data: root mean sq anom $integral dY$ [ ENACTS version0 dekadal rainfall rfe_merged_vs_meanrfe

root mean sq anom ∫dY [ ENACTS version0 dekadal rainfall rfe_merged_vs_meanrfe_merged ] : Rainfall data Expressed as Percentages of the Long-Term Average data

dekadal rainfall rfe_merged_vs_meanrfe_merged rfe_merged_vs_meanrfe_merged int_dY Rainfall data Expressed as Percentages of the Long-Term Average from ENACTS version0: version 0: CHIRPS-BLENDED and CRU.

Independent Variables (Grids)

Time: grid: /T (days since 1960-01-01) ordered [ (1-10 Jan 1981) (11-20 Jan 1981) (21-31 Jan 1981) ... (21-31 Mar 2024)] N= 1557 pts :grid
Latitude (latitude): grid: /Y (degree_north) ordered (11.8S) to (23.15N) by 0.05 N= 700 pts :grid

Other Info

bufferwordsize

CE

null

colorscalename

precip_colors

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null

datatype

doublearraytype

maxncolor

254

units

0.000174532925199433 radian north

history

root mean sq anom $integral dY$ [ ENACTS version0 dekadal rainfall rfe_merged_vs_meanrfe_merged ]

Averaged over T2[0.5, 1080.5] minimum 0.0% data present
Averaged over X[21.88E, 51.43E]

Last updated: Fri, 19 Apr 2024 06:43:57 GMT
Expires: Thu, 11 Apr 2024 00:00:00 GMT

Data Views

	T	Y
root mean sq anom $integral dY$ [ ENACTS version0 dekadal rainfall rfe_merged_vs_meanrfe_merged ] [ Y \| T]	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 Y T
Differentiate along Y T
Take differences along Y T

Average over Y T | Y T |
RMS (root mean square with mean *not* removed) over Y T | Y T |
RMSA (root mean square with mean removed) over Y T | Y T |
Maximum over Y T | Y T |
Minimum over Y T | Y T |
Detrend (best-fit-line) over Y T | Y T |

Note on units