Evaluation of 33 Years of Quantitative Precipitation Forecasting at the NMC

Olson, David A.; Junker, Norman W.; Korty, B.

doi:10.1175/1520-0434(1995)010<0498:eoyoqp>2.0.co;2

Cited by 195 publications

(131 citation statements)

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“…Precipitation is one of the most difficult parameters to forecast in numerical weather prediction (Olson et al, 1995;Wang and Seaman, 1997). The question if an increase in horizontal resolution can produce more skilful precipitation forecasts is discussed, among others, by Ducrocq et al (2002), Lagouvardos et al (2003), Kotroni and Lagouvardos (2004).…”

Section: Introductionmentioning

confidence: 99%

Verification of precipitation forecasts of MM5 model over Epirus, NW Greece, for various convective parameterization schemes

Sindosi

Bartzokas

Kotroni

et al. 2012

Nat. Hazards Earth Syst. Sci.

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Abstract. The mesoscale meteorological model MM5 is applied to 22 selected days with intense precipitation in the region of Epirus, NW Greece. At first, it was investigated whether and to what extend an increased horizontal resolution (from 8 to 2 km) improves the quantitative precipitation forecasts. The model skill was examined for the 12-h accumulated precipitation recorded at 14 meteorological stations located in Epirus and by using categorical and descriptive statistics. Then, the precipitation forecast skill for the 2 km grid was studied: (a) without and (b) with the activation of a convective parameterization scheme. From the above study, the necessity of the use of a scheme at the 2 km grid is assessed. Furthermore, three different convective parameterization schemes are compared: (a) Betts-Miller, (b) Grell and (c) Kain-Fritsch-2 in order to reveal the scheme, resulting in the best precipitation forecast skill in Epirus. Kain-Fritsch-2 and Grell give better results with the latter being the best for the high precipitation events.

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Section: Introductionmentioning

confidence: 99%

Verification of precipitation forecasts of MM5 model over Epirus, NW Greece, for various convective parameterization schemes

Sindosi

Bartzokas

Kotroni

et al. 2012

Nat. Hazards Earth Syst. Sci.

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“…Uma possibilidade da fonte de erros do modelo pode ser devido às saídas do modelo representarem um valor médio da grade, enquanto que a variável precipitação apresenta grande variação espacial. Olson et al (1995) salientaram que a complexidade física dos processos de precipitação e as escalas de tempoespaço, envolvidas em tais processos, não são resolvidas satisfatoriamente pelos modelos numéricos. Mesmo os modelos com alta resolução de Previsão Numérica de Tempo (PNT) não conseguem estimar satisfatoriamente os campos de precipitação por meio de suas parametrizações.…”

Section: Avaliação Dos Prognósticos De Precipitação Simulada Pelo Modunclassified

Avaliação dos prognósticos de precipitação simulada pelo modelo BRAMS na Amazônia Ocidental na estação chuvosa

et al. 2011

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ResumoO objetivo deste estudo é avaliar o modelo de previsão numérica do tempo BRAMS (Brazilian Regional Atmospheric Modelling System), a partir da comparação entre os valores preditos e os observados (dados do NCEP/NOAA (National Centers of Environmental Predictions/ National Oceanic and Atmospheric Administration) e do satélite TRMM (Tropical Rainfall Measuring Mission)). O modelo foi assimilado com dados do modelo global do NCEP/NOAA e do CPTEC/INPE. Foram realizadas comparações entre os valores preditos e os observados através da raiz do erro quadrático médio (RMSE) e do erro médio (ME) para os prognósticos de precipitação para os horizontes de 24, 48, 72 e 96 horas, do período

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“…However, it is one of the most difficult variables to forecast, because of its inherent spatial and temporal variability (Wilson and Vallée, 2002;Antolik, 2000). For this reason, the temporal and spatial scales involved are not yet solved satisfactorily by the available numerical models (Olson et al, 1995). Ramos (2000), studying artificial neural networks (ANNs) and multiple linear regression (MLR), found that the neural-network method performed better than the linearregression method, although both showed good performance for monthly and seasonal rainfall.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

Santos

Mendes

Torres

2016

Nonlin. Processes Geophys.

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Abstract. Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANNs) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon; northeastern Brazil; and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model output and observed monthly precipitation. We used general circulation model (GCM) experiments for the 20th century (RCP historical; 1970-1999 and two scenarios (RCP 2.6 and 8.5; 2070-2100. The model test results indicate that the ANNs significantly outperform the MLR downscaling of monthly precipitation variability.

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Evaluation of 33 Years of Quantitative Precipitation Forecasting at the NMC

Cited by 195 publications

References 0 publications

Verification of precipitation forecasts of MM5 model over Epirus, NW Greece, for various convective parameterization schemes

Verification of precipitation forecasts of MM5 model over Epirus, NW Greece, for various convective parameterization schemes

Avaliação dos prognósticos de precipitação simulada pelo modelo BRAMS na Amazônia Ocidental na estação chuvosa

Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

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