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∂Y mean [ ENACTS version0 dekadal rainfall rfe_merged_vs_meanrfe_merged ] : ∂Y Rainfall data Expressed as Percentages of the Long-Term Average data
dekadal rainfall rfe_merged_vs_meanrfe_merged rfe_merged_vs_meanrfe_merged rfe_merged_vs_meanrfe_merged ∂Y Rainfall data Expressed as Percentages of the Long-Term Average from ENACTS version0: version 0: CHIRPS-BLENDED and CRU.Independent Variables (Grids)
- Longitude (longitude)
- grid: /X (degree_east) ordered (21.88E) to (51.43E) by 0.05 N= 592 pts :grid
- Latitude (latitude)
- grid: /Y (degree_north) ordered (11.75S) to (23.1N) by 0.05 N= 698 pts :grid
Other Info
- CE
- null
- colorscalename
- precip_colors
- CS
- null
- datatype
- realarraytype
- maxncolor
- 254
- units
- 0.572957795130823 radian-1 north
- history
- $partialdiff sub Y$ mean [ ENACTS version0 dekadal rainfall rfe_merged_vs_meanrfe_merged ]
- Averaged over T2[0.5, 1080.5] minimum 0.0% data present
Averaged over T[1-10 Jan 1981, 0144 1 Oct 2024]
- Averaged over T2[0.5, 1080.5] minimum 0.0% data present
Last updated: Mon, 28 Oct 2024 09:31:03 GMT
Expires: Fri, 11 Oct 2024 00:00:00 GMT
Data Views
Views
data as colors
data as contours
colors with land
contours with land
colors with coasts| X | Y | |
 $partialdiff sub Y$ mean [ ENACTS version0 dekadal rainfall rfe_merged_vs_meanrfe_merged ] [ X Y |] | M | M |
|---|
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
- Differentiate along X Y
- Take differences along X Y
RMS (root mean square with mean *not* removed) over X Y | X Y |
RMSA (root mean square with mean removed) over X Y | X Y |
Maximum over X Y | X Y |
Minimum over X Y | X Y |
Detrend (best-fit-line) over X Y | X Y |
Note on units