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.
Vannitsem, S, Bremnes, JB, Demaeyer, J, Evans, GR, Flowerdew, J, Hemri, S, Lerch, S, Roberts, N, Theis, S, Atencia, A, Bouallègue, ZB, Bhend, J, Dabernig, M, de Cruz, L, Hieta, L, Mestre, O, Moret, L, Plenković, IO, Schmeits, M, Taillardat, M, van den Bergh, J, van Schaeybroeck, B, Whan, K & Ylhaisi, J 2021, 'Statistical postprocessing for weather forecasts review, challenges, and avenues in a big data world', Bulletin of the American Meteorological Society, vol. 102, no. 3, pp. E681-E699. https://doi.org/10.1175/BAMS-D-19-0308.1
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., ... 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), E681-E699. https://doi.org/10.1175/BAMS-D-19-0308.1
@article{1c9c0278af4f4a62b323d6cdfd1148ca,
title = "Statistical postprocessing for weather forecasts review, challenges, and avenues in a big data world",
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.",
keywords = "Bias, Data science, Model output statistics, Operational forecasting, Probability forecasts/models/distribution, Regression",
author = "St{\'e}phane Vannitsem and Bremnes, {John Bj{\o}rnar} and Jonathan Demaeyer and Evans, {Gavin R.} and Jonathan Flowerdew and Stephan Hemri and Sebastian Lerch and Nigel Roberts and Susanne Theis and Aitor Atencia and Bouall{\`e}gue, {Zied Ben} and Jonas Bhend and Markus Dabernig and {de Cruz}, Lesley and Leila Hieta and Olivier Mestre and Lionel Moret and Plenkovi{\'c}, {Iris Odak} and Maurice Schmeits and Maxime Taillardat and {van den Bergh}, Joris and {van Schaeybroeck}, Bert and Kirien Whan and Jussi Ylhaisi",
note = "Funding Information: Acknowledgments. The constructive comments of the two reviewers were highly appreciated. Useful comments from Tim Hewson and Mark Liniger on an earlier version of this paper are also very much appreciated. The work of Jonathan Demaeyer and St\u00E9phane Vannitsem is partly supported by the EUMETNET module \u201CPost-processing\u201D of the NWP cooperation program. Sebastian Lerch acknowledges support by the Deutsche Forschungsgemeinschaft through SFB/TRR 165 \u201CWaves to Weather.\u201D Bert Van Schaeybroeck and St\u00E9phane Vannitsem are partly supported by the project MEDSCOPE that has received funding from EU\u2019s H2020 Research and Innovation Program under Grant Agreement 690462. Publisher Copyright: {\textcopyright} 2021 American Meteorological Society For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy.",
year = "2021",
month = jan,
day = "1",
doi = "10.1175/BAMS-D-19-0308.1",
language = "English",
volume = "102",
pages = "E681--E699",
journal = "Bulletin of the American Meteorological Society",
issn = "0003-0007",
publisher = "American Meteorological Society",
number = "3",
}