data: ALISIOS NextGen CHIRPS Seasonal OLE2 NCEP-CFSv2 Skill Pearson

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ALISIOS NextGen CHIRPS Seasonal OLE2 NCEP-CFSv2 Skill Pearson: Pearson - CCA Deterministic Hindcasts data

Pearson - CCA Deterministic Hindcasts from ALISIOS NextGen CHIRPS Seasonal OLE2 NCEP-CFSv2 Skill: Forecast Skills.

Independent Variables (Grids)

Forecast Lead Time in Months: grid: /L (months) ordered (2.5 months) to (4.5 months) by 1.0 N= 3 pts :grid
S: grid: /S (months since 01-Jan) periodic (Jan) to (Dec) by 1.0 N= 12 pts :grid
Longitud (longitude): grid: /X (degree_east) ordered (117.6837W) to (28.2837W) by 0.1 N= 895 pts :grid
Latitud (latitude): grid: /Y (degree_north) ordered (32.6663N) to (49.975S) by 0.10005 N= 827 pts :grid

Other Info

CE: 1
colorscalename: correlationcolorscale
CS: -1
datatype: realarraytype
maxncolor: 254
missing_value: -999
scale_max: 1.0
scale_min: -1.0
colorscale

Last updated: Sun, 15 Dec 2024 14:42:00 GMT

Data Views

	L	S	X	Y
ALISIOS NextGen CHIRPS Seasonal OLE2 NCEP-CFSv2 Skill Pearson[ 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