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∂Y min ∫dS [ ICPAC Forecasts CPT Rainfall Seasonal Forecast Forecast ] : ∂Y forecast data
Forecast int_dS min ∂Y 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
- 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 (11.375S) to (22.875N) by 0.25 N= 138 pts :grid
Other Info
- CE
- null
- CS
- null
- datatype
- realarraytype
- file_missing_value
- -999.0
- maxncolor
- 254
- missing_value
- NaN
- units
- 0.00477464829275686 meter radian-1 north year
- history
- min over S[0000 16 Jan 2001, 0000 16 Feb 2020]
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 | X | Y | |
 $partialdiff sub Y$ min $integral dS$ [ ICPAC Forecasts CPT Rainfall Seasonal Forecast Forecast ] 2.5 months[ X Y | 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 X Y
- Differentiate along X Y
- Take differences along X Y
RMS (root mean square with mean *not* removed) over X Y | X Y |
RMSA (root mean square with mean removed) over X Y | X Y |
Maximum over X Y | X Y |
Minimum over X Y | X Y |
Detrend (best-fit-line) over X Y | X Y |
Note on units