min [ MGAP CPT CFSv2_AtlPac_sst PearsonR ]: Pearson correlation skill values data
MGAP CPT CFSv2_AtlPac_sst PearsonR PearsonR Pearson correlation skill values 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 (59.5W) to (52.5W) by 1.0 N= 8 pts :grid
Latitude (latitude)
grid: /Y (degree_north) ordered (35.5S) to (29.5S) by 1.0 N= 7 pts :grid
Other Info
bufferwordsize
4
CE
0.9960159
colorscalename
correlationcolorscale
CS
-0.9960159
datatype
realarraytype
file_missing_value
-9999.0
maxncolor
254
missing_value
NaN
pointwidth
1.0
units
unitless
history
min [ MGAP CPT CFSv2_AtlPac_sst PearsonR ]
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 min over T[16 Jan - 15 Feb, 16 Dec - 15 Jan]
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.