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mean root mean sq anom ∂Y ∫dS [ ZMD CPT GFDL-CM2p5-FLOR-A06 Prob ] : ∂Y Tercile Forecast data
CPT GFDL-CM2p5-FLOR-A06 Prob int_dS partial_Y partial_Y ∂Y Tercile Forecast from ZMD: Zambia Meteorological Department.Independent Variables (Grids)
- S (forecast_reference_time)
- grid: /S (months since 1960-01-01) ordered (0000 16 Jul 2020) to (0000 16 Dec 2021) by 1.0 N= 18 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
- 0.0477464829275686 radian-1 north year
- history
- mean root mean sq anom $partialdiff sub Y$ $integral dS$ [ ZMD CPT GFDL-CM2p5-FLOR-A06 Prob ]
- Averaged over Y[17.77436S, 8.225641S] minimum 0.0% data present
Averaged over X[19.97455E, 34.02546E] C[Below, Above] minimum 0.0% data present
- Averaged over Y[17.77436S, 8.225641S] 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
line plots
color bars
scatter| S | |
 mean root mean sq anom $partialdiff sub Y$ $integral dS$ [ ZMD CPT GFDL-CM2p5-FLOR-A06 Prob ] [ | S] | M |
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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 S
- Differentiate along S
- Take differences along S
RMS (root mean square with mean *not* removed) over S |
RMSA (root mean square with mean removed) over S |
Maximum over S |
Minimum over S |
Detrend (best-fit-line) over S |
Note on units