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root mean sq ∫dY [ UCSB CHIRPS v1p8 dekad prcp ] : precipitation data
CHIRPS v1p8 dekad prcp int_dY int_dY precipitation from UCSB: UC Santa Barbara. isIndependent Variables (Grids)
Other Info
- bufferwordsize
- 8
- CE
- null
- colorscalename
- prcp_dekadrate_max600_smooth
- CS
- null
- datatype
- doublearraytype
- file_missing_value
- -9999.0
- maxncolor
- 254
- missing_value
- NaN
- standard_name
- lwe_precipitation_rate
- units
- 1.74532925199433×10-05 meter radian north
- history
- Averaged over X[180W, 180] Y[50.025S, 50.025N] T[6-15 Jan 1981, 2400 5 May 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: 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