served from digilib.icpac.net
∫dT detrended-bfl ∂Y detrended-bfl [ ENACTS version0 daily rainfall rfe ] : ∂Y precipitation data
daily rainfall rfe adif partial_Y adif ∂Y precipitation from ENACTS version0: version 0: CHIRPS-BLENDED and CRU.Independent Variables (Grids)
- Time (time)
- grid: /T (days since 1970-01-01) ordered (0000 1 Jan 1981) to (0000 1 Jan 2021) by 1.0 N= 14611 pts :grid
- Longitude (longitude)
- grid: /X (degree_east) ordered (20.975E) to (52.025E) by 0.05 N= 622 pts :grid
- Latitude (latitude)
- grid: /Y (degree_north) ordered (11.99675S) to (22.20111N) by 0.04999687 N= 685 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
- 0.0572957795130824 meter radian-1 north
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| T | X | Y | |
 ENACTS version0 daily rainfall[rfe
] | |||
 $integral dT$ detrended-bfl $partialdiff sub Y$ detrended-bfl [ ENACTS version0 daily rainfall rfe ] [ X Y | T] | M | 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 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