Assimilation of Ground‐Based Microwave Radiometer Temperature Observations Into a Convective‐Scale NWP Model for Fog Forecast Improvement

Abstract During the SOuthwest FOGs 3D (SOFOG3D) campaign, a unique network of eight ground‐based microwave radiometers (MWRs) has been deployed over the southwestern part of France in order to improve fog forecasting, among other objectives. Observations have been acquired from mid‐autumn to mid‐spring 2019/2020 with the aim of assessing the impact of the assimilation of such observations in the Application de la Recherche à l'Opérationnel à Méso‐Échelle (AROME‐France) numerical weather prediction model during fog conditions. For that purpose, temperature profiles derived from MWR measurements at six different locations have been assimilated for a time period of three months with a three‐dimensional variational assimilation system similar to the one used operationally in the AROME‐France numerical weather prediction model. Positive impacts on AROME‐France one‐hour background and analysis have been obtained, as well as statistically significant temperature improvements for up to eight hours of forecast during all weather conditions. Positive impacts are also demonstrated during fog conditions, both for a single case study and statistically for temperature and fog forecast scores over several fog events.

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Publication Details

Journal
Quarterly Journal of the Royal Meteorological Society
Published
2024-11-28
DOI
https://doi.org/10.1002/qj.4893
Citations
4
Primary Topic
Meteorological Phenomena and Simulations
Type
article
Field-Weighted Citation Impact
1.51

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article

Assimilation of Ground‐Based Microwave Radiometer Temperature Observations Into a Convective‐Scale NWP Model for Fog Forecast Improvement

Ulrich Löhnert, Gary E. Thomas, Philippe Chambon, Pauline Martinet et al.
4 citations
Quarterly Journal of the Royal Meteorological Society
Meteorological Phenomena and Simulations
1.51
article

Assimilation of Ground‐Based Microwave Radiometer Temperature Observations Into a Convective‐Scale NWP Model for Fog Forecast Improvement

Ulrich Löhnert, Gary E. Thomas, Philippe Chambon, Pauline Martinet, Vinciane Unger, Emiliano Orlandi, Thierry Huet, Maxime Hervo, Pierre Brousseau, Jean‐François Georgis
article en
4 citations

Abstract

Abstract During the SOuthwest FOGs 3D (SOFOG3D) campaign, a unique network of eight ground‐based microwave radiometers (MWRs) has been deployed over the southwestern part of France in order to improve fog forecasting, among other objectives. Observations have been acquired from mid‐autumn to mid‐spring 2019/2020 with the aim of assessing the impact of the assimilation of such observations in the Application de la Recherche à l'Opérationnel à Méso‐Échelle (AROME‐France) numerical weather prediction model during fog conditions. For that purpose, temperature profiles derived from MWR measurements at six different locations have been assimilated for a time period of three months with a three‐dimensional variational assimilation system similar to the one used operationally in the AROME‐France numerical weather prediction model. Positive impacts on AROME‐France one‐hour background and analysis have been obtained, as well as statistically significant temperature improvements for up to eight hours of forecast during all weather conditions. Positive impacts are also demonstrated during fog conditions, both for a single case study and statistically for temperature and fog forecast scores over several fog events.

Quarterly Journal of the Royal Meteorological SocietyVol. 151(766)
Centre National de la Recherche Scientifique (FR), Federal Office of Meteorology and Climatology MeteoSwiss (CH), European Centre for Medium-Range Weather Forecasts (GB), Université Fédérale de Toulouse Midi-Pyrénées (FR), University of Cologne (DE), Office National d'Études et de Recherches Aérospatiales (FR), Centre National de Recherches Météorologiques (FR), Laboratoire d'Aérologie (FR), Météo-France (FR)
European Cooperation in Science and Technology
Climate action
Openalex Percentile: Top 19%
Meteorological Phenomena and Simulations
1.51
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