气候变化领域集成服务门户(CCPortal): Statistical postprocessing for weather forecasts review, challenges, and avenues in a big data world

CCPortal

DOI	10.1175/BAMS-D-19-0308.1
	Statistical postprocessing for weather forecasts review, challenges, and avenues in a big data world
	Vannitsem S.; Bremnes J.B.; Demaeyer J.; Evans G.R.; Flowerdew J.; Hemri S.; Lerch S.; Roberts N.; Theis S.; Atencia A.; Bouallègue Z.B.; 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.
发表日期	2021
ISSN	00030007
起始页码	E681
结束页码	E699
卷号	102 期号:3
英文摘要	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. © 2021 American Meteorological Society For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy.
英文关键词	Bias; Data science; Model output statistics; Operational forecasting; Probability forecasts/models/distribution; Regression
语种	英语
scopus关键词	Large dataset; Engineering community; High-quality observations; Numerical weather prediction; Operational applications; Post-processing techniques; Prognostic variables; Space-time correlation; Theoretical development; Weather forecasting
来源期刊	Bulletin of the American Meteorological Society
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/177718
作者单位	Royal Meteorological Institute of Belgium, European Meteorological Network (EUMETNET), Brussels, Belgium; Norwegian Meteorological Institute, Oslo, Norway; Met Office, Exeter, United Kingdom; Federal Office of Meteorology and Climatology, MeteoSwiss, Zurich, Switzerland; Institute for Stochastics, Karlsruhe Institute of Technology, Karlsruhe, Germany; MetOffice@Reading, Met Office, United Kingdom; Deutscher Wetterdienst, Offenbach, Germany; Zentralanstalt für Meteorologie und Geodynamik, Vienna, Austria; European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom; Federal Office of Meteorology and Climatology, MeteoSwiss, Zurich, Switzerland; Royal Meteorological Institute of Belgium, Brussels, Belgium; Finnish Meteorological Institute, Helsinki, Finland; Météo-France, CNRM-UMR 3589, Toulouse, France; Croatian Meteorological and Hydrological Service, Zagreb, Croatia; Royal Netherlands Meteorological Institute (KNMI), De Bilt, Netherlands
推荐引用方式 GB/T 7714	Vannitsem S.,Bremnes J.B.,Demaeyer J.,et al. Statistical postprocessing for weather forecasts review, challenges, and avenues in a big data world[J],2021,102(3).
APA	Vannitsem S..,Bremnes J.B..,Demaeyer J..,Evans G.R..,Flowerdew J..,...&Ylhaisi J..(2021).Statistical postprocessing for weather forecasts review, challenges, and avenues in a big data world.Bulletin of the American Meteorological Society,102(3).
MLA	Vannitsem S.,et al."Statistical postprocessing for weather forecasts review, challenges, and avenues in a big data world".Bulletin of the American Meteorological Society 102.3(2021).