data: $integral dX$ $integral dS$ [ ICPAC Forecasts CPT Rainfall Seasonal History PearsonR ]

∫dX ∫dS [ ICPAC Forecasts CPT Rainfall Seasonal History PearsonR ] : Pearson correlation skill values data

PearsonR int_dS int_dS 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 1960-01-01) ordered (16 Dec 1959 - 15 Jan 1960) to (16 Nov 1960 - 15 Dec 1960) by 1.0 N= 12 pts :grid
Longitude (longitude): grid: /X (degree_east) ordered (21.875E) to (51.375E) by 0.25 N= 119 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: correlationcolorscale
CS: null
datatype: doublearraytype
file_missing_value: -999.0
maxncolor: 254
missing_value: NaN
units: 0.00145444104332861 radian east year

Last updated: Thu, 25 Feb 2021 08:46:12 GMT

Data Views

	L	S	X	Y
$integral dX$ $integral dS$ [ ICPAC Forecasts CPT Rainfall Seasonal History PearsonR ] 2.5 months[ X Y \| S L]	M	M	M	M

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