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max ∂Y mean [ MGAP CPT PearsonR ] : ∂Y Pearson correlation skill values data
MGAP CPT PearsonR PearsonR partial_Y ∂Y Pearson correlation skill values from SOURCES: datos de MGAP SNIA prueba. isIndependent Variables (Grids)
Other Info
- CE
- null
- colorscalename
- correlationcolorscale
- CS
- null
- datatype
- realarraytype
- file_missing_value
- -999.0
- maxncolor
- 254
- missing_value
- NaN
- pointwidth
- 3.0
- units
- 57.2957795130823 radian-1 north
- history
- $partialdiff sub Y$ mean [ MGAP CPT PearsonR ] 4.5 months
- 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[Sep, Nov] minimum 0.0% data present
max $partialdiff sub Y$ mean [ MGAP CPT PearsonR ]- max over X[59.75W, 52.25W] Y[29.5S, 35.5S] L[2.5 months, 4.5 months]
- 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
 doc[] |
| MGAP CPT[hindcast_date
InputObs
target_date
CrossValHind
PearsonR
PredErrorVars
CFSv2_AtlPac_sst
Forecast
] |
 max $partialdiff sub Y$ mean [ MGAP CPT PearsonR ] [ |] |
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
- Differentiate along
- Take differences along
RMS (root mean square with mean *not* removed) over
RMSA (root mean square with mean removed) over
Maximum over
Minimum over
Detrend (best-fit-line) over
Note on units