served from IRI/LDEO Climate Data Library
∫dX [ UCSB CHIRPS v2p0 monthly global NMME_deg1p0 precipitation ]: CHIRPS precipitation data
precipitation CHIRPS precipitation from UCSB CHIRPS v2p0 monthly global NMME_deg1p0: CHIRPS monthly precipitation regridded to 1.0 deg. lat/lon resolution to match NMME models between 49S and 49N.Independent Variables (Grids)
- Time (time)
- grid: /T (months since 1960-01-01) ordered (Jan 1981) to (Feb 2024) by 1.0 N= 518 pts :grid
- Longitude
- grid: /X (degree_east) ordered (0.5W) to (0.5W) by 1.0 N= 361 pts :grid
- Latitude
- grid: /Y (degree_north) ordered (49S) to (49N) by 1.0 N= 99 pts :grid
Other Info
- bufferwordsize
- 8
- CE
- 1500.0
- colorscalename
- prcp_monthlyrate_max1500_smooth
- CS
- 0.0
- datatype
- doublearraytype
- file_missing_value
- -9999.0
- maxncolor
- 254
- missing_value
- NaN
- pointwidth
- 1.0
- standard_name
- lwe_precipitation_rate
- units
- 0.00020943951023932 meter radian east year-1
- history
- $integral dX$ [ UCSB CHIRPS v2p0 monthly global NMME_deg1p0 precipitation ]
- Boxes with less than 25.0% dropped
Boxes with less than 25.0% dropped
- Boxes with less than 25.0% dropped
- colorscale
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: Wed, 13 Mar 2024 21:30:12 GMT
Expires: Mon, 01 Apr 2024 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