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Journal Articles Bulletin of the American Meteorological Society Year : 2021

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

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John Bjørnar Bremnes
  • Function : Author
Jonathan Demaeyer
  • Function : Author
Gavin Evans
  • Function : Author
Jonathan Flowerdew
  • Function : Author
Stephan Hemri
Sebastian Lerch
Nigel Roberts
  • Function : Author
Susanne Theis
  • Function : Author
Aitor Atencia
  • Function : Author
Zied Ben Bouallègue
  • Function : Author
Jonas Bhend
  • Function : Author
Markus Dabernig
  • Function : Author
Lesley de Cruz
  • Function : Author
Leila Hieta
  • Function : Author
Lionel Moret
  • Function : Author
Iris Odak Plenković
  • Function : Author
Maurice Schmeits
  • Function : Author
Joris van den Bergh
  • Function : Author
Bert van Schaeybroeck
  • Function : Author
Kirien Whan
  • Function : Author
Jussi Ylhaisi
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Abstract

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.
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Dates and versions

meteo-03543712 , version 1 (04-03-2022)

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Attribution - CC BY 4.0

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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⟩
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