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∫dT ∂T root mean sq [ ENACTS version0 daily rainfall0p25 rfe ] : ∂T precipitation data
daily rainfall0p25 rfe rfe partial_T ∂T precipitation from ENACTS version0: version 0: CHIRPS-BLENDED and CRU.Independent Variables (Grids)
- Time (time)
- grid: /T (julian_day) ordered (1 Jan 1981) to (31 Dec 2020) by 1.0 N= 14610 pts :grid
- Latitude
- grid: /Y (degree_north) ordered (12.02175S) to (22.22611N) by 0.2499844 N= 138 pts :grid
Other Info
- bufferwordsize
- 8
- CE
- null
- colorscalename
- prcp_dailyrate_max100_smooth
- CS
- null
- datatype
- doublearraytype
- file_missing_value
- -9999.0
- maxncolor
- 254
- missing_value
- NaN
- pointwidth
- 1.0
- standard_name
- lwe_precipitation_rate
- units
- mm /day
- standard units*
- 1.15740740740741×10-08 meter second-1
- history
- ENACTS version0 daily rainfall0p25 rfe
- X regridded on ENACTS version0 daily rainfall rfe
Y regridded on ENACTS version0 daily rainfall rfe
$integral dT$ $partialdiff sub T$ root mean sq [ ENACTS version0 daily rainfall0p25 rfe ]- Averaged over X[20.8498E, 52.1502E] minimum 0.0% data present
- X regridded on ENACTS version0 daily rainfall rfe
Last updated: Mon, 22 Feb 2021 09:33:15 GMT
Data Views
Views
data as colors
data as contours| T | Y | |
 $integral dT$ $partialdiff sub T$ root mean sq [ ENACTS version0 daily rainfall0p25 rfe ] [ 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
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