data: $integral dL$ mean $integral dT$ [ MGAP CPT PearsonR ]

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∫dL mean ∫dT [ MGAP CPT PearsonR ] : Pearson correlation skill values data

MGAP CPT PearsonR int_dT int_dT int_dT Pearson correlation skill values from SOURCES: datos de MGAP SNIA prueba.

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
Longitude (longitude): grid: /X (degree_east) ordered (59.75W) to (52.25W) by 0.5 N= 16 pts :grid
Latitude (latitude): grid: /Y (degree_north) ordered (29.25S) to (35.75S) by 0.5 N= 14 pts :grid

Other Info

bufferwordsize

CE

null

colorscalename

correlationcolorscale

CS

null

datatype

doublearraytype

file_missing_value

-999.0

maxncolor

254

missing_value

NaN

pointwidth

3.0

units

0.00694444444444444 year²

history

$integral dL$ mean $integral dT$ [ MGAP CPT PearsonR ]

Output from CPT for 12 3-month running seasons for 1960-2009 between ECHAM4p5 GCM and CRU dataset, CPT recompiled on Mac 9.04 version
Averaged over T[0000 1 Sep 1960, 0000 1 Dec 1960] minimum 0.0% data present

Last updated: Thu, 04 Feb 2016 15:18:08 GMT

Data Views

	L	X	Y
doc[]
MGAP CPT[Forecast PredErrorVars hindcast_date InputObs CFSv2_AtlPac_sst CrossValHind PearsonR target_date ]
$integral dL$ mean $integral dT$ [ MGAP CPT PearsonR ] [ X Y \| L]	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 L
Differentiate along X Y L
Take differences along X Y L