Recalibrating wind‐speed forecasts using regime‐dependent ensemble 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.

Download Full-text

Multivariate ensemble Model Output Statistics using empirical copulas

Quarterly Journal of the Royal Meteorological Society ◽

10.1002/qj.2414 ◽

2014 ◽

Vol 141 (688) ◽

pp. 945-952 ◽

Cited By ~ 41

Author(s):

Daniel S. Wilks

Keyword(s):

Ensemble Model ◽

Model Output ◽

Ensemble Model Output Statistics ◽

Model Output Statistics

Download Full-text

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

Nonlinear Processes in Geophysics ◽

10.5194/npg-28-467-2021 ◽

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).

Download Full-text