data: $partialdiff sub S$ $integral dL$ root mean sq [ MGAP CPT PredErrorVars ]

∂_S ∫dL root mean sq [ MGAP CPT PredErrorVars ] : ∂_S prediction error variance data

MGAP CPT PredErrorVars PredErrorVars int_dL int_dL partial_S ∂_S prediction error variance from SOURCES: datos de MGAP SNIA prueba.

Independent Variables (Grids)

L: grid: /L (months) ordered (2.0 months) to (5.0 months) by 1.0 N= 4 pts :grid
S (forecast_reference_time): grid: /S (months since 1960-01-01) ordered [ (0000 1 Oct 2012 - 1200 8 Jan 2013) (1200 8 Jan 2013 - 1200 23 Jun 2013) (1200 23 Jun 2013 - 2400 30 Sep 2013)] :grid
Longitude (longitude): grid: /X (degree_east) ordered (59.75W) to (52.25W) by 0.5 N= 16 pts :grid

Other Info

CE

null

CS

null

datatype

realarraytype

file_missing_value

-999.0

missing_value

NaN

pointwidth

3.0

units

mm /month

standard units*

0.012 meter year^-1

history

$partialdiff sub S$ $integral dL$ root mean sq [ MGAP CPT PredErrorVars ]

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 Y[36S, 29S] minimum 0.0% data present

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

Data Views

	L	S	X
$partialdiff sub S$ $integral dL$ root mean sq [ MGAP CPT PredErrorVars ] [ X \| S 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 S L
Differentiate along X S L
Take differences along X S L

∂S ∫dL root mean sq [ MGAP CPT PredErrorVars ] : ∂S prediction error variance data

Independent Variables (Grids)

Other Info

Data Views

Filters

∂_S ∫dL root mean sq [ MGAP CPT PredErrorVars ] : ∂_S prediction error variance data