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min ∂S root mean sq ∫dS [ MGAP CPT PredErrorVars ] : ∂S prediction error variance data
MGAP CPT PredErrorVars int_dS int_dS int_dS partial_S ∂S 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
- 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
- $partialdiff sub S$ root mean sq $integral dS$ [ MGAP CPT PredErrorVars ] 16-30 Oct 2013
- 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
min $partialdiff sub S$ root mean sq $integral dS$ [ MGAP CPT PredErrorVars ]- min over S[16 Sep 2012 - 15 Oct 2012, 16-30 Oct 2013]
- 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
Views
data as colors
data as contours
line plots
color bars
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 L
- Differentiate along X L
- Take differences along X L
RMS (root mean square with mean *not* removed) over X L | X L |
RMSA (root mean square with mean removed) over X L | X L |
Maximum over X L | X L |
Minimum over X L | X L |
Detrend (best-fit-line) over X L | X L |
Note on units