served from IRI/LDEO Climate Data Library
∫dX [ Ethiopia NMA monthly climatologies tmin ]: tmin 1983-2010 average data
Ethiopia NMA monthly climatologies tmin tmin 1983-2010 average from SOURCES: the IRI/LDEO collection of climate data.Independent Variables (Grids)
- Time
- grid: /T (months since 01-Jan) periodic (Jan) to (Dec) by 1.0 N= 12 pts :grid
- Longitude (longitude)
- grid: /X (degree_east) ordered (33E) to (48E) by 0.1 N= 151 pts :grid
- Latitude (latitude)
- grid: /Y (degree_north) ordered (3.05N) to (14.95N) by 0.09999999 N= 120 pts :grid
Other Info
- bufferwordsize
- 8
- CE
- 32
- colorscalename
- temp_colors
- CS
- 0
- datatype
- doublearraytype
- file_missing_value
- -9999
- fnname
- masklt
- maxncolor
- 254
- missing_value
- NaN
- units
- 0.0174532925199433 degree_Kelvin radian east
- history
- $integral dX$ [ Ethiopia NMA monthly climatologies tmin ]
- Boxes with less than 0.0% dropped
Averaged over T2[1983, 2010] minimum 0.0% data present
- Boxes with less than 0.0% dropped
- colorscale
Last updated: Tue, 08 Dec 2020 21:54:43 GMT
Data Views
Views
data as colors
data as contours
colors with land
contours with land
colors with coasts| T | X | Y | |
 $integral dX$ [ Ethiopia NMA monthly climatologies tmin ][ X Y T |] | M | M | M |
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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 X Y T
- Differentiate along X Y T
- Take differences along X Y T
RMS (root mean square with mean *not* removed) over X Y T | X Y X T Y T | X Y T |
RMSA (root mean square with mean removed) over X Y T | X Y X T Y T | X Y T |
Maximum over X Y T | X Y X T Y T | X Y T |
Minimum over X Y T | X Y X T Y T | X Y T |
Detrend (best-fit-line) over X Y T | X Y X T Y T | X Y T |
Note on units