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∫dY ∂S ∫dY [ ICPAC Forecasts CPT Rainfall Seasonal History PearsonR ] : ∂S Pearson correlation skill values data
PearsonR int_dY partial_S ∂S Pearson correlation skill values from ICPAC Forecasts CPT Rainfall Seasonal History: Predictand, Hindcast and Skill.Independent Variables (Grids)
- Forecast Lead Time in Months
- grid: /L (months) ordered [ (2.5)] :grid
- S
- grid: /S (months since 01-Jan) periodic (Dec) to (Nov) by 1.0 N= 12 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 (11.75S) to (23.25N) by 0.25 N= 141 pts :grid
Other Info
- bufferwordsize
- 8
- CE
- 1
- colorscalename
- correlationcolorscale
- CS
- -1
- datatype
- doublearraytype
- file_missing_value
- -999.0
- maxncolor
- 254
- missing_value
- NaN
- units
- 0.00365540903744051 radian2 north year-1
- colorscale
Last updated: Thu, 25 Feb 2021 08:46:12 GMT
Data Views
Views
data as colors
data as contours
colors with land
contours with land
colors with coasts| L | S | X | Y | |
 $integral dY$ $partialdiff sub S$ $integral dY$ [ ICPAC Forecasts CPT Rainfall 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