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min ∫dS mean ∂Y root mean sq [ IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast PROBABILIDADESFORESCAST ] : ∂Y Probabilidades - Precipitacion data
PROBABILIDADESFORESCAST PROBABILIDADESFORESCAST PROBABILIDADESFORESCAST partial_Y partial_Y int_dS ∂Y Probabilidades - Precipitacion from IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast: Forecast and Error. isIndependent Variables (Grids)
Other Info
- bufferwordsize
- 8
- CE
- null
- colorscalename
- halfgreyscale
- CS
- null
- datatype
- doublearraytype
- file_missing_value
- 0
- fnname
- maskle
- maxncolor
- 254
- missing_value
- NaN
- pointwidth
- 3.0
- units
- ids /degree_north months
- history
- [ dominant_class ( IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast ProbForecast Superior a lo Normal ) + masklt ( { [ dominant_class ( IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast ProbForecast ) - 1. ] * 11. } , 22 ) ] + [ dominant_class ( IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast ProbForecast Normal ) + masknotrange ( { [ dominant_class ( IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast ProbForecast ) - 1. ] * 11. } , 10 , 12 ) ]
- dominant_class [ IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast ProbForecast Superior a lo Normal ] + masklt [ ( { dominant_class [ IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast ProbForecast ] - 1. } * 11. ) , 22 ]
- dominant_class [ IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast ProbForecast Superior a lo Normal ]
dominant_class over ProbForecast[<35, >80]
- masklt [ ( { dominant_class [ IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast ProbForecast ] - 1. } * 11. ) , 22 ]
dominant_class over C[Debajo de lo Normal, Superior a lo Normal]
- dominant_class [ IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast ProbForecast Superior a lo Normal ]
- dominant_class [ IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast ProbForecast Normal ] + masknotrange [ ( { dominant_class [ IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast ProbForecast ] - 1. } * 11. ) , 10 , 12 ]
- dominant_class [ IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast ProbForecast Normal ]
dominant_class over ProbForecast[<35, >80]
- masknotrange [ ( { dominant_class [ IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast ProbForecast ] - 1. } * 11. ) , 10 , 12 ]
dominant_class over C[Debajo de lo Normal, Superior a lo Normal]
- dominant_class [ IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast ProbForecast Normal ]
- dominant_class [ IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast ProbForecast Superior a lo Normal ] + masklt [ ( { dominant_class [ IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast ProbForecast ] - 1. } * 11. ) , 22 ]
- dominant_class [ IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast ProbForecast Debajo de lo Normal ] + maskgt [ ( { dominant_class [ IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast ProbForecast ] - 1. } * 11. ) , 0 ]
- dominant_class [ IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast ProbForecast Debajo de lo Normal ]
dominant_class over ProbForecast[<35, >80]
- maskgt [ ( { dominant_class [ IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast ProbForecast ] - 1. } * 11. ) , 0 ]
dominant_class over C[Debajo de lo Normal, Superior a lo Normal]
- dominant_class [ IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast ProbForecast Debajo de lo Normal ]
- Averaged over L[0.0 months, 5.0 months] minimum 0.0% data present
$integral dS$ mean $partialdiff sub Y$ root mean sq [ IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast PROBABILIDADESFORESCAST ]- Averaged over X[60W, 52W] Y[35.5S, 29.5S] minimum 0.0% data present
min $integral dS$ mean $partialdiff sub Y$ root mean sq [ IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast PROBABILIDADESFORESCAST ]- min over S[0000 1 Jan 2000, 0000 1 Apr 2026]
- [ dominant_class ( IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast ProbForecast Superior a lo Normal ) + masklt ( { [ dominant_class ( IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast ProbForecast ) - 1. ] * 11. } , 22 ) ] + [ dominant_class ( IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast ProbForecast Normal ) + masknotrange ( { [ dominant_class ( IRI Forecast GZ200_CFSV2-PRECIP_GPCC Forecast ProbForecast ) - 1. ] * 11. } , 10 , 12 ) ]
Last updated: Mon, 31 Jul 2017 15:25:55 GMT
Expires: Sat, 05 Aug 2017 00:00:00 GMT
Data Views
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