Statistical Postprocessing for Weather Forecasts: Review, Challenges, and Avenues in a Big Data World

Stéphane Vannitsem (1) , John Bjørnar Bremnes , Jonathan Demaeyer , Gavin Evans , Jonathan Flowerdew , Stephan Hemri , Sebastian Lerch , Nigel Roberts , Susanne Theis , Aitor Atencia , Zied Ben Bouallègue , Jonas Bhend , Markus Dabernig , Lesley de Cruz , Leila Hieta , Olivier Mestre (2, 3) , Lionel Moret , Iris Odak Plenković , Maurice Schmeits , Maxime Taillardat (2, 3) , Joris van den Bergh , Bert van Schaeybroeck , Kirien Whan , Jussi Ylhaisi

1 IRM - Institut Royal Météorologique de Belgique [Bruxelles] - Royal Meteorological Institute of Belgium
2 DIRSE - Météo-France Direction Interrégionale Sud-Est
3 CNRM - Centre national de recherches météorologiques

Abstract Statistical postprocessing techniques are nowadays key components of the forecasting suites in many national meteorological services (NMS), with, for most of them, the objective of correcting the impact of different types of errors on the forecasts. The final aim is to provide optimal, automated, seamless forecasts for end users. Many techniques are now flourishing in the statistical, meteorological, climatological, hydrological, and engineering communities. The methods range in complexity from simple bias corrections to very sophisticated distribution-adjusting techniques that incorporate correlations among the prognostic variables. The paper is an attempt to summarize the main activities going on in this area from theoretical developments to operational applications, with a focus on the current challenges and potential avenues in the field. Among these challenges is the shift in NMS toward running ensemble numerical weather prediction (NWP) systems at the kilometer scale that produce very large datasets and require high-density high-quality observations, the necessity to preserve space–time correlation of high-dimensional corrected fields, the need to reduce the impact of model changes affecting the parameters of the corrections, the necessity for techniques to merge different types of forecasts and ensembles with different behaviors, and finally the ability to transfer research on statistical postprocessing to operations. Potential new avenues are also discussed.

Domaines: Océan, Atmosphère; Machine Learning [stat.ML]; Méthodologie [stat.ME]; Applications [stat.AP]; Statistiques [math.ST]; Dynamique Chaotique [nlin.CD]; Intelligence artificielle [cs.AI]; Physique Atmosphérique et Océanique [physics.ao-ph]

Stéphane Vannitsem, John Bjørnar Bremnes, Jonathan Demaeyer, Gavin Evans, Jonathan Flowerdew, et al.. Statistical Postprocessing for Weather Forecasts: Review, Challenges, and Avenues in a Big Data World. Bulletin of the American Meteorological Society, 2021, 102 (3), pp.E681-E699. ⟨10.1175/BAMS-D-19-0308.1⟩. ⟨meteo-03543712⟩