data: OLE2 CPT ESCENARIO1 PROBABILIDADES

OLE2 CPT ESCENARIO1 PROBABILIDADES: Probabilidades - Precipitación data

OLE2 CPT ESCENARIO1 PROBABILIDADES.

Independent Variables (Grids)

Forecast Lead Time in Months: grid: /L (months) ordered [ (2.5)] :grid
Hecho en (forecast_reference_time): grid: /S (months since 1960-01-01) ordered (0000 1 Jan 2000) to (0000 1 Nov 2025) by 1.0 N= 311 pts :grid
Longitud (longitude): grid: /X (degree_east) ordered (117.75W) to (28.25W) by 0.5 N= 180 pts :grid
Latitud (latitude): grid: /Y (degree_north) ordered (32.75N) to (62.75S) by 0.5 N= 192 pts :grid

Other Info

Key
1.0	<35 Bajo
2.0	35-40 Bajo
3.0	40-45 Bajo
4.0	45-50 Bajo
5.0	50-55 Bajo
6.0	55-60 Bajo
7.0	60-65 Bajo
8.0	65-70 Bajo
9.0	70-75 Bajo
10.0	75-80 Bajo
11.0	>80 Bajo
12.0	<35 Normal
13.0	35-40 Normal
14.0	40-45 Normal
15.0	45-50 Normal
16.0	50-55 Normal
17.0	55-60 Normal
18.0	60-65 Normal
19.0	65-70 Normal
20.0	70-75 Normal
21.0	75-80 Normal
22.0	>80 Normal
23.0	<35 Sobre
24.0	35-40 Sobre
25.0	40-45 Sobre
26.0	45-50 Sobre
27.0	50-55 Sobre
28.0	55-60 Sobre
29.0	60-65 Sobre
30.0	65-70 Sobre
31.0	70-75 Sobre
32.0	75-80 Sobre
33.0	>80 Sobre

bufferwordsize

CE

colorscalename

halfgreyscale

CS

datatype

realarraytype

file_missing_value

fnname

maskle

maxncolor

254

missing_value

NaN

pointwidth

scale_max

33.0

scale_min

1.0

units

ids

history

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

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

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

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

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

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

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

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

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

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

colorscale

Last updated: Thu, 06 Nov 2025 07:30:02 GMT

Data Views

	L	S	X	Y
OLE2 CPT ESCENARIO1[ODDSWETDAYS AFCTMAX AFCWET TMAX PROBABILIDADESTMAX SPEARMAN ABOVETMED PROPENSIONWETDAYS BELOWWET PROPENSIONTMED ABOVETMAX ABOVE ODDS AFC ABOVETMIN BELOWTMAX SPEARMANTMED ODDSTMAX BELOWTMIN PROPENSION TMED BELOW PROBABILIDADESTMED ODDSTMIN SPEARMANTMAX PROBABILIDADES TMIN PROBABILIDADESTMIN WETDAY AFCTMIN BELOWTMED PROPENSIONTMIN PRECIPITACION PROPENSIONTMAX SPEARMANWET AFCTMED SPEARMANTMIN ODDSTMED PROBABILIDADESWETDAYS target_date ABOVEWET ]
grid: /X (degree_east) ordered (117.75W) to (28.25W) by 0.5 N= 180 pts :grid			M
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 Nov 2025) by 1.0 N= 311 pts :grid		M
OLE2 CPT ESCENARIO1 PROBABILIDADES 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