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∫dY ∂X [ ICPAC Forecasts CPT Rainfallv2 Seasonal History PearsonR ] : ∂X Pearson correlation skill values data
PearsonR partial_X ∂X Pearson correlation skill values from ICPAC Forecasts CPT Rainfallv2 Seasonal History: Predictand, Hindcast and Skill.Independent Variables (Grids)
- Forecast Lead Time in Months
- grid: /L (months) ordered [ (2.5)] :grid
- Hindcast Issue Date
- grid: /S (months since 01-Jan) periodic (0000 1 Jan) to (0000 1 Dec) by 1.0 N= 12 pts :grid
- Longitude (longitude)
- grid: /X (degree_east) ordered (20.75E) to (52.75E) by 0.5 N= 65 pts :grid
- Latitude (latitude)
- grid: /Y (degree_north) ordered (13.75S) to (24.75N) by 0.5 N= 78 pts :grid
Other Info
- bufferwordsize
- 8
- CE
- 1
- colorscalename
- correlationcolorscale
- CS
- -1
- datatype
- doublearraytype
- file_missing_value
- -9999.0
- maxncolor
- 254
- missing_value
- NaN
- pointwidth
- 0
- units
- unitless
- colorscale
Last updated: Wed, 12 May 2021 17:45:02 GMT
Data Views
Views
data as colors
data as contours
colors with land
contours with land
colors with coasts| L | S | X | Y | |
 $integral dY$ $partialdiff sub X$ [ ICPAC Forecasts CPT Rainfallv2 Seasonal History PearsonR ] 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