min [ mean ∂Y ∫dY ( ENACTS version0 dekadal rainfall rfe_merged_vs_meanrfe_merged ) climatology ]: ∂Y Rainfall data Expressed as Percentages of the Long-Term Average data
dekadal rainfall rfe_merged_vs_meanrfe_merged int_dY partial_Y partial_Y partial_Y
∂Y Rainfall data Expressed as Percentages of the Long-Term Average from ENACTS version0: version 0: CHIRPS-BLENDED and CRU.
Independent Variables (Grids)
- Latitude (latitude)
- grid: /Y (degree_north) ordered (11.775S) to (23.125N) by 0.05 N= 699 pts :grid
Other Info
- CE
- null
- colorscalename
- precip_colors
- CS
- null
- datatype
- realarraytype
- maxncolor
- 254
- units
- percent
- standard units*
- 0.01
- history
- mean $partialdiff sub Y$ $integral dY$ [ ENACTS version0 dekadal rainfall rfe_merged_vs_meanrfe_merged ]
- Averaged over T2[0.5, 1080.5] minimum 0.0% data present
Averaged over X[21.83E, 51.48E]
mean $partialdiff sub Y$ $integral dY$ [ ENACTS version0 dekadal rainfall rfe_merged_vs_meanrfe_merged ] climatology- Averaged over T2[1981, 2357 30 Dec 2022 - 2357 30 Dec 2023]
min [ mean $partialdiff sub Y$ $integral dY$ ( ENACTS version0 dekadal rainfall rfe_merged_vs_meanrfe_merged ) climatology ]- min over T[0000 1 Jan - 0000 11 Jan, 0000 21 Dec - 0000 1 Jan]
Last updated: Thu, 16 May 2024 08:15:58 GMT
Expires: Sat, 11 May 2024 00:00:00 GMT
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 Y
- Differentiate along Y
- Take differences along Y
Average over
Y
|
RMS (root mean square with mean *not* removed) over
Y
|
RMSA (root mean square with mean removed) over
Y
|
Maximum over
Y
|
Minimum over
Y
|
Detrend (best-fit-line) over
Y
|
Note on units