Titel
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
Autor*in
Guannan Hu
Department of Meteorology, School of Mathematical, Physical and Computational Sciences, University of Reading
Autor*in
Sarah L. Dance
Department of Meteorology, School of Mathematical, Physical and Computational Sciences, University of Reading
Autor*in
Ross N. Bannister
Department of Meteorology, School of Mathematical, Physical and Computational Sciences, University of Reading
... show all
Abstract
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 10 and 12 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 focused 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.
Stichwort
convection-permitting data assimilationcovariance modellingmultiscale data assimilationnovel observationsoperational data assimilation systems
Objekt-Typ
Sprache
Englisch [eng]
Persistent identifier
https://phaidra.univie.ac.at/o:2045818
Erschienen in
Titel
Atmospheric Science Letters
Band
24
Ausgabe
1
ISSN
1530-261X
Erscheinungsdatum
2022
Verlag
Wiley
Erscheinungsdatum
2022
Zugänglichkeit
Rechteangabe
© 2022 The Authors

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