data: KMD Forecast CPT Rainfall Seasonal History PearsonR

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

PearsonR 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
S: grid: /S (days since 1960-01-01) periodic [ (0000 2 Jan) (0000 2 Feb) (0000 1 Mar) (0000 1 Apr) (0000 1 May) (0000 1 Jun) (0000 1 Jul) (0000 1 Aug) (0000 1 Sep) (0000 1 Oct) (0000 1 Nov) (0000 1 Dec)] :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
Conventions: COARDS
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
KMD Forecast CPT Rainfall Seasonal History[PearsonR hindcast_date CrossValHind hindcast_length InputObs final_season ]
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