served from maproom.metmalawi.gov.mw
min [ ∂X ( UCSB CHIRPS v1p8 monthly global precipitation ) anomalies ]: ∂X CHIRPS precipitation data
CHIRPS v1p8 monthly global precipitation precipitation precipitation partial_X partial_X ∂X CHIRPS precipitation from UCSB: UC Santa Barbara.Independent Variables (Grids)
- Time (time)
- grid: /T (months since 1960-01-01) ordered (Jan 1981) to (Dec 2025) by 1.0 N= 540 pts :grid
- Longitude (longitude)
- grid: /X (degree_east) periodic [ (179.95W) (179.9W) (179.85W) ... (179.95E)] N= 7200 pts :grid
Other Info
- bufferwordsize
- 4
- CE
- null
- colorscalename
- prcp_monthlyrate_max1500_smooth
- CS
- null
- datatype
- realarraytype
- file_missing_value
- -9999.0
- maxncolor
- 254
- missing_value
- NaN
- pointwidth
- 1.0
- standard_name
- lwe_precipitation_rate
- units
- 0.687549354156988 meter radian-1 east year-1
- history
- min [ $partialdiff sub X$ ( UCSB CHIRPS v1p8 monthly global precipitation ) anomalies ]
- Averaged over T2[1981, 2025] minimum 0.0% data present
min over Y[49.95N, 49.95S]
- Averaged over T2[1981, 2025] minimum 0.0% data present
References
Funk, C. C, Peterson, P. J., Landsfeld, M. F., Pedreros, D. H., Verdin, J. P., Rowland, J. D., Romero, B. E., Husak, G. J. Michaelsen, J. C., and Verdin, A. P., 2014, A quasi-global precipitation time series for drought monitoring: U. S. Geological Survey Data Series 832, 4 p., http://dx.doi.org/110.3133/ds832.
Last updated: Thu, 02 Nov 2023 07:43:25 GMT
Expires: Sun, 01 Feb 2026 00:00:00 GMT
Data Views
Views
data as colors
data as contours| T | X | |
 min [ $partialdiff sub X$ ( UCSB CHIRPS v1p8 monthly global precipitation ) anomalies ][ X | T] | 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 T
- Differentiate along X T
- Take differences along X T
RMS (root mean square with mean *not* removed) over X T | X T |
RMSA (root mean square with mean removed) over X T | X T |
Maximum over X T | X T |
Minimum over X T | X T |
Detrend (best-fit-line) over X T | X T |
Note on units