served from maproom.metmalawi.gov.mw
∂T [ UCSB CHIRPS v1p8 monthly global precipitation ] anomalies: ∂T CHIRPS precipitation data
CHIRPS v1p8 monthly global precipitation partial_T partial_T partial_T ∂T CHIRPS precipitation from UCSB: UC Santa Barbara.Independent Variables (Grids)
- Time
- grid: /T (days since 1960-01-01) (360) ordered [ (0000 22 Jun 1981 - 0000 22 Aug 1981) (0000 22 Aug 1981 - 0000 23 Oct 1981) (0000 23 Oct 1981 - 0000 25 Jan 1982) ... (0002 12 Nov 2025 - 0002 13 Feb 2026)] N= 209 pts :grid
- Longitude (longitude)
- grid: /X (degree_east) periodic (180W) to (179.95E) by 0.05 N= 7200 pts :grid
- Latitude (latitude)
- grid: /Y (degree_north) ordered (49.975N) to (49.975S) by 0.05 N= 2000 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
- 0.144 meter year-2
- history
- Averaged over T2[16 Jan 1981 - 15 Jan 1982, 16 Jan 2024 - 15 Jan 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 Jun 2025 00:00:00 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 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