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root mean sq anom ∫dS [ ICPAC Forecasts CPT Rainfallv2 Seasonal History PearsonR ] : Pearson correlation skill values data
PearsonR int_dS Pearson correlation skill values from ICPAC Forecasts CPT Rainfallv2 Seasonal History: Predictand, Hindcast and Skill.Independent Variables (Grids)
- Forecast Lead Time in Months
- grid: /L (months) ordered [ (2.5)] :grid
- Longitude (longitude)
- grid: /X (degree_east) ordered (20.5E) to (53E) by 0.5 N= 66 pts :grid
- Latitude (latitude)
- grid: /Y (degree_north) ordered (13.5S) to (24.5N) by 0.5 N= 77 pts :grid
Other Info
- bufferwordsize
- 8
- CE
- null
- colorscalename
- correlationcolorscale
- CS
- null
- datatype
- doublearraytype
- file_missing_value
- -9999.0
- maxncolor
- 254
- missing_value
- NaN
- pointwidth
- 0
- units
- months
- standard units*
- 0.0833333333333333 year
- history
- Averaged over S[0000 16 Dec 1959, 0000 16 Dec 1960] minimum 0.0% data present
Last updated: Wed, 12 May 2021 17:45:02 GMT
Data Views
Views
data as colors
data as contours
colors with land
contours with land
colors with coasts
line plots
color bars| L | X | Y | |
 root mean sq anom $integral dS$ [ ICPAC Forecasts CPT Rainfallv2 Seasonal History PearsonR ] 2.5 months[ X Y | L] | M | M | M |
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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