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ICPAC Forecasts CPT Rainfall Seasonal Forecast PredErrorVars: prediction error variance data
prediction error variance 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
- Forecast Issue Date (forecast_reference_time)
- grid: /S (months since 1960-01-01) ordered (0000 1 Feb 2001) to (0000 1 Feb 2020) by 1.0 N= 229 pts :grid
- Longitude (longitude)
- grid: /X (degree_east) ordered (22E) to (51.25E) by 0.25 N= 118 pts :grid
- Latitude (latitude)
- grid: /Y (degree_north) ordered (11.5S) to (23N) by 0.25 N= 139 pts :grid
Other Info
- bufferwordsize
- 8
- CE
- null
- colorscalename
- rainbowcolorscale
- CS
- 0
- datatype
- doublearraytype
- maxncolor
- 256
- missing_value
- -999.0
- scale_min
- 0.0
- units
- mm2
- standard units*
- 10-06 meter2
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
colors with land
contours with land
colors with coasts| L | S | X | Y | |
 ICPAC Forecasts CPT Rainfall Seasonal Forecast PredErrorVars 2.5 months[ X Y | S L] | M | 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 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