served from dlibrary.snia.gub.uy
root mean sq anom ∂Y [ MGAP CPT PredErrorVars ] : ∂Y prediction error variance data
MGAP CPT PredErrorVars partial_Y ∂Y prediction error variance from SOURCES: datos de MGAP SNIA prueba.Independent Variables (Grids)
- Latitude (latitude)
- grid: /Y (degree_north) ordered (29.5S) to (35.5S) by 0.5 N= 13 pts :grid
Other Info
- bufferwordsize
- 8
- CE
- null
- CS
- null
- datatype
- doublearraytype
- file_missing_value
- -999.0
- missing_value
- NaN
- pointwidth
- 3.0
- units
- 0.687549354156988 meter radian-1 north year-1
- history
- root mean sq anom $partialdiff sub Y$ [ 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 X[59.75W, 52.25W] S[0000 1 Sep 2012, 0000 1 Nov 2013] L[2.5 months, 4.5 months] minimum 0.0% data present
- 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
line plots
color bars
scatter
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 Y
- Differentiate along Y
- Take differences along Y
RMS (root mean square with mean *not* removed) over Y |
RMSA (root mean square with mean removed) over Y |
Maximum over Y |
Minimum over Y |
Detrend (best-fit-line) over Y |
Note on units