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The Impacts of Assimilating Various Densities of Uncrewed Aircraft System Observations on Regional NWP Forecasts in an OSSE.

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Title: The Impacts of Assimilating Various Densities of Uncrewed Aircraft System Observations on Regional NWP Forecasts in an OSSE.
Authors: Murdzek, Shawn S.1,2 (AUTHOR) shawn.murdzek@colorado.edu, Ladwig, Terra T.2 (AUTHOR), Houston, Adam L.3 (AUTHOR), James, Eric P.2 (AUTHOR)
Source: Monthly Weather Review. May2026, Vol. 154 Issue 5, p1-22. 22p.
Subjects: Data assimilation, Numerical weather forecasting, Weather forecasting, Drone aircraft, Troposphere
Geographic Terms: United States
Abstract: Uncrewed aircraft systems (UAS) have emerged as an option for increasing the number of routine observations within the in situ observational gap in the lower troposphere. Before deploying a nationwide network of UAS, however, it is necessary to determine what impact UAS observations will have on weather forecast model accuracy and assess the relative benefits of various UAS networks. Our goal is to help address this knowledge gap by examining the impact of assimilating varying densities of UAS observations on Rapid Refresh Forecast System (RRFS) forecasts. To do this, an observing system simulation experiment (OSSE) is used that consists of two week-long nature runs over the contiguous United States. Five different networks in which UAS execute hourly vertical profiles up to 2 km AGL are examined, with the spacing between UAS sites varying between 300 and 35 km. Results show positive impacts from assimilating UAS, with observations from the 35-km UAS network reducing 6-hour root-mean-squared errors by over 15% in the lower atmosphere. It is also shown that the benefit per UAS in the bulk verification statistics decreases as more UAS are added to the network. Examining a low cloud ceiling case shows that UAS can improve cloud forecasts when there are minimal clouds at the analysis time owing to a better representation of above-ground moisture, though the UAS impact was minimal when using the coarsest UAS network. Altogether, these results suggest that UAS can improve RRFS forecasts and benefits can be obtained from less than a hundred UAS. [ABSTRACT FROM AUTHOR]
Copyright of Monthly Weather Review is the property of American Meteorological Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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An: 194578194
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  Data: The Impacts of Assimilating Various Densities of Uncrewed Aircraft System Observations on Regional NWP Forecasts in an OSSE.
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  Data: <searchLink fieldCode="DE" term="%22Data+assimilation%22">Data assimilation</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+weather+forecasting%22">Numerical weather forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Weather+forecasting%22">Weather forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Drone+aircraft%22">Drone aircraft</searchLink><br /><searchLink fieldCode="DE" term="%22Troposphere%22">Troposphere</searchLink>
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  Data: Uncrewed aircraft systems (UAS) have emerged as an option for increasing the number of routine observations within the in situ observational gap in the lower troposphere. Before deploying a nationwide network of UAS, however, it is necessary to determine what impact UAS observations will have on weather forecast model accuracy and assess the relative benefits of various UAS networks. Our goal is to help address this knowledge gap by examining the impact of assimilating varying densities of UAS observations on Rapid Refresh Forecast System (RRFS) forecasts. To do this, an observing system simulation experiment (OSSE) is used that consists of two week-long nature runs over the contiguous United States. Five different networks in which UAS execute hourly vertical profiles up to 2 km AGL are examined, with the spacing between UAS sites varying between 300 and 35 km. Results show positive impacts from assimilating UAS, with observations from the 35-km UAS network reducing 6-hour root-mean-squared errors by over 15% in the lower atmosphere. It is also shown that the benefit per UAS in the bulk verification statistics decreases as more UAS are added to the network. Examining a low cloud ceiling case shows that UAS can improve cloud forecasts when there are minimal clouds at the analysis time owing to a better representation of above-ground moisture, though the UAS impact was minimal when using the coarsest UAS network. Altogether, these results suggest that UAS can improve RRFS forecasts and benefits can be obtained from less than a hundred UAS. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Monthly Weather Review is the property of American Meteorological Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1175/MWR-D-25-0175.1
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      – Code: eng
        Text: English
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      – SubjectFull: Weather forecasting
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      – SubjectFull: Drone aircraft
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      – SubjectFull: Troposphere
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      – SubjectFull: United States
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      – TitleFull: The Impacts of Assimilating Various Densities of Uncrewed Aircraft System Observations on Regional NWP Forecasts in an OSSE.
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            – D: 01
              M: 05
              Text: May2026
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              Y: 2026
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