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∂T ∂X ∂Y ∂Y [ ENACTS version0 daily rainfall rfe ] : ∂T ∂X ∂Y ∂Y precipitation data
daily rainfall rfe partial_Y partial_Y partial_Y partial_X ∂T ∂X ∂Y ∂Y precipitation from ENACTS version0: version 0: CHIRPS-BLENDED and CRU.Independent Variables (Grids)
- Time (time)
- grid: /T (julian_day) ordered (1200 1 Jan 1981 - 1200 2 Jan 1981) to (1200 30 Dec 2020 - 1200 31 Dec 2020) by 1.0 N= 14609 pts :grid
- Longitude (longitude)
- grid: /X (degree_east) ordered (21.0E) to (52.0E) by 0.05 N= 621 pts :grid
- Latitude (latitude)
- grid: /Y (degree_north) ordered (11.94675S) to (22.15112N) by 0.04999688 N= 683 pts :grid
Other Info
- bufferwordsize
- 4
- CE
- 100.0
- colorscalename
- prcp_dailyrate_max100_smooth
- CS
- 0.0
- datatype
- realarraytype
- file_missing_value
- -9999.0
- maxncolor
- 254
- missing_value
- NaN
- pointwidth
- 1.0
- standard_name
- lwe_precipitation_rate
- units
- 2.51965112759372×10-08 meter radian-3 north second-2
- colorscale
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 | |
 $partialdiff sub T$ $partialdiff sub X$ $partialdiff sub Y$ $partialdiff sub Y$ [ ENACTS version0 daily rainfall rfe ] [ X Y | T] | M | M | M |
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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