data: KMD Forecast CPT Rainfall Seasonal History PearsonR

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KMD Forecast CPT Rainfall Seasonal History PearsonR: Pearson correlation skill values data

Pearson correlation skill values from KMD Forecast CPT Rainfall 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 (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: 1
colorscalename: correlationcolorscale
CS: -1
datatype: doublearraytype
maxncolor: 254
missing_value: -999.0
scale_max: 1.0
scale_min: -1.0
units: unitless
colorscale

Last updated: Wed, 29 May 2024 07:29:02 GMT

Data Views

	L	S	X	Y
grid: /X (degree_east) ordered (22E) to (51.25E) by 0.25 N= 118 pts :grid			M
grid: /Y (degree_north) ordered (11.5S) to (23N) by 0.25 N= 139 pts :grid				M
grid: /S (months since 01-Jan) periodic (0000 1 Jan) to (0000 1 Dec) by 1.0 N= 12 pts :grid		M
KMD Forecast 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