The LAMP QPF Products. Part I: Model Development

Charba, Jerome P.

doi:10.1175/1520-0434(1998)013<0934:tlqppi>2.0.co;2

Cited by 13 publications

(6 citation statements)

References 22 publications

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“…The third type of predictors contains linearized predictors calculated in a way similar to that used by Charba (1998). The idea is to linearize relationships between the predictors and predictands.…”

Section: Predictorsmentioning

confidence: 99%

Nowcasting of 1-h precipitation using radar and NWP data

Sokol¹

2006

Journal of Hydrology

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Section: Predictorsmentioning

confidence: 99%

Nowcasting of 1-h precipitation using radar and NWP data

Sokol¹

2006

Journal of Hydrology

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“…Regression techniques such as model output statistics (MOS) can be used to generate probabilities of exceeding thresholds (Glahn and Lowry 1972;Klein and Glahn 1974;Bermowitz 1975;Charba 1998;Antolik 2000), or to generate quantiles of expected precipitation (Bremnes 2004;Friederichs and Hense 2007). Applequist et al (2002) found that logistic regression can outperform standard regression, and Hamill et al (2004) found that this can be further refined by using logistic regression on power-transformed forecasts.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging

Sloughter

Raftery

Gneiting

et al. 2007

Monthly Weather Review

357

343

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Bayesian model averaging (BMA) is a statistical way of postprocessing forecast ensembles to create predictive probability density functions (PDFs) for weather quantities. It represents the predictive PDF as a weighted average of PDFs centered on the individual bias-corrected forecasts, where the weights are posterior probabilities of the models generating the forecasts and reflect the forecasts' relative contributions to predictive skill over a training period. It was developed initially for quantities whose PDFs can be approximated by normal distributions, such as temperature and sea level pressure. BMA does not apply in its original form to precipitation, because the predictive PDF of precipitation is nonnormal in two major ways: it has a positive probability of being equal to zero, and it is skewed. In this study BMA is extended to probabilistic quantitative precipitation forecasting. The predictive PDF corresponding to one ensemble member is a mixture of a discrete component at zero and a gamma distribution. Unlike methods that predict the probability of exceeding a threshold, BMA gives a full probability distribution for future precipitation. The method was applied to daily 48-h forecasts of 24-h accumulated precipitation in the North American Pacific Northwest in 2003-04 using the University of Washington mesoscale ensemble. It yielded predictive distributions that were calibrated and sharp. It also gave probability of precipitation forecasts that were much better calibrated than those based on consensus voting of the ensemble members. It gave better estimates of the probability of high-precipitation events than logistic regression on the cube root of the ensemble mean.

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“…The development sample is dominated by small observed precipitation amounts with the result that the regression algorithm best predicts these small amounts. To partly overcome this problem, some of the predictors might be "linearized" with respect to precipitation amount (Charba, 1998). However, Charba's technique is timeconsuming and requires some subjectivity in application.…”

Section: Regression Models and Selection Of Predictorsmentioning

confidence: 99%

“…The regression model reduces systematic errors of the NWP models and improves local forecasts by taking into account local relationships between the predictors and predictands. Historically, MOS has been applied to the forecast of various meteorological quantities such as temperature, wind and precipitation (Glahn and Lowry, 1972;Krzysztofowitz et al, 1993;Knuepffer, 1996;Sigrest and Krysztofowitz, 1998;Charba, 1998;Antolik, 2000;Maini et al, 2002;Kalnay, 2002;Yuval andHsieh, 2003, Sokol, 2003a).…”

Section: Introductionmentioning

confidence: 99%

MOS based forecast of 6-hourly area precipitation

Sokol

2006

Stud Geophys Geod

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A statistical post-processing methodology for application to numerical weather prediction (NWP) model outputs for precipitation forecast is proposed. The postprocessing is based on the model output statistics approach. The statistical relationships are described by the multiple linear regression model, which is complemented by an iteration procedure to further correct the regression outputs. Prognostic fields of the ALADIN/LACE (Aire Limitée Adaptation Dynamique Développement InterNational/ Limited Area Modelling in Central Europe) NWP model are used for the forecast of 6-hourly areal precipitation amounts at 15 river basins. The NWP model integration starts at 00UTC and forecasts are calculated for lead times of +12, +18, +24 and +30 hours. The post-processing models are developed separately for each lead time and for separate warm (April to September) and cool (October to March) seasons. The forecasts are focused on large precipitation amounts. Using all the combinations, data from four years (1999)(2000)(2001)(2002) are divided into calibration data (3 years), where the models are developed, and verification data. The models are evaluated by examining the root-meansquare error (RMSE), bias, and correlation coefficient (CC) on the verification data samples.The results show that the additional iteration procedure increases the forecast accuracy for a given range of precipitation amounts and simultaneously does not deteriorate the bias, a situation which can arise when negative regression outputs are set to zero. The post-processing method improves the forecast of the NWP model in terms of RMSE and CC. For large precipitation amounts during the summer season, the decrease of RMSE reaches 10% to 20% depending upon the applied method of verification. For the cool season, the decrease is somewhat smaller (7% to 15%).

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The LAMP QPF Products. Part I: Model Development

Cited by 13 publications

References 22 publications

Nowcasting of 1-h precipitation using radar and NWP data

Nowcasting of 1-h precipitation using radar and NWP data

Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging

MOS based forecast of 6-hourly area precipitation

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