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Applications of Uncrewed Aerial Vehicles (UAVs) in Winter Precipitation-Type Forecasts

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  • 1 School of Meteorology, University of Oklahoma, Norman, Oklahoma
  • | 2 Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma
  • | 3 NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
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Abstract

Temperature and humidity profiles in the lowest 3 km of the atmosphere provide crucial information in determining the precipitation type, which aids forecasters in relaying winter-weather risks. In response to the challenges associated with forecasting mixed-phase environments, this study employs uncrewed aerial vehicles (UAVs) to explore the efficacy of high-resolution temporal and vertical measurements in winter-weather environments. On 19 February 2019, boundary layer measurements of an Oklahoma winter storm were collected by a UAV and radiosondes. UAV observations show a pronounced surface-based subfreezing layer that corresponds to observed ice pellets at the surface. This is in contrast to the High-Resolution Rapid Refresh (HRRR) model analyses, which show a subfreezing layer near the surface that is 3°C warmer than both the UAV and radiosonde observations. Using a spectral-bin-microphysics algorithm designed to provide hydrometeor-phase diagnosis throughout the vertical column, it was found that UAV measurements can improve discrimination between hydrometer types in environments near 0°C. A numerical-modeling study of the same winter-weather event illustrates the potential benefit of vertically sampling a mixed-phase environment at multiple mesonet sites and highlights future scientific and operational questions to be addressed by the UAV community.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: David L. Boren, daniel.tripp@noaa.gov

Abstract

Temperature and humidity profiles in the lowest 3 km of the atmosphere provide crucial information in determining the precipitation type, which aids forecasters in relaying winter-weather risks. In response to the challenges associated with forecasting mixed-phase environments, this study employs uncrewed aerial vehicles (UAVs) to explore the efficacy of high-resolution temporal and vertical measurements in winter-weather environments. On 19 February 2019, boundary layer measurements of an Oklahoma winter storm were collected by a UAV and radiosondes. UAV observations show a pronounced surface-based subfreezing layer that corresponds to observed ice pellets at the surface. This is in contrast to the High-Resolution Rapid Refresh (HRRR) model analyses, which show a subfreezing layer near the surface that is 3°C warmer than both the UAV and radiosonde observations. Using a spectral-bin-microphysics algorithm designed to provide hydrometeor-phase diagnosis throughout the vertical column, it was found that UAV measurements can improve discrimination between hydrometer types in environments near 0°C. A numerical-modeling study of the same winter-weather event illustrates the potential benefit of vertically sampling a mixed-phase environment at multiple mesonet sites and highlights future scientific and operational questions to be addressed by the UAV community.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: David L. Boren, daniel.tripp@noaa.gov
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