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∂S root mean sq anom ∂S [ ICPAC Forecasts CPT Rainfall Seasonal Forecast Forecast ] : ∂S ∂S forecast data
Forecast partial_S partial_S ∂S ∂S forecast from ICPAC Forecasts CPT Rainfall Seasonal Forecast: Forecast and Error.Independent Variables (Grids)
- Forecast Lead Time in Months
- grid: /L (months) ordered [ (2.5)] :grid
- S (forecast_reference_time)
- grid: /S (months since 1960-01-01) ordered (16 Feb 2001 - 15 Mar 2001) to (16 Dec 2019 - 15 Jan 2020) by 1.0 N= 227 pts :grid
- Latitude (latitude)
- grid: /Y (degree_north) ordered (11.5S) to (23N) by 0.25 N= 139 pts :grid
Other Info
- CE
- null
- CS
- null
- datatype
- realarraytype
- file_missing_value
- -999.0
- maxncolor
- 254
- missing_value
- NaN
- units
- 0.144 meter year-2
- history
- Averaged over X[22E, 51.25E] minimum 0.0% data present
Last updated: Thu, 25 Feb 2021 08:46:12 GMT
Expires: Tue, 09 Mar 2021 00:00:00 GMT
Data Views
Views
data as colors
data as contours| L | S | Y | |
 $partialdiff sub S$ root mean sq anom $partialdiff sub S$ [ ICPAC Forecasts CPT Rainfall Seasonal Forecast Forecast ] 2.5 months[ Y | S L] | M | M | 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 Y S
- Differentiate along Y S
- Take differences along Y S
RMS (root mean square with mean *not* removed) over Y S | Y S |
RMSA (root mean square with mean removed) over Y S | Y S |
Maximum over Y S | Y S |
Minimum over Y S | Y S |
Detrend (best-fit-line) over Y S | Y S |
Note on units