data: $integral dC$ $partialdiff sub C$ root mean sq [ ZMD CPT GFDL-CM2p5-FLOR-A06 Prob ]

∫dC ∂_C root mean sq [ ZMD CPT GFDL-CM2p5-FLOR-A06 Prob ] : ∂_C Tercile Forecast data

CPT GFDL-CM2p5-FLOR-A06 Prob Prob partial_C int_dC int_dC ∂_C Tercile Forecast from ZMD: Zambia Meteorological Department.

Independent Variables (Grids)

Tercile Categories: grid: /C (ids) unordered [ (Below) (Normal) (Above)] :grid
S (forecast_reference_time): grid: /S (days since 1960-01-01) ordered [ (Aug 2020) (Sep 2020) (Oct 2020) (Nov 2020) (Dec 2020) (Jan 2021) (Feb 2021) (Mar 2021) (Apr 2021) (May 2021) (Jun 2021) (Jul 2021) (Aug 2021) (Sep 2021) (Oct 2021) (Nov 2021)] :grid
Latitude (latitude): grid: /Y (degree_north) ordered (17.9S) to (8.1S) by 0.251282 N= 40 pts :grid

Other Info

CE: null
colorscalename: tercileclassesscale
CS: null
datatype: realarraytype
file_missing_value: -1.0
maxncolor: 254
missing_value: NaN
pointwidth: 0
units: percent /ids ids
history: Averaged over X[19.97455E, 34.02546E] 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

	C	S	Y
$integral dC$ $partialdiff sub C$ root mean sq [ ZMD CPT GFDL-CM2p5-FLOR-A06 Prob ] [ Y C \| 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 Y C S
Differentiate along Y C S
Take differences along Y C S

∫dC ∂C root mean sq [ ZMD CPT GFDL-CM2p5-FLOR-A06 Prob ] : ∂C Tercile Forecast data

Independent Variables (Grids)

Other Info

Data Views

Filters

∫dC ∂_C root mean sq [ ZMD CPT GFDL-CM2p5-FLOR-A06 Prob ] : ∂_C Tercile Forecast data