Ensemble model output statistics as a probabilistic site-adaptation tool for satellite-derived and reanalysis solar irradiance

2020 ◽  
Vol 12 (1) ◽  
pp. 016102 ◽  
Author(s):  
Dazhi Yang
Keyword(s):  
Solar Irradiance ◽  
Ensemble Model ◽  
Model Output ◽  
Ensemble Model Output Statistics ◽  
Model Output Statistics

scholarly journals Calibrated Ensemble Forecasts Using Quantile Regression Forests and Ensemble Model Output Statistics

2016 ◽  
Vol 144 (6) ◽  
pp. 2375-2393 ◽  
Author(s):  
Maxime Taillardat ◽  
Olivier Mestre ◽  
Michaël Zamo ◽  
Philippe Naveau
Keyword(s):  
Quantile Regression ◽  
Weather Forecasting ◽  
Real Data ◽  
Ensemble Model ◽  
Model Output ◽  
General Measure ◽  
Ensemble Forecasts ◽  
Probabilistic Forecasts ◽  
Ensemble Model Output Statistics ◽  
Model Output Statistics

Abstract Ensembles used for probabilistic weather forecasting tend to be biased and underdispersive. This paper proposes a statistical method for postprocessing ensembles based on quantile regression forests (QRF), a generalization of random forests for quantile regression. This method does not fit a parametric probability density function (PDF) like in ensemble model output statistics (EMOS) but provides an estimation of desired quantiles. This is a nonparametric approach that eliminates any assumption on the variable subject to calibration. This method can estimate quantiles using not only members of the ensemble but any predictor available including statistics on other variables. The method is applied to the Météo-France 35-member ensemble forecast (PEARP) for surface temperature and wind speed for available lead times from 3 up to 54 h and compared to EMOS. All postprocessed ensembles are much better calibrated than the PEARP raw ensemble and experiments on real data also show that QRF performs better than EMOS, and can bring a real gain for human forecasters compared to EMOS. QRF provides sharp and reliable probabilistic forecasts. At last, classical scoring rules to verify predictive forecasts are completed by the introduction of entropy as a general measure of reliability.

scholarly journals Calibrated ensemble forecasts of the height of new snow using quantile regression forests and ensemble model output statistics

2021 ◽  
Vol 28 (3) ◽  
pp. 467-480
Author(s):  
Guillaume Evin ◽  
Matthieu Lafaysse ◽  
Maxime Taillardat ◽  
Michaël Zamo
Keyword(s):  
Quantile Regression ◽  
Specific Humidity ◽  
Ensemble Model ◽  
Model Output ◽  
Post Processing ◽  
Ensemble Forecasts ◽  
Mountain Areas ◽  
Processing Methods ◽  
Ensemble Model Output Statistics ◽  
Model Output Statistics

Abstract. Height of new snow (HN) forecasts help to prevent critical failures of infrastructures in mountain areas, e.g. transport networks and ski resorts. The French national meteorological service, Météo-France, operates a probabilistic forecasting system based on ensemble meteorological forecasts and a detailed snowpack model to provide ensembles of HN forecasts. These forecasts are, however, biased and underdispersed. As for many weather variables, post-processing methods can be used to alleviate these drawbacks and obtain meaningful 1 to 4 d HN forecasts. In this paper, we compare the skill of two post-processing methods. The first approach is an ensemble model output statistics (EMOS) method, which can be described as a nonhomogeneous regression with a censored shifted Gamma distribution. The second approach is based on quantile regression forests, using different meteorological and snow predictors. Both approaches are evaluated using a 22 year reforecast. Thanks to a larger number of predictors, the quantile regression forest is shown to be a powerful alternative to EMOS for the post-processing of HN ensemble forecasts. The gain of performance is large in all situations but is particularly marked when raw forecasts completely miss the snow event. This type of situation happens when the rain–snow transition elevation is overestimated by the raw forecasts (rain instead of snow in the raw forecasts) or when there is no precipitation in the forecast. In that case, quantile regression forests improve the predictions using the other weather predictors (wind, temperature, and specific humidity).

scholarly journals Calibrated ensemble forecasts of the height of new snow using quantile regression forests and Ensemble Model Output Statistics

2021 ◽  
Author(s):  
Guillaume Evin ◽  
Matthieu Lafaysse ◽  
Maxime Taillardat ◽  
Michaël Zamo
Keyword(s):  
Quantile Regression ◽  
Specific Humidity ◽  
Ensemble Model ◽  
Model Output ◽  
Post Processing ◽  
Ensemble Forecasts ◽  
Mountain Areas ◽  
Processing Methods ◽  
Ensemble Model Output Statistics ◽  
Model Output Statistics

Abstract. Height of new snow (HN) forecasts help to prevent critical failures of infrastructures in mountain areas, e.g. transport networks, ski resorts. The French national meteorological service, Meteo-France, operates a probabilistic forecasting system based on ensemble meteorological forecasts and a detailed snowpack model to provide ensembles of HN forecasts. These forecasts are however significantly biased and underdispersed. As for many weather variables, post-processing methods can be used to alleviate these drawbacks and obtain meaningful 1-day to 4-day HN forecasts. In this paper, we compare the skill of two post-processing methods. The first approach is an ensemble model output statistics (EMOS) method, which can be described as a Nonhomogeneous Regression with a Censored Shifted Gamma distribution. The second approach is based on quantile regression forests, using different meteorological and snow predictors. Both approaches are evaluated using a 22-year reforecast. Thanks to a larger number of predictors, the quantile regression forest is shown to be a powerful alternative to EMOS for the post-processing of HN ensemble forecasts. The gain of performance is important in all situations but is particularly marked when raw forecasts completely miss the snow event. This type of situations happens when the rain-snow transition elevation is overestimated by the raw forecasts (rain instead of snow in the raw forecasts) or when there is no precipitation in the forecast. In that case, quantile regression forests improve the predictions using the other weather predictors (wind, temperature, specific humidity).

scholarly journals Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation

2005 ◽  
Vol 133 (5) ◽  
pp. 1098-1118 ◽  
Author(s):  
Tilmann Gneiting ◽  
Adrian E. Raftery ◽  
Anton H. Westveld ◽  
Tom Goldman
Keyword(s):  
Sea Level ◽  
Ensemble Prediction ◽  
Ensemble Model ◽  
Model Output ◽  
Sea Level Pressure ◽  
Forecast Bias ◽  
Level Pressure ◽  
Probabilistic Forecasts ◽  
Ensemble Model Output Statistics ◽  
Model Output Statistics

Abstract Ensemble prediction systems typically show positive spread-error correlation, but they are subject to forecast bias and dispersion errors, and are therefore uncalibrated. This work proposes the use of ensemble model output statistics (EMOS), an easy-to-implement postprocessing technique that addresses both forecast bias and underdispersion and takes into account the spread-skill relationship. The technique is based on multiple linear regression and is akin to the superensemble approach that has traditionally been used for deterministic-style forecasts. The EMOS technique yields probabilistic forecasts that take the form of Gaussian predictive probability density functions (PDFs) for continuous weather variables and can be applied to gridded model output. The EMOS predictive mean is a bias-corrected weighted average of the ensemble member forecasts, with coefficients that can be interpreted in terms of the relative contributions of the member models to the ensemble, and provides a highly competitive deterministic-style forecast. The EMOS predictive variance is a linear function of the ensemble variance. For fitting the EMOS coefficients, the method of minimum continuous ranked probability score (CRPS) estimation is introduced. This technique finds the coefficient values that optimize the CRPS for the training data. The EMOS technique was applied to 48-h forecasts of sea level pressure and surface temperature over the North American Pacific Northwest in spring 2000, using the University of Washington mesoscale ensemble. When compared to the bias-corrected ensemble, deterministic-style EMOS forecasts of sea level pressure had root-mean-square error 9% less and mean absolute error 7% less. The EMOS predictive PDFs were sharp, and much better calibrated than the raw ensemble or the bias-corrected ensemble.

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