data: $integral dS$ [ IRI Forecast SST_OBS-PRECIP

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∫dS [ IRI Forecast SST_OBS-PRECIP_GPCC History 2AFC ]: 2AFC data

2AFC 2AFC 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
S (forecast_reference_time): grid: /S (months since 1960-01-01) ordered (0000 16 Dec 1999) to (0000 16 May 2026) by 1.0 N= 318 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: 8
CE: null
colorscalename: precip_colors
CS: null
datatype: doublearraytype
file_missing_value: -999.0
maxncolor: 254
missing_value: NaN
pointwidth: 3.0
units: months
standard units*: 0.0833333333333333 year

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 ]
$integral dS$ [ IRI Forecast SST_OBS-PRECIP_GPCC History 2AFC ][ 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