Progress, challenges and future steps in data assimilation for convection-permitting numerical weather prediction: report on the virtual meeting held on 10 and 12 November 2021

Hu, G. ORCID: https://orcid.org/0000-0003-4305-3658, Dance, S. L. ORCID: https://orcid.org/0000-0003-1690-3338, Bannister, R. N. ORCID: https://orcid.org/0000-0002-6846-8297, Chipilski, H. G., Guillet, O., Macpherson, B., Weissmann, M. and Yussouf, N. (2023) Progress, challenges and future steps in data assimilation for convection-permitting numerical weather prediction: report on the virtual meeting held on 10 and 12 November 2021. Atmospheric Science Letters, 24 (1). e1130. ISSN 1530-261X doi: 10.1002/asl.1130

Abstract/Summary

In November 2021, the Royal Meteorological Society Data Assimilation (DA) Special Interest Group and the University of Reading hosted a virtual meeting on the topic of DA for convection-permitting numerical weather prediction. The goal of the meeting was to discuss recent developments and review the challenges including methodological developments and progress in making the best use of observations. The meeting took place over two half days on the 10th and 12th November, and consisted of six talks and a panel discussion. The scientific presentations highlighted some recent work from Europe and the USA on convection-permitting DA including novel developments in the assimilation of observations such as cloud-affected satellite radiances in visible channels, ground-based profiling networks, aircraft data and radar reflectivity data, as well as methodological advancements in background and observation error covariance modelling and progress in operational systems. The panel discussion focussed on key future challenges including the handling of multiscales (synoptic-, meso- and convective-scales), ensemble design, the specification of background and observation error covariances, and better use of observations. These will be critical issues to address in order to improve short-range forecasts and nowcasts of hazardous weather.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/106780
Identification Number/DOI	10.1002/asl.1130
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO) Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Royal Meteorological Society
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