Statistical and machine learning methods for postprocessing ensemble forecasts of wind gusts

Schulz, Benedikt; Lerch, Sebastian

doi:10.5194/egusphere-egu21-1326

Cited by 2 publications

(1 citation statement)

References 58 publications

(109 reference statements)

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“…Different EMOS extensions can include more predictors, in particular the boosting extension (Messner et al, 2017). Schulz and Lerch (2021) compare a gradient boosting extension of EMOS (EMOS-GB) to many machine learning methods for postprocessing ensemble forecasts of wind gusts, using a truncated logistic distribution. The performances of EMOS-GB and other machine-learning-based postprocessing methods are promising.…”

Section: Comparison Of Performances Between Qrf and Emos Approachesmentioning

confidence: 99%

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

Evin

Lafaysse

Taillardat

et al. 2021

Nonlin. Processes Geophys.

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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).

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Section: Comparison Of Performances Between Qrf and Emos Approachesmentioning

confidence: 99%

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

Evin

Lafaysse

Taillardat

et al. 2021

Nonlin. Processes Geophys.

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Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review

Salcedo-Sanz,

Pérez-Aracil,

Ascenso

et al. 2023

Theor Appl Climatol

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Atmospheric extreme events cause severe damage to human societies and ecosystems. The frequency and intensity of extremes and other associated events are continuously increasing due to climate change and global warming. The accurate prediction, characterization, and attribution of atmospheric extreme events is, therefore, a key research field in which many groups are currently working by applying different methodologies and computational tools. Machine learning and deep learning methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric extreme events. This paper reviews machine learning and deep learning approaches applied to the analysis, characterization, prediction, and attribution of the most important atmospheric extremes. A summary of the most used machine learning and deep learning techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. The critical literature review has been extended to extreme events related to rainfall and floods, heatwaves and extreme temperatures, droughts, severe weather events and fog, and low-visibility episodes. A case study focused on the analysis of extreme atmospheric temperature prediction with ML and DL techniques is also presented in the paper. Conclusions, perspectives, and outlooks on the field are finally drawn.

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Statistical and machine learning methods for postprocessing ensemble forecasts of wind gusts

Cited by 2 publications

References 58 publications

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

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

Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review

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