Joint estimation of sea ice and atmospheric state from microwave imagers in operational weather forecasting

Abstract Satellite‐observed microwave radiances provide information on both surface and atmosphere. For operational weather forecasting, information on atmospheric temperature, humidity, cloud, and precipitation is inferred directly using all‐sky radiance data assimilation. In contrast, information...

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Published in:Quarterly Journal of the Royal Meteorological Society
Main Author: Geer, Alan J.
Format: Article in Journal/Newspaper
Language:English
Published: Wiley 2024
Subjects:
Online Access:http://dx.doi.org/10.1002/qj.4797
https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/qj.4797
id crwiley:10.1002/qj.4797
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spelling crwiley:10.1002/qj.4797 2024-09-15T18:34:21+00:00 Joint estimation of sea ice and atmospheric state from microwave imagers in operational weather forecasting Geer, Alan J. 2024 http://dx.doi.org/10.1002/qj.4797 https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/qj.4797 en eng Wiley http://creativecommons.org/licenses/by/4.0/ Quarterly Journal of the Royal Meteorological Society ISSN 0035-9009 1477-870X journal-article 2024 crwiley https://doi.org/10.1002/qj.4797 2024-07-09T04:14:22Z Abstract Satellite‐observed microwave radiances provide information on both surface and atmosphere. For operational weather forecasting, information on atmospheric temperature, humidity, cloud, and precipitation is inferred directly using all‐sky radiance data assimilation. In contrast, information on the surface state, such as sea‐surface temperature (SST) and sea‐ice concentration (SIC), is typically provided through third‐party retrieval products. Scientifically, this is a sub‐optimal use of the observations, and practically it has disadvantages such as time delays of more than 48 h. A better solution is to estimate the surface and atmospheric state jointly from the radiance observations. This has not been possible until now, due to incomplete knowledge of the surface state and the radiative transfer that links this to the observed radiances. A new approach based on an empirical state and an empirical sea‐ice surface emissivity model is used here to add sea‐ice state estimation, including SIC, to the European Centre for Medium‐range Weather Forecasts atmospheric data assimilation system. The sea‐ice state is estimated using augmented control variables at the observation locations. The resulting SIC estimates are of good quality and they highlight apparent defects in the existing OCEAN5 sea‐ice analysis. The SIC estimates can also be used to track giant icebergs, which may provide a novel maritime application for passive microwave radiances. Further, the SIC estimates should be suitable for onward use in coupled ocean–atmosphere data assimilation. There is also increased coverage of microwave observations in the proximity of sea ice, leading to improved atmospheric forecasts out to day 4 in the Southern Ocean. Article in Journal/Newspaper Sea ice Southern Ocean Wiley Online Library Quarterly Journal of the Royal Meteorological Society
institution Open Polar
collection Wiley Online Library
op_collection_id crwiley
language English
description Abstract Satellite‐observed microwave radiances provide information on both surface and atmosphere. For operational weather forecasting, information on atmospheric temperature, humidity, cloud, and precipitation is inferred directly using all‐sky radiance data assimilation. In contrast, information on the surface state, such as sea‐surface temperature (SST) and sea‐ice concentration (SIC), is typically provided through third‐party retrieval products. Scientifically, this is a sub‐optimal use of the observations, and practically it has disadvantages such as time delays of more than 48 h. A better solution is to estimate the surface and atmospheric state jointly from the radiance observations. This has not been possible until now, due to incomplete knowledge of the surface state and the radiative transfer that links this to the observed radiances. A new approach based on an empirical state and an empirical sea‐ice surface emissivity model is used here to add sea‐ice state estimation, including SIC, to the European Centre for Medium‐range Weather Forecasts atmospheric data assimilation system. The sea‐ice state is estimated using augmented control variables at the observation locations. The resulting SIC estimates are of good quality and they highlight apparent defects in the existing OCEAN5 sea‐ice analysis. The SIC estimates can also be used to track giant icebergs, which may provide a novel maritime application for passive microwave radiances. Further, the SIC estimates should be suitable for onward use in coupled ocean–atmosphere data assimilation. There is also increased coverage of microwave observations in the proximity of sea ice, leading to improved atmospheric forecasts out to day 4 in the Southern Ocean.
format Article in Journal/Newspaper
author Geer, Alan J.
spellingShingle Geer, Alan J.
Joint estimation of sea ice and atmospheric state from microwave imagers in operational weather forecasting
author_facet Geer, Alan J.
author_sort Geer, Alan J.
title Joint estimation of sea ice and atmospheric state from microwave imagers in operational weather forecasting
title_short Joint estimation of sea ice and atmospheric state from microwave imagers in operational weather forecasting
title_full Joint estimation of sea ice and atmospheric state from microwave imagers in operational weather forecasting
title_fullStr Joint estimation of sea ice and atmospheric state from microwave imagers in operational weather forecasting
title_full_unstemmed Joint estimation of sea ice and atmospheric state from microwave imagers in operational weather forecasting
title_sort joint estimation of sea ice and atmospheric state from microwave imagers in operational weather forecasting
publisher Wiley
publishDate 2024
url http://dx.doi.org/10.1002/qj.4797
https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/qj.4797
genre Sea ice
Southern Ocean
genre_facet Sea ice
Southern Ocean
op_source Quarterly Journal of the Royal Meteorological Society
ISSN 0035-9009 1477-870X
op_rights http://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.1002/qj.4797
container_title Quarterly Journal of the Royal Meteorological Society
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