data: $integral dX$ $integral dS$ [ OLE2 CPT ESCENARIO1 PROBABILIDADESWETDAYS ]

served from 129.236.110.25

∫dX ∫dS [ OLE2 CPT ESCENARIO1 PROBABILIDADESWETDAYS ] : Probabilidades -Días Lluviosos data

∫dX ∫dS [ OLE2 CPT ESCENARIO1 PROBABILIDADESWETDAYS ] .

Independent Variables (Grids)

Forecast Lead Time in Months: grid: /L (months) ordered [ (2.5)] :grid
S (forecast_reference_time): grid: /S (days since 1960-01-01) ordered [ (16 Dec 1999 - 15 Jan 2000) (16 Jan 2000 - 15 Feb 2000) (16 Feb 2000 - 15 Mar 2000) ... (16 Nov 2025 - 15 Dec 2025)] N= 312 pts :grid
Longitude (longitude): grid: /X (degree_east) ordered (118W) to (28W) by 0.5 N= 181 pts :grid
Latitud (latitude): grid: /Y (degree_north) ordered (32.75N) to (62.75S) by 0.5 N= 192 pts :grid

Other Info

CE

null

colorscalename

halfgreyscale

CS

null

datatype

realarraytype

file_missing_value

fnname

maskle

maxncolor

254

missing_value

NaN

pointwidth

units

ids months degree_east

history

[ dominant_class ( OLE2 CPT ESCENARIO1 WETDAY Sobre la Normal ) + masklt ( { [ dominant_class ( OLE2 CPT ESCENARIO1 WETDAY ) - 1. ] * 11. } , 22 ) ] + [ dominant_class ( OLE2 CPT ESCENARIO1 WETDAY Normal ) + masknotrange ( { [ dominant_class ( OLE2 CPT ESCENARIO1 WETDAY ) - 1. ] * 11. } , 10 , 12 ) ]

dominant_class [ OLE2 CPT ESCENARIO1 WETDAY Sobre la Normal ] + masklt [ ( { dominant_class [ OLE2 CPT ESCENARIO1 WETDAY ] - 1. } * 11. ) , 22 ]

dominant_class [ OLE2 CPT ESCENARIO1 WETDAY Sobre la Normal ]
dominant_class over WETDAY[<35, >80]

masklt [ ( { dominant_class [ OLE2 CPT ESCENARIO1 WETDAY ] - 1. } * 11. ) , 22 ]
dominant_class over C[Bajo la Normal, Sobre la Normal]

dominant_class [ OLE2 CPT ESCENARIO1 WETDAY Normal ] + masknotrange [ ( { dominant_class [ OLE2 CPT ESCENARIO1 WETDAY ] - 1. } * 11. ) , 10 , 12 ]

dominant_class [ OLE2 CPT ESCENARIO1 WETDAY Normal ]
dominant_class over WETDAY[<35, >80]

masknotrange [ ( { dominant_class [ OLE2 CPT ESCENARIO1 WETDAY ] - 1. } * 11. ) , 10 , 12 ]
dominant_class over C[Bajo la Normal, Sobre la Normal]

dominant_class [ OLE2 CPT ESCENARIO1 WETDAY Bajo la Normal ] + maskgt [ ( { dominant_class [ OLE2 CPT ESCENARIO1 WETDAY ] - 1. } * 11. ) , 0 ]

dominant_class [ OLE2 CPT ESCENARIO1 WETDAY Bajo la Normal ]
dominant_class over WETDAY[<35, >80]

maskgt [ ( { dominant_class [ OLE2 CPT ESCENARIO1 WETDAY ] - 1. } * 11. ) , 0 ]
dominant_class over C[Bajo la Normal, Sobre la Normal]

Last updated: Sat, 06 Dec 2025 07:30:02 GMT
Expires: Mon, 05 Jan 2026 00:00:00 GMT

Data Views

	L	S	X	Y
OLE2 CPT ESCENARIO1[SPEARMANTMAX BELOWTMED ABOVETMAX PROPENSIONTMIN ODDSTMIN BELOWTMAX ODDSTMAX SPEARMANTMED PROBABILIDADES PROPENSION PROBABILIDADESTMED TMED ABOVETMED PROBABILIDADESWETDAYS TMIN target_date ABOVE SPEARMANTMIN PROBABILIDADESTMIN PROPENSIONWETDAYS AFCTMIN PROPENSIONTMAX BELOW ODDS ODDSTMED WETDAY ODDSWETDAYS SPEARMANWET AFC AFCTMED AFCWET ABOVETMIN SPEARMAN PRECIPITACION PROBABILIDADESTMAX TMAX BELOWTMIN ABOVEWET BELOWWET PROPENSIONTMED AFCTMAX ]
grid: /X (degree_east) ordered (117.75W) to (28.25W) by 0.5 N= 180 pts :grid			O=-0.25
grid: /Y (degree_north) ordered (32.75N) to (62.75S) by 0.5 N= 192 pts :grid				M
grid: /S (months since 1960-01-01) ordered (0000 1 Jan 2000) to (0000 1 Dec 2025) by 1.0 N= 312 pts :grid		D
$integral dX$ $integral dS$ [ OLE2 CPT ESCENARIO1 PROBABILIDADESWETDAYS ] 2.5 months[ X Y \| L S]	M	M	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 S
Differentiate along X Y S
Take differences along X Y S