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∫dX root mean sq ∂S ∫dY [ ZMD CPT GFDL-CM2p5-FLOR-A06 Prob ] : ∂S Tercile Forecast data
CPT GFDL-CM2p5-FLOR-A06 Prob int_dY partial_S partial_S ∂S Tercile Forecast from ZMD: Zambia Meteorological Department.Independent Variables (Grids)
- S (forecast_reference_time)
- grid: /S (months since 1960-01-01) ordered (Aug 2020) to (Nov 2021) by 1.0 N= 16 pts :grid
- Longitude (longitude)
- grid: /X (degree_east) ordered (19.97455E) to (34.02546E) by 0.2509091 N= 57 pts :grid
- Latitude (latitude)
- grid: /Y (degree_north) ordered (18.02564S) to (7.97436S) by 0.251282 N= 41 pts :grid
Other Info
- bufferwordsize
- 8
- CE
- null
- colorscalename
- tercileclassesscale
- CS
- null
- datatype
- doublearraytype
- file_missing_value
- -1.0
- maxncolor
- 254
- missing_value
- NaN
- pointwidth
- 0
- units
- 3.65540903744051×10-05 radian2 east year-1
- history
- Averaged over C[Below, Above] minimum 0.0% data present
Last updated: Thu, 10 Feb 2022 00:03:57 GMT
Expires: Sat, 12 Mar 2022 00:00:00 GMT
Data Views
Views
data as colors
data as contours
colors with land
contours with land
colors with coasts| S | X | Y | |
 $integral dX$ root mean sq $partialdiff sub S$ $integral dY$ [ ZMD CPT GFDL-CM2p5-FLOR-A06 Prob ] [ X Y | S] | M | M | M |
|---|
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 S
- Differentiate along X Y S
- Take differences along X Y S
RMS (root mean square with mean *not* removed) over X Y S | X Y X S Y S | X Y S |
RMSA (root mean square with mean removed) over X Y S | X Y X S Y S | X Y S |
Maximum over X Y S | X Y X S Y S | X Y S |
Minimum over X Y S | X Y X S Y S | X Y S |
Detrend (best-fit-line) over X Y S | X Y X S Y S | X Y S |
Note on units