Impact of Mode-S Enhanced Surveillance Weather Observations on Weather Forecasts over the MetCoOp Northern European Model Domain

Magnus Lindskog aSwedish Meteorological and Hydrological Institute, Norrköping, Sweden

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Roohollah Azad bNorwegian Meteorological Institute, Oslo, Norway

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Siebren de Haan cKoninklijk Nederlands Meteorologisch Instituut, De Bilt, Netherlands

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Jesper Blomster aSwedish Meteorological and Hydrological Institute, Norrköping, Sweden

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Martin Ridal aSwedish Meteorological and Hydrological Institute, Norrköping, Sweden

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Abstract

Meteorological Cooperation on Operational Numeric Weather Prediction (MetCoOp) is a northern European collaboration on operational numerical weather prediction based on a common limited-area, kilometer-scale ensemble system. The initial states of this model are produced using a three-dimensional, variational, data assimilation scheme utilizing a large number of observations from conventional in situ measurements, weather radars, global navigation satellite systems, advanced scatterometer data, and satellite radiances. Since 2019, the MetCoOp system was enhanced by utilization of observations based on selective mode (Mode-S) enhanced surveillance (EHS) reports that are broadcast by aircraft in response to interrogation from air traffic control radar. These observations, obtained from the European Meteorological Aircraft Derived Data Center, are used to derive indirect information of atmospheric wind speed and temperature. The use of these observations compensated for the considerably reduced number of direct aircraft observations encountered as an effect of the COVID-19 pandemic. The MetCoOp observation handling system is described, with emphasis on Mode-S EHS data. The quality of these observations is evaluated, and we show that they are suitable to be used in MetCoOp data assimilation. The impact on average forecast verification scores of the additional Mode-S EHS data is slightly positive. The benefit of using Mode-S EHS was demonstrated for an individual case and also a demonstration of utilizing the Stockholm Arlanda receiver data in assimilation has been performed.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Magnus Lindskog, magnus.lindskog@smhi.se

Abstract

Meteorological Cooperation on Operational Numeric Weather Prediction (MetCoOp) is a northern European collaboration on operational numerical weather prediction based on a common limited-area, kilometer-scale ensemble system. The initial states of this model are produced using a three-dimensional, variational, data assimilation scheme utilizing a large number of observations from conventional in situ measurements, weather radars, global navigation satellite systems, advanced scatterometer data, and satellite radiances. Since 2019, the MetCoOp system was enhanced by utilization of observations based on selective mode (Mode-S) enhanced surveillance (EHS) reports that are broadcast by aircraft in response to interrogation from air traffic control radar. These observations, obtained from the European Meteorological Aircraft Derived Data Center, are used to derive indirect information of atmospheric wind speed and temperature. The use of these observations compensated for the considerably reduced number of direct aircraft observations encountered as an effect of the COVID-19 pandemic. The MetCoOp observation handling system is described, with emphasis on Mode-S EHS data. The quality of these observations is evaluated, and we show that they are suitable to be used in MetCoOp data assimilation. The impact on average forecast verification scores of the additional Mode-S EHS data is slightly positive. The benefit of using Mode-S EHS was demonstrated for an individual case and also a demonstration of utilizing the Stockholm Arlanda receiver data in assimilation has been performed.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Magnus Lindskog, magnus.lindskog@smhi.se
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