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

dc.contributor.authorVannitsem, Stéphane
dc.contributor.authorBremnes, J. B.
dc.contributor.authorDemaeyer, Jonathan
dc.contributor.authorEvans, G.R.
dc.contributor.authorFlowerdew, J.
dc.contributor.authorHemri, S.
dc.contributor.authorLerch, S.
dc.contributor.authorRoberts, N.
dc.contributor.authorTheis, S.
dc.contributor.authorAtencia, A.
dc.contributor.authorBen Bouallègue, Z.
dc.contributor.authorBhend, Z.
dc.contributor.authorDabernig, J.
dc.contributor.authorDe Cruz, Lesley
dc.contributor.authorHieta, L.
dc.contributor.authorMestre, O.
dc.contributor.authorMoret, L.
dc.contributor.authorPlencovic, I.O.
dc.contributor.authorSchmeits, M.
dc.contributor.authorTaillardat, M.
dc.contributor.authorVan den Bergh, Joris
dc.contributor.authorVan Schaeybroeck, Bert
dc.contributor.authorWhan, K.
dc.contributor.authorYlhaisi, J.
dc.date2021-03
dc.date.accessioned2022-02-16T14:53:11Z
dc.date.available2022-02-16T14:53:11Z
dc.identifier.citationVannitsem, S., Bremnes, J. B., Demaeyer, J., Evans, G. R., Flowerdew, J., Hemri, S., Lerch, S., Roberts, N., Theis, S., Atencia, A., Ben Bouallègue, Z., Bhend, J., Dabernig, M., De Cruz, L., Hieta, L., Mestre, O., Moret, L., Plenković, I. O., Schmeits, M., Taillardat, M., Van den Bergh, J., Van Schaeybroeck, B., Whan, K., & Ylhaisi, J. Statistical Postprocessing for Weather Forecasts: Review, Challenges, and Avenues in a Big Data World, Bulletin of the American Meteorological Society, 102(3), E681-E699, 2021. 85. Vannitsem, S., Demaeyer, J., & Ghil, M. Extratropical low-frequency variability with ENSO forcing: A reduced-order coupled model study. Journal of Advances in Modeling Earth Systems, 13, e2021MS002530. https://doi.org/10.1029/2021MS002530,2021.en_US
dc.identifier.urihttps://orfeo.belnet.be/handle/internal/9795
dc.descriptionStatistical postprocessing techniques are nowadays key components of the forecastingsuites 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.en_US
dc.languageengen_US
dc.publisherAmerican Meteorological Societyen_US
dc.titleStatistical Postprocessing for Weather Forecasts: Review, Challenges, and Avenues in a Big Data Worlden_US
dc.typeArticleen_US
dc.subject.frascatiEarth and related Environmental sciencesen_US
dc.audienceScientificen_US
dc.source.titleBulletin of the American Meteorological Societyen_US
dc.source.volume102en_US
dc.source.issue2en_US
dc.source.pageE681-E699en_US
dc.relation.projectPP module of EUMETNETen_US
Orfeo.peerreviewedYesen_US
dc.identifier.doi10.1175/BAMS-D-19-0308.1


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