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IRI CPT JAS total_rainfall History PearsonR_prcp: Precipitation Pearson's Correlation data
Precipitation Pearson's Correlation from IRI CPT JAS total_rainfall History: Predicand, Hindcast and Skill.Independent Variables (Grids)
- Longitude (longitude)
- grid: /X (degree_east) ordered (16.75W) to (19.75E) by 0.5 N= 74 pts :grid
- Latitude (latitude)
- grid: /Y (degree_north) ordered (5.25N) to (19.75N) by 0.5 N= 30 pts :grid
Other Info
- bufferwordsize
- 8
- CE
- 1
- colorscalename
- correlationcolorscale
- CS
- -1
- datatype
- doublearraytype
- maxncolor
- 254
- scale_max
- 1.0
- scale_min
- -1.0
- units
- unitless
- colorscale
Last updated: Wed, 02 May 2018 19:38:40 GMT
Data Views
Views
data as colors
data as contours
colors with land
contours with land
colors with coasts
line plots
color bars| X | Y | |
 IRI CPT JAS total_rainfall History[CrossValPred_prcp
hindcast_date
Input_prcp
PearsonR_prcp
] | ||
 IRI CPT JAS total_rainfall History PearsonR_prcp[ X Y |] | M | M |
|---|
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
- Differentiate along X Y
- Take differences along X Y
RMS (root mean square with mean *not* removed) over X Y | X Y |
RMSA (root mean square with mean removed) over X Y | X Y |
Maximum over X Y | X Y |
Minimum over X Y | X Y |
Detrend (best-fit-line) over X Y | X Y |
Note on units