrix[T][T_out]mulsum/">Live
Example Link</a></font>
var2
Variable to be multiplied and averaged
false
2
mulavg2
Multiplies and averages over a set of independent variables
independent variables that will be renamed to match the independent variables averaged over
4
ivar_renamed
true
ivar
3
false
independent variables over which the product var1*var2 will be averaged
mulavg3
mulavg
multiplies and averages over a set of independent variables
true
Minimum fraction of data that must be present (i.e., fraction not indicated as missing) within the selected domain in order for the average to be calculated. If minfrac is not present, then a missing value is returned. If minfrac is not given, then the average is calculated regardless of the amount of data present in domain
mulavg4
5
minfrac
mulavg
Variable multiplied and averaged over a set of grids
var'
false
mulavg5
6
mulavg1
var1
false
Variable to be multiplied and averaged
1
OpÃ©rations Matricielles
Operaciones Matriciales
Matrix Operations
spatial grids over which svd is to be found
space
2
false
svd_dataset
4
dataset comprised of the singular value decomposition of <i>var</i>, where structures (Ss), time series (Ts), singular values (sv), and normalized eigenvalues (evaln) are variables.
SOURCES .CAC .ssta
<br>{ Y cosd } [ X Y ] [T] svd
<p>
i.e.
<a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.CAC/.ssta/%7BY/cosd%7D%5BX/Y%5D%5BT%5Dsvd/">SVD Example</a>, particularly
<a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.CAC/.ssta/%7BY/cosd%7D%5BX/Y%5D%5BT%5Dsvd/.svdview.html">svdview</a>.
<p>
As a check of normalization, plot<p>
Ss
dup mul
Y cosd mul
[X Y]average
<p> or <p>
Ts
sv div
dup mul
[T]average
<p>
Both should be a constant 1. Note that this could be more consisely written as
<pre>
Ss
W mul
Ss[X Y]mulavg
</pre>
because the kernel is returned in W. We write it this way to make it clearer that the adjoint is Ss W mul, and that one applies the adjoint with mulavg rather than mulsum. svd normally computes in the smaller of the two spaces, and uses the adjoint to compute the vectors in the larger space.
But svd returns results even if there is missing data (it uses generalized inverses rather than adjoints to find the vectors in the alternate space).
<pre>
Ss Ts [ev] mulsum
</pre>
gives the original data, and that Ss is normalized rms (weighted) 1. Combining those two equations suggests that if we write
the original data as D, we should be able to compute the time series with
<pre>
Ss
W mul
D [X Y]mulavg
</pre>
<p>
For further details, please see the
<a href="http://iridl.ldeo.columbia.edu/dochelp/StatTutorial/SVD/">SVD Tutorial</a>.
Computes singular value decomposition
2
N
false
number of modes to include in rotation
performs varimax rotation on the results of svd
var1'
false
2xtoNaN83
Variable with missing_values replaced by NaNs
3
2xtoNaN8
<br>
<b><u>Description</u></b><br><br><b>2xtoNaN8</b> is a function that
converts missing value indicators in up to two immediately previous
variables on the stack to "NaN" ("not a number").<br><br><b><u>Example</u></b><br><br><font color="#008000">SOURCES
.NOAA .NCEP .CPC .CAMS .anomaly .temp<br>SOURCES .NOAA .NCEP .CPC .PRECL
.prcp<br>2xtoNaN8</font><br><br>In this example, missing values of -9999.
in the CAMS temperature anomaly variable and of -99.9 in the PRECL
precipitation variable are converted to 'NaN'.<br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.CPC/.CAMS/.anomaly/.temp/SOURCES/.NOAA/.NCEP/.CPC/.PRECL/.prcp/2xtoNaN8/">Live
Example Link</a><br>
2xtoNaN8
Changes missing_values into NaNs. Uses either missing_value or valid_range
flags. Outputs are both real*4 or both real*8.
var2'
2xtoNaN84
4
false
Variable with missing_values replaced by NaNs
1
Variable with missing_values
2xtoNaN81
false
var1
Changes missing_values into NaNs. Uses either missing_value or valid_range
flags. Outputs are both real*4 or both real*8.
2xtoNaN82
false
Variable with missing_values
2
var2
dataset
dataset to be altered
false
1
uses a 1D variable to replace the independent variable on which it depends
var
2
false
1D dependent variable to be used as the new independent variable
3
name to use for independent variable
true
newname
string
character argument
use_as_grid
<p style="margin-top: 0">
dup type /stringtype eq {cvn} if
</p>
<p style="margin-top: 0">
dup type /nametype eq {3}{2} ifelse
</p>
<p style="margin-top: 0">
/use_as_grid publicproc:
</p>
<p style="margin-top: 0">
dup type /nametype eq { cvx exch 1 object exch /name exch def } if
</p>
<p style="margin-top: 0">
streamgrids exch
</p>
<p style="margin-top: 0">
datatype /stringtype eq {toname}if
</p>
<p style="margin-top: 0">
ndim RECHUNK
</p>
<p style="margin-top: 0">
name units ordered 4 -1 roll getrealization NewGRID
</p>
<p style="margin-top: 0">
3 -1 roll 5 object 1 index name exch def 3 1 roll
</p>
<p style="margin-top: 0">
replaceGRID
</p>
<p style="margin-top: 0">
:publicproc
</p>
new dataset with var as independent variable
4
dataset'
false
restructures a variable by combining independent variables that are the result of a split by partitiongrid
false
3
output variable where all the independent variables name are consolidated with their sub-grids into single ivars, i.e. the tiling is removed
newvar
ivars
set of coarse-scale independent variables to restructure
2
false
var
1
false
input variable to be restructured
unifygrids
false
7
regridLinear7
Variable after regridding
var2'
Wght
false
4
regridLinear4
Regriller
Regrids a variable by averaging. Indicated grids of one variable are regridded to match those of another variable. The function is commonly used when comparing multiple variables that are dependent on different spatial grids (see correlation example below).
false
grid
independent variable to be sampled along
3
4
var1-sampled
sampled variable -- all values of grid found in var2 have been dropped.
false
samples variable by keeping only what is missing from a second variable
variable to be sampled
1
var1
false
3 /SAMPLE_MISSING publicproc: dup type /arraytype eq { dup 0 get totype /gridtype eq } if { 0 get .name 2 index 1 index getgridbyname nip 3 1 roll getgridbyname nip } if {mydataset initial_grid remove_grid}inputs mydataset initial_grid /districtmap unitmatrix streamgrids pop remove_grid SAMPLE [streamgrids pop]sum 0 maskgt streamgrids initial_grid .name cvlit renameGRID SELECT 1output :publicproc
false
where list of values to remove is taken from
2
var2
SAMPLE_MISSING
SOURCES .NOAA .NCEP .CPC .Merged_Analysis .monthly .v0407 .ver2 .prcp_est
<br>SOURCES .CDIAC .tr051 .precipitation .anomalies
<br>[X Y] regridAverage
<p>
OR
<p>
SOURCES .NOAA .NCEP .CPC .Merged_Analysis .monthly .v0407 .ver2 .prcp_est
<br>T (Jan 1980) (Dec 2003) RANGE
<br>SOURCES .NOAA .NCDC .ERSST .version2 .SST
<br>T (Jan 1980) (Dec 2003) RANGE
<br>[X Y]0.5 regridAverage
<br>[T]correlate
<p>Note that in the second example, the <i>minfrac</i> value of 0.5 requires that at least half of the input data that go into a particular output gridpoint have non-missing values in order for that gridpoint to receive a non-missing value in the regridding (i.e., keeps land from expanding outward).
5
Minimum fraction of data that must be present (i.e., fraction not indicated as missing) within the selected domain in order for the average to be calculated. If minfrac is not present, then a missing value is returned. If minfrac is not given, then the average is calculated regardless of the amount of data present in domain.
minfrac
true
false
2
variable whose grid(s) are to be regridded
var2
Regrids a variable by averaging. Indicated grids of one variable are regridded to match those of another variable. The function is commonly used when comparing multiple variables that are dependent on different spatial grids (see correlation example below).
weights to be used in the averaging. Note: not generally needed unless regridding to a very coarse spatial grid.
4
true
weights
var2regridded
false
Same as <i>var2</i> except it is now dependent on <i>grids</i> as they are defined in <i>var1</i>.
6
regridAverage
grids
false
3
grid(s) of <i>var2</i> to be regridded to match those of <i>var1</i>. Note: use caution when applying the function to temporal grids to make sure that the desired output is actually returned, particularly when regridding a coarse temporal grid to a relatively fine temporal grid.
variable (i.e, its grids) on which regridding is based
var1
false
1
Regrillado
false
var2
2
Variable to be regridded
regridLB2
regridLB5
var2'
5
false
Regridded variable
Regrids variable2 to match variable1 by using left bounds, i.e. grid is
ordered, variable2's grid is a subset of variable1's grid, the values of
the variable1 grid are bounded by the values of the variable2 grid, and
the variable2 values that are on the left of that pair are used as the
matching values
regridLB4
false
var1
Unchanged variable1
4
false
Variable to be compared to
1
regridLB1
var1
regridLB3
Independent variable to be applied to to variables
false
ivar
3
regridLB
regrids variable2 to match variable1 by using left bounds, i.e. grid is
ordered, variable2's grid is a subset of variable1's grid, the values of the
variable1 grid are bounded by the values of the variable2 grid, and the
variable2 values that are on the left of that pair are used as the matching
values.
regridLB
<br>
<b><u>Description</u></b><br><br><b>GRID</b> regrids an existing
independent variable (grid) associated with a data set or variable with a
newly-defined evenly-spaced grid based upon user-defined limits and
spacing.<br><br>In the function arguments "var" is a data variable that
depends on the independent variable "grid", "lowbound" is the minimum
value of the new grid being defined, "step" is the evenly-spaced interval
of the new grid in the units of "grid", and "highbound" is the upper limit
of the new grid.<br><br><b><u>Example</u></b><br><br>In the following
example, a gridded data set of monthly mean short wave radiances is
spatially regridded globally (from -180° to 179° in longitude and -90° to
90° in latitude) from 2.5° lat/lon grid spacing to 1° lat/lon spacing.
Bi-linear interpolation is used to regrid from the coarser to the finer
grid.<br><br><font color="#008000">SOURCES .ERBE .global .monthly .srb<br>X
-180 1 179 GRID<br>Y -90 1 90 GRID</font><br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.ERBE/.global/.monthly/.srb/X/-180/1/179/GRID/Y/-90/1/90/GRID/">Live
Example Link</a>
regridvar
false
6
<i>var</i> regridded onto newly-defined grid
<p>
Data at new grid points are based on linear interpolation of original <i>var</i>.
grid (i.e., independent variable) on which new grid specifications (e.g., lowbound, step, highbound) are to be applied
false
2
grid
variable to be regridded
var
1
false
3
false
lower limit of new grid definition
lowbound
Regrids a variable onto a newly-defined, evenly-spaced grid (i.e., independent variable)
width (in units of <i>grid</i>) between new grid points
<p>
If n is not an integer, where n=(<i>highbound</i>-<i>lowbound</i>)/<i>step</i>, then <i>step</i> is assigned closest value that allows n to be an integer.
step
false
4
upper limit of new grid definition
highbound
5
false
GRID
Regrids a variable onto a newly-defined, evenly-spaced grid (i.e., independent variable)
ship track
false
3
transit3
Y(S)
false
transit5
Returns the position on the given grid of the ship track
D(S)
5
given a variable D(X,Y) and a ship track X(S), Y(S), returns D(S).
false
2
X(S)
transit2
ship track
Given a variable D(X,Y) and a ship track X(S), Y(S), returns D(S).
transit
transit
false
transit1
1
D(X,Y)
false
S
4
transit4
Regridding
var2
Variable to be regridded
2
regridLinear2
false
5
regridLinear5
true
Wmin
regridLinear3
ivar'
false
Independent variable to be applied to variables
3
regridLinear
Regrids by averaging
regridLinear
var1
false
regridLinear6
6
Unchanged variable
regridLinear1
var1
1
false
Variable to be regridded
regrids by two point linear interpretation
false
finegrid
3
subgrid for the partition
step size for coarse-grid; width of sub-grid.
false
2
step
false
ivar
1
independent variable to be split
outer grid of the partition
coarsegrid
false
4
partitiongrid
splits an independent variable into two parts: a coarse scale grid and a fine-scale subgrid. The two ivars point to each other with sophisticates and isSophisticatedBy.
2
variable or constant to be compared to <i>var/num1</i>
var/num2
false
3
maxvar/num
false
maximum, or variable containing the maximum of <i>var1/num</i> and <i>var2/num</i>. <p>If a variable, <i>maxvar/num</i> is dependent on the same grids as are <i>var1/num</i> and <i>var2/num</i>, but the grid domains are limited to the domain common between <i>var1/num</i> and <i>var2/num</i>. In the example below, the temporal and spatial domain of <i>maxvar/num</i> is limited to the domains common bewteen the <i>var1/num</i> and <i>var2/num</i> - Jan 1981-Dec 1986 for the Tropical Atlantic.
<u><b>Description</b></u><br><b>max</b> returns the maximum of two
variables or two numbers<br><u><b>Example</b></u><br><font color="#008000">SOURCES
.CHRIS .CARD .taux<br>SOURCES .Morliere .taux<br>max
</font>
<p>
OR
</p>
<p>
<font color="#008000">5 pi max<br></font><font color="#800000"><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.CHRIS/.CARD/.taux/SOURCES/.CHRIS/.CARD/.tauy/mag/SOURCES/.Morliere/.taux/SOURCES/.Morliere/.tauy/mag/max/figviewer.html?my.help=more+options&map.T.plotvalue=first+to+last&map.Y.units=degree_north&map.Y.plotlast=29N&map.url=X+Y+fig-+colors+coasts+-fig&map.domain=+%7B+%2Fmax+0+1.331128+plotrange+%2FT+323.5+plotvalue+%7D&map.domainparam=+%2Fplotaxislength+432+psdef+%2Fplotborder+72+psdef+%2FXOVY+null+psdef&map.zoom=Zoom&map.Y.plotfirst=19S&map.X.plotfirst=59W&map.X.units=degree_east&map.X.modulus=360&map.X.plotlast=13E&map.max.plotfirst=0&map.max.units=dynes%2Fcm2&map.max.plotlast=1.331128&map.newurl.grid0=X&map.newurl.grid1=Y&map.newurl.land=draw+coasts&map.newurl.plot=colors&map.plotaxislength=432&map.plotborder=72&map.fnt=Helvetica&map.fntsze=12&map.XOVY=auto&map.color_smoothing=1&map.iftime=25&map.mftime=25&map.fftime=200">Live
Example Link</a></font>
</p>
Returns the maximum of two variables or two numbers
Returns the maximum of two variables or two numbers
max
var/num1
1
variable or constant to be compared to <i>var2</i>
false
SOURCES .NOAA .NODC .WOA01 .Grid-1x1 .Monthly .an .salinity
<br>/missing_value -99.9999 def
<br>0 replaceNaN
1
false
variable containing missing values to be replaced
<p>
Note that missing values must be identified with the <i>missing_value</i> attribute. If they are not, then the attribute can be defined as in the example below.
var
false
varfull
3
<i>var</i> with all missing values identified by the <i>missing_value</i> attribute replaced by <i>value</i>
Replaces missing values in a variable with a selected value (or variable)
false
2
value or variable that will replace missing values
value
replaceNaN
Replaces missing values in a variable with a selected value (or variable)
false
2
var'
Variable converted to random data uniformly distributed onto gaussian data
1
var
false
Variable to be converted
gaussianinv
Converts random data uniformly distributed on to gaussian data
erf
computes error function from variable
C
false
2
A
false
1
3
<i>var</i> with data values greater than or equal to <i>maskval</i> replaced by missing value indicator
maskvar
false
SOURCES .NOAA .NCEP .CPC .GMSM .w
<br>100 maskge
maskge
2
false
threshold value on which mask is based
maskval
Flags data values less than a specified threshold
false
var
variable on which flags are to be applied
1
Flags data values less than a specified threshold
false
2
threshold value used to apply flags
flagval
flaggedvar
binary version of <i>var</i>, where values less and greater than <i>flagval</i> are assigned values of 1 and 0, respectively
3
false
<br>
<u><b>Description</b></u><br><br><b>flaglt</b> replaces values of the
input variable "<i>var</i>" with a value of "1" if the input values
are less than the user-specified threshold value "<i>flagval</i>" and
replaces values of the input variable "var" with a value of "0" if the
input values are greater than or equal to the user-specified threshold
value "<i>flagval</i>".<br><br><u><b>Example</b></u><br><br>In
the following example "12 <b>flaglt</b>" is applied to monthly
climatological precipitation values from the UEA TS2.1 data set to denote
very dry locations that receive less than 12 mm of precipitation per
month, on average.<br><br><font color="#008000">SOURCES .UEA .CRU .TS2p1
.climatology .c7100 .prcp <br>12 flaglt</font><br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.UEA/.CRU/.TS2p1/.climatology/.c7100/.prcp/12/flaglt/">Live
Example Link</a><br>
flaglt
Flags data values less than and equal to a specified threshold
false
var
1
variable on which flags are to be applied
flagle
flagval
false
threshold value used to apply flags
2
Flags data values less than and equal to a specified threshold
<br>
<u><b>Description</b></u><br><br><b>flagle</b> replaces values of the
input variable "<i>var</i>" with a value of "1" if the input values
are less than or equal to the user-specified threshold value "<i>flagval</i>"
and replaces values of the input variable "var" with a value of "0" if the
input values are greater than the user-specified threshold value "<i>flagval</i>".<br><br><u><b>Example</b></u><br><br>In
the following example, "28 flagle" replaces monthly sea surface
temperature values less than or equal to 28°C with a "1" and SST values
greater than 28°C with a "0".<br><br><font color="#008000">SOURCES
.NOAA .NCDC .ERSST .version2 .SST<br>28 flagle</font><br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCDC/.ERSST/.version2/.SST/28/flagle/">Live
Example Link</a><br>
3
false
binary version of <i>var</i>, where values less than or equal to <i>flagval</i> and greater than <i>flagval</i> are assigned values of 1 and 0, respectively
flaggedvar
Flags data values greater than a specified threshold
flaggt
<br>
<u><b>Description</b></u><br><br><b>flaggt</b> replaces values of the
input variable "<i>var</i>" with a value of "1" if the input values
are greater than the user-specified threshold value "<i>flagval</i>"
and replaces values of the input variable "var" with a value of "0" if the
input values are less than or equal to the user-specified threshold value "<i>flagval</i>".<br><br><u><b>Example</b></u><br><br>In
the following example <b>flaggt</b> is used to mark those months in which
stations in the ANEEL daily precipitation data set have no more than one
day of precipitation data missing per month. The "dataflag" command is
first applied to the daily precipitation data to flag with a "1" those
days with non-missing precipitation data, and to flag with a "0" those
days with missing data. "monthlyAverage" is applied to these daily flags
of "1"s and "0"s to calculate the fraction of days per month with
non-missing data, assigning one value per month. "0.96 <b>flaggt</b>"
is applied to these monthly fractional values and replaces them with a
value of "1" if at least 96% of days in the month (which is at least all
but one day per month, no matter which month) have non-missing
precipitation values and a value of "0" if more than one day per month has
missing precipitation.<br><br><font color="#008000">SOURCES .ANEEL
.prcp_sta .prcp<br>dataflag<br>monthlyAverage<br>0.96 flaggt</font><br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.ANEEL/.prcp_sta/.prcp/dataflag/monthlyAverage/0.96/flaggt/">Live
Example Link</a><br>
false
1
variable on which flags are to be applied
var
binary version of <i>var</i>, where values greater and less than <i>flagval</i> are assigned values of 1 and 0, respectively
flaggedvar
false
3
Flags data values greater than a specified threshold
flagval
threshold value used to apply flags
2
false
binary version of <i>var</i>, where values greater than and equal to <i>flagval</i> and less than <i>flagval</i> are assigned values of 1 and 0, respectively
flaggedvar
3
false
false
2
flagval
threshold value used to apply flags
Flags data values greater than or equal to a specified threshold
1
var
variable on which flags are to be applied
false
flagge
<br>
<u><b>Description</b></u><br><br><b>flagge</b> replaces values of the
input variable "<i>var</i>" with a value of "1" if the input values
are greater than or equal to the user-specified threshold value "<i>flagval</i>"
and replaces values of the input variable "var" with a value of "0" if the
input values are less than the user-specified threshold value "<i>flagval</i>".<br><br><u><b>Example</b></u><br><br>In
the following example flagge is used to identify and help count the number
of stations in the GHCN precipitation data set that received precipitation
greater than or equal to 500 millimeters in December 2006. "<i>500
flagge</i>" replaces precipitation data values of at least 500 mm with a
value of "1" and replaces precipitation values below 500 mm with a value
of "0". "<i>[IWMO] sum</i>" sums the flagged data values
over the station grid "IWMO" to give a count of the number of stations
with at least 500 mm of precipitation.<br><br><font color="#008000">SOURCES
.NOAA .NCDC .GHCN .v2beta<br>prcp<br>T (Dec 2006) VALUE<br>500 flagge<br>[IWMO]sum</font><br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCDC/.GHCN/.v2beta/prcp/T/%28Dec%202006%29VALUE/500/flagge%5BIWMO%5Dsum/">Live
Example Link</a>
Flags data values greater than or equal to a specified threshold
variable on which mask is to be applied
false
1
var
Masks out data values greater than or equal to a specified threshold
Masks out data values greater than or equal to a specified threshold
false
7
dist
normalized frequency distribution of <i>var</i> within range and intervals specified by <i>lower</i>, <i>upper</i>, and <i>step</i>
upper
false
upper bound of range over which distribution is to be found
4
2
DATA
false
Returns the normalized frequency distribution of a set of data for a specified range and step interval.
3
lower
lower bound of range over which distribution is to be found
false
Returns the normalized frequency distribution of a set of data for a specified range and step interval.
normalizeddistrib1D
false
5
width of intervals (in units of var) used to find distribution
step
SOURCES .NOAA .NCEP .CPC .GMSM .w
<br>Y (20) VALUE
<br>X (50) VALUE
<br>T (Jul 1948-2003) VALUES
<br>DATA 0 50 2 RANGESTEP
<br>normalizeddistrib1D
false
var
variable (i.e., data) of which distribution is to be found
1
RANGESTEP
6
false
constantdata
constant
Constant to be applied
false
2
constantdata2
false
variable which matches the input variable but has constant values
var'
3
constantdata3
constantdata
returns constant data
<p style="margin-top: 0">
<br>
<u><b>Description</b></u><br><br><b>constantdata</b> changes a variable
to a user-selected constant value. This may come in handy, for instance,
if you want to compare a variable to a constant value.<br><br><u><b>Example</b></u><br><br><font color="#008000">SOURCES
.KEELING .MAUNA_LOA .co2<br></font>
</p>
<p style="margin-top: 0">
<font color="#008000">dup<br></font>
</p>
<p style="margin-top: 0">
<font color="#008000">340 constantdata</font><br><br>This example makes
a copy of the famous Mauna Loa monthly CO2 concentration time series and
applies a constant value of 340 (parts per million, in this case) to all
points in time in the copy.<br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.KEELING/.MAUNA_LOA/.co2/dup/340/constantdata/">Live
Example Link</a>
</p>
Returns constant data
false
constantdata1
Variable to be changed
1
var
false
Ratio of alpha to beta
alpha/beta
4
abrat4
Ã©quation d'Ã©tat de l'eau de mer
ecuaciÃ³n del estado del agua del mar
oceanic equation of state
abrat
false
abrat3
3
P
Pressure
<br>
<u><b>Description</b></u><br><br><b>abrat</b> uses potential temperature,
salinity, and pressure values in the ocean to calculate the ratio of alpha
(the thermal expansion coefficient) to beta (the saline contraction
coefficient).<br><br><u><b>References</b></u><br><br>McDougall, T. J.,
1987: Neutral Surfaces. <i>Journal of Physical Oceanography</i>, <b>17</b>,
1950-1964.<br><br><u><b>Example</b></u><br><br><font color="#008000">SOURCES
.GEOSECS .THETA<br>SOURCES .GEOSECS .SAL<br>SOURCES .GEOSECS .PRESS<br>abrat</font><br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.GEOSECS/.THETA/SOURCES/.GEOSECS/.SAL/SOURCES/.GEOSECS/.PRESS/abrat/">Live
Example Link</a><br>
abrat2
S
false
Salinity
2
Potential Temperature
abrat1
1
theta
false
abrat
ratio of alpha to beta (oceanic eq. of state)
Ratio of alpha to beta
median
computes the median
median
Computes the median
var
false
2
median
median
Variable from which median will be derived
1
median1
A
false
stream'
ratios3
Pairwise ratios along grid of variable
3
false
ivar
ratios2
false
2
Independent variable along which function will be applied
false
var
1
ratios1
Variable to which function will be applied
Takes pairwise ratios along independent variable of variable
ratios
takes pairwise ratios along independent variable of variable
ratios
data
false
1
dataset or variable
computer seasonal Maximum over time
3
time periods within which maxima will be look for
season length
false
<p style="margin-top: 0">
seasonalMax can be used to pick out a season from data that depends on
daily to monthly time steps, and find the maximum value within each
season.
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
As soon as an original time grid point overlaps the target season, its
associated value is considered in full for being a potential minimum. In
other words, no weighting is applied for original time grid whether they
are fully overlapped or partially overlapped by the targeted season.
This also means that original non-even bumpy grids (like dekad grids)
deal with each grid point equally regardless of their point width.
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
For instance, if you ask a (Nov-Jan) seasonalMax over a weekly time grid
for ssta (see below), the value at time 28 Oct - 3 Nov will be checked
for a potential maximum value, even though only 3 days of it fall in
November.
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
The above also means that it makes no sense to apply seasonalMax on
extensive, or cumulated, variables, such as total dekadal precipitation
expressed against a dekadal grid, since finding a maximum over 10-day
total rainfall and say 11-day total rainfall makes no sense. Besides, if
the edges of the target season don't coincide with edges of the original
time grid, there is no rational to figure out how to take into account
the value for those overlapped time grids. However this can be overcome
by changing units to a units by time step small enough not to be
overlapped by the target season definition, for instance in the previous
example: mm/day.
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
For example, consider
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
SOURCES .NOAA .NCEP .EMC .CMB .GLOBAL .Reyn_SmithOIv2 .weekly .ssta
</p>
<p style="margin-top: 0">
T (Nov-Jan) seasonalMax
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
This is also accessible <a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.EMC/.CMB/.GLOBAL/.Reyn_SmithOIv2/.weekly/.ssta/T/%28Nov-Jan%29seasonalMin/">here</a>.
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
It can also be used to pick out seasons that start and end within
months, with a slightly different syntax, e.g.
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
SOURCES .NOAA .NCEP .EMC .CMB .GLOBAL .Reyn_SmithOIv2 .weekly .ssta
</p>
<p style="margin-top: 0">
T (6 Nov - 8 Jan) seasonalMax
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
which is also accessible <a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.EMC/.CMB/.GLOBAL/.Reyn_SmithOIv2/.weekly/.ssta/T/%286%20Nov%20-%208%20Jan%29seasonalMin/">here
</a>
</p>
<p style="margin-top: 0">
<a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.EMC/.CMB/.GLOBAL/.Reyn_SmithOIv2/.weekly/.ssta/T/%286%20Nov%20-%208%20Jan%29seasonalMin/">
</a> </p>
<p style="margin-top: 0">
Converting a variable from mm (or mm/dekad) in mm/day before applying a
seasonalMax accessible <a href="iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.FEWS/.Africa/.TEN-DAY/.RFEv2/.est_prcp/T/differential_div/T/(6 Nov - 8 Jan)/seasonalMin/">here</a>.
</p>
time grid of <b>data</b>, units in days to months.
false
2
Tgrid
seasonalMax
computes seasonal Maximum over time
computes seasonal Minimum over time
3
time periods within which minima will be look for
season length
false
false
dataset or variable
1
data
false
seasonal minimum
seasonal minimum of <b>data</b> within each <b>season length</b>
5
2
time grid of <b>data</b>, units in days to months.
Tgrid
false
seasonalMin
true
minfrac
<p style="margin-top: 0">
minimum fraction of data that must be non-missing within <b>season length</b>
to look for a minimum, otherwise returns missing for that season.
Default is 0.
</p>
4
<p style="margin-top: 0">
seasonalMin can be used to pick out a season from data that depends on
daily to monthly time steps, and find the minimum value within each
season.
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
As soon as an original time grid point overlaps the target season, its
associated value is considered in full for being a potential minimum. In
other words, no weighting is applied for original time grid whether they
are fully overlapped or partially overlapped by the targeted season.
This also means that original non-even bumpy grids (like dekad grids)
deal with each grid point equally regardless of their point width.
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
For instance, if you ask a (Nov-Jan) seasonalMin over a weekly time grid
for ssta (see below), the value at time 28 Oct - 3 Nov will be checked
for a potential minimum value, even though only 3 days of it fall in
November.
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
The above also means that it makes no sense to apply seasonalMin on
extensive, or cumulated, variables, such as total dekadal precipitation
expressed against a dekadal grid, since finding a minimum over 10-day
total rainfall and say 11-day total rainfall makes no sense. Besides, if
the edges of the target season don't coincide with edges of the original
time grid, there is no rational to figure out how to take into account
the value for that overlapped time grid. However this can be overcome by
changing units to a units by time step small enough not to be overlapped
by the target seasoa definition, for instance in the previous example:
mm/day.
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
For example, consider
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
SOURCES .NOAA .NCEP .EMC .CMB .GLOBAL .Reyn_SmithOIv2 .weekly .ssta
</p>
<p style="margin-top: 0">
T (Nov-Jan) seasonalMin
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
This is also accessible <a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.EMC/.CMB/.GLOBAL/.Reyn_SmithOIv2/.weekly/.ssta/T/%28Nov-Jan%29seasonalMin/">here</a>.
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
It can also be used to pick out seasons that start and end within
months, with a slightly different syntax, e.g.
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
SOURCES .NOAA .NCEP .EMC .CMB .GLOBAL .Reyn_SmithOIv2 .weekly .ssta
</p>
<p style="margin-top: 0">
T (6 Nov - 8 Jan) seasonalMin
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
which is also accessible <a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.EMC/.CMB/.GLOBAL/.Reyn_SmithOIv2/.weekly/.ssta/T/%286%20Nov%20-%208%20Jan%29seasonalMin/">here
</a>
</p>
<p style="margin-top: 0">
<a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.EMC/.CMB/.GLOBAL/.Reyn_SmithOIv2/.weekly/.ssta/T/%286%20Nov%20-%208%20Jan%29seasonalMin/">
</a> </p>
<p style="margin-top: 0">
Converting a variable from mm (or mm/dekad) in mm/day before applying a
seasonalMin accessible <a href="iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.FEWS/.Africa/.TEN-DAY/.RFEv2/.est_prcp/T/differential_div/T/(6 Nov - 8 Jan)/seasonalMin/">here</a>.
</p>
computes seasonal Minimum over time
seasonal maximum of <b>data</b> within each <b>season length</b>
seasonal Maximum
false
5
4
minfrac
<p style="margin-top: 0">
minimum fraction of data that must be non-missing within <b>season length</b>
to look for a maximum, otherwise returns missing for that season.
Default is 0.
</p>
true
false
grid(s) over which the box average is to be calculated
grid
2
<br>
<b><u>Description</u></b><br><br>For the latest variable on the stack, <b>boxAverage</b>
calculates non-overlapping, unweighted averages over windows of length <i>interval</i>
over the specified grid, starting with the first point in the grid. No
average is calculated for any incomplete interval at the end of the grid. <i>interval
</i>is in the units of the grid being averaged over. The optional argument <i>minfrac</i>
has a default value of 0. Note that the units of the grid itself in the
result are not changed, only the spacing and number of calculated values
assigned to the grid.<br><br><b><u>Example</u></b><br><br><font color="#008000">SOURCES
.IRI .EPPF .environment .thermocline_depth<br>T (Jan 1950) (Nov 1998) RANGE<br>T
3 1. boxAverage</font><br><br>In this example boxAverage calculates
3-month, non-overlapping averages over the time (T) grid of the
thermocline_depth variable, starting with January 1950. With a minfrac of
1.0, 3-month averages will be returned for only for those intervals with
three months of non-missing data. Three-month averages are returned for
January-March 1950, April-June 1950, ..., July-September 1998. Because the
final interval in the time grid is incomplete (October-November 1998), no
average is returned for it.<br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.IRI/.EPPF/.environment/.thermocline_depth/T/%28Jan%201950%29%28Nov%201998%29RANGE/T/3/1./boxAverage/">Live
Example Link</a><br><br>
<p>
<b>Note:</b> This function should NOT be used with a discontinuous
domain like the T grid shown below:
</p>
<p>
<font color="#008000">SOURCES .IRI .EPPF .environment .thermocline_depth<br><i>T
(Jan-Mar 1950-1998) VALUES</i><br>T 3 boxAverage</font>
</p>
Calcuates the box average. Commonly used for creating seasonal averages. Note: function should only be used with continuous data domain (see example below).
avgvar
5
box average of <i>var</i>, where the non-overlapping intervals are of length <i>interval</i>
false
var
variable to be averaged
1
false
4
true
Minimum fraction of data that must be present (i.e., fraction not indicated as missing) within the selected domain in order for the box average to be calculated. If minfrac is not present, then a missing value is returned. If minfrac is not given, then the box average is calculated regardless of the amount of data present in domain.
minfrac
boxAverage
Calcuates the box average. Commonly used for creating seasonal averages. Note: function should only be used with continuous data domain (see example below).
false
interval
3
width of interval (in units of <i>grid</i>).
dens
dens3
3
Pressure
P
false
unecso '81 density (insitu) of seawater
false
dens1
T
Temperature
1
<p style="margin-top: 0">
<br>
<b><u>Description</u></b><br><br><b>dens</b> calculates the density of
seawater (in g/cm<sup>3</sup>) given input variables temperature (T),
salinity (S), and pressure (P), in that order, using the international
equation of state for seawater, IES80 (UNESCO 1981).<br><br><b><u>Reference</u></b><br><br>UNESCO,
1981: Tenth report of the joint panel on oceanographic tables and
standards. UNESCO Tech. Paper in Marine Science 36, 25pp.<br><br><b><u>Example</u></b><br><br>The
following example calculates the density of seawater based on samples of
measured temperature, salinity, and pressure from the full data set of
GEOSECS observational profiles.<br><br><font color="#008000">SOURCES
.GEOSECS .TEMP </font>
</p>
<p style="margin-top: 0">
<font color="#008000">SOURCES .GEOSECS .SAL </font>
</p>
<p style="margin-top: 0">
<font color="#008000">SOURCES .GEOSECS .PRESS </font>
</p>
<p style="margin-top: 0">
<font color="#008000">dens</font><br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.GEOSECS/.TEMP/SOURCES/.GEOSECS/.SAL/SOURCES/.GEOSECS/.PRESS/dens/">Live
Example Link</a>
</p>
dens2
false
2
S
Salinity
dens
Unecso '81 density (insitu) of seawater
density
4
false
dens4
Unecso '81 density (insitu) of seawater
restricted_ds/var
5
dataset or variable (<i>ds/var</i>) restricted to only data associated with <i>grid</i> values within <i>range_low</i> and <i>range_high</i>, inclusive;
applied to all variables in the dataset if no variable selected
false
grid in which values will be selected
false
2
grid
SOURCES .CAC
<br>Y (10S) (10N) RANGEEDGES
<p>
OR
<p>
SOURCES .CAC .ssta
<br>Y -10 10 RANGEEDGES
4
false
upper threshold of range (upper edge of closest grid box)
range_high
removes list of VALUES from variable (complement to VALUES)
new variable without listed values
4
data_out
false
false
value list
values to be dropped from set
3
removeVALUES
independent variable along which selection is to be performed
2
ivar
false
data_variable
false
variable on which selection is to be performed
1
removes list of VALUES from variable (complement to VALUES)
false
ds/var
1
dataset or variable dependent on the grid that will be sampled along
Identifies continuous range of values in one grid (i.e., independent variable) for which data will be selected.
RANGEEDGES
lower threshold of range (lower edge of closest grid box)
range_low
3
false
Identifies continuous range of values in one grid (i.e., independent variable) for which data will be selected.
interval at which data is lagged between low and high. Default value is 1.
true
4
step
lagvar
Same as <i>var</i> except:
<br>1. The values of <i>var</i> associated with the first <i>low</i> gridpoints and last <i>high</i> gridpoints of <i>grid</i> are assigned missing values.
<br>2. <i>lagvar</i> is dependent on a new grid, <i>grid</i>_lag, which has a minumum value of <i>low</i>, maximum value of <i>high</i>, and a step interval of <i>step</i>.
6
false
low
3
minimum of range of grid steps to lag. Negative (positive) values indicate a forward (backward) shift in the data along <i>grid</i>. For instance, if <i>low</i> = -6, a data value assigned to <i>grid</i> = n before <b>shiftdata</b> is applied (or, equivalently, to <i>grid</i> = n at <i>grid</i>_lag = 0 once <b>shiftdata</b> is applied) is assigned to <i>grid</i> = n - (-6) = n + 6 at <i>grid</i>_lag = -6 when <b>shiftdata</b> is applied.
false
Shifts data to create lagged versions of the data. Commonly used to calculate lag correlations.
shiftdata
false
grid over which data is to be shifted
grid
2
5
false
maximum of range of grid steps to lag. Negative (positive) values indicate a forward (backward) shift in the data along <i>grid</i>. For instance, if <i>high</i> = +6, a data value assigned to <i>grid</i> = n before <b>shiftdata</b> is applied (or, equivalently, to <i>grid</i> = n at <i>grid</i>_lag = 0 once <b>shiftdata</b> is applied) is assigned to <i>grid</i> = n - (+6) = n - 6 at <i>grid</i>_lag = +6 when <b>shiftdata</b> is applied.
high
SOURCES .Indices .soi .standardized
<br>T (Jan 1985) (Dec 2003) RANGEEDGES
<br>T -6 1 6 shiftdata
Shifts data to create lagged versions of the data. Commonly used to calculate lag correlations.
1
var
false
data to be shifted
Returns the frequency distribution of a set of data for a specified range and step interval. Commonly used to create histograms.
upper bound of range over which distribution is to be found
false
upper
4
8
frequency distribution of <i>var</i> within range and interval specified by <i>lower</i>, <i>upper</i>, and <i>step</i>
false
dist
Returns the frequency distribution of a set of data for a specified range and step interval. Commonly used to create histograms.
<br>
<u><b>Description</b></u><br><br><b>distrib1D</b> returns the frequency
distribution (as binned counts) of data from an input variable based upon
a user-specified binning interval and range limits defined in the DATA
lower upper step RANGESTEP command. In doing this, <b>distrib1D</b>
creates a new grid of bins defined by the RANGESTEP command that has the
same name as the input variable.<br><br><u><b>Example</b></u><br><br>In
this example soil moisture values from July 1948-2003 and the grid box
that includes the coordinates 20° N, 50° E have been selected, leaving
soil moisture values that vary only over the time grid. <i>DATA 0 50 2
RANGESTEP </i>specifies a selection of soil moisture values from 0 to 50
mm and a binning of 2 mm. <b>distrib1D</b> uses these specifications to
produce a count of the number of soil moisture observations in the
selection that fall within each 2 mm bin from 0 to 50 mm. The limits of
the RANGESTEP command serve as bin centers. Therefore, in this example,
there are 26 bins, namely -1 (actually 0)-1, 1-3, 3-5, ..., 47-49, 49-51
(actually 50). The grid of bins is named "w".<br><br><font color="#008000">SOURCES
.NOAA .NCEP .CPC .GMSM .w<br>Y (20) VALUE<br>X (50) VALUE<br>T (Jul
1948-2003) VALUES<br>DATA 0 50 2 RANGESTEP<br>distrib1D</font><br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.CPC/.GMSM/.w/Y/%2820%29VALUE/X/%2850%29VALUE/T/%28Jul%201948-2003%29VALUES/DATA/0/50/2/RANGESTEP/distrib1D/">Live
Example Link</a><br><br>In the following example using the GHCN monthly
precipitation data set, precipitation values for stations in Madagascar
and its vicinity have been selected for January 1971 to December 2000. The <i>DATA
0 1200 50 RANGESTEP</i> command specifies the selection of precipitation
values from 0 to 1200 mm and defines 50 mm-wide bins. Finally, in the <b>distrib1D</b>
command, the inclusion of IWMO, the name of the station ID grid, in
brackets means that a frequency distribution will be constructed for each
station separately. If "[IWMO]" had been excluded, a single frequency
distribution would have been produced using the collective precipitation
values from all the selected stations. This can be generalized to
multiple grids (if the data depend upon additional grids) as well.<br><br><font color="#008000">SOURCES
.NOAA .NCDC .GHCN .v2beta<br>lon (41.5) (53.0) masknotrange SELECT<br>lat
(-28.5) (-8.0) masknotrange SELECT<br>T (Jan 1971) (Dec 2000) RANGE<br>.prcp<br>DATA
0 1200 50 RANGESTEP<br>[IWMO]distrib1D</font><br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCDC/.GHCN/.v2beta/lon/%2841.5%29%2853.0%29masknotrange/SELECT/lat/%28-28.5%29%28-8.0%29masknotrange/SELECT/T/%28Jan%201971%29%28Dec%202000%29RANGE/.prcp/DATA/0/1200/50/RANGESTEP%5BIWMO%5Ddistrib1D/">Live
Example Link</a><br><br>
step
false
5
width of intervals (in units of <i>var</i>) used in distribution
2
DATA
false
RANGESTEP
false
6
lower bound of range over which distribution is to be found
lower
false
3
7
true
depends_on
independent variables that the distribution is to depend on. Default is to compute over all independent variables, thus depends_on is empty
false
1
var
variable (i.e., data) of which distribution is to be found
distrib1D
CatÃ©gorisation
gives the distributions of values for a given variable d(x,y,z,t) by
returning a new variable f(d) which gives the number of occurrences of data points nearest the values d_i which are equally spaced from min to max in steps of step.
distrib
distrib
Gives the distributions of values for a given variable d(x,y,z,t) by
returning a new variable f(d) which gives the number of occurrences of
data points nearest the values d_i which are equally spaced from min to
max in steps of step
distrib2
f(d)
false
Variable returning the number of occurrences of data points nearest the values d_i which are equally spaced from min to max in steps of step
2
d (x,y,z,t)
1
distrib1
false
Variable to be used
classify
false
classes
variable in categorical form
3
false
1
weights
variable of weights in complete disjunctive form
returns class with the highest weight
dominant_class
returns class with the highest weight
<b>dominant_class </b>transforms the variable from <i>complete disjunctive
form</i> (a variable of weights (0 to 1) with an independent variable that
corresponds to the list of classes) to <i>categorical form </i>(a variable
of integer values, numbering the classes from 1 to N) by choosing the
class with the highest non-zero weight (first class is used in the case of multiple
classes with the same non-zero weight). When multiple independent variables are specified, dominant_class combines the multiple class sets, creating the multivariate class that corresponds to the combination of the class sets.
classlist
one or more independent variables which list the classes
2
false
forme catÃ©gorielle
forma categÃ³rica
categorical form
forme disjonctive
forma disyuntiva
complete disjunctive form
Classifies data into categories, i.e. labels ranges of values.
false
weights
output. There is an additional grid consisting of the N+1 names, and the
values are 0, 1, or missing depending on whether the data was between the
values given in the <i>classify</i> number set.
This variable is sometimes referred to as being in <i>complete disjunctive form</i>.
4
Classifies data into categories, i.e. labels ranges of values.
classes
true
2
alternating names and numbers, starting and ending with a name, so that there are N+1 names and N numbers
<br>
<b><u>Description</u></b><br><br><b>classify</b> is used to assign ranges
of values from a variable into user-defined classes. Given a variable with
a given range of values, the classify statement accepts a list of
alternating class names and constants which define the boundaries between
the classes within that range. As a result, a new grid composed of the
defined classes is created, and the values from the input variable are
transformed into flags of 0 (not a member of the class), 1 (is a member of
the class), or NaN (not a number -- missing). This is best illustrated
with an example.<br><br><b><u>Examples</u></b><br><br><font color="#008000">SOURCES
.KAPLAN .Indices .NINO3 .avOS<br>T (Jan 1901) (Dec 1990) RANGE<br>T 3
boxAverage<br>[T]percentileover<br>{LaNina 0.2 Neutral 0.8 ElNino}(ENSO)classify</font><br><br>This
example first takes non-overlapping 3-month seasonal averages of sea
surface temperature anomalies (SSTA) from the NINO3 region of the
equatorial Pacific Ocean over the period January 1901 to December 1990.
This gives a single time series of seasonal sea surface temperature
anomalies. The first time step is Jan-Mar 1901, the second is Apr-Jun
1901, and so on until Oct-Dec 1990.<br><br>Then, the SSTA values are
converted into percentiles, from 0. to 1., using <b>[T]percentileover</b>.
The most negative SSTA values in the distribution are assigned a value
near zero, and the most positive values are assigned a value near one,
with intermediate values ranging between these extremes.<br><br>The next
line comprises the <b>classify</b> statement and its parameters. The class
names and the boundaries between them are placed within the curly braces.
Since the input variable is composed of percentiles that range between 0.
and 1. the class boundaries should fall within this range. In this case,
any values below 0.2 (in the lowest 20% of the distribution) are
classified as 'LaNina', values between 0.2 and 0.8 are classified as
'Neutral', and values from 0.8 upward (in the upper 20% of the
distribution) are classified as 'ElNino'.<br><br>Whereas the input
variable was a function of time (T) only, after <b>classify</b> was
applied the output variable became a function of both time (T) and class,
which was named after the variable (avOS). So, for a given season and a
given class, the output variable will have a value of either 0 (not a
member of the class), 1 (is a member of the class), or NaN (missing). The
Live Example Link below is followed by a link to a table from the same
calculation, which will help to illustrate the meaning of the output.<br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.KAPLAN/.Indices/.NINO3/.avOS/T/%28Jan%201901%29%28Dec%201990%29RANGE/T/3/boxAverage%5BT%5Dpercentileover/%7BLaNina/0.2/Neutral/0.8/ElNino%7D%28ENSO%29classify/">Live
Example Link</a><br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.KAPLAN/.Indices/.NINO3/.avOS/T/%28Jan%201901%29%28Dec%201990%29RANGE/T/3/boxAverage%5BT%5Dpercentileover/%7BLaNina/0.2/Neutral/0.8/ElNino%7D%28ENSO%29classify/ngridtable/3+ncoltable.html?tabopt.N=4&tabopt.1=text&tabopt.2=text&tabopt.3=text&tabopt.4=blankNaN&NaNmarker=&tabtype=html&eol=LF+%28unix%29">Table
Link</a><br><br><b>classify</b> makes it very handy to calculate
composites, such as long-term average seasonal precipitation conditioned
upon the state of ENSO. The following example shows an application of the <b>classify</b>
statement to illustrate a relationship between ENSO and monsoon rainfall
in India.<br><br><font color="#008000">expert SOURCES .Indices .india
.rainfall<br>SOURCES .KAPLAN .Indices .NINO3 .avOS<br>T (Oct 1901) (Dec
1990) RANGE<br>T 4 boxAverage<br>T 12 STEP<br>[T]percentileover<br>{LaNina
0.2 Neutral 0.8 ElNino}(ENSO)classify<br>T 4 shiftdatashort<br>[T]weighted-average<br>table:
1 :table</font><br><br>A weighted average of the June-September all-India
rainfall index is taken with the classification (0 or 1) of
October-January seasonal SSTAs as either 'LaNina', 'Neutral', or 'ElNino'
to illustrate differences in long-term mean June-September Indian monsoon
rainfall based upon the state of ENSO later in the year (an ENSO-based
composite of seasonal precipitation).<br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.Indices/.india/.rainfall/SOURCES/.KAPLAN/.Indices/.NINO3/.avOS/T/%28Oct%201901%29%28Dec%201990%29RANGE/T/4/boxAverage/T/12/STEP%5BT%5Dpercentileover/%7BLaNina/0.2/Neutral/0.8/ElNino%7D%28ENSO%29classify/T/4/shiftdatashort%5BT%5Dweighted-average/table:/1/:table/">Live
Example Link<br></a><br>The following example is similar to the previous
one except that instead of using a single precipitation time series for
India it uses CMAP gridded precipitation values that vary in space over
India. It produces composite maps of June-September 1979-2006 seasonal
average precipitation according to ENSO state (LaNina, Neutral, and
ElNino) over south Asia.<br><br><font color="#008000">SOURCES .NOAA .NCEP
.CPC .Merged_Analysis .monthly .v0703 .ver2 .prcp_est </font><br><font color="#008000">X
60. 100. RANGEEDGES </font><br><font color="#008000">Y 0 40 RANGEEDGES </font><br><font color="#008000">T
(Jun 1979) (Sep 2006) RANGE </font><br><font color="#008000">T 4
runningAverage </font><br><font color="#008000">T 12 STEP </font><br><font color="#008000">SOURCES
.KAPLAN .Indices .NINO3 .avOS </font><br><font color="#008000">T (Oct
1979) (Dec 2006) RANGE </font><br><font color="#008000">T 4 boxAverage </font><br><font color="#008000">T
12 STEP </font><br><font color="#008000">[T]percentileover </font><br><font color="#008000">{LaNina
0.2 Neutral 0.8 ElNino}(ENSO)classify </font><br><font color="#008000">T 4
shiftdatashort </font><br><font color="#008000">[T]weighted-average</font><br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.CPC/.Merged_Analysis/.monthly/.v0703/.ver2/.prcp_est/X/60./100./RANGEEDGES/Y/0/40/RANGEEDGES/T/%28Jun%201979%29%28Sep%202006%29RANGE/T/4/runningAverage/T/12/STEP/SOURCES/.KAPLAN/.Indices/.NINO3/.avOS/T/%28Oct%201979%29%28Dec%202006%29RANGE/T/4/boxAverage/T/12/STEP%5BT%5Dpercentileover/%7BLaNina/0.2/Neutral/0.8/ElNino%7D%28ENSO%29classify/T/4/shiftdatashort%5BT%5Dweighted-average/">Live
Example Link </a><br><br>Note that in the special case of a variable in <i>categorical
form</i> (i.e. which has integral values that are chosen from a list), and
that list is given with the variable, then the list of classes and
transitions can be omitted. For example,<br><br><font color="#008000">SOURCES .NASA .ISLSCP .GDSLAM .Hydrology-Soils
.soils .texture classify </font><br><br>transforms the variable from <i>categorical
form </i>(a 3D variable of integer values) to <i>complete disjunctive form</i>
(a 4D variable of weights (0 or 1) where the added dimension has the list
of possibilities defined with the original categorical dataset). This
dataset can now be regridded or factor-analyzed.<font color="#008000"> </font><br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NASA/.ISLSCP/.GDSLAM/.Hydrology-Soils/.soils/.texture/classify/">Live
Example Link</a>
3
facet
true
name of new independent variable (name of var if omitted)
1
input data to be classified
var
false
CategorizaciÃ³n
Categorization
returns bivariate counts
distrib2D
Returns bivariate counts
C
Bivariate counts of the variables above
false
3
distrib2D3
distrib2D
A
Variable to be processed
1
distrib2D1
false
2
B
Variable to be processed
distrib2D2
false
openquery
Opens a query
openquery1
1
false
db
openquery4
dataset
false
4
opens a query
false
2
query
openquery2
3
false
ivar
openquery3
openquery
Returns the maximum value of a variable over a selected grid(s)
SOURCES .NOAA .NCEP .CPC .CAMS .station .temperature .temp
<br>IWMO (1001) VALUES
<br>[T]0.9 maxover
<p>
OR
<p>
SOURCES .NOAA .NCEP .CPC .GMSM .w
<br>X 112 153 RANGE
<br>Y -44 -11 RANGE
<br>[X Y]maxover
variable of which maximum value is found
var
false
1
maxover
2
grid(s) over which maximum value is found
grid
false
Minimum fraction of data that must be present (i.e., fraction not indicated as missing) within the selected domain in order for the maximum value to be found. If <i>minfrac</i> is not present, then a missing value is returned. If <i>minfrac</i> is not given, then the maximum value is found regardless of the amount of data present in domain.
true
minfrac
3
maximum value of <i>var</i> within selected domain of <i>grid</i>
<p>
<i>maxvar/num</i> is no longer dependendent on <i>grid</i>, but is still dependent on any other grids that <i>var</i> depended on (if any)
4
false
maxvar/num
Returns the maximum value of a variable over a selected grid(s)
setmissing_value
changes the missing value flag for missing data to value
var'
3
false
Variable with missing data replaced with a given value
setmissing value3
Value to replace missing data
false
value
2
setmissing value2
false
1
var
setmissing value1
Variable with missing data
Changes the missing value flag for missing data to value
setmissing_value
openqueryby
Opens a query indexed by indexvar
opens a query indexed by indexvar
query
2
openqueryby2
false
openqueryby
4
false
query indexed by indexvar
openqueryby4
dataset
db
1
false
openquery1
indexvar
Grid by which query will be indexed
3
false
openqueryby3
Calculates the potential temperature
3
false
potemp3
P
Pressure
potemp
S
false
potemp2
2
Salinity
potemp
calculates the potential temperature
potemp1
T
false
Temperature
1
4
Potential temperature
potemp4
false
theta
Selects values of a grid based on values of the indicated variable. Commonly used to select stations - station ids (grid) selected based on lat/lon (variables) values (see Live Example).
<u><b>Description </b></u><br><b>SELECT</b> is used to select values of a
grid based on values of the indicated variable. Commonly used to select
stations - station ids (grid) selected based on lat/lon (variables) values<br><u><b>Examples</b></u><br><font color="#008000">lat
SELECT</font><br><font color="#800000"><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCDC/.GDCN/lon/290/340/masknotrange/SELECT/lat/-10/10/masknotrange/SELECT/">Live
Example Link</a></font><br>
Selects values of a grid based on values of the indicated variable. Commonly used to select stations - station ids (grid) selected based on lat/lon (variables) values (see Live Example).
false
dataset to be selected from
1
ds
var
2
variable used to make selection of grid values
false
3
selected ds
new dataset selected from <i>ds</i>. All the values of the independent variable of <i>var</i> that are marked missing in <i>var</i> are eliminated
SELECT
converts a variable A to a variable B using a table B(C=A) and linear
interpolation. Out of rangevalues beyond half a grid step are NaN.
BofA=C
Converts a variable A to a variable B using a table B(C=A) and linear interpolation. Out of range values beyond half a grid step are NaN
B'
New stream using a table B(C=A) and linear interpolation.
Out of range values beyond half a grid step are NaN.
false
4
BofA=C4
false
B
2
Variable to be converted
BofA=C2
BofA=C3
3
false
C
BofA=C
BofA=C-bounded
A
1
BofA=C-bounded1
Variable to be converted
false
<p style="margin-top: 0">
Converted stream using table B(C=A) and linear interpolation
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
Out of range values are pegged to the extreme values
</p>
BofA=C-bounded4
false
4
B'
B
2
false
BofA=C-bounded2
Variable to be converted
BofA=C-bounded
Converts a variable A to a variable B using a table B(C=A) and linear
interpolation. Out of range values are pegged to the extreme values
converts a variable A to a variable B using a table B(C=A) and linear
interpolation. Out of range values are pegged to the extreme values.
false
BofA=C-bounded3
3
C
Transitividad
Table Lookup
false
2
CofA=B2
Variable to be converted
B
Converts a variable A to a variable B using a table B(C) and linear
interpolation. Out of range values beyond half a grid step are NaN
CofA=B
converts a variable A to a variable C using a table B(C) and linear
interpolation. Out of range values beyond half a grid step are NaN.
<p style="margin-top: 0">
<br>
<b><u>Description</u></b><br><br><b>CofA=B</b> accepts two variables (A
and B) as input and converts variable A values to values expressed in
terms of variable B based upon a table of corresponding values (B(C)) in
variable B and linear interpolation between the points specified in the
table. Out-of-range values beyond half a grid step are assigned NaN
values.<br><br><b><u>Example</u></b><br><br>In the following example the
input variable A is CAMS-OPI gridded monthly precipitation, which
depends upon longitude (X), latitude (Y), and months (T) from Jan 1979
to Dec 2000. Input variable B is gridded observed monthly precipitation,
which depends upon grids X and Y (which match with X and Y from variable
A) and months (T) specified from Jan 1969 to Dec 1998. The purpose of
this particular script is to use a specific 30-year set of precipitation
values (variable B) to calculate precipitation percentiles by month of
year and then express precipitation values from the shorter time series
(variable A) in terms of the percentiles from variable B's climatology
at each (X,Y) grid point.
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
Here, the time grid for variable B is split into a grid of 12 months per
year (T) and a grid of 30 years (T2) from 1969 to 1998. The function
"replacebypercentile" is applied to the T2 grid of years in variable B,
with the result that the T2 grid is replaced by a new grid named
"percentile" that has points at 0.0, 0.33, 0.5, 0.67, and 1, containing
the precipitation values that correspond to those percentiles in the
30-year climatology for each month of the year at each (X,Y) grid point.
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
Based upon the correspondence between percentile and precipitation
values in variable B function (the table B(C))
"[percentile]CofA=B-bounded" then converts the time series of monthly
precipitation values in variable A to percentiles by month of year using
linear interpolation between the percentile points specified in the
"percentile" grid in variable B. Any precipitation values in variable A
that fall outside the range of precipitation values that correspond to
percentiles between 0 and 1 in variable B will be assigned missing
values (NaN).<br><br><font color="#008000">SOURCES .NOAA .NCEP .CPC
.CAMS_OPI .v0208 .mean .prcp<br>T (Jan 1979) (Dec 2000) RANGE </font>
</p>
<p style="margin-top: 0">
<font color="#008000">SOURCES .IRI .FD .Seasonal_Forecast .Observations
.monthly .prcp </font>
</p>
<p style="margin-top: 0">
<font color="#008000">T (Jan 1969) (Dec 1998) RANGE </font>
</p>
<p style="margin-top: 0">
<font color="#008000">T 12 splitstreamgrid </font>
</p>
<p style="margin-top: 0">
<font color="#008000">[T2]0.0 0.33 0.5 0.67 1. 0.5 replacebypercentile </font>
</p>
<p style="margin-top: 0">
<font color="#008000">[percentile]CofA=B</font><br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.CPC/.CAMS_OPI/.v0208/.mean/.prcp/T/%28Jan%201979%29%28Dec%202000%29RANGE/SOURCES/.IRI/.FD/.Seasonal_Forecast/.Observations/.monthly/.prcp/T/%28Jan%201969%29%28Dec%201998%29RANGE/T/12/splitstreamgrid%5BT2%5D0.0/0.33/0.5/0.67/1./0.5/replacebypercentile%5Bpercentile%5DCofA=B/">Live
Example Link</a>
</p>
CofA=B
Variable to be converted
CofA=B1
false
A
1
CofA=B3
3
C
false
C'
false
Converted variable using table B(C) and linear interpolation
Out of range values beyond half a grid step are NaN
CofA=B4
4
false
independent variable that is going to be replaced by constant values of <i>S</i>
Z
3
Interpolates onto surfaces
A(S)
5
output data: A goes from surfaces of constant Z to surfaces of constant S by linear interpolation
false
2
S(Z)
input surface information (needs to be monotonic in the independent variable <i>Z</i> so that it can be used as a coordinate)
value(s) of the new <i>S</i> grid. If omitted, then it uses a list of S
values, in which case S needs to have discrete, repeated values.
true
4
S
toS
1
A(Z)
false
input data to be interpolated onto surfaces of <i>S</i>
Interpolates onto surfaces
C
3
false
CofA=B-bounded3
Converts a variable A to a variable B using a table B(C) and linear
interpolation. Out of range values are pegged to the extreme values
CofA=B-bounded4
Converted variable using table B(C) and linear interpolation
Out of range values beyond half a grid step are pegged to the extreme values
4
C'
false
<p style="margin-top: 0">
<br>
<b><u>Description</u></b><br><br><b>CofA=B-bounded</b> accepts two
variables (A and B) as input and converts variable A values to values
expressed in terms of variable B based upon a table of corresponding
values (B(C)) in variable B and linear interpolation between the points
specified in the table. Out-of-range values are pegged to the extreme
values.<br><br><b><u>Example</u></b><br><br>In the following example the
input variable A is CAMS-OPI gridded monthly precipitation, which
depends upon longitude (X), latitude (Y), and months (T) from Jan 1979
to Dec 2000. Input variable B is gridded observed monthly precipitation,
which depends upon grids X and Y (which match with X and Y from variable
A) and months (T) specified from Jan 1969 to Dec 1998. The purpose of
this particular script is to use a specific 30-year set of precipitation
values (variable B) to calculate precipitation percentiles by month of
year and then express precipitation values from the shorter time series
(variable A) in terms of the percentiles from variable B's climatology
at each (X,Y) grid point.
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
Here, the time grid for variable B is split into a grid of 12 months per
year (T) and a grid of 30 years (T2) from 1969 to 1998. The function
"replacebypercentile" is applied to the T2 grid of years in variable B,
with the result that the T2 grid is replaced by a new grid named
"percentile" that has points at 0.0, 0.33, 0.5, 0.67, and 1, containing
the precipitation values that correspond to those percentiles in the
30-year climatology for each month of the year at each (X,Y) grid point.
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
Based upon the correspondence between percentile and precipitation
values in variable B function (the table B(C))
"[percentile]CofA=B-bounded" then converts the time series of monthly
precipitation values in variable A to percentiles by month of year using
linear interpolation between the percentile points specified in the
"percentile" grid in variable B. The word "bounded" in the name of the
function indicates that any precipitation values in variable A that fall
outside the range of precipitation values that correspond to percentiles
between 0 and 1 in variable B will be assigned to either 0 or 1,
depending upon whether the value is beyond the lower or upper end of the
range, respectively.<br><br><font color="#008000">SOURCES .NOAA .NCEP
.CPC .CAMS_OPI .v0208 .mean .prcp<br>T (Jan 1979) (Dec 2000) RANGE </font>
</p>
<p style="margin-top: 0">
<font color="#008000">SOURCES .IRI .FD .Seasonal_Forecast .Observations
.monthly .prcp </font>
</p>
<p style="margin-top: 0">
<font color="#008000">T (Jan 1969) (Dec 1998) RANGE </font>
</p>
<p style="margin-top: 0">
<font color="#008000">T 12 splitstreamgrid </font>
</p>
<p style="margin-top: 0">
<font color="#008000">[T2]0.0 0.33 0.5 0.67 1. 0.5 replacebypercentile </font>
</p>
<p style="margin-top: 0">
<font color="#008000">[percentile]CofA=B-bounded</font><br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.CPC/.CAMS_OPI/.v0208/.mean/.prcp/T/%28Jan%201979%29%28Dec%202000%29RANGE/SOURCES/.IRI/.FD/.Seasonal_Forecast/.Observations/.monthly/.prcp/T/%28Jan%201969%29%28Dec%201998%29RANGE/T/12/splitstreamgrid%5BT2%5D0.0/0.33/0.5/0.67/1./0.5/replacebypercentile%5Bpercentile%5DCofA=B-bounded/">Live
Example Link</a>
</p>
CofA=B-bounded
Variable to be converted
CofA=B-bounded2
2
B
false
1
A
CofA=B-bounded1
Variable to be converted
false
converts a variable A to a variable C using a table B(C) and linear
interpolation. Out of range values are pegged to the extreme values.
CofA=B-bounded
TransitivitÃ©
Reads a dataset from THREDDS server
Reads a dataset from a THREDDS server
readthredds
2
readthredds2
Normally a dataset containing other net references
object
false
1
false
readthredds1
url
readthredds
eexp
<i>Result</i> will be of the same type as <i>A</i> (i.e., either a variable or constant).
Result
2
Calculates base e to the power of a variable or constant
A
the power to which base e is to be taken
1
Calculates base e to the power of a variable or constant
<br>
<u><b>Description</b></u><br><br><b>eexp</b> evaluates the base of the
natural logarithm (e), raised to the power indicated by an input variable
or constant <i>A</i>, i.e., e<i><sup>A</sup></i>.<br><br><u><b>Example</b></u><br><br>In
the following example, e raised to the power of 5 is evaluated.<br><br><font color="#008000">5
eexp</font><br><br><a href="http://iridl.ldeo.columbia.edu/expert/5/eexp/">Live
Example Link</a>
products3
var'
false
Pairwise products along grid of variable
3
2
false
products2
ivar
Independent variable along which function will be applied
var
Variable to which function will be applied to
1
false
products1
takes pairwise products along independent variable of variable
products
Takes pairwise products along independent variable of variable
gaussianlat
Returns gaussian grid with n points
grid
gaussianlat2
false
Gaussian grid with n points
2
gaussianlat1
1
false
n
returns gaussian grid with n points.
gaussianlat
Applies zonal derivative to variable in spectral coordinates
var'
false
Variable after zonal derivative in spectral coordinates has been applied
partialeast2
2
4
definite integral over <i>ivar</i> from <i>low</i> to <i>high</i>
integral
Returns integral evaluated between two limits
Returns integral evaluated between two limits
definite-integral
function to be integrated
integrand
0
independent variable to be integrated over
1
ivar
low
lower limit
2
<br>
<u><b>Description</b></u><br><br><b>definite-integral</b> calculates the
definite integral of the latest variable on the stack over the grid
(independent variable) and limits (<i>low</i> and <i>high</i>) specified
in the command.<br><br><u><b>Example</b></u><br><br><font color="#008000">SOURCES
.NOAA .NCEP-NCAR .CDAS-1 .DAILY .Intrinsic .PressureLevel .reld<br>T (1
Jan 2007) VALUE<br>P 600 10 definite-integral</font><br><br>In this
example global gridded relative divergence values for 1 January 2007 are
selected for all available vertical pressure levels from the NCEP/NCAR
Reanalysis data set. Then, the integral of the relative divergence values
is taken over the vertical pressure grid (P) from 600 to 10 hectopascals
(mb), presumably from near the level of non-divergence to the top of the
atmosphere in the Reanalysis data set. To the extent that the relative
divergence values are accurate, the result of the calculation should be
proportional to the vertical velocity at 600 hPa. Maps of the result of
this calculation and the Reanalysis pressure vertical velocity for the
same day are given here:<br><br>
<table border="0" cellpadding="1" cellspacing="1">
<tr>
<td align="center">
Vertical Integral of Relative Divergence<br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP-NCAR/.CDAS-1/.DAILY/.Intrinsic/.PressureLevel/.reld/T/%281%20Jan%202007%29VALUE/P/600/10/definite-integral/figviewer.html?my.help=more+options&map.Y.units=degree_north&map.Y.plotlast=90N&map.url=X+Y+fig-+colors+coasts+lakes+-fig&map.domain=+%7B+Y+-90.+90.+plotrange+%7D&map.domainparam=+%2Fplotaxislength+432+psdef+%2Fplotborder+72+psdef&map.zoom=Zoom&redraw.x=22&redraw.y=9&map.Y.plotfirst=90S&map.X.plotfirst=1.25W&map.X.units=degree_east&map.X.modulus=360&map.X.plotlast=1.25W&map.int_dP.plotfirst=-0.0051178&map.int_dP.units=100+kilogram+meter-1+second-3&map.int_dP.plotlast=0.00511725&map.newurl.grid0=X&map.newurl.grid1=Y&map.newurl.land=draw+coasts&map.newurl.plot=colors&map.plotaxislength=300&map.plotborder=72&map.fnt=Helvetica&map.fntsze=12&map.XOVY=auto&map.color_smoothing=1&map.framelbl=framelabelstart&map.framelabeltext=&map.iftime=25&map.mftime=25&map.fftime=200"><img src="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP-NCAR/.CDAS-1/.DAILY/.Intrinsic/.PressureLevel/.reld/T/%281%20Jan%202007%29VALUE/P/600/10/definite-integral/X+Y+fig-+colors+coasts+-fig+//int_dP/-0.0051178/0.00511725/plotrange/Y/-90/90/plotrange//plotaxislength+300+psdef//plotborder+72+psdef//XOVY+null+psdef+.gif"></a><br><img src="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP-NCAR/.CDAS-1/.DAILY/.Intrinsic/.PressureLevel/.reld/T/%281%20Jan%202007%29VALUE/P/600/10/definite-integral/X+Y+fig-+colors+coasts+-fig+.auxfig+//int_dP/-0.0051178/0.00511725/plotrange/Y/-90/90/plotrange//plotaxislength+300+psdef//plotborder+72+psdef//XOVY+null+psdef+.gif">
</td>
<td align="center">
Pressure Vertical Velocity at 600 hPa<br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP-NCAR/.CDAS-1/.DAILY/.Intrinsic/.PressureLevel/.vvel/P/600/VALUE/T/%281%20Jan%202007%29VALUE/figviewer.html?my.help=more+options&map.Y.units=degree_north&map.Y.plotlast=90N&map.url=X+Y+fig-+colors+coasts+-fig&map.domain=+%7B+%2Fvvel+-0.6+0.6+plotrange+Y+-90+90+plotrange+%7D&map.domainparam=+%2Fplotaxislength+432+psdef+%2Fplotborder+72+psdef+%2FXOVY+null+psdef&map.zoom=Zoom&redraw.x=24&redraw.y=-3&map.Y.plotfirst=90S&map.X.plotfirst=1.25W&map.X.units=degree_east&map.X.modulus=360&map.X.plotlast=1.25W&map.vvel.plotfirst=-0.6&map.vvel.units=Pa%2Fs&map.vvel.plotlast=0.6&map.newurl.grid0=X&map.newurl.grid1=Y&map.newurl.land=draw+coasts&map.newurl.plot=colors&map.plotaxislength=300&map.plotborder=72&map.fnt=Helvetica&map.fntsze=12&map.XOVY=auto&map.color_smoothing=1&map.framelbl=framelabelstart&map.framelabeltext=&map.iftime=25&map.mftime=25&map.fftime=200"><img src="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP-NCAR/.CDAS-1/.DAILY/.Intrinsic/.PressureLevel/.vvel/P/600/VALUE/T/%281%20Jan%202007%29VALUE/X+Y+fig-+colors+coasts+-fig+//vvel/-0.6/0.6/plotrange/Y/-90/90/plotrange//plotaxislength+300+psdef//plotborder+72+psdef//XOVY+null+psdef+.gif"></a><br><img src="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP-NCAR/.CDAS-1/.DAILY/.Intrinsic/.PressureLevel/.vvel/P/600/VALUE/T/%281%20Jan%202007%29VALUE/X+Y+fig-+colors+coasts+-fig+.auxfig+//vvel/-0.6/0.6/plotrange/Y/-90/90/plotrange//plotaxislength+300+psdef//plotborder+72+psdef//XOVY+null+psdef+.gif">
</td>
</tr>
</table>
<p>
<a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP-NCAR/.CDAS-1/.DAILY/.Intrinsic/.PressureLevel/.reld/T/%281%20Jan%202007%29VALUE/P/600/10/definite-integral/">Live
Example Link</a>
</p>
<p>
</p>
<br>
3
upper limit
high
diferencial
false
var
Variable of which partial derivative will be taken along grid
partial1
1
Independent variable along which partial derivative of variable will be taken
partial2
ivar
false
2
Partial derivative of stream along grid
false
3
partial3
var'
Takes partial derivative of variable along grid
partial
partial
takes partial derivative of variable along grid
integrate
Independent variable to be applied to variable
integrate3
3
false
ivar
integrate
Takes integral with respect to independent variable starting with initial
value
Stream from which integral will be taken
1
var
integrate1
false
takes integral with respect to independent variable starting with initial value
integrate4
Integral of variable with respect to grid starting with initial value
false
4
var'
initial
false
2
integrate2
false
2
ivar
independent variable that var depends upon
multiplies by the differential corresponding to an independent variable
<p style="margin-top: 0">
This operator is essential in converting between various measures of
precipitation, particularly in going from rates to accumulations or in
changing rates from per day to per month.
</p>
<p style="margin-top: 0">
In this case we start with kg/m<sup>2</sup>/s
</p>
<pre>
SOURCES .NOAA .NCEP-NCAR .CDAS-1 .MONTHLY .Diagnostic .surface
.prate
c:0.001 (m3 kg-1) :c
mul
(mm/day) unitconvert
T (days since 1960-01-01) streamgridunitconvert
T differential_mul
T (months since 1960-01-01) streamgridunitconvert
T differential_div
</pre>
false
variable to be operated upon
1
var
var is multiplied by the differential of ivar
var2
false
3
differential_mul
Converts units
unitconvert
3
false
var'
Variable with units converted
units
Unit to be converted to
false
2
false
var
Variable whose units are to be converted
1
This Data Library makes some effort to interpret and manipulate the units
given for each variable. The code is based on the <a href="http://www.unidata.ucar.edu/software/udunits/">udunits</a>
package, though we had to make a number of changes in order to arrive at
something suitable for our needs. Some of this documentation is taken from
the udunits documentation. A unit is the amount by which a physical
quantity is measured. For example:
<table border="2">
<tr>
<td>
Physical Quantity
</td>
<td>
Possible Unit
</td>
</tr>
<tr>
<td>
time
</td>
<td>
weeks
</td>
</tr>
<tr>
<td>
distance
</td>
<td>
centimeters
</td>
</tr>
<tr>
<td>
power
</td>
<td>
watts
</td>
</tr>
</table>
<p>
 
</p>
For the most part, units are purely multiplicitive, i.e. if we multiply a
velocity in m/s times the time in s, we end up with a distance in m. This
technique of dimensional analysis is a powerful constraint in checking
scientific calculations. Some units (also called scales) also have an
implicit origin or base value. For example, zero on the Celsius scale
corresponds to 273.15 on the Kelvin scale. So if we want to convert a
Celsius temperature to a Kelvin temperature, we have to add 273.15. On the
other hand, if we have a Celsius temperature anomaly (i.e. deviation from
its normal value), it is already a Kelvin temperature anomaly, and
requires no conversion. If we remove the mean, for example, from a
temperature, it then loses its origin and becomes a 'anomaly' unit.
Unfortunately, we use the word Celsius or Kelvin to refer both to the
scale and the anomaly unit. To help diminish the impact of this ambiguity,
we have chosen the convention to refer to temperature units as
Celsius_scale or Kelvin_scale, while the temperature anomaly equivalent is
degree_Celsius or degree_Kelvin. (The plain names are considered to be
scales.) Note that scales (i.e. units with origins) do not really work
properly in dimensional analysis, a reflection of the fact that in most
cases a reference value should be removed before such a quantity is
manipulated. To create a units grammer that is readily manipulated by
machine, we follow the convention that units are separated by spaces, /
denotes division, and m2 corresponds to meters squared while <b>m2 s-1</b>
would be meters squared per second. The origin mentioned earlier is
denoted by <b>above</b>, i.e. Celsius is defined as <b>degree_Kelvin above
273.1</b>5. @, <b>from</b>, and <b>since</b> are all synonyms for <b>above</b>.
1
marks start
c:
false
:c
<u>Description</u></b><br><br>The c: :c construct allows the user to define a constant with units. Units are optional, but must be enclosed within parentheses if included.<br><br><b><u>Example</u></b><br><br><font color="#008000">SOURCES .NOAA .NCEP-NCAR .CDAS-1 .DAILY .Diagnostic .surface .prate<br>c: 0.001 (m3 kg-1) :c<br>mul<br>c: 1000 (mm m-1) :c<br>mul<br>c: 86400 (s day-1) :c<br>mul</font><br><br>This example uses the c: :c construct to convert the units of NCEP-NCAR Reanalysis precipitation from kg m-2 s-1 to mm/day through the use of three multiplied conversion factors.<br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP-NCAR/.CDAS-1/.DAILY/.Diagnostic/.surface/.prate/c:/0.001/%28m3%20kg-1%29:c/mul/c:/1000/%28mm%20m-1%29:c/mul/c:/86400/%28s%20day-1%29:c/mul/">Live Example Link</a>
4
false
constant
returned variable with <b>value</b> and <b>units</b>.
Defines a numeric constant with units and (optionally) other attributes
Defines a numeric constant with units and (optionally) other attributes
numeric value of constant
false
value
2
units of constant
units
true
3
unitÃ©s
unidades
units
diffÃ©rentiel
divides by an underlying differential
3
var multiplied by the differential of ivar
var2
false
2
false
independent variable that var depends on
ivar
input variable
1
var
false
differential_div
var
false
1
partialnorth1
Variable to which meridional derivative will be applied to
false
2
Variable after meridional derivative applied
partialnorth2
var'
Applies meridional derivative to variable in spectral coordinates
partialnorth
applies meridional derivative to variable in spectral coordinates
partialnorth
takes integral with respect to independent variable.
ivar
Independent variable to be applied to variable
2
false
integral2
integral
integral
Integral wtih respect to grid
var'
integral3
false
3
Takes integral with respect to independent variable
Stream to which grid will be applied
var
false
1
integral1
differential
applies zonal derivative to variable in spectral coordinates
partialeast
false
var
Variable to which zonal derivative will be applied
1
partialeast
partialeast
var
1
changetruncation1
false
Variable to be changed
Changes variable truncations to nsp
changes variable truncations to nsp
changetruncation
changetruncation
false
changetruncation3
3
var'
New spectral truncation level
false
2
nsp
changetruncation2
Applies Laplacian to variable in spectral coordinates
New variable to which Laplacian has been appllied
laplacian2
false
var'
2
var
laplacian1
1
Variable to which Laplacian will be applied
false
laplacian
laplacian
applies Laplacian to variable in spectral coordinates
3
false
number of latitude points
tsptogau3
ny
converts spectral coefficients to lon/lat
tsptogau
2
number of longitude points
tsptogau2
nx
false
false
4
tsptogau4
var'
Variable converted to lon/lat
tsptogau
Converts spectral coefficients to lon/lat
Variable to be converted
1
false
var
tsptogau1
Spectral Transformation
Converts lon/lat to spectral coefficients using spectral harmonics for
velocity
var'
false
3
Variable converted to spectral coefficients velocity
2
false
spectral truncation
nwave
var
1
Variable to be converted
false
gautotspUV
Transformation Spectrale
Converts spectral coefficients to lon/lat using spectral harmonics for velocity
var'
false
4
Variable converted to lon/lat velocities
ny
false
3
number of latitude points
2
number of longitude points
nx
false
Variable to be converted
var
1
false
tsptogauUV
gautotsp
Spectral coefficients
3
var'
false
gautotsp3
false
2
nwave
spectral truncation
gautotsp2
var
gautotsp1
false
Lon/Lat
1
converts lon/lat to spectral coefficients
Converts lon/lat to spectral coefficients
gautotsp
TransformaciÃ³n Espectral
tsptogauR
Converts spectral coefficients to lon/lat
false
Variable after conversion
var'
4
ny
3
number of latitude points
false
false
nx
number of longitude points
2
var
Variable to be converted
1
false
applies inverse laplacian to variable in spectral coordinates
Applies inverse laplacian to variable in spectral coordinates
invlaplacian
2
var'
false
Inverse laplacian applied to stream in spectral coordinates
invlaplacian2
var
false
1
invlaplacian1
Varibable to which inverse laplacian will be applied
invlaplacian
Takes pairwise ln ratios along grid of variable
takes pairwise ln ratios along grid of variable
lnratios3
3
pairwise ln ratios taken along grid of stream
false
var'
2
lnratios2
grid
Grid to be appliced to pairwise ln ratios
false
var
1
lnratios1
false
Variable from which pairwise ln rations along grid of stream will be taken
lnratios
lnratios
abs
<br>
<b><u>Description</u></b><br><br><b>abs</b> applies sqrt[<i>num</i><sup>2</sup>]
to a variable stream of real numbers or a constant (<i>num</i>) to produce
its numerical magnitude without regard to its sign.<br><br><b><u>Example</u></b><br><br><font color="#008000">SOURCES
.NOAA .NCEP-NCAR .CDAS-1 .MONTHLY .Diagnostic .above_ground .u<br>abs </font><br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP-NCAR/.CDAS-1/.MONTHLY/.Diagnostic/.above_ground/.u/abs/">Live
Example Link</a><br>
absnum
absolute value of <i>num</i>
false
2
Returns the absolute value of a variable or a constant
num
false
variable or constant of which the absolute value is to be found
1
Returns the absolute value of a variable or a constant
converts daily data to dekadal by averaging
<p style="margin-top: 0">
<br>
<u><b>Description</b></u><br><br>A dekad is a unit of time with a
peculiar definition. There are three dekads in a calendar month. The
first ten days of a month constitute the first dekad of the month. The
second ten days constitute the second dekad of the month, and the
remaining days (8 to 11 days, depending upon the month) constitute the
third dekad.<br><br><b>dekadalAverage</b> calculates dekadal average
values from an input variable containing daily data.<br><br><u><b>Example</b></u><br><br><font color="#008000">SOURCES
.NOAA .NCEP .CPC .FEWS .SAsia .RFEv2 .DAILY .est_prcp<br>T (1 Jan 2007)
(30 Apr 2007) RANGE<br>dekadalAverage</font><br><br>This example selects
daily precipitation estimates from the CPC/FEWS South Asia RFE product
for the period 1 January to 30 April 2007 and calculates the dekadal
averages of the daily values such that there are average precipitation
values for 1-10 January, 11-20 January, 21-31 January, 1-10 February
2007, etc. The underlying time grid remains a daily grid, but the
average values are assigned to the dekadal intervals.<br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.CPC/.FEWS/.SAsia/.RFEv2/.DAILY/.est_prcp/T/%281%20Jan%202007%29%2830%20Apr%202007%29RANGE/dekadalAverage/">Live
Example Link</a><br>
</p>
data averaged by dekad
dekadalvar
false
3
dekadalAverage
dailyvar
false
1
daily data to be averaged, with units of units
2
true
minfrac
Minimum fraction of data that must be present (i.e., fraction not indicated as missing) within each dekad in order for each average to be calculated. If minfrac is not present, then a missing value is returned. If minfrac is not given, then the average is calculated regardless of the amount of data present.
converts daily data to dekadal by averaging
readgrib
Reads data from GRIB files
false
Data read from GRIB file
dataset
readgrib3
2
Name of file
1
filename
false
readgrib1
Reads data from GRIB files
readgrib
true
3
Minimum fraction of data that must be present (i.e., fraction not indicated as missing) within the selected domain in order for the function to be performed. If minfrac is not present, then a missing value is returned. If minfrac is not given, then the function is performed regardless of the amount of data present in domain.
minfrac
standardized data calculated by removing the mean from <i>var</i> and dividing by its standard deviation
false
stdvar
4
Standardizes a set of data by removing its mean and dividing the result by its standard deviation
Standardizes a set of data by removing its mean and dividing the result by its standard deviation
standardize
variable (i.e., data) to be standardized
1
var
false
SOURCES .NOAA .NCDC .ERSST .version2 .SST
<br>T (Jan 1974) (Dec 2003) RANGE
<br>[T]standardize
false
grids
2
grid(s) (i.e., independent variables) over which the mean and standard deviation are calculated
Ã‰chantillonage par Variable
Muestro por Variable
Sample by Variable
Samples variable1 along grid using variable2=grid
2
var2
Variable to be used
samplealong2
false
samples variable1 along grid using variable2=grid
var1
false
Variable to be sampled
1
sample-along1
false
var1'
Variable after being sampled along grid
4
sample-along4
sample-along
sample-along
false
Independent variable=variable2
sample-along3
ivar
3
scale_factor
1
new scale_factor for variable
false
scales
stream so that it has attributes scale_factor and add_offset
5
scaled data
scaled_variable
false
SCALE
false
0
variable to be scaled
variable
SOURCES .NOAA .NCEP .EMC .CMB .GLOBAL .Reyn_SmithOIv2 .weekly .ssta<br>
0.01 0 -999 SCALE<br>
toi2
true
missing_value
new missing_value for variable
3
scales
stream so that it has attributes scale_factor and add_offset
new add_offset for variable
2
add_offset
false
SM121
4
false
number of passes of smoothing to be applied
n
Applies two-dimensional 121 smoothing to a variable
2
false
grids
grids (1 or 2) over which 121 smoothing is to be applied
SOURCES .NOAA .NCEP .CPC .CAMS_OPI .climatology .prcp
<br>[X Y] 1 SM121
variable to be smoothed
false
1
var
Applies two-dimensional 121 smoothing to a variable
smoothvar
false
5
<i>var</i> after <i>n</i> passes of 121 smoothing has been applied over <i>grids</i>
5
var'
Variable sampled along one coordinate
false
false
range_high
Upper threshold of range (center of closest grid box)
4
Lower threshold of range (center of closest grid box)
false
range_low
3
Independent variable to be applied to variable
2
false
ivar
1
var
false
Variable to be sampled along
Samples variable along one coordinate. Works by creating a child object of the variable
RANGESPAN
int
interval (in units of <i>grid</i>) at which data will be selected
false
3
STEP
false
<i>ds/var</i> restricted to only data associated with <i>grid</i> values
sampled according to <i>int</i>
4
restricted_ds/var
false
1
dataset or variable (i.e., data) dependent on the grid that will sampled along
ds/var
Identifies interval at which data will be selected along one grid (i.e., independent variable). Commonly used with runningAverage to create and select single season averages.
Identifies interval at which data will be selected along one grid (i.e., independent variable). Commonly used with runningAverage to create and select single season averages.
<b><u>Description</u></b><br><br>The <b>STEP</b> command samples values at
interval <i>int</i> along the specified grid <i>grid</i>. The interval is
defined in terms of the units of <i>grid</i>, even if values along <i>grid</i>
are only defined and available at some other spacing.<br><br>For example,
a common calculation is to calculate 3-month seasonal averages from
monthly values using "T 3 <b>boxAverage</b>". Even though 3-month
seasonal average values have been calculated with the application of <b>boxAverage</b>
on grid "T", the underlying time grid is still monthly, not seasonal, and
the <b>STEP</b> command operates on this underlying monthly grid.<br><br><b><u>Example</u></b><br><br><font color="#008000">expert<br>SOURCES
.NOAA .NCEP .EMC .CMB .GLOBAL .Reyn_SmithOIv2 .monthly .sst<br>T (Jan
1982) (Dec 2000) RANGE<br>T 3 boxAverage<br>T 6 STEP</font><br><br>In the
example above, monthly sea surface temperatures from the Reynolds and
Smith version 2 dataset have been selected for the globe for the period
January 1982 to December 2000. The "T 3 <b>boxAverage</b>" command
has been used to calculate 3-month seasonal averages of SST such that the
first seasonal value is for January-March 1982 (centered on February 1982
on the monthly time grid), the second value is for April-June 1982
(centered on May 1982), the third seasonal value is for July-September
1982 (centered on August 1982), and so on. When "T 6 <b>STEP</b>" is
applied, values are sampled every six months, starting with the first
available value. Therefore, in this case, the first seasonal value,
January-March 1982, centered on February 1982, is selected, the second
seasonal value centered on May 1982 is skipped, the third seasonal value,
for July-September 1982, centered on August 1982 is selected, and so on.<br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.EMC/.CMB/.GLOBAL/.Reyn_SmithOIv2/.monthly/.sst/T/%28Jan%201982%29%28Dec%202000%29RANGE/T/3/boxAverage/T/6/STEP/">Live
Example Link</a><br><br>In the following example, one daily maximum
temperature value is sampled every seven days.<br><br><font color="#008000">expert<br>SOURCES
.NOAA .NCDC .GHCN_Daily .version1<br>STNID 30586 VALUE<br>.TMAX<br>T (1
Jan 1948) last RANGE<br>T 7 STEP</font><br><br>Here, daily maximum
temperature values from a station in the GHCN Daily Version 1 dataset have
been selected, starting from 1 January 1948 and continuing through the end
of the available record. With the application of "T 7 <b>STEP</b>",
starting from Thursday, 1 January 1948, the maximum temperature for this
station is sampled once per week, every Thursday, through the rest of the
data record.<br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCDC/.GHCN_Daily/.version1/STNID/30586/VALUE/.TMAX/T/%281%20Jan%201948%29last/RANGE/T/7/STEP/">Live
Example Link</a><br>
2
grid to be sampled along
false
grid
3
output named unit matrix depending on grid and grid_out
false
unitmatrix
name of output matrix
2
false
name
false
1
independent variable for the matrix
grid
creates a unit matrix
creates a unit matrix
unitmatrix
false
ds/var
1
dataset or variable to be summed
2
grid(s) over which the sum is taken
grid
false
sum
SOURCES .NOAA .NCDC .GDCN .PCPN
<br>ISTA 19210401 VALUE
<br>T (1 Jan 1900) (31 Dec 1970) RANGE
<br>[T]0.95 sum
<p>
OR
<p>
SOURCES .NOAA .NCEP .CPC .FEWS .TEN-DAY .RFEv2 .est_prcp
<br>T (1 Jan 2003) (31 Dec 2003) RANGE
<br>[T]sum
<p>
OR
<p>
SOURCES .NOAA .NCEP .CPC .FEWS .TEN-DAY .RFEv2 .est_prcp
<br>Y (6.5) (10) RANGE
<br>X (0) (5) RANGE
<br>T (6 Jun 2004) VALUES
<br>[X Y]sum
Returns the sum of a dataset or variable over a specified grid
Minimum fraction of data that must be present (i.e., fraction not indicated as missing) within the selected domain in order for the sum to be calculated. If <i>minfrac</i> is not present, then a missing value is returned. If <i>minfrac</i> is not given, then the sum is calculated regardless of the amount of data present in domain.
3
true
minfrac
Returns the sum of a dataset or variable over a specified grid
false
returned_sum
4
resulting value when the sum of <i>ds/var</i> is taken over <i>grid</i><p>
<i>returned_sum</i> will be of the same type as <i>ds/var</i> (i.e., either a dataset or a variable).
false
longitude coordinates for stations used in the analysis
longitude
1
<br>
<b><u>Detailed Syntax</u></b><br><br><i>longitudes</i><br><i>latitudes</i><br><i>data</i><br><b>[</b><i>IWMO</i><b>]</b>
<b>weaver:</b><br><b>X</b> <i>xmin</i> <i>xmax</i> <i>xstep</i> <b>RANGEEDGESTEP</b><br><b>Y</b>
<i>ymin</i> <i>ymax</i> <i>ystep</i> <b>RANGEEDGESTEP</b><br><i>nsdec</i> <b>setdecimal</b><br><i>nsp</i>
<b>setpass</b><br><i>nsc</i> <b>setcenterweight</b><br><i>tfsw</i> <b>setweave</b><br><i>tfse</i>
<b>setemphasize</b><br><b>:weaver</b><br><br><b><u>Input</u></b><br><br><i>longitudes</i>
is a stream of longitude coordinates for stations used in the analysis<br><i>latitudes</i>
is a stream of latitude coordinates for stations used in the analysis<br><i>data</i>
is a stream of data values at the stations<br><i>IWMO</i> is the name of
the grid of station identifiers<br><br><b><u>Parameters</u></b><br><br><b>X</b>
<i>xmin</i> <i>xmax</i> <i>xstep</i> <b>RANGEEDGESTEP</b> : specification
of the east-west domain of the analysis to be produced, in terms of
minimum and maximum longitude values and the grid step, in degrees. The
default values for <i>xmin</i>, <i>xmax</i>, and <i>xstep</i> are -180.,
180., and 2., respectively.<br><br><b>Y</b> <i>ymin</i> <i>ymax</i> <i>ystep</i>
<b>RANGEEDGESTEP</b> : specification of the north-south domain of the
analysis to be produced, in terms of minimum and maximum latitude values
and the grid step, in degrees. The default values for <i>ymin</i>, <i>ymax</i>,
and <i>ystep</i> are -90., 90., and 2., respectively.<br><br><i>nsdec</i> <b>setdecimal</b>
: <i>nsdec</i> is an integer that sets the number of decimal places after
the decimal point that should appear in the final values for each grid
point in the analysis. The default value is 3.<br><br><i>nsp</i> <b>setpass</b>
: <i>nsp</i> is an integer that specifies the number of times, above the
minimum of 2, that the "EMFASZ" subroutine of weaver is applied to the
analysis. This option applies only if both <b>setweave</b> and <b>setemphasize</b>
are set to "true". The default value is 2.<br><br><i>nsc</i> <b>setcenterweight</b>
: <i>nsc</i> is a number that controls whether and to what extent
weighting from surrounding grid points are applied to a given grid point
as refining passes are made through the analysis. If <i>nsc</i> is a
negative number, the original value of a grid point is preserved and is
not affected by surrounding grid points. If nsc is zero or larger, the
original value at a grid point will be refined using surrounding grid
points. The weight of the grid point will be equal to the product of the
sum of the weights given to the surrounding points and the value at the
grid point itself. This option applies only if <b>setweave</b> is set to
"true". The default value is -1.<br><br><i>tfsw</i> <b>setweave</b>
: <i>tfsw</i> can hold a value of either "true" or "false". If <b>setweave</b>
is "false" then the value assigned to any given grid box is simply the
unweighted average of the station values that fall within that grid box,
and a missing value is assigned to grid boxes that do not contain
stations. If <b>setweave</b> is "true" then the "WEAVE" interpolation
subroutine in <b>weaver</b>, which applies a 15-point weight from
surrounding grid points, is applied to the analysis grid. The default
value is "true".<br><br><i>tfse</i> <b>setemphasize</b> : <i>tfse</i>
can hold a value of either "true" or "false". If setemphasize is set to
"true", the "EMFASZ" subroutine in <b>weaver</b> is
applied to the analysis. Its purpose is to re-emphasize minima or maxima
in the analysis field that may have been over-smoothed. This option
applies only if <b>setweave</b> is set to "true". The default value is
"false".<br><br><b><u>Description</u></b><br><br><b>weaver</b>
is an objective analysis scheme developed at the U.S. Climate Prediction
Center in the 1970s. In the Data Library <b>weaver</b> is almost always
used with the <b>setweave</b> option set to "false". With <b>setweave</b>
set to "false", the analysis value assigned to each grid box is simply the
arithmetic average of the station values that fall within the grid box,
without any distance weighting applied. Grid boxes that do not contain
stations are assigned a missing value.<br><br><b><u>Example</u></b><br><br><font color="#008000">SOURCES
.NOAA .NCEP .CPC .CAMS .station .temperature<br>lon lat temp [IWMO]<br>weaver:<br>X
0 360 2 RANGEEDGESTEP<br>Y -90 90 2 RANGEEDGESTEP<br>false setweave<br>:weaver</font><br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.CPC/.CAMS/.station/.temperature/lon/lat/temp%5BIWMO%5Dweaver:/X/0/360/2/RANGEEDGESTEP/Y/-90/90/2/RANGEEDGESTEP/false/setweave/:weaver/">Live
Example Link</a>
data
false
3
data values at station
Performs a simplified weaver objective analysis
gridded data returned from objective analysis
<p>
For more information, please see the <a href="http://iridl.ldeo.columbia.edu/dochelp/StatTutorial/Interpolation/#Weaver">weaver examples</a> in the Statistical Analysis Tutorial
5
griddeddata
false
latitude
false
2
latitude coordinates for stations used in the analysis
Performs a simplified weaver objective analysis
false
grid(s) over which analysis is to be performed (most often the station id grid)
4
statgrid
:weaver
changes missing_values into NaNs. Uses either missing_value or valid_range
flags. output is real*4 or real*8
toNaN8
Changes missing_values into NaNs. Uses either missing_value or valid_range flags. output is real*4 or real*8
var'
2
false
toNaN82
Variable with missing values replaced with NaNs
var
false
toNaN81
1
Variable with missing values
toNaN8
Calculates the cosine of a number or variable (given in radians)
Calculates the cosine of a number or variable (given in radians)
Result
2
<i>Result</i> will be of the same type as <i>A</i> (i.e., either a variable or constant).
variable or constant (in radians) of which the cosine is to be taken
A
1
cos
<br>
<b><u>Description</u></b><br><br><b>cos</b> calculates the cosine of the
latest variable or constant item on the stack, expecting an input value in
radians rather than degrees.<br><br><b><u>Example</u></b><br><br><font color="#008000">pi
cos</font><br><br><a href="http://iridl.ldeo.columbia.edu/expert/pi/cos/">Live
Example Link</a>
5
last
false
Last value on ivar
7
New ivar evenly spaced from first to last with step
var'
false
ivar
false
2
Independent variable to be applied
evengridAverage
Step between first and last on new ivar
step
false
4
false
var
1
Variable to be regridded
First value on ivar
3
first
false
Regrids a variable to a new grid by averaging
true
6
Wmin
Returns the minimum value of a variable over a selected grid(s)
Returns the minimum value of a variable over a selected grid(s)
minover
var
false
variable of which minimum value is found
1
false
2
grid(s) over which minimum value is found
grid
Minimum fraction of data that must be present (i.e., fraction not indicated as missing) within theselected domain in order for the minimum value to be found. If minfrac is not present, then a missing value is returned. If minfrac is not given, then the minimum value is found regardless of the amount of data present in domain.
minfrac
true
3
SOURCES .NOAA .NCEP .CPC .CAMS .station .temperature .temp
<br>IWMO (1001) VALUES
<br>[T]0.9 minover
<p>
OR
<p>
SOURCES .NOAA .NCEP .CPC .GMSM .w
<br>X 112 153 RANGE
<br>Y -44 -11 RANGE
<br>[X Y]minover
4
minvar/num
minimum value of <i>var</i> within selected domain of <i>grid</i>
<p>
<i>minvar/num</i> is no longer dependendent on <i>grid</i>, but is still dependent on any other grids that <i>var</i> depended on (if any)
false
Explicit:endLoop
boucle
bucle
looping
marks the end of a Loop started by beginLoop
2
outvar
false
output variable: it is the same as loopvar for beginLoop, except that it also has data corresponding to the last value of beginLoop's ivar.
loopvar
Loop variable (used by beginLoop for values subsequent to the initial condition)
false
1
endLoop
marks the beginning of a loop
beginLoop
<table align="right">
<tr>
<td align="center">
<a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.CAC/.ssta/T/%28Nov%201982%29VALUE/grid://name//T/def//units/%28months%20since%201960-01-01%29def/274.5/1./300.5/:grid/beginLoop/0.9/mul/endLoop/figviewer.html?my.help=more+options&map.T.plotvalue=16+Oct+1982+-+15+Feb+1985&map.Y.units=degree_north&map.Y.plotlast=30N&map.url=X+Y+fig-+colors+land+-fig&map.domain=+%7B+/T+274.+301.+plotrange+%7D&map.domainparam=+/plotaxislength+300+psdef+/plotborder+72+psdef+/XOVY+null+psdef&map.zoom=Zoom&map.Y.plotfirst=30S&map.X.plotfirst=123E&map.X.units=degree_east&map.X.modulus=360&map.X.plotlast=69W&map.ssta.plotfirst=-4.2&map.ssta.units=degree_Celsius&map.ssta.plotlast=4.2&map.newurl.grid0=X&map.newurl.grid1=Y&map.newurl.land=draw+land&map.newurl.plot=colors&map.plotaxislength=300&map.plotborder=72&map.fnt=Helvetica&map.fntsze=12&map.XOVY=auto&map.color_smoothing=1&map.framelbl=framelabelstart&map.framelabeltext=&map.iftime=25&map.mftime=25&map.fftime=200" onmouseover="self.status='Click here to explore the data';return true"><img nosave="#DEFAULT" height="179" border="0" alt="Click for options and more information" width="372" title="Click for options and more information" src="http://iridl.ldeo.columbia.edu/expert/SOURCES/.CAC/.ssta/T/%28Nov%201982%29VALUE/grid://name//T/def//units/%28months%20since%201960-01-01%29def/274.5/1./300.5/:grid/beginLoop/0.9/mul/endLoop/X+Y+fig-+colors+land+-fig+//T/274.5/301./plotrange+//plotaxislength+300+psdef//plotborder+72+psdef//XOVY+null+psdef+.gif">
</a><br>
<i>x = CAC [ ssta * 0.9 ]</i>
</td>
</tr>
</table>
<h4>
Simple decay of an initial map
</h4>
For example, a model that simply decays an initial SSTA anomaly could be
written as
<pre>SOURCES .CAC .ssta
T (Nov 1982) VALUE
grid:
/name /T def
/units (months since 1960-01-01) def
274.5 1. 300.5 :grid
beginLoop
0.9 mul
endLoop</pre>
<br clear="all">
<table align="right">
<tr>
<td align="center">
<a href="http://iridl.ldeo.columbia.edu/expert/c:/1/:c/grid://name//T/def//units/%28months%20since%201960-01-01%29def/274.5/1./300.5/:grid/beginLoop/0.9/mul/endLoop/figviewer.html?my.help=more+options&map.unnamed.units=unitless&map.unnamed.plotlast=1.&map.url=T+fig-+line+-fig&map.domainparam=+/plotaxislength+432+psdef+/plotborder+72+psdef&map.zoom=Zoom&map.unnamed.plotfirst=0.06461077&map.T.plotfirst=0000+1+Nov+1982&map.T.units=months+since+1960-01-01&map.T.calendar=360&map.T.plotlast=2400+30+Jan+1985&map.newurl.grid0=T&map.newurl.plot=line&map.plotaxislength=432&map.plotborder=72&map.fnt=Helvetica&map.fntsze=12&map.XOVY=auto&map.color_smoothing=1&map.framelbl=framelabelstart&map.framelabeltext=" onmouseover="self.status='Click here to explore the data';return true"><img nosave="#DEFAULT" height="222" border="0" alt="Click for options and more information" title="Click for options and more information" width="372" src="http://iridl.ldeo.columbia.edu/expert/c:/1/:c/grid://name//T/def//units/%28months%20since%201960-01-01%29def/274.5/1./300.5/:grid/beginLoop/0.9/mul/endLoop/T+fig-+line+-fig+//unnamed/0.06461077/1./plotrange+//plotaxislength+300+psdef//plotborder+72+psdef//XOVY+null+psdef+.gif">
</a><br>
<i>x = unnamed * 0.9</i>
</td>
</tr>
</table>
<h4>
Simple Decay from initial condition 1.
</h4>
<pre> c: 1 :c
grid:
/name /T def
/units (months since 1960-01-01) def
274.5 1. 300.5 :grid
beginLoop
0.9 mul
endLoop</pre>
3
false
loopvar
loop variable: first time through, it is equal to the initial condition (initial). Subsequently, it is equal to what is passed to endLoop (or the increment applied by Explicit:endLoop or L4cycle:endLoop). This happens one less time than the number of elements in ivar.
values that label the interations of the loop
ivar
false
2
initial condition for the loop
false
initial
1
L4cycle:endLoop
ends a Lorenz 4-cycle scheme integration loop
L4cycle:endLoop
Ends a Lorenz 4-cycle scheme integration loop
L4cycle:endLoop2
false
h
2
false
dh
1
L4cycle:endLoop1
Explicit:endLoop2
h
false
2
dh
false
1
Explicit:endLoop1
Explicit:endLoop
ends an explicit integration loop
Ends an explicit integration loop
Pairwise sums along independent variable of variable. This is the variable
equivalent of integralgrid
pairsums
pairsums
3
pairsums3
false
Pairwaise sums along grid of stream
This is the stream equivalent of integralgrid
var'
pairsums2
ivar
2
Independent variable along which pairwise sums will be taken
false
Variable of which pairwise sums along the grid will be taken
false
var
pairsums1
1
pairwise sums along independent variable of variable. This is the variable equivalent of integralgrid
The resulting sum of <i>A </i> and <i>B</i>. <p>If either <i>A</i> or <i>B</i> are variables, then <i>sum</i> will be a variable. If both <i>A</i> and <i>B</i> are constants, then <i>sum</i> will also be a constant.
sum
3
A
1
variable or constant to be added
<br>
<b><u>Description</u></b><br><br><b>add</b> adds together the two
previously listed variables or constants to produce one resulting variable
or constant.<br><br><b><u>Example</u></b><br><br><font color="#008000">SOURCES
.NOAA .NCEP .CPC .GMSM .w<br>5 add</font><br><br>This example simply adds
a value of 5 to all GMSM soil moisture values.<br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.CPC/.GMSM/.w/5/add/">Live
Example Link</a>
B
2
variable or constant to be added
add
Adds the last two items on the stack
Adds the last two items on the stack
isolines2
false
X
2
Y
isolines3
3
false
1
false
Variable from which data will be computed from
var
isolines1
computes isolines from a gridded dataset. Should be changed to output a
geometry.
isolines
Computes isolines from a gridded dataset. Should be changed to output a geometry
isolines7
GRDS
false
7
YS
isolines6
6
false
5
false
XS
isolines5
isolines
4
false
levels
isolines4
false
indvar
2
grid(s) (i.e., independent variables) over which the root mean square is to be calculated
SOURCES .NOAA .NCEP .CPC .FEWS .TEN-DAY .RFEv2 .est_prcp
<br>[T]rmsaover
Minimum fraction of data that must be present (i.e., fraction not indicated as missing) within the selected domain in order for the average to be calculated. If minfrac is not present, then a missing value is returned. If minfrac is not given, then the average is calculated regardless of the amount of data present in domain.
minfrac
3
true
root mean square anomaly of <i>var</i> over <i>indvar</i>
<p> <i>rmsavar/num</i> is no longer dependendent on <i>indvar</i>, but is still dependent on any other grids that <i>var</i> depended on (if any)
false
4
rmsavar/num
variable of which the root mean square is to be calculated
var
false
1
rmsaover
Returns the root mean square averaged over selected grids (i.e., independent variables) with the mean removed
Returns the root mean square averaged over selected grids (i.e., independent variables) with the mean removed
Variable after sample has been taken along ivar1
false
var'
4
false
ivar2
Independent variable to which ivar1 is based on
3
false
Independent variable from which variable will be sampled along
2
ivar1
var
1
Variable to be sampled
false
Samples variable along ivar1 according to ivar2
SAMPLE
false
3
NewName
new name of <i>grid</i>
SOURCES .NASA .GSFC .TOMS .NIMBUS7 .monthly
<br>T (Time) renameGRID
1
ds/var
dataset or variable (i.e., data) dependent on grid to be renamed
false
ds_var2
false
4
<i>ds/var</i> dependent on renamed grid
renameGRID
Assigns new name to an existing grid (i.e., independent variable)
Assigns new name to an existing grid (i.e., independent variable)
2
grid to be renamed
false
grid
var
variable (i.e., data) of which distribution is to be found
1
false
false
dist
7
cumulative frequency distribution of <i>var</i> within range and intervals specified by <i>lower</i>, <i>upper</i>, and <i>step</i>
width of intervals (in units of <i>var</i>) used to find distribution
step
false
5
false
upper bound of range over which distribution is to be found
4
upper
SOURCES .NOAA .NCEP .CPC .GMSM .w
<br>Y (20) VALUE
<br>X (50) VALUE
<br>T (Jul 1948-2003) VALUES
<br>DATA 0 50 0.5 RANGESTEP
<br>integrateddistrib1D
integrateddistrib1D
DATA
false
2
Returns the cumulative frequency distribution of a set of data for a specified range and step interval.
3
false
lower
lower bound of range over which distribution is to be found
false
6
RANGESTEP
poestudnt2
3
<p style="margin-top: 0">
variable or constant y that is positive quantiles of the Student's t-distribution.
</p>
y
dof
false
<p style="margin-top: 0">
degree of freedom
</p>
2
1
<p style="margin-top: 0">
two-tailed probability probability between 0 and 2.
</p>
t
false
Computes the threshold value of a probability of exceeding in a Student-t
distribution
Calculates positive quantiles of the Student's t-distribution at the
two-tailed probability pr.
<p>
Example:
</p>
<p>
This is the value of the SST with a 50% chance to be exceeded:
</p>
<p>
SOURCES .NOAA .NCEP .EMC .CMB .GLOBAL .Reyn_SmithOIv2 .monthly .sst
</p>
<p>
dup dup
</p>
<p>
[T]average
</p>
<p>
exch
</p>
<p>
[T]rmsaover
</p>
<p>
0.5 dup
</p>
<p>
2.0 mul
</p>
<p>
5 -1 roll
</p>
<p>
dataflag
</p>
<p>
[T]sum
</p>
<p>
1 sub
</p>
<p>
poestudnt2
</p>
<p>
exch round
</p>
<p>
-2.0 mul
</p>
<p>
1.0 add
</p>
<p>
mul
</p>
<p>
mul
</p>
<p>
add
</p>
poestudnt
<p style="margin-top: 0">
variable of constant y that is a probability of exceedance in a
Student-t distribution of x if applied to t=(x-f)/s
</p>
y
3
false
dof
2
<p style="margin-top: 0">
degree of freedom
</p>
false
<p style="margin-top: 0">
variable or constant t of the form t=(x-s)/f
</p>
1
t
Computes the probability of exceeding in a Student-t distribution
calculates the probability of exceedance of a Student-t distribution when
applied to t=(x-f)/s where f is the mean and s the standard deviation of
the distribution.
<p>
Example
</p>
<p style="margin-top: 0">
This is the probability of SST to exceed 20 deg C
</p>
<p style="margin-top: 0">
</p>
<p style="margin-top: 0">
SOURCES .NOAA .NCEP .EMC .CMB .GLOBAL .Reyn_SmithOIv2 .monthly .sst
</p>
<p style="margin-top: 0">
dup dup
</p>
<p style="margin-top: 0">
[T]rmsaover
</p>
<p style="margin-top: 0">
exch
</p>
<p style="margin-top: 0">
[T]average
</p>
<p style="margin-top: 0">
-1 mul
</p>
<p style="margin-top: 0">
20 add
</p>
<p style="margin-top: 0">
exch div
</p>
<p style="margin-top: 0">
exch dataflag [T]sum
</p>
<p style="margin-top: 0">
1 sub
</p>
<p style="margin-top: 0">
poestudnt
</p>
Returns the cumulative frequency distribution of a set of data for a specified range and step interval.
divides var1 by var2. Points less than minimum in var2 become NaN
in the output
Var1 divided by var2
Points less than minimum in var2 replaced with NaN
var'
normalize4
4
false
normalize3
minimum
Minimum value allowed before being replaced with NaN
false
3
normalize
normalize2
Variable dividing var1
2
false
var2
Divides var1 by var2. Points less than minimum in var2 become NaN in the
output
Variable to be divided by var2
false
1
normalize1
var1
normalize
variable or constant of which the natural log is to be taken
1
A
ln
Calculates the natural log of a number or variable
SOURCES .NOAA .NCDC .ERSST .version2 .SST
<br>ln
logarithme nÃ©pÃ©rien
logaritmo neperiano
natural logarithm
2
<i>Result</i> will be of the same type as <i>A</i> (i.e., either a variable or constant).
Result
Calculates the natural log of a number or variable
maskle
SOURCES .NOAA .NCDC .ERSST .version2 .SST
<br>28 maskle
Masks out data values less than or equal to a specified threshold
Masks out data values less than or equal to a specified threshold
maskedvar
<i>var</i> with data values less than or equal to <i>maskval</i> replaced by missing value indicator
3
false
false
var
variable on which mask is to be applied
1
false
2
threshold value on which mask is basd
maskval
C
false
2
A
false
1
erfc
<p style="margin-top: 0">
computes complementary error function from variable
</p>
3
Variable after reordered
false
var'
Independent variables corresponding to n dimensions
false
2
ivar
var
1
false
Variable to be reordered
REORDER
Reorders variable so that the chunk for the variable has n dimensions and is ordered with the fastest varying dimension corresponding to ivar1, the second to ivar2, ect
datarank
<br>
<u><b>Description</b></u><br><br><b>datarank</b> converts the n values
along the grid(s) of an input variable to integer ranks from 1 to n along
the specified grid(s) in the data set, with 1 corresponding to the largest
input value and n corresponding to the smallest value.<br><br><u><b>Example</b></u><br><br><font color="#008000">SOURCES
.NOAA .NCDC .ERSST .version2 .SST<br>X (-170) (-120) RANGE<br>Y (-5) (5)
RANGE<br>T (Jan 1971) (Dec 2000) RANGE<br>[X Y]average<br>T 12
splitstreamgrid<br>[T2]datarank</font><br><br>In this example, monthly sea
surface temperature values from the ERSST data set are selected from 1971
to 2000 and spatially averaged over the Nino 3.4 region in the equatorial
Pacific to produce a single monthly time series for the region. The "T 12
splitstreamgrid" command splits the monthly time grid into two component
grids -- "T", which is a 12-point periodic grid that indexes the months of
the year, and "T2", which is a 30-point grid that indexes the years 1971
to 2000. [T2] <b>datarank</b> converts the SST values to ranks along the
"T2" grid of years. As a result, for each month of the year, SST values
from 1971 to 2000 are ranked from largest (1) to smallest (30).<br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCDC/.ERSST/.version2/.SST/X/%28-170%29%28-120%29RANGE/Y/%28-5%29%285%29RANGE/T/%28Jan%201971%29%28Dec%202000%29RANGE%5BX/Y%5Daverage/T/12/splitstreamgrid%5BT2%5Ddatarank/">Live
Example Link</a><br><br>
Ranks data over a selected independent variable(s)
false
grids
grid(s) (i.e., independent variables) over which data is to be ranked
2
same as <i>var</i> except values of <i>rankvar</i> are the ranks of corresponding values of <i>var</i>; <i>rankvar</i> is dependent on the same grids as <i>var</i>
3
rankvar
false
var
data to be ranked
1
false
Ranks data over a selected independent variable(s)
Unchanged variable
false
3
matchtruncation3
var1
matchtruncation
matchtruncation
false
matchtruncation1
1
var1
Variable to be compared to
matchtruncation2
var2
2
false
Variable to be truncated to match the first
Changes second variable to match truncation of the first
4
var2'
false
matchtruncation4
Variable truncated to match the first
changes second variable to match truncation of the first
yearly-anomalies
Calculates anomalies based on climatology of all selected years
Calculates anomalies based on climatology of all selected years
false
anomvar
<i>var</i> with monthly climatology subtracted (i.e., monthly anomalies of <i>var</i>)
2
var
1
data of which anomalies are to be calculated
<p>
Note that the data must be monthly (i.e., units of time grid must be months).
false
SOURCES .UNH .CSRC .RivDIS .dischrg
<br>ISTA 518 VALUE
<br>T (Jan 1930) (Dec 1979) RANGE
<br>yearly-anomalies
Combines two grids (i.e., independent variables) that were created by the splitstreamgrid function (i.e., undoes splitstreamgrid)
3
false
varout
same as <i>var</i> except now dependent on <i>grid_combined</i> instead of <i>grid</i> and <i>grid</i>2
Combines two grids (i.e., independent variables) that were created by the splitstreamgrid function (i.e., undoes splitstreamgrid)
SOURCES .NOAA .NCEP .EMC .CMB .GLOBAL .Reyn_SmithOIv2 .monthly .sst
<br>T (Jan 1982) (Dec 2003) RANGE
<br>T 12 splitstreamgrid
<br>T unsplitstreamgrid
grid containing combined values of <i>grid</i> and <i>grid</i>2
4
false
grid_combined
variable dependent on grids to be combined
false
1
var
unsplitstreamgrid
grid
2
false
grid that was split with splitstreamgrid (see <i>grid_in</i> and <i>gridout</i> in splitstreamgrid function documentation)
randomdata
randomdata
Random numbers
false
randomdata2
var'
2
1
Variable to be converted
false
randomdata1
var
returns a variable of random numbers
Returns a variable of random numbers
SOURCES .NOAA .NCDC .GDCN
<br>ISTA 305801 305811 VALUES
<p>
OR
<p>
SOURCES .NOAA .NCDC .GDCN .TMAX
<br>ISTA (305801) (305811) VALUES
Identifies values along one grid (i.e., independent variable) for which data will be selected
3
value in grid to be selected
val1
grid in which values will be selected
2
grid
VALUES
ds/var
1
dataset or variable dependent on the grid that will be sampled along
false
valn
5
value in grid to be selected
dataset or variable (<i>ds/var</i>) restricted to only data associated with <i>grid</i> values <i>val1</i>, ..., <i>valn</i>; applied to all variables in the dataset if no variable selected
6
restricted_ds/var
Identifies values along one grid (i.e., independent variable) for which data will be selected
...
4
values in grid to be selected
Shifts a grid (i.e., independent variable) by a specified number of grid points
shiftGRID
num
number of grid points the grid is to be shifted. <i>num</i>>0 (<i>num</i><0) shifts grid forwards (backwards)
3
false
false
ds/var
1
data dependent on the grid (i.e., independent variable) to be shifted
4
false
ds/varshift
same as <i>ds/var</i> with <i>grid</i> relabeled according to a shift of <i>num</i> grid points
SOURCES .IRI .FD .ECHAM4p5 .Forecast .psst .ensemble12 .MONTHLY .PressureLevel-SF .phi
<br>M -12 shiftGRID
<p>OR<p>
SOURCES .IRI .Analyses .SPI .SPI-CMAP0407v1_3-Month
<br>T
/pointwidth 1 def
<br>pop
<br>T 1 shiftGRID
Shifts a grid (i.e., independent variable) by a specified number of grid points
2
grid
false
grid (i.e., independent variable) to be shifted
Calculates the sine of a number or variable (given in degrees)
<i>Result</i> will be of the same type as <i>A</i> (i.e., either a variable or constant).
2
Result
A
1
variable or constant (in degrees) of which the sine is to be taken
sind
Calculates the sine of a number or variable (given in degrees)
90 sind
Changes second stream to match truncation of the first
returns mean square of A values. If [ grid1 ... ] is given, then the mean
AM will
meansqover
meansqover
3
false
meansqover3
AM
false
2
grid
meansqover2
meansqover1
A
false
1
ginverse
Computes the (transposed) generalized inverse with the ivar1 grids as the first dimentsion of the matrix and the ivar2 grids as the second dimension of the matrix
inverse
Generalized inverse
5
false
4
optional taper to be added to diagonal of covariance before inverting. Written as fraction of the total variance, i.e. .001 signifies a .1% taper
stab
true
false
ivar2
3
Second dimension of the matrix
ivar1
false
2
First dimension of the matrix
Variable from which inverse will be computed
1
false
var
Calculates the square root of a number or variable. The sign (+/-) of argument is preserved in the result.
false
1
variable or constant of which the square root is to be taken
var/num
sqrtsgn
-9 sqrtsgn
Result
2
square root of <i>var/num</i> with the same sign as <i>var/num</i>. <p>
Result will be of the same type as <i>var/num</i> (i.e., either a variable or constant).
false
Calculates the square root of a number or variable. The sign (+/-) of argument is preserved in the result.
<br>
<b><u>Description</u></b><br><br><b>addGRID</b> attaches a new
single-valued grid (independent variable) to the latest variable on the
stack.<br><br><b><u>Example</u></b><br><br><font color="#008000">SOURCES
.UEA .CRU .Jones .CRUTEM3 .tanom<br>/Z /meters ordered [2] NewGRID<br>addGRID</font><br><br>This
example creates and adds a new, single-valued height (Z) grid to the
CRUTEM3 land-only surface temperature anomaly data set. "/Z /meters
ordered [2] NewGRID" creates a new grid called "Z", with units in meters,
that is ordered, and has a single value of 2. <b>addGRID</b> then attaches
this grid to the temperature anomaly data set.<br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.UEA/.CRU/.Jones/.CRUTEM3/.tanom//Z//meters/ordered%5B2%5DNewGRID/addGRID/">Live
Link Example</a>
addGRID
Adds a single-valued independent variable
3
Variable with single-valued independent variable added
var'
false
2
ivar
Independent variable to be applied to variable
false
1
var
false
Variable to be manipulated
2
variable
false
variable to be added
add_variable
dup type /stringtype eq {cvn}if dup type /nametype eq {3}{2}ifelse /add_variable publicproc: dup type /nametype eq {exch 1 object exch cvx /name exch def}if exch 1 object exch name exch def :publicproc
adds a variable to a dataset
dataset
1
dataset to contain additional variable
false
new dataset with added variable
false
4
dataset'
true
new name of variable
new name
3
Organisation
OrganizaciÃ³n
Organizing
false
beta
beta4
4
beta
3
Pressure
beta3
P
false
2
false
beta2
S
Salinity
beta
<br>
<u><b>Description</b></u><br><br><b>beta</b> uses potential temperature,
salinity, and pressure values in the ocean to calculate beta (the saline
contraction coefficient).<br><br><u><b>References</b></u><br><br>McDougall,
T. J., 1987: Neutral Surfaces. <i>Journal of Physical Oceanography</i>, <b>17</b>,
1950-1964.<br><br><u><b>Example</b></u><br><br><font color="#008000">SOURCES
.GEOSECS .THETA<br>SOURCES .GEOSECS .SAL<br>SOURCES .GEOSECS .PRESS<br>beta</font><br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.GEOSECS/.THETA/SOURCES/.GEOSECS/.SAL/SOURCES/.GEOSECS/.PRESS/beta/">Live
Example Link</a><br>
theta
beta1
false
1
Potential Temperature
saline contraction coefficient in the ocean
combines variables into a single variable
false
input dataset that variables are selected from
1
A
grouptogrid
3
AV
false
concatenation of variables: there is an additional grid <i>M</i> whose values are the names of the variables
combines variables into a single variable
name
2
name(s) of variables to be included in variable
false
<p style="margin-top: 0">
<br>
<u><b>Description</b></u><br><br><b>grouptogrid</b> groups data from a
set of n dependent variables (<i>name<sub>1</sub></i>, <i>name<sub>2</sub></i>,
..., <i>name<sub>n</sub></i>) from a data set in the Data Library as if
they were all from one variable dependent upon a newly-created grid (M)
with grid points named after the original dependent variables.<br><br><u><b>Example</b></u><br><br>In
the following example, <b>grouptogrid</b> is applied to the monthly mean
zonal (u) and meridional (v) wind variables (which originally depend
upon pressure level (P), time (T), longitude (X), and latitude (Y)
grids) from the NCEP-NCAR Reanalysis data set. The result is a single
variable containing both the zonal and meridional wind data, but which
is now indexed by the original P, T, X, and Y grids and, additionally, a
new 2-point grid named "M" whose grid values are named "u" (to which the
zonal wind values are assigned) and "v" (to which the meridional wind
values are assigned).<br><br><font color="#008000">SOURCES .NOAA
.NCEP-NCAR .CDAS-1 .MONTHLY .Intrinsic .PressureLevel<br>{u v}grouptogrid</font><br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP-NCAR/.CDAS-1/.MONTHLY/.Intrinsic/.PressureLevel/%7Bu/v%7Dgrouptogrid/">Live
Example Link</a>
</p>
Calculates a monthly climatology
false
monthly climatology of <i>var</i> based on all selected years. <i>climovar</i> is dependent on a time grid with 12 grid points (i.e., Jan, Feb, ..., Nov, Dec).
climovar
2
1
data of which monthly climatology is to be calculated
<p>Note that the data must be monthly (i.e., units of time grid must be months).
var
false
yearly-climatology
Calculates a monthly climatology
SOURCES .UNH .CSRC .RivDIS .dischrg
<br>ISTA 518 VALUE
<br>T (Jan 1930) (Dec 1979) RANGE
<br>yearly-climatology
Computes eigenvalues and eigenvectors, returns low to high (but indexes the hightest one as 1
eigrs
3
Eigenvectors
Z
false
Eigenvalues
2
D
false
false
Covariance
1
Q
gridtomatchnamed
4
false
Variable1 regridded to match variable2
var1'
false
3
Name of object
object name
var2
2
false
Variable to which variable1 will be regridded to
var1
Variable to be regridded
false
1
<br>
<b><u>Description</u></b><br><br><b>addGRIDlast</b> attaches a new
single-valued grid (independent variable) to the latest variable on the
stack and makes the grid the last grid in the variable chunk.<br><br><b><u>Example</u></b><br><br><font color="#008000">SOURCES
.UEA .CRU .Jones .CRUTEM3 .tanom<br>/Z /meters ordered [2] NewGRID<br>addGRIDlast</font><br><br>This
example creates and adds a new, single-valued height (Z) grid to the
CRUTEM3 land-only surface temperature anomaly data set. "/Z /meters
ordered [2] NewGRID" creates a new grid called "Z", with units in meters,
that is ordered, and has a single value of 2. <b>addGRIDlast</b> then
attaches this grid to the temperature anomaly data set, making it the last
grid in the data chunk.<br><br>In the Live Link Example the top line of
the data set page shows the name of the selected data set and variable and
the ordering of the data set grids. In this example, using <b>addGRIDlast</b>,
the grids are ordered as [X Y | T Z]. If addGRID were used instead, the
grids would be ordered as [X Y | Z T].<br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.UEA/.CRU/.Jones/.CRUTEM3/.tanom//Z//meters/ordered%5B2%5DNewGRID/addGRIDlast/">Live
Link Example</a>
addGRIDlast
Adds a single-valued independent variable as the slowest-varying independent variable
false
var'
Variable with single-valued independent variable as the slowest-varying independent variable added
3
ivar
false
2
Independent variable to be applied to variable
Variable to be manipulated
1
var
Calculates the logarithm of a number or variable, with base 10
6 log
log
Calculates the logarithm of a number or variable, with base 10
Result
2
<i>Result</i> will be of the same type as <i>A</i> (i.e., either a variable or constant).
A
1
variable or constant of which the log (base 10) is taken
var
false
1
variable on which mask is to be applied
Masks out data values less than a specified threshold
SOURCES .NOAA .NCDC .ERSST .version2 .SST
<br>28 masklt
maskval
false
2
threshold value on which mask is based
<i>var</i> with data values less than <i>maskval</i> replaced by missing value indicator
false
3
maskedvar
Masks out data values less than a specified threshold
masklt
4
keeps independent variables averaged over in output as single values grids
true
keepgrids
1
variable to be averaged
false
var
Calculates the average
Calculates the average
5
<i>var</i> averaged over <i>grid</i>. <p><i>avgvar</i> is no longer dependent on <i>grid</i>, but is still dependent on any other grids that <i>var</i> depended on (if any)
false
avgvar/num
true
minfrac
Minimum fraction of data that must be present (i.e., fraction not indicated as missing) within the selected domain in order for the average to be calculated. If minfrac is not present, then a missing value is returned. If minfrac is not given, then the average is calculated regardless of the amount of data present in domain.
3
average
false
grid(s) over which the average is to be calculated
grid
2
average
<br>
<b><u>Description</u></b><br><br><b>average</b> calculates a simple
average, over the grid(s) included in the brackets, of the values in the
latest variable on the stack. The optional <i>minfrac</i> argument is a
number between 0. and 1.0 that indicates what fraction of data must be
non-missing over the averaging grid in order for a non-missing result to
be produced. If <i>minfrac</i> is not specified, its default value is 0.,
and an average is produced using all available data over the averaging
grid.<br><br><b><u>Example</u></b><br><br><font color="#008000">SOURCES
.NOAA .NCDC .ERSST .version2 .SST<br>X (170W) (120W) RANGE<br>Y (5S) (5N)
RANGE<br>[X Y]average</font><br><br>This example averages over both
longitude (X) and latitude (Y) to produce an average sea surface
temperature value (for each time step) over the equatorial Pacific Ocean.<br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCDC/.ERSST/.version2/.SST/X/%28170W%29%28120W%29RANGE/Y/%285S%29%285N%29RANGE%5BX/Y%5Daverage/">Live
Example Link</a>
<p>
OR
</p>
<p>
<font color="#008000">SOURCES .NOAA .NCDC .GHCN .v2beta .prcp<br>IWMO
60354001 VALUE<br>T (Jan 1960) (Dec 1979) RANGE<br>[T]0.5 average</font><br><br>This
example produces the average monthly precipitation value for Algiers,
Algeria, over the period January 1960 to December 1979, if at least half
the monthly precipitation values over that time period are not missing.<br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCDC/.GHCN/.v2beta/.prcp/IWMO/60354001/VALUE/T/%28Jan%201960%29%28Dec%201979%29RANGE%5BT%5D0.5/average/">Live
Example Link</a>
</p>
changes missing_values into NaNs. Uses either missing_value or valid_range
flags. output is real*4.
toNaN
toNaN
Changes missing_values into NaNs. Uses either missing_value or valid_range flags. output is real*4
Variable with missing values replaced with NaNs
false
var'
2
toNaN2
1
var
Variable with missing values
toNaN1
false
Masks out all values of a variable not included in the indicated range. Commonly used to select stations within a particular lat/lon range (see Live Example).
3
upper threshold of range
range_max
variable on which mask will be applied
1
variable
restricted_var
<i>variable</i> with all values outside of range specified by <i>range_min</i> and <i>range_max</i> masked out
4
masknotrange
range_min
2
lower threshold of range
Masks out all values of a variable not included in the indicated range. Commonly used to select stations within a particular lat/lon range (see Live Example).
<u><b>Description</b></u><br><font color="#008000"><b>masknotrange</b></font><font color="#000000">
masks out all values of a variable not included in the indicated range.
Commonly used to select stations within a particular lat/lon range.</font><font color="#008000"><br></font><u><b>Example</b></u><br><font color="#008000">lat
-20 20 masknotrange</font><br><font color="#800000"><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCDC/.GDCN/lon/290/340/masknotrange/SELECT/lat/-10/10/masknotrange/SELECT/">Live
Example Link</a></font>
pi sin
Calculates the sine of a number or variable (given in radians)
2
Result
<i>Result</i> will be of the same type as <i>A</i> (i.e., either a variable or constant).
variable or constant (in radians) of which the sine is to be taken
1
A
sin
Calculates the sine of a number or variable (given in radians)
3
selectbyNaN3
false
var1'
Variable with missing_values replaced
Variable from which missing values will be replaced with
var2
selectbyNaN2
2
false
1
false
selectbyNaN1
Variable from which missing values will be replaced
var1
selectbyNaN
Replaces missing_values in variable2 with variable1. Documentation carried
primarily from variable1, however, making it a bit different that a
reversed replaceNaN
selectbyNaN
replaces missing_values in variable2 with variable1. Documentation carried
primarily from variable1, however, making it a bit different that a reversed
replaceNaN.
6
lagvar
false
Same as <i>var</i> except:
<br>1. <i>grid</i> has smaller range of values such that its minimum value is now (original minimum - <i>low</i>) and its maximum value is now (original maximum - <i>high</i>)
<br>2. <i>lagvar</i> dependent on new grid, <i>grid</i>_lag, which has a minumum value of <i>low</i>, maximum value of <i>high</i>, and a step interval of <i>step</i>
1
false
data to be shifted
var
Shifts data to create lagged versions of the data. Commonly used to calculate lag correlations.
false
3
minimum of range of grid steps to lag. Negative (positive) values indicate a forward (backward) shift in the data along <i>grid</i>. For instance, if <i>low</i> = -6, a data value assigned to <i>grid</i> = n before <b>shiftdatashort</b> is applied (or, equivalently, to <i>grid</i> = n at <i>grid</i>_lag = 0 once <b>shiftdatashort</b> is applied) is assigned to <i>grid</i> = n - (-6) = n + 6 at <i>grid</i>_lag = -6 when <b>shiftdatashort</b> is applied.
low
Shifts data to create lagged versions of the data. Commonly used to calculate lag correlations.
maximum of range of grid steps to lag. Negative (positive) values indicate a forward (backward) shift in the data along <i>grid</i>. For instance, if <i>high</i> = +6, a data value assigned to <i>grid</i> = n before <b>shiftdatashort</b> is applied (or, equivalently, to <i>grid</i> = n at <i>grid</i>_lag = 0 once <b>shiftdatashort</b> is applied) is assigned to <i>grid</i> = n - (+6) = n - 6 at <i>grid</i>_lag = +6 when <b>shiftdatashort</b> is applied.
false
high
5
<u><b>Description</b></u><br><b>shiftdatashort</b> shifts data to create
lagged versions of the data. Commonly used to calculate lag correlations<br><u><b>Example</b></u><br><font color="#008000">SOURCES
.Indices .soi .standardized<br>T (Jan 1985) (Dec 2003) RANGEEDGES<br>T -6
1 6 shiftdatashort<br></font><font color="#800000"><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.EMC/.CMB/.GLOBAL/.Reyn_SmithOIv2/.monthly/.ssta/T/%28Jan%201985%29%28Dec%202003%20%29RANGEEDGES/SOURCES/.Indices/.soi/.standardized/T/%28Jan%201985%29%28Dec%202003%29RANGEEDGES/T/-6/1/6/shiftdatashort%5BT%5Dcorrelate/">Live
Example Link</a></font>
2
false
grid over which data is to be shifted
grid
interval at which data is lagged between <i>low</i> and <i>high</i>. Default value is 1.
step
4
true
shiftdatashort
grids
grid set describing domain to be detrended over
2
false
detrends with a best-fit-line
true
3
minfrac
Minimum fraction of data that must be present (i.e., fraction not
indicated as missing) within the selected domain in order for the data to
be used in the best-fit. If minfrac is not present, then a missing value
is returned. If minfrac is not given, then the bestfit is calculated
regardless of the amount of data present in the domain.
<p style="margin-top: 0"> While detrending data can be statistically important, it is important to remember that you could be removing the primary signal. For example, consider </p> <p style="margin-top: 0"> <img src="http://iridl.ldeo.columbia.edu/SOURCES/.KEELING/.MAUNA_LOA/.co2/T+fig-+line+-fig//plotborder+72+psdef//plotaxislength+432+psdef+.gif"> </p> <p style="margin-top: 0"> </p> <p style="margin-top: 0"> where the point is that there is a long-term trend in the CO2 levels. One can, however, easily detrend the line with </p> <pre>SOURCES .KEELING .MAUNA_LOA .co2 [T]detrend-bfl</pre> <p style="margin-top: 0"> which would result in </p> <p style="margin-top: 0"> </p> <p style="margin-top: 0"> <img src="http://iridl.ldeo.columbia.edu/expert/SOURCES/.KEELING/.MAUNA_LOA/.co2%5BT%5Ddetrend-bfl/T+fig-+line+-fig//plotborder+72+psdef//plotaxislength+432+psdef+.gif"> </p> <p style="margin-top: 0"> </p> <p style="margin-top: 0"> Note that if we were dealing with a multi-dimensional datasets (such as X Y T representing longitude, latitute, and time), then applying [T] detrend-bfl results in a separate line being calculated at each spatial point. </p>
detrend-bfl
variable to be detrended
var
false
1
4
detrended-var
detrended version of the variable
false
DÃ©tection Tendance
DetecciÃ³n Tendencia
Detrending
detrend-bfl
converts daily data to yearly by averaging
yearlyAverage
false
yearlyvar
3
yearly average of dailyvar
2
Minimum fraction of data that must be present (i.e., fraction not indicated as missing) within each year in order for each average to be calculated. If minfrac is not present, then a missing value is returned. If minfrac is not given, then the average is calculated regardless of the amount of data present.
minfrac
true
dailyvar
daily data to be averaged, with units of units
1
false
converts daily data to yearly by averaging
FillNaN
Modifies a variable so that if it has a missing data flag the missing data is filled in.
FillNaN
modifies a variable so that if it has a missing data flag the missing data is filled in.
missing data is filled in where missing data flag existed
false
4
var'
FillNaN2
false
1
FillNaN1
var
Original stream to be modified
FillNaN2
false
ivar
2
Independent variable to be used if variable is missing data
Value to replace missing data when no there is no data available from grid
default
FillNaN3
3
false
3
rotatedata3
Y
false
false
D'
Rotated variable
rotatedata9
9
7
ox
false
rotatedata7
rotatedata6
false
alpha
6
rotatedata
4
rotatedata4
false
X'
rotatedata
false
rotatedata5
5
Y'
rotates a variable onto a new grid (X'Y')
Rotates a variable onto a new grid (X'Y')
D
Variable to be rotated
rotatedata1
false
1
oy
8
rotatedata8
false
X
false
2
rotatedata2
SOURCES .NOAA .NCDC .ERSST .version2 .SST<br>28 sub
sub
false
B
2
variable or constant to be subtracted from <i>A</i> (i.e., the subtrahend)
Subtracts the item on the top of stack from the item below it.
A
false
variable or constant from which <i>B</i> is subtracted (i.e., the minuend)
1
Subtracts the item on the top of stack from the item below it.
3
The resulting difference when <i>B</i> is subtracted from <i>A</i>. <p>If either <i>A</i> or <i>B</i> are variables, then <i>difference</i> will be a variable. If both <i>A</i> and <i>B</i> are constants, then <i>difference</i> will also be a constant.
difference
false
false
xhi
3
east longitude
georect
<p style="margin-top: 0">
<br>
<u><b>Description</b></u><br><br><b>georect</b> defines a rectangular
region from user-defined latitude/longitude coordinates within an input
variable that depends upon latitude and longitude, creating a geometry
variable "<i>rectangle</i>" as a result. To define the rectangle,
the user provides four arguments in the following order: <i>xlo</i> (the
western-most longitude), <i>ylo</i> (the southern-most latitude), <i>xhi</i>
(the eastern-most longitude), and <i>yhi</i> (the northern-most
latitude).<br><br><u><b>Example</b></u><br><br>In the following example, <b>georect</b>
is used in conjunction with the Reynolds and Smith Version 2 SSTA data
set and the <b>weighted-average</b> function to calculate the Niño3.4
Index in the central Pacific. The Niño3.4 region is defined by the
western-most longitude "-170°", the southern-most latitude "-5°", the
eastern-most longitude "-120°", and the northern-most latitude "5°". "[X
Y] weighted-average" calculates the spatial average of the SSTA values
over the Niño3.4 region defined by <b>georect</b>. The use of <b>georect</b>
also ensures that area weighting using the cosine of latitude is applied
in the "weighted average" calculation.
</p>
<p style="margin-top: 0">
</p>
<font color="#008000">SOURCES .NOAA .NCEP .EMC .CMB .GLOBAL
.Reyn_SmithOIv2 .monthly .ssta<br>-170 -5 -120 5 georect<br>[X
Y]weighted-average</font>
<p style="margin-top: 0">
<br>
<a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.EMC/.CMB/.GLOBAL/.Reyn_SmithOIv2/.monthly/.ssta/-170/-5/-120/5/georect%5BX/Y%5Dweighted-average/">Live
Example Link</a>
</p>
Defines a lat/lon rectangle
Defines a lat/lon rectangle
false
2
ylo
south latitude
4
north latitude
false
yhi
west longitude
1
xlo
false
5
a lat/lon rectangle as a geometry variable
false
rectangle
SOURCES .NOAA .NCDC .GHCN .v2 .raw .mean .temp
<br>IWMO 72494000 VALUE
<br>0.6 32 RESCALE
Rescales data by multiplying by a scalar and then adding a scalar
false
scale
scale factor by which <i>var</i> is multiplied
2
1
false
var
variable to be rescaled
Rescales data by multiplying by a scalar and then adding a scalar
scaledvar
false
rescaled version of <i>var</i>. <p>Missing values in <i>var</i> are changed to NaN. Rescaling is generally noted within the history attribute.
4
RESCALE
scalar this is added after <i>var</i> is multipled by <i>scale<i>
3
false
offset
Multiplies the last two items on the stack.
SOURCES .NOAA .NCDC .CIRS .ClimateDivision .tmp<br>1 mul
1
A
variable or constant to be multipled
false
3
false
product
The resulting product when <i>A</i> and <i>B</i> are multiplied together. <p>If either <i>A</i> or B</i> are variables, then <i>product</i> will be a variable. If both <i>A</i> and <i>B</i> are constants, then <i>product</i> will also be a constant.
mul
Multiplies the last two items on the stack.
variable or constant to be multiplied
B
2
false
Masks out data values greater than a specified threshold
false
var
variable on which mask is to be applied
1
maskgt
maskvar
false
<i>var</i> with data values greater than <i>maskval</i> replaced by missing value indicator
3
Masks out data values greater than a specified threshold
maskval
threshold value on which mask is based
2
false
SOURCES .NOAA .NCEP .CPC .GMSM .w
<br>100 maskgt
gridtomatch
Returns variable1 regridded to match variable2
3
var1'
Variable1 regridded to match variable2
false
false
Variable to which variable1 will be regridded to match
2
var2
false
1
Variable to be regridded
var1
<br>
<u><b>Description</b></u><br><br><b>differences</b> calculates pairwise
differences of n values along a specified grid of an input variable (var).
In the resulting output (var') the n-1 difference values are assigned to
the midpoints of the intervals of the original grid along which the
differences were calculated. If x<sub>1</sub> is a value in variable "var"
at time T=1 and x<sub>2</sub> is a value at time T=2, then the difference
of the two values along the time grid is calculated as x<sub>2</sub> - x<sub>1</sub>
and the result is assigned to T=1.5. As pairwise differences are
calculated along a grid, if one or both of the values in a pair is a
missing value, the result of the difference is a missing value.<br><br><u><b>Example</b></u><br><br><font color="#008000">SOURCES
.NOAA .NCEP .EMC .CMB .GLOBAL .Reyn_SmithOIv2 .monthly .ssta<br>[T]differences</font><br><br>In
this example the difference between monthly sea surface temperature
anomalies from one month to the next along the time grid (T) are
calculated in the global gridded Reynolds and Smith OIv2 SSTA data set.<br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.EMC/.CMB/.GLOBAL/.Reyn_SmithOIv2/.monthly/.ssta%5BT%5Ddifferences/">Live
Example Link</a><br><br><br><br><br>
Calculates pairwise differences along specified grid(s)
false
grid(s) along which pairwise differences are to be calculated
grid
2
differences
3
var'
Pairwise differences of <i>var</i> along <i>grid</i>. <i>var'</i> is dependent on the same grids as <i>var</i>, but <i>grid</i> has one less grid point (i.e., last grid point dropped) and the grid point values (i.e., labels) in <i>grid</i> indicate the differences that were calculated.
false
Calculates pairwise differences along specified grid(s)
1
variable of which pairwise differences are to be calculated
false
var
false
median
4
median of <i>var</i> with respect to <i>grids</i>
<p>
<i>median</i> is not dependent on <i>grids</i>, but is dependent on any other grids that <i>var</i> depended on (if any). It is also dependent on a new grid , percentile, which contains a single value, 0.5, indicating that <i>median</i> represents the 50th percentile.
Calculates the median value (i.e., 50th percentile) of a variable over selected grids (i.e., independent variables)
SOURCES .NASA .GSFC .TOMS .NIMBUS7 .monthly .ai
<br>[T] 0.9 medianover
2
grids
grid(s) (i.e., independent variables) over which median is to be calculated
false
Minimum fraction of data that must be present (i.e., fraction not indicated as missing) within the selected domain in order for the median to be calculated. If minfrac is not present, then a missing value is returned. If minfrac is not given, then the median is calculated regardless of the amount of data present in the domain.
3
minfrac
true
false
1
var
variable of which median is to be calculated
medianover
Calculates the median value (i.e., 50th percentile) of a variable over selected grids (i.e., independent variables)
1
var
false
variable dependent on grid to be split
splitstreamgrid
Splits grid (i.e., independent variable) into two grids. Commonly-used to create climatologies (see live example below).
step interval on which grid split is based
false
step
3
varout
false
4
same as <i>var</i> except now dependent on two newly-defined grids:
<br>1) <i>grid_in</i> now contains <i>step</i> points. In the example below, <i>grid_in</i> (i.e., T) contains values of Jan, Feb, ..., Dec.
<br>
2) <i>newgrid</i> contains <i>n</i>/<i>step</i> points with a step interval of <i>step</i>. In the example below, <i>newgrid</i> (i.e., T2) contains values of 1982, 1983, ..., 2003.
Splits grid (i.e., independent variable) into two grids. Commonly-used to create climatologies (see live example below).
<u><b>Description</b></u><br><b>splitstreamgrid</b> splits grid (i.e.,
independent variable) into two grids. Commonly used to create
climatologies.<br><u><b>Example</b></u><br><font color="#008000">SOURCES
.NOAA .NCEP .EMC .CMB .GLOBAL .Reyn_SmithOIv2 .monthly .sst<br>T (Jan
1982) (Dec 2003) RANGE<br>T 12 splitstreamgrid<br></font><font color="#800000"><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.EMC/.CMB/.GLOBAL/.Reyn_SmithOIv2/.monthly/.sst/T/%28Jan%201982%29%28Dec%202003%29RANGE/T/12/splitstreamgrid%5BT2%5Daverage/">Live
Example Link</a></font>
false
grid to be split, containing <i>n</i> points
grid_in
2
2
false
grids
grid(s) over which percentiles are to be calculated
Calculates specified percentiles, based on non-parametric methods.
1
false
data values of which percentiles are to be calculated
var
replacebypercentile
minfrac
Minimum fraction of data that must be present (i.e., fraction not indicated as missing) within the selected domain in order for the percentiles to be calculated. If minfrac is not present, then a missing value is returned.
4
false
percentile value(s) to be calculated
false
percentiles
3
5
data values corresponding to <i>percentiles</i>; same as <i>var</i> except <i>grids</i> have been replaced by a single grid called <i>percentile</i>, which contains the values indicated by <i>percentiles</i>.
var'
false
Calculates specified percentiles, based on non-parametric methods.
<br>
<b><u>Description</u></b><br><br><b>replacebypercentile</b> applied to an
input variable replaces independent variables <i>grids</i> with a grid of
n percentiles (percentiles<sub>1</sub>, percentiles<sub>2</sub>, ...,
percentiles<sub>n</sub>) named "percentile" whose values are specified by
the user, and assigns corresponding input data values to the percentile
points. Non-missing values are assigned to points in the new "percentile"
grid only if the fraction of values that are non-missing along the
original <i>grids</i> exceeds the user-specified "minfrac" value, which
should be between 0 and 1.<br><br><b><u>Example</u></b><br><br>In the
following example, the input variable is July sea surface temperatures
between 1854 and 2004 from the ERSST data set. The SST variable depends
initially upon longitude (X), latitude (Y), and month (T). The command
"[T] 0.1 0.25 0.5 0.75 0.9 0 replacebypercentile" replaces the time grid
"T" with a new grid named "percentile" that has points at 0.1, 0.25, 0.5,
0.75, and 0.9. Then, for each (X,Y) gridpoint it assigns the July SST
values that are nearest to the specified percentile grid points to those
percentile grid points. In this case, minfrac = 0, so an SST value is
assigned to a percentile grid point as long as at least 0% of July SST
values at the (X,Y) grid point are non-missing.<br><br>The result is an
SST variable that depends upon the grids X, Y, and percentile, which has 5
points. At each (X,Y) grid point, the SST values assigned to the
"percentile" grid points are those closest to the 0.1, 0.25, 0.5, 0.75,
and 0.9 percentiles from the set of July SST values between 1854 and 2004.<br><br><font color="#008000">SOURCES
.NOAA .NCDC .ERSST .version2 .SST<br>T (Jul 1854-2004) RANGE<br>[T]0.1
0.25 0.5 0.75 0.9 0 replacebypercentile</font><br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCDC/.ERSST/.version2/.SST/T/%28Jul%201854-2004%29RANGE%5BT%5D0.1/0.25/0.5/0.75/0.9/0/replacebypercentile/">Live
Example Link</a>
laggedcovariance4
lags
false
4
period
laggedcovariance3
3
false
cov
lagged covariance
laggedcovariance6
6
false
false
5
laggedcovariance5
Egrid
Computes lagged covariance $C(E,E,S,L)$. If Tgrid is a variable, uses the
first grid as the time grid
laggedcovariance
2
false
Tgrid
laggedcovariance2
computes lagged covariance $C(E,E,S,L)$. If Tgrid is a variable, uses the
first grid as the time grid.
false
1
var
laggedcovariance1
5
running average of <i>var</i>, where the overlapping intervals are of length <i>interval</i>
avgvar
false
SOURCES .NOAA .NCDC .GHCN .v2beta .prcp
<br>IWMO 63450000 VALUE
<br>T 12 runningAverage
runningAverage
false
grid(s) over which the running average is to be calculated
2
grid
Calculates the running average
width (in units of <i>grid</i>) of the overlapping interval
false
3
interval
1
var
false
variable to be averaged
Calculates the running average
4
minfrac
Minimum fraction of data that must be present (i.e., fraction not indicated as missing) within the selected domain in order for the running average to be calculated. If minfrac is not present, then a missing value is returned. If minfrac is not given, then the running average is calculated regardless of the amount of data present in domain.
true
computes inverse error function from variable
2
C
false
A
false
1
erfinv
false
3
vectormag
magnitude of vector with components of <i>var1</i> and <i>var2</i>. <p><i>vectormag</i> is dependent on all of the grids on which <i>var1</i> and <i>var2</i> are dependent.
SOURCES .NOAA .NCEP-NCAR .CDAS-1 .MONTHLY .Intrinsic .PressureLevel .u
<br>SOURCES .NOAA .NCEP-NCAR .CDAS-1 .MONTHLY .Intrinsic .PressureLevel .v
<br>P 850 VALUE
<br>mag
false
1
first component of vector
var1
Calculates the vector magnitude
second component of variable
var2
false
2
mag
Calculates the vector magnitude
sign
false
1 or -1
3
fourier transform3
Fourier_transform
Computes fouriertransform
false
1
fourier transform1
Variable to be transformed
var
fourier transform2
Domain to be transformed to frequency or wavelength
2
false
ivar
var'
4
false
stream after fouier transform computed
fourier transform4
computes fouriertransform
Fourier Transform
SAMPLEUB
Variable after sampled along ivar1
4
false
var'
3
Independent variable that ivar1 will be compared to
false
ivar2
false
2
Independent variable along which variable will be sampled
ivar1
false
var
Variable to be sampled
1
Samples variable along ivar1 according to ivar2, returning highest grid value in ivar1 it value in ivar2
<br>
<b><u>Description</u></b><br><br><b>correlate</b> calculates the Pearson
product moment correlation for the two latest items on the stack over the
indicated grid. For the correlation to be computed, the gridding of the
two items on the stack must match.<br><br><b><u>Example</u></b><br><br><font color="#008000">SOURCES
.NOAA .NCEP .CPC .GMSM .w<br>T (Jan 1969) (Dec 1998) RANGE<br>X (-8) (20)
RANGE<br>Y (8) (20) RANGE<br>SOURCES .DEKLIM .VASClimO .PrcpClim
.Resolution-0p5x0p5 .prcp<br>T (Jan 1969) (Dec 1998) RANGE<br>X (-8) (20)
RANGE<br>Y (8) (20) RANGE<br>[T]correlate</font><br><br>In this example,
GMSM monthly soil moisture values are correlated over the time grid with
monthly precipitation values from the VASClim0 data set for a region of
West Africa over the period January 1969 to December 1998. The gridding of
the two data sets matches in both space (0.5 deg. lat/lon resolution) and
time. The result is a single map of correlation coefficients over the
defined region of West Africa.<br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.CPC/.GMSM/.w/T/%28Jan%201969%29%28Dec%201998%29RANGE/X/%28-8%29%2820%29RANGE/Y/%288%29%2820%29RANGE/SOURCES/.DEKLIM/.VASClimO/.PrcpClim/.Resolution-0p5x0p5/.prcp/T/%28Jan%201969%29%28Dec%201998%29RANGE/X/%28-8%29%2820%29RANGE/Y/%288%29%2820%29RANGE%5BT%5Dcorrelate/">Live
Example Link</a><br>
<p>
OR
</p>
<p>
<font color="#008000">SOURCES .NOAA .NCEP .CPC .CAMS_OPI .v0208 .anomaly
.prcp<br>T (Jan 1980) (Dec 2003) RANGE<br>X (-150) (-80) RANGE<br>Y
(-10) (10) RANGE<br>SOURCES .NOAA .NCEP .EMC .CMB .GLOBAL
.Reyn_SmithOIv2 .monthly .sst<br>T (Jan 1980) (Dec 2003) RANGE<br>X
(-150) (-80) RANGE<br>Y (-10) (10) RANGE<br>[X Y]regridAverage<br>[X
Y]correlate</font><br><br>In this example, CAMS_OPI monthly
precipitation data are correlated in space with Reynolds and Smith OI
Version 2 monthly sea surface temperatures over the equatorial Pacific
Ocean. The result is a monthly time series of values from January 1980
to December 2003. The [X Y]regridAverage command was used to spatially
regrid the SST data set (at 1.0 deg. lat/lon resolution) to match the
gridding of the precipitation data set (at 2.5 deg. lat/lon resolution)
before taking the correlation.<br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP/.CPC/.CAMS_OPI/.v0208/.anomaly/.prcp/T/%28Jan%201980%29%28Dec%202003%29RANGE/X/%28-150%29%28-80%29RANGE/Y/%28-10%29%2810%29RANGE/SOURCES/.NOAA/.NCEP/.EMC/.CMB/.GLOBAL/.Reyn_SmithOIv2/.monthly/.sst/T/%28Jan%201980%29%28Dec%202003%29RANGE/X/%28-150%29%28-80%29RANGE/Y/%28-10%29%2810%29RANGE%5BX/Y%5DregridAverage%5BX/Y%5Dcorrelate/">Live
Example Link</a><br><br><br>
</p>
false
1
variable to be correlated with <i>var2</i>
var1
correlate
5
false
Pearson-Product Moment Correlation coefficient of <i>var1</i> and <i>var2</i> over <i>grids</i>.
<p>
<i>coefficient</i> is not dependent on <i>grids</i>, but is dependent on any other grids that <i>var1</i> or <i>var2</i> depended on (if any).
coefficient
Calculates the Pearson Product-Moment Correlation coefficient of two variables over specified grids (i.e., independent variables)
var2
variable to be correlated with <i>var1</i>
<p>
Note that <i>var1</i> and <i>var2</i> should have similarly-defined <i>grids</i>. Regridding one variable to match the other may be necessary (see example below).
2
false
grids
3
false
grid(s) (i.e., independent variables) over which correlation coefficient is to be calculated
Calculates the Pearson Product-Moment Correlation coefficient of two variables over specified grids (i.e., independent variables)
Minimum fraction of data that must be present (i.e., fraction not indicated as missing) within the selected domain in order for the correlation to be calculated. If minfrac is not present, then a missing value is returned. If minfrac is not given, then the correlation is calculated regardless of the amount of data present in the domain.
4
minfrac
true
false
Temperature
pressure1
1
T
S
pressure2
Salinity
2
false
Computes hydrostatic pressure by integrating from the surface
Height
pressure3
3
Z
false
computes hydrostatic pressure by integrating from the surface
pressure
false
P
pressure4
Hydrostatic pressure
4
pressure
maskbybin
3
false
Mask with additional grid (the bins) that is either NaN or 1
maskbybin4
var'
2
false
maskbybin2
bin edges
false
var
Variable to be sorted
1
maskbybin1
Bins data into bins from lo to high by step, returning a mask with an
additional grid (the bins) that is either NaN or 1
maskbybin
bins data into bins from lo to high by step, returning a mask with an
additional grid (the bins) that is either NaN or 1
SAMPLELB
4
false
Variable with lowest value in ivar1 greater than or equal to value in ivar2
var'
3
Independent variable from which ivar1 is based
ivar2
false
false
ivar1
Independent variable from which variable is sampled along
2
var
false
1
Variable to be sampled
Samples variable along ivar1 according to ivar2, returning lowest value in ivar1 greater than or equal to value in ivar2
sort_by
sorteddataset
false
4
sorted dataset where <i>grid </i>is replaced by<i> rank. </i>Rank value 1
corresponds to the maxium value of variable.
3
independent variables to be replaced by rank
false
grid
false
2
variable
1
dataset
false
dataset to be sorted
sorts dataset variables by replacing grid with rank, rank being calculated from variable.
normalizeddistrib2D
normalizeddistrib2D
Computes the distribution of A vs B (see distrib2D) and then renormalizes
by the integral along B. This new variable has the property that the
integral along B is 1
computes the distribution of A vs B (see distrib2D) and then renormalizes
by the integral along B. This new variable has the property that the
integral along B is 1.
3
var
false
Distribution of A vs B and then renormalizes by integral along B
Has the property that the integral along B is 1
normalizeddistrib2D3
false
normalizeddistrib2D2
2
B
Variable from which distribution will be calculated against A and then
renormalized by the integral along it
1
false
Variable from which distribution will be calculated against B and then renormalized by the integral along B
A
normalizeddistrib2D
1
false
var
variable of which the root mean square is to be calculated
Returns the root mean square averaged over selected grids (i.e., independent variables)
rmsover
SOURCES .NOAA .NCDC .ERSST .version2 .SST <br>yearly-anomalies
<br>[T] 0.8 rmsover
rmsvar/num
root mean square of <i>var</i> over <i>indvar</i>
<p>
<i>rmsvar/num</i> is no longer dependendent on <i>indvar</i>, but is still dependent on any other grids that <i>var</i> depended on (if any)
false
4
minfrac
Minimum fraction of data that must be present (i.e., fraction not indicated as missing) within the selected domain in order for the average to be calculated. If minfrac is not present, then a missing value is returned. If minfrac is not given, then the average is calculated regardless of the amount of data present in domain.
true
3
2
false
grid(s) (i.e., independent variables) over which the root mean square is to be calculated
indvar
Returns the root mean square averaged over selected grids (i.e., independent variables)
false
var
3
integrateddistrib2D3
Integrated normalized distribution
The stream is marked so that contours will be at 66\% and 96\% widths as well
as at the median
false
integrateddistrib2D2
Variable from which normalized distribution will be calulated and integrated
B
2
Variable from which normalized distribution will be calulated and integrated
integrateddistrib2D1
A
1
false
integrateddistrib2D
Computes the normalized distribution and then integrates it over the second variable B, thus the
values will range from zero to one. The variable is marked so that
contours will be at 66\% and 96\% widths as well as at the median.
integrateddistrib2D
computes the normalized distribution and then integates it, thus the
values will range from zero to one. The stream is marked so that contours
will be at 66\% and 96\% widths as well as at the median.
1
false
var/num1
variable or constant to be compared to <i>var/num2</i>
Returns the minimum of two variables or two numbers
SOURCES .NOAA .NODC .WOA01 .Grid-1x1 .Monthly .an .O2
<br>SOURCES .NOAA .NODC .WOA98 .MONTHLY .analyzed .O2
<br>min
<p>
OR
<p>
3 pi min
minimum, or variable containing the minimum, of <i>var1/num</i> and <i>var2/num</i>. <p>If <i>minvar/num</i> is a variable, then it is dependent on the same grids as are <i>var/num1</i> and <i>var/num2</i>, but the grid domains are limited to the domain common between <i>var/num1</i> and <i>var/num2</i>.
false
minvar/num
3
2
var/num2
false
variable or constant to be compared to <i>var/num1</i>
Returns the minimum of two variables or two numbers
min
Z
false
2
independent variable that is going to be replaced by S
false
value(s) of the new <i>S</i> grid
3
S
invertontogrid
false
S(Z)
input surface information (needs to be monotonic in the independent variable <i>Z</i> so that it can be used as a coordinate)
1
output data: <i>Z</i> values for surfaces of constant <i>S</i>
4
Z(S)
Inverts onto surfaces
Inverts onto surfaces
Calculates the square root of a number or variable
Calculates the square root of a number or variable
SOURCES .UEA .CRU .New .CRU05 .monthly .prcp <br>
sqrt
var/num
1
variable or constant of which the square root is to be taken
square root of <i>var/num</i>. <p>
Result will be of the same type as <i>var/num</i> (i.e., either a variable or constant).
Result
2
sqrt
<br>
<u><b>Description</b></u><br><br><b>div</b> divides the next to last item
on the stack (the dividend) by the last item on the stack (the divisor) to
produce a resulting quotient. If one of the two items is a variable that
depends on at least one grid, the resulting quotient will be a variable as
well. If both items in the operation are constants, the result will be a
constant.<br><br><u><b>Example</b></u><br><br>In this example, Reanalysis
global mean sea level pressure values at all grid locations and times are
divided by a constant value of 100.<br><br><font color="#008000">SOURCES
.NOAA .NCEP-NCAR .CDAS-1 .MONTHLY .Intrinsic .MSL .pressure<br>100 div</font><br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP-NCAR/.CDAS-1/.MONTHLY/.Intrinsic/.MSL/.pressure/100/div/">Live
Example Link</a><br><br>In the following example, the Reanalysis gridded
pressure level temperature is divided by the virtual temperature variable
in the same data set. Since the two variables match completely along the
X, Y, and T grids, and along a sample of the P grid, the quotient is
calculated gridpoint by gridpoint.<br><br><font color="#008000">SOURCES
.NOAA .NCEP-NCAR .CDAS-1 .MONTHLY .Intrinsic .PressureLevel .temp<br>SOURCES
.NOAA .NCEP-NCAR .CDAS-1 .MONTHLY .Intrinsic .PressureLevel .vtemp<br>div</font><br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.NOAA/.NCEP-NCAR/.CDAS-1/.MONTHLY/.Intrinsic/.PressureLevel/.temp/SOURCES/.NOAA/.NCEP-NCAR/.CDAS-1/.MONTHLY/.Intrinsic/.PressureLevel/.vtemp/div/">Live
Example Link</a><br>
quotient
3
false
The resulting quotient when <i>A</i> is divided by <i>B</i>. <p>If either <i>A</i> or <i>B</i> are variables, then <i>quotient</i> will be a variable. If both <i>A</i> and <i>B</i> are constants, then <i>quotient</i> will also be a constant.
Divides the last two items on the stack.
Divides the last two items on the stack.
div
B
variable or constant by which <i>A</i> is divided (i.e.,the divisor)
false
2
1
variable or constant to be divided by <i>B</i> (i.e.,the dividend)
false
A
false
1
dataset or variable dependent on the grid that will be sampled along
ds/var
Identifies continuous range of values in one grid (i.e., independent variable) for which data will be selected.
restricted_ds/var
false
5
dataset or variable (<i>ds/var</i>) restricted to only data associated with <i>grid</i> values within <i>range_low</i> and <i>range_high</i>, inclusive; applied to all variables in the dataset if no variable selected
RANGE
SOURCES .CAC <br>
Y (10S) (10N) RANGE
<p>OR<p>
SOURCES .CAC .ssta<br>
Y -10 10 RANGE
lower threshold of range (center of closest grid box)
3
range_low
Identifies continuous range of values in one grid (i.e., independent variable) for which data will be selected.
upper threshold of range (center of closest grid box)
range_high
4
grid
grid in which values will be selected
2
4
density
Unecso '81 density (insitu) of seawater minus 1
densa4
false
<p style="margin-top: 0">
<br>
<b><u>Description</u></b><br><br><b>densa</b> calculates the density of
seawater above 1 g/cm<sup>3</sup> given input variables temperature (T),
salinity (S), and pressure (P), in that order, using the international
equation of state for seawater, IES80 (UNESCO 1981).<br><br><b><u>Reference</u></b><br><br>UNESCO,
1981: Tenth report of the joint panel on oceanographic tables and
standards. UNESCO Tech. Paper in Marine Science 36, 25pp.<br><br><b><u>Example</u></b><br><br>The
following example calculates the density of seawater based on samples of
measured temperature, salinity, and pressure from the full data set of
GEOSECS observational profiles.<br><br><font color="#008000">SOURCES
.GEOSECS .TEMP </font>
</p>
<p style="margin-top: 0">
<font color="#008000">SOURCES .GEOSECS .SAL </font>
</p>
<p style="margin-top: 0">
<font color="#008000">SOURCES .GEOSECS .PRESS </font>
</p>
<p style="margin-top: 0">
<font color="#008000">densa</font><br><br><a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.GEOSECS/.TEMP/SOURCES/.GEOSECS/.SAL/SOURCES/.GEOSECS/.PRESS/densa/">Live
Example Link</a>
</p>
Salinity
S
densa2
2
false
densa
3
P
false
Pressure
densa3
densa
T
densa1
1
false
Temperature
unecso '81 density (insitu) of seawater minus 1
Unecso '81 density (insitu) of seawater minus 1
false
geometry gridded to match griddedvar
3
griddedgeometry
geometry
false
geometry variable which is to be rasterized.
2
griddedvar
reference gridded variable to be matched
false
1
rasterizes a geometry to match a gridded variable
rasterize
rasterizes a geometry to match a gridded variable
ev
false
4
Eigenvalues of matrix
3
Independent variable to be applied to calculation of eigenvalues
false
ivar2
false
ivar1
Independent variable to be applied to calculation of eigenvalues
2
Variable from which eigenvalues will be calculated
false
var
1
eigenvalues
Calulates eigenvalues of a matrix
cptv10
creates a CPT v10 file from the input dataset/variable
grid or variable that gives the time
4
time
false
false
spatial grids
3
either a pair of spatial grids or the station grid
2
true
variable list
optional list of variables to be extracted from the dataset (default is to include all of them)
<p style="margin-top: 0">
<b><font face="SansSerif">cptv10 </font></b>allows one to explicitly
specify variables, spatial, and temporal grids for output to a cptv10
file. Since CPT is most useful as a tool to analyze results from Data
Library analyses (as opposed to direct downloads of datasets that
explicitly exist in the data library), the flexibility of explicitly
specifying information for CPT is useful.
</p>
<h4>
Downloading a precipitation analysis with no explicit time
</h4>
<pre>
SOURCES .IRI .FD .ECHAM4p5 .Forecast
.ca_sst .ensemble24 .MONTHLY .prec
(mm/day) unitconvert
[M]average
S (1 Dec 1957-2008) VALUES
L 0.5 2.5 RANGEEDGES
[L] /keepgrids average
[X Y][S L add]cptv10
</pre>
This example accesses the precipitation record, changes the units,
ensemble averages, selects Dec starts, and averages over the first three
lags (retaining the lag (L) grid so that it can be used to compute time
later). The last line then specifies X Y as the spatial grids and the sum
of start and lead time as time.
<p>
<a href="http://iridl.ldeo.columbia.edu/expert/SOURCES/.IRI/.FD/.ECHAM4p5/.Forecast/.ca_sst/.ensemble24/.MONTHLY/.prec/(mm/day)unitconvert%5BM%5Daverage/S/(1%20Dec%201957-2008)VALUES/L/0.5/2.5/RANGEEDGES%5BL%5D//keepgrids/average%5BX/Y%5D%5BS/L/add%5Dcptv10/">Results</a>
<h4>
Downloading two precipitation analyses as a pair of fields for CPT
</h4>
<pre>
SOURCES .NOAA .NCEP .EMC .CFS
.MONTHLY .surface .prate
L (1.5) (3.5) RANGEEDGES
S (0000 1 Sep 1982-2008) VALUES
X (30E) (100E) RANGEEDGES
Y (30N) (30S) RANGEEDGES
[M]average
[L] /keepgrids average
c: /name /water_density def 998 (kg/m3) :c
div
(mm/day) unitconvert
SOURCES .IRI .MP .RESEARCH .COUPLED .GLOBAL
.ECHAM4p5-MOM3-AC1 .ATM .Surface .prec
L (1.5) (3.5) RANGEEDGES
S (0000 1 Sep 1982-2008) VALUES
X (30E) (100E) RANGEEDGES
Y (30N) (30S) RANGEEDGES
[M]average
[L] /keepgrids average
(mm/day) unitconvert
[ X Y ] regridLinear
{NOAA_prcp IRI_prcp}ds
[X Y][S L add ]cptv10</pre>
In this example, we compute seasonal average precipitation from two
different models. Extract data from the monthly CFS, Sep starts averaged
from 1.5 to 3.5 month leads. Note that we keep the L grid in the average
so that we can use it later to compute time from start time and lead.
<pre>
SOURCES .NOAA .NCEP .EMC .CFS
.MONTHLY .surface .prate
L (1.5) (3.5) RANGEEDGES
S (0000 1 Sep 1982-2008) VALUES
X (30E) (100E) RANGEEDGES
Y (30N) (30S) RANGEEDGES
[M]average
[L] /keepgrids average
</pre>
Because this model outputs precipitation in kg /m2 /s, we need to divide
by water density to allow unit conversion to mm/day.
<pre>
c: /name /water_density def 998 (kg/m3) :c
div
(mm/day) unitconvert
</pre>
Now we extract data from the monthly MOM3, again Sep starts averaged from
1.5 to 3.5 month leads. The mm/day unit conversion does not involve water
density this time.
<pre>
SOURCES .IRI .MP .RESEARCH .COUPLED .GLOBAL
.ECHAM4p5-MOM3-AC1 .ATM .Surface .prec
L (1.5) (3.5) RANGEEDGES
S (0000 1 Sep 1982-2008) VALUES
X (30E) (100E) RANGEEDGES
Y (30N) (30S) RANGEEDGES
[M]average
[L] /keepgrids average
(mm/day) unitconvert
</pre>
Now we regrid the IRI model analysis to match the NOAA model analysis
<pre>
[ X Y ] regridLinear
</pre>
We bundle the two results into a new dataset, calling them NOAA_prcp and
IRI_prcp respectively.
<pre>
{NOAA_prcp IRI_prcp}ds
</pre>
We then invoke cptv10, specifying X Y spatial grids, and computing time
from the sum of start time S and lead time L. Note that the width of three
months associated with L is inherited by T, so that the output is
correctly labelled as seasonal averages.
<pre>
[X Y][S L add ]cptv10
</pre>
<a href="http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.EMC/.CFS/.MONTHLY/.surface/.prate/L/(1.5)(3.5)RANGEEDGES/S/(0000%201%20Sep%201982-2008)VALUES/X/(30E)(100E)RANGEEDGES/Y/(30N)(30S)RANGEEDGES%5BM%5Daverage%5BL%5D//keepgrids/average/c://name//water_density/def/998/(kg/m3):c/div/(mm/day)unitconvert/SOURCES/.IRI/.MP/.RESEARCH/.COUPLED/.GLOBAL/.ECHAM4p5-MOM3-AC1/.ATM/.Surface/.prec/L/(1.5)(3.5)RANGEEDGES/S/(0000%201%20Sep%201982-2008)VALUES/X/(30E)(100E)RANGEEDGES/Y/(30N)(30S)RANGEEDGES%5BM%5Daverage%5BL%5D//keepgrids/average/(mm/day)unitconvert%5BX/Y%5DregridLinear/%7BNOAA_prcp/IRI_prcp%7Dds%5BX/Y%5D%5BS/L/add%5Dcptv10/">Results</a><a>
<h4>Downloading dual field Station data</h4>
<pre>
SOURCES .NOAA .NCDC .USHCN
state
(30) dup masknotrange
SELECT
T (Jun 1960-1979) VALUES
{raw .prcp
raw .mean .temp
lon lat elev Name}[ID][T] cptv10
</pre>
This example uses the state code to pick out a subset of the stations,
picks June data from 1960-1979, then selected prcp and temp data, with
lon, lat, elev, and Name station information.
<p>
<a href="http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCDC/.USHCN/state/(30)dup/masknotrange/SELECT/T/(Jun 1960-1979)VALUES/{raw/.prcp/raw/.mean/.temp/lon/lat/elev/Name}[ID][T]/cptv10/">Results</a>
cpt file page which gives links to the CPT files
false
5
output file
inputdata
dataset or variable to be put in cptv10 file
1
false
Converts daily data to pentad (i.e., five-day) data by averaging
1
false
daily data to be averaged, with units of <i>units</i>
dailyvar
minfrac
true
Minimum fraction of data that must be present (i.e., fraction not indicated as missing) within each pentad in order for each pentad average to be calculated. If minfrac is not present, then a missing value is returned. If minfrac is not given, then the average is calculated regardless of the amount of data present.
2
SOURCES .NOAA .NCEP .CPC .FEWS .DAILY .est_prcp
<br>0.8 pentadAverage
<br>/units (mm/day) def
<p>
OR
<p>
SOURCES .NOAA .NCDC .GDCN .TMAX
<br>ISTA 253175 VALUE
<br>pentadAverage
3