served from digilib.icpac.net
∫dX mean ∂Y [ ENACTS version0 daily rainfall0p25 rfe ] : ∂Y precipitation data
daily rainfall0p25 rfe partial_Y partial_Y partial_Y ∂Y precipitation from ENACTS version0: version 0: CHIRPS-BLENDED and CRU.Independent Variables (Grids)
- Longitude
- grid: /X (degree_east) ordered (20.975E) to (52.025E) by 0.2504032 N= 125 pts :grid
- Latitude
- grid: /Y (degree_north) ordered (11.89676S) to (22.10112N) by 0.2499844 N= 137 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
- $partialdiff sub Y$ [ ENACTS version0 daily rainfall0p25 rfe ]
- X regridded on ENACTS version0 daily rainfall rfe
Y regridded on ENACTS version0 daily rainfall rfe
$integral dX$ mean $partialdiff sub Y$ [ ENACTS version0 daily rainfall0p25 rfe ]- Averaged over T[1200 31 Dec 1980 - 1200 1 Jan 1981, 2400 31 Dec 2020] 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
colors with land
contours with land
colors with coasts
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