data: MGAP CPT CFSv2_AtlPac

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MGAP CPT CFSv2_AtlPac_sst PredErrorVars: prediction error variance data

MGAP CPT CFSv2_AtlPac_sst prediction error variance 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
Forecast Issue Date (forecast_reference_time): grid: /S (months since 1960-01-01) ordered (0000 1 Sep 2014) to (0000 1 Oct 2014) N= 2 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 (35.5S) to (29.5S) by 1.0 N= 7 pts :grid

Other Info

bufferwordsize: 4
datatype: realarraytype
missing_value: -999.0
pointwidth: 0
scale_min: 0.0
units: mm /day
standard units*: 1.15740740740741×10^-08 meter second^-1
history: 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

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

Data Views

	L	S	X	Y
MGAP CPT CFSv2_AtlPac_sst PredErrorVars[ 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