root mean sq ∫dIWMO [ mean ( { percentile_over [ T2 75% min ] [ NOAA NCDC GCPS MONTHLY STATION mean prcp ] } * masklt { [ percentile_over ( T ) ( KAPLAN Indices NINO3 avOS ) ] , 1 } ) * unnamed ] : percentile data
root mean sq
∫dIWMO [ mean ( { percentile_over [ T2 75% min ] [ NOAA NCDC GCPS MONTHLY STATION mean prcp ] } * masklt { [ percentile_over ( T ) ( KAPLAN Indices NINO3 avOS ) ] , 1 } ) * unnamed ] .
Independent Variables (Grids)
- avOS
- grid: /avOS (ids) unordered [ (LaNina) (Neutral) (ElNino)] :grid
Other Info
- bufferwordsize
- 8
- CE
- null
- colorscalename
- tercileclassesscale
- CS
- null
- datatype
- doublearraytype
- maxncolor
- 254
- missing_value
- NaN
- percentile
- Probability
- units
- percent ids
- history
- mean [ ( percentile_over { T2 75% min } { NOAA NCDC GCPS MONTHLY STATION mean prcp } ) * masklt ( { percentile_over [ T ] [ KAPLAN Indices NINO3 avOS ] } , 1 ) ]
- Boxes with less than 0.0% dropped
Averaged over T2[1901, 1990] minimum 0.0% data present
root mean sq $integral dIWMO$ [ mean ( { percentile_over [ T2 75% min ] [ NOAA NCDC GCPS MONTHLY STATION mean prcp ] } * masklt { [ percentile_over ( T ) ( KAPLAN Indices NINO3 avOS ) ] , 1 } ) * unnamed ] - Averaged over prcp[dry, wet] T[Jan-Mar, Oct-Dec] IWMO[99900, 9885849] minimum 0.0% data present
Last updated: Mon, 12 Sep 2016 21:13:52 GMT
Filters
Here are some filters that are useful for manipulating data. There
are actually many more available, but they have to be entered
manually. See
Ingrid
Function Documentation for more information.
- Monthly Climatology calculates
a monthly climatology by averaging over all years.
- anomalies calculates the difference
between the (above) monthly climatology and the original data.
- Integrate along avOS
- Differentiate along avOS
- Take differences along avOS
Average over
avOS
|
RMS (root mean square with mean *not* removed) over
avOS
|
RMSA (root mean square with mean removed) over
avOS
|
Maximum over
avOS
|
Minimum over
avOS
|
Detrend (best-fit-line) over
avOS
|
Note on units