National Weather Service Data Needs for Short-Term Forecasts and the Role of Unmanned Aircraft in Filling the Gap: Results from a Nationwide Survey

Adam L. Houston Department of Earth and Atmospheric Sciences, University of Nebraska, Lincoln, Nebraska

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Lisa M. PytlikZillig Public Policy Center, University of Nebraska, Lincoln, Nebraska

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Janell C. Walther Public Policy Center, University of Nebraska, Lincoln, Nebraska

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Abstract

Inclusion of unmanned aircraft systems (UAS) into the weather surveillance network has the potential to improve short-term (<1 day) weather forecasts through direct integration of UAS-collected data into the forecast process and assimilation into numerical weather prediction models. However, one of the primary means by which the value of any new sensing platform can be assessed is through consultation with principal stakeholders. National Weather Service (NWS) forecasters are principal stakeholders responsible for the issuance of short-term forecasts. The purpose of the work presented here is to use results from a survey of 630 NWS forecasters to assess critical data gaps that impact short-term forecast accuracy and explore the potential role of UAS in filling these gaps. NWS forecasters view winter precipitation, icing, flood, lake-effect/lake-enhanced snow, turbulence, and waves as the phenomena principally impacted by data gaps. Of the 10 high-priority weather-related characteristics that need to be observed to fill critical data gaps, 7 are either measures of precipitation or related to precipitation-producing phenomena. The three most important UAS capabilities/characteristics required for useful data for weather forecasting are real-time or near-real-time data, the ability to integrate UAS data with additional data gathered by other systems, and UASs equipped with cameras to verify forecasts and monitor weather. Of the three operation modes offered for forecasters to consider, targeted surveillance is considered to be the most important compared to fixed site profiling or transects between fixed sites.

© 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: Adam L. Houston, ahouston2@unl.edu

Abstract

Inclusion of unmanned aircraft systems (UAS) into the weather surveillance network has the potential to improve short-term (<1 day) weather forecasts through direct integration of UAS-collected data into the forecast process and assimilation into numerical weather prediction models. However, one of the primary means by which the value of any new sensing platform can be assessed is through consultation with principal stakeholders. National Weather Service (NWS) forecasters are principal stakeholders responsible for the issuance of short-term forecasts. The purpose of the work presented here is to use results from a survey of 630 NWS forecasters to assess critical data gaps that impact short-term forecast accuracy and explore the potential role of UAS in filling these gaps. NWS forecasters view winter precipitation, icing, flood, lake-effect/lake-enhanced snow, turbulence, and waves as the phenomena principally impacted by data gaps. Of the 10 high-priority weather-related characteristics that need to be observed to fill critical data gaps, 7 are either measures of precipitation or related to precipitation-producing phenomena. The three most important UAS capabilities/characteristics required for useful data for weather forecasting are real-time or near-real-time data, the ability to integrate UAS data with additional data gathered by other systems, and UASs equipped with cameras to verify forecasts and monitor weather. Of the three operation modes offered for forecasters to consider, targeted surveillance is considered to be the most important compared to fixed site profiling or transects between fixed sites.

© 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: Adam L. Houston, ahouston2@unl.edu
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  • Bocklisch, F., S. F. Bocklisch, and J. F. Krems, 2012: Sometimes, often, and always: Exploring the vague meanings of frequency expressions. Behav. Res. Methods, 44, 144157, https://doi.org/10.3758/s13428-011-0130-8.

    • Search Google Scholar
    • Export Citation
  • Chilson, P. B., and Coauthors, 2019: Moving towards a network of autonomous UAS atmospheric profiling stations for observations in the Earth’s lower atmosphere: The 3D Mesonet concept. Sensors, 19, 2720, https://doi.org/10.3390/s19122720.

    • Search Google Scholar
    • Export Citation
  • Cione, J. J., and Coauthors, 2020: Eye of the storm: Observing hurricanes with a small unmanned aircraft system. Bull. Amer. Meteor. Soc., 101, E186E205, https://doi.org/10.1175/BAMS-D-19-0169.1.

    • Search Google Scholar
    • Export Citation
  • Dabberdt, W. F., and Coauthors, 2005: Multifunctional mesoscale observing networks. Bull. Amer. Meteor. Soc., 86, 961982, https://doi.org/10.1175/BAMS-86-7-961.

    • Search Google Scholar
    • Export Citation
  • Flagg, D. D., and Coauthors, 2018: On the impact of unmanned aerial system observations on numerical weather prediction in the coastal zone. Mon. Wea. Rev., 146, 599622, https://doi.org/10.1175/MWR-D-17-0028.1.

    • Search Google Scholar
    • Export Citation
  • Gaskell, G., M. W. Bauer, J. Durant, and N. C. Allum, 1999: Worlds apart? The reception of genetically modified foods in Europe and the US. Science, 285, 384387, https://doi.org/10.1126/science.285.5426.384.

    • Search Google Scholar
    • Export Citation
  • Gupta, N., A. R. Fischer, and L. J. Frewer, 2012: Socio-psychological determinants of public acceptance of technologies: A review. Public Understanding Sci., 21, 782795, https://doi.org/10.1177/0963662510392485.

    • Search Google Scholar
    • Export Citation
  • Heinselman, P. L., D. S. LaDue, and H. Lazrus, 2012: Exploring impacts of rapid-scan radar data on NWS warning decisions. Wea. Forecasting, 27, 10311044, https://doi.org/10.1175/WAF-D-11-00145.1.

    • Search Google Scholar
    • Export Citation
  • Houston, A. L., J. C. Walther, L. M. PytlikZillig, and J. Kawamoto, 2020: Initial assessment of unmanned aircraft system characteristics required to fill data gaps for short-term forecasts: Results from focus groups and interviews. J. Oper. Meteor., 8, 111120, https://doi.org/10.15191/nwajom.2020.0809.

    • Search Google Scholar
    • Export Citation
  • Jensen, A. A., and Coauthors, 2021: Assimilation of a coordinated fleet of uncrewed aircraft system observations in complex terrain: EnKF system design and preliminary assessment. Mon. Wea. Rev., 149, 14591480, https://doi.org/10.1175/MWR-D-20-0359.1.

    • Search Google Scholar
    • Export Citation
  • Koch, S. E., M. Fengler, P. B. Chilson, K. L. Elmore, B. Argrow, D. L. Andra Jr., and T. Lindley, 2018: On the use of unmanned aircraft for sampling mesoscale phenomena in the preconvective boundary layer. J. Atmos. Oceanic Technol., 35, 22652288, https://doi.org/10.1175/JTECH-D-18-0101.1.

    • Search Google Scholar
    • Export Citation
  • Leuenberger, D., A. Haefele, N. Omanovic, M. Fengler, G. Martucci, B. Calpini, and O. Fuhrer, 2020: Improving high-impact numerical weather prediction with lidar and drone observations. Bull. Amer. Meteor. Soc., 101, E1036E1051, https://doi.org/10.1175/BAMS-D-19-0119.1.

    • Search Google Scholar
    • Export Citation
  • McFarquhar, G. M., and Coauthors, 2020: Current and future uses of UAS for improved forecasts/warnings and scientific studies. Bull. Amer. Meteor. Soc., 101, E1322E1328, https://doi.org/10.1175/BAMS-D-20-0015.1.

    • Search Google Scholar
    • Export Citation
  • National Academies of Sciences, Engineering, and Medicine, 2018: The Future of Atmospheric Boundary Layer Observing, Understanding, and Modeling: Proceedings of a Workshop. National Academies Press, 70 pp., https://doi.org/10.17226/25138.

    • Search Google Scholar
    • Export Citation
  • NRC, 2009: Observing Weather and Climate from the Ground Up: A Nationwide Network of Networks. National Academies Press, 250 pp., https://doi.org/10.17226/12540.

    • Search Google Scholar
    • Export Citation
  • Vömel, H., and Coauthors, 2018: NCAR/EOL Community Workshop on Unmanned Aircraft Systems for Atmospheric Research. Boulder, CO, NCAR, https://doi.org/10.5065/D6X9292S.

    • Search Google Scholar
    • Export Citation
  • Walther, J., L. PytlikZillig, C. Detweiler, and A. Houston, 2019: How people make sense of drones used for atmospheric science (and other purposes): Hopes, concerns, and recommendations. J. Unmanned Veh. Syst., 7, 219234, https://doi.org/10.1139/juvs-2019-0003.

    • Search Google Scholar
    • Export Citation
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