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root mean sq root mean sq ∫dY [ OLE2 CPT ESCENARIO1 PROBABILIDADESTMAX ] : Probabilidades -Temperatura Máxima data
root mean sq root mean sq ∫dY [ OLE2 CPT ESCENARIO1 PROBABILIDADESTMAX ] . isIndependent Variables (Grids)
- Forecast Lead Time in Months
- grid: /L (months) ordered [ (2.5)] :grid
Other Info
- bufferwordsize
- 8
- CE
- null
- colorscalename
- halfgreyscale
- CS
- null
- datatype
- doublearraytype
- file_missing_value
- 0
- fnname
- maskle
- maxncolor
- 254
- missing_value
- NaN
- pointwidth
- 0
- units
- ids degree_north
- history
- [ dominant_class ( OLE2 CPT ESCENARIO1 TMAX Sobre la Normal ) + masklt ( { [ dominant_class ( OLE2 CPT ESCENARIO1 TMAX ) - 1. ] * 11. } , 22 ) ] + [ dominant_class ( OLE2 CPT ESCENARIO1 TMAX Normal ) + masknotrange ( { [ dominant_class ( OLE2 CPT ESCENARIO1 TMAX ) - 1. ] * 11. } , 10 , 12 ) ]
- dominant_class [ OLE2 CPT ESCENARIO1 TMAX Sobre la Normal ] + masklt [ ( { dominant_class [ OLE2 CPT ESCENARIO1 TMAX ] - 1. } * 11. ) , 22 ]
- dominant_class [ OLE2 CPT ESCENARIO1 TMAX Sobre la Normal ]
dominant_class over TMAX[<35, >80]
- masklt [ ( { dominant_class [ OLE2 CPT ESCENARIO1 TMAX ] - 1. } * 11. ) , 22 ]
dominant_class over C[Bajo la Normal, Sobre la Normal]
- dominant_class [ OLE2 CPT ESCENARIO1 TMAX Sobre la Normal ]
- dominant_class [ OLE2 CPT ESCENARIO1 TMAX Normal ] + masknotrange [ ( { dominant_class [ OLE2 CPT ESCENARIO1 TMAX ] - 1. } * 11. ) , 10 , 12 ]
- dominant_class [ OLE2 CPT ESCENARIO1 TMAX Normal ]
dominant_class over TMAX[<35, >80]
- masknotrange [ ( { dominant_class [ OLE2 CPT ESCENARIO1 TMAX ] - 1. } * 11. ) , 10 , 12 ]
dominant_class over C[Bajo la Normal, Sobre la Normal]
- dominant_class [ OLE2 CPT ESCENARIO1 TMAX Normal ]
- dominant_class [ OLE2 CPT ESCENARIO1 TMAX Sobre la Normal ] + masklt [ ( { dominant_class [ OLE2 CPT ESCENARIO1 TMAX ] - 1. } * 11. ) , 22 ]
- dominant_class [ OLE2 CPT ESCENARIO1 TMAX Bajo la Normal ] + maskgt [ ( { dominant_class [ OLE2 CPT ESCENARIO1 TMAX ] - 1. } * 11. ) , 0 ]
- dominant_class [ OLE2 CPT ESCENARIO1 TMAX Bajo la Normal ]
dominant_class over TMAX[<35, >80]
- maskgt [ ( { dominant_class [ OLE2 CPT ESCENARIO1 TMAX ] - 1. } * 11. ) , 0 ]
dominant_class over C[Bajo la Normal, Sobre la Normal]
- dominant_class [ OLE2 CPT ESCENARIO1 TMAX Bajo la Normal ]
- Averaged over S[0000 1 Jan 2000, 0000 1 Mar 2025] minimum 0.0% data present
root mean sq root mean sq $integral dY$ [ OLE2 CPT ESCENARIO1 PROBABILIDADESTMAX ]- Averaged over X[118W, 28W] Y[63S, 33N] minimum 0.0% data present
- [ dominant_class ( OLE2 CPT ESCENARIO1 TMAX Sobre la Normal ) + masklt ( { [ dominant_class ( OLE2 CPT ESCENARIO1 TMAX ) - 1. ] * 11. } , 22 ) ] + [ dominant_class ( OLE2 CPT ESCENARIO1 TMAX Normal ) + masknotrange ( { [ dominant_class ( OLE2 CPT ESCENARIO1 TMAX ) - 1. ] * 11. } , 10 , 12 ) ]
Last updated: Thu, 06 Mar 2025 07:30:01 GMT
Expires: Sat, 05 Apr 2025 00:00:00 GMT
Data Views
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 root mean sq root mean sq $integral dY$ [ OLE2 CPT ESCENARIO1 PROBABILIDADESTMAX ] 2.5 months[ L |] | M |
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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
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