max ∫dIWMO [ mean ( { percentile_over [ T2 75% min ] [ NOAA NCDC GCPS MONTHLY STATION mean prcp ] } * masklt { [ percentile_over ( T ) ( KAPLAN Indices NINO3 avOS ) ] , 1 } ) * unnamed ] : percentile data
max
∫dIWMO [ mean ( { percentile_over [ T2 75% min ] [ NOAA NCDC GCPS MONTHLY STATION mean prcp ] } * masklt { [ percentile_over ( T ) ( KAPLAN Indices NINO3 avOS ) ] , 1 } ) * unnamed ] .
Independent Variables (Grids)
 Time (time)
 grid: /T (months since 01Jan) periodic (JanMar) to (OctDec) by 3.0 N= 4 pts :grid
Other Info
 CE
 null
 colorscalename
 tercileclassesscale
 CS
 null
 datatype
 realarraytype
 maxncolor
 254
 missing_value
 NaN
 percentile
 Probability
 units
 percent ids
 history
 mean [ ( percentile_over { T2 75% min } { NOAA NCDC GCPS MONTHLY STATION mean prcp } ) * masklt ( { percentile_over [ T ] [ KAPLAN Indices NINO3 avOS ] } , 1 ) ]
 Boxes with less than 0.0% dropped
Averaged over T2[1901, 1990] minimum 0.0% data present
max $integral dIWMO$ [ mean ( { percentile_over [ T2 75% min ] [ NOAA NCDC GCPS MONTHLY STATION mean prcp ] } * masklt { [ percentile_over ( T ) ( KAPLAN Indices NINO3 avOS ) ] , 1 } ) * unnamed ]  max over prcp[, ] avOS[LaNina, ElNino] IWMO[99900, 9885849]
Last updated: Wed, 13 Feb 2019 20:27:28 GMT
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 T
 Differentiate along T
 Take differences along T
Average over
T

RMS (root mean square with mean *not* removed) over
T

RMSA (root mean square with mean removed) over
T

Maximum over
T

Minimum over
T

Detrend (bestfitline) over
T

Note on units