served from dlibrary.snia.gub.uy
∂L ∫dX ∂S [ MGAP CPT CFSv2_AtlPac_sst PredErrorVars ] : ∂L ∂S prediction error variance data
MGAP CPT CFSv2_AtlPac_sst PredErrorVars partial_S int_dX ∂L ∂S prediction error variance from SOURCES: datos de MGAP SNIA prueba.Independent Variables (Grids)
- L
- grid: /L (months) ordered (3.0 months) to (4.0 months) N= 2 pts :grid
- S (forecast_reference_time)
- grid: /S (months since 1960-01-01) ordered [ (Sep 2014)] :grid
- Longitude (longitude)
- grid: /X (degree_east) ordered (60W) to (52W) by 1.0 N= 9 pts :grid
- Latitude (latitude)
- grid: /Y (degree_north) ordered (35.5S) to (29.5S) by 1.0 N= 7 pts :grid
Other Info
- datatype
- realarraytype
- file_missing_value
- -999.0
- missing_value
- NaN
- pointwidth
- 0
- units
- 2.90888208665722×10-08 meter radian east second-1 year-2
- 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
Views
data as colors
data as contours
colors with land
contours with land
colors with coasts
line plots
color bars
MGAP CPT[
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 L
- Differentiate along X Y L
- Take differences along X Y L
RMS (root mean square with mean *not* removed) over X Y L | X Y X L Y L | X Y L |
RMSA (root mean square with mean removed) over X Y L | X Y X L Y L | X Y L |
Maximum over X Y L | X Y X L Y L | X Y L |
Minimum over X Y L | X Y X L Y L | X Y L |
Detrend (best-fit-line) over X Y L | X Y X L Y L | X Y L |
Note on units