data: IRI Forecast SST_OBS-PRECIP

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IRI Forecast SST_OBS-PRECIP_GPCC History PearsonR: Pearson correlation skill values data

Pearson correlation skill values from IRI Forecast SST_OBS-PRECIP_GPCC History: Predicand, Hindcast and Skill.

Independent Variables (Grids)

Forecast Lead Time in Months: grid: /L (months) ordered (1.5 months) to (3.5 months) by 1.0 N= 3 pts :grid
Forecast Issue Date (forecast_reference_time): grid: /S (months since 1960-01-01) ordered (0000 1 Jan 2000) to (0000 1 May 2026) by 1.0 N= 317 pts :grid
Longitude (longitude): grid: /X (degree_east) ordered (59.5W) to (52.5W) by 1.0 N= 8 pts :grid
Latitude (latitude): grid: /Y (degree_north) ordered (29.5S) to (35.5S) by 1.0 N= 7 pts :grid

Other Info

bufferwordsize: 4
CE: 0.9960159
colorscalename: correlationcolorscale
CS: -0.9960159
datatype: realarraytype
maxncolor: 254
missing_value: -999.0
pointwidth: 3.0
scale_max: 1.0
scale_min: -1.0
units: unitless
standard units*: unitless
colorscale

Last updated: Mon, 31 Jul 2017 15:28:00 GMT
Expires: Sat, 05 Aug 2017 00:00:00 GMT

Data Views

	L	S	X	Y
doc[]
IRI Forecast SST_OBS-PRECIP_GPCC History[target_date InputObs hindcast_date PearsonR CrossValPred 2AFC SpearmanR YInputData ]
IRI Forecast SST_OBS-PRECIP_GPCC History PearsonR[ 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 L
Differentiate along X Y S L
Take differences along X Y S L