data: root mean sq anom root mean sq [ OLE2 CPT ESCENARIO1 PROBABILIDADESTMIN ]

served from 129.236.110.25

root mean sq anom root mean sq [ OLE2 CPT ESCENARIO1 PROBABILIDADESTMIN ] : Probabilidades -Temperatura Mínima data

root mean sq anom root mean sq [ OLE2 CPT ESCENARIO1 PROBABILIDADESTMIN ] . is

Independent Variables (Grids)

Forecast Lead Time in Months: grid: /L (months) ordered [ (2.5)] :grid

Other Info

bufferwordsize

CE

null

colorscalename

halfgreyscale

CS

null

datatype

doublearraytype

file_missing_value

fnname

maskle

maxncolor

254

missing_value

NaN

pointwidth

units

ids

history

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

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

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

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

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

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

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

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

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

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

root mean sq [ OLE2 CPT ESCENARIO1 PROBABILIDADESTMIN ]

Averaged over Y[63S, 33N] S[0000 1 Jan 2000, 0000 1 Mar 2025] minimum 0.0% data present

root mean sq anom root mean sq [ OLE2 CPT ESCENARIO1 PROBABILIDADESTMIN ]

Averaged over X[117.75W, 28.25W] minimum 0.0% data present

Last updated: Thu, 06 Mar 2025 07:30:01 GMT
Expires: Sat, 05 Apr 2025 00:00:00 GMT

Data Views

	L	S	X	Y
OLE2 CPT ESCENARIO1[AFCTMAX PROBABILIDADESTMIN ABOVEWET BELOWTMED TMAX PROBABILIDADESTMAX AFCWET ODDSWETDAYS PROPENSIONWETDAYS SPEARMAN AFCTMED PROPENSIONTMED SPEARMANTMIN BELOWWET ABOVETMAX ABOVETMIN BELOWTMAX ABOVE ODDS ODDSTMAX ODDSTMIN AFC BELOWTMIN SPEARMANTMED PROPENSION TMED PROBABILIDADESTMED PROPENSIONTMIN SPEARMANTMAX TMIN BELOW PROBABILIDADES WETDAY AFCTMIN PROPENSIONTMAX PRECIPITACION SPEARMANWET ODDSTMED ABOVETMED PROBABILIDADESWETDAYS target_date ]
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 Mar 2025) by 1.0 N= 303 pts :grid		M
OLE2 CPT ESCENARIO1 ABOVE[ X Y \| S]		M	M	M
root mean sq anom root mean sq [ OLE2 CPT ESCENARIO1 PROBABILIDADESTMIN ] 2.5 months[ L \|]	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
Differentiate along
Take differences along

Average over
RMS (root mean square with mean *not* removed) over
RMSA (root mean square with mean removed) over
Maximum over
Minimum over
Detrend (best-fit-line) over

Note on units