data: max [ S , max $integral dS$ ( OLE2 CPT ESCENARIO1 PROPENSIONTMIN ) ]

served from 129.236.110.25

max [ S , max ∫dS ( OLE2 CPT ESCENARIO1 PROPENSIONTMIN ) ]: Hecho en, Propensión -Temperatura Mínima data

max [ S , max ∫dS ( OLE2 CPT ESCENARIO1 PROPENSIONTMIN ) ].

Independent Variables (Grids)

Hecho en (forecast_reference_time): grid: /S (months since 1960-01-01) ordered (0000 1 Jan 2000) to (0000 1 Dec 2025) by 1.0 N= 312 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

calendar

360

datatype

realarraytype

fnname

max

gridid

175490048

missing_value

NaN

pointwidth

scale_max

null

scale_min

null

standard_name

forecast_reference_time

units

months since 1960-01-01

standard units*

0.0833333333333333 ( years since 1960-01-01 00:00.000000000 )

history

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

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

dominant_class [ OLE2 CPT ESCENARIO1 ODDSTMIN Sobre la Normal ]
dominant_class over ODDSTMIN[0, >9.0]

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

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

dominant_class [ OLE2 CPT ESCENARIO1 ODDSTMIN Normal ]
dominant_class over ODDSTMIN[0, >9.0]

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

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

dominant_class [ OLE2 CPT ESCENARIO1 ODDSTMIN Bajo la Normal ]
dominant_class over ODDSTMIN[0, >9.0]

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

max $integral dS$ [ OLE2 CPT ESCENARIO1 PROPENSIONTMIN ]

max over S[0000 16 Dec 1999, 0000 16 Dec 2025]

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

Data Views

	S	X	Y
OLE2 CPT ESCENARIO1[PROPENSIONTMIN PROBABILIDADES BELOWTMED TMED PROBABILIDADESTMED ABOVETMAX PROPENSION BELOW PROBABILIDADESWETDAYS AFCTMIN SPEARMAN TMIN AFCTMED BELOWTMAX SPEARMANTMAX PRECIPITACION PROPENSIONTMAX SPEARMANWET AFC ODDSTMED WETDAY PROBABILIDADESTMIN ABOVEWET AFCTMAX PROPENSIONWETDAYS target_date AFCWET TMAX PROBABILIDADESTMAX SPEARMANTMIN ODDSWETDAYS ODDS PROPENSIONTMED BELOWWET ABOVETMIN ABOVE ABOVETMED BELOWTMIN ODDSTMIN ODDSTMAX SPEARMANTMED ]
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
max [ S , max $integral dS$ ( OLE2 CPT ESCENARIO1 PROPENSIONTMIN ) ][ \| S X Y]	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 S X Y
Differentiate along S X Y
Take differences along S X Y