served from maproom.metmalawi.gov.mw
root mean sq anom ∂T [ UCSB CHIRPS v2p0 daily global 0p05 prcp ] : ∂T precipitation data
CHIRPS v2p0 daily global 0p05 prcp partial_T ∂T precipitation from UCSB: UC Santa Barbara. isIndependent Variables (Grids)
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
- 1.33959190672154×10-13 meter second-2
- history
- root mean sq anom $partialdiff sub T$ [ UCSB CHIRPS v2p0 daily global 0p05 prcp ]
- created by Climate Hazards Group
Averaged over X[179.975W, 179.975E] Y[49.975N, 49.975S] T[1200 1 Jan 1981 - 1200 2 Jan 1981, 1200 30 Apr 2025 - 1200 1 May 2025] minimum 0.0% data present
- created by Climate Hazards Group
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: Tue, 01 Jul 2025 00:00:00 GMT
Data Views
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
- Differentiate along
- Take differences along
RMS (root mean square with mean *not* removed) over
RMSA (root mean square with mean removed) over
Maximum over
Minimum over
Detrend (best-fit-line) over
Note on units