• Ancel, E., F. M. Capristan, J. V. Foster, and R. C. Condotta, 2017: Real-time risk assessment framework for unmanned aircraft system (UAS) traffic management (UTM). 17th AIAA Aviation Technology, Integration, and Operations Conf., Denver, CO, American Institute of Aeronautics and Astronautics, AIAA 2017-3273, https://doi.org/10.2514/6.2017-3273.

    • Crossref
    • Export Citation
  • Ancell, B. C., 2013: Nonlinear characteristics of ensemble perturbation evolution and their application to forecasting high-impact events. Wea. Forecasting, 28, 13531365, https://doi.org/10.1175/WAF-D-12-00090.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Arterburn, D. R., C. T. Duling, and N. R. Goli, 2017: Ground collision severity standards for UAS operating in the National Airspace System (NAS). 17th AIAA Aviation Technology, Integration, and Operations Conf., Denver, CO, American Institute of Aeronautics and Astronautics, AIAA 2017-3778, https://doi.org/10.2514/6.2017-3778.

    • Crossref
    • Export Citation
  • Aviation Safety Information Analysis and Sharing, 2010: Weather-related aviation accident study. Federal Aviation Administration Tech. Rep., 71 pp., https://www.asias.faa.gov/i/studies/2003-2007weatherrelatedaviationaccidentstudy.pdf.

  • Baklanov, A. A., and et al. , 2011: The nature, theory, and modeling of atmospheric planetary boundary layers. Bull. Amer. Meteor. Soc., 92, 123128, https://doi.org/10.1175/2010BAMS2797.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barlow, J. F., 2014: Progress in observing and modelling the urban boundary layer. Urban Climate, 10, 216240, https://doi.org/10.1016/j.uclim.2014.03.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barr, L. C., R. L. Newman, E. Ancel, C. M. Belcastro, J. V. Foster, J. K. Evans, and D. H. Klyde, 2017: Preliminary risk assessment for small unmanned aircraft systems. 17th AIAA Aviation Technology, Integration, and Operations Conf., Denver, CO, American Institute of Aeronautics and Astronautics, AIAA 2017-3272, https://doi.org/10.2514/6.2017-3272.

    • Crossref
    • Export Citation
  • Belcastro, C. M., R. L. Newman, J. K. Evans, D. H. Klyde, L. C. Barr, and E. Ancel, 2017: Hazards identification and analysis for unmanned aircraft system operations. 17th AIAA Aviation Technology, Integration, and Operations Conf., Denver, CO, American Institute of Aeronautics and Astronautics, AIAA 2017-3269, https://doi.org/10.2514/6.2017-3269.

    • Crossref
    • Export Citation
  • Benjamin, S. G., and et al. , 2016: A North American hourly assimilation and model forecast cycle: The Rapid Refresh. Mon. Wea. Rev., 144, 16691694, https://doi.org/10.1175/MWR-D-15-0242.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bir, C., and D. C. Viano, 2004: Design and injury assessment criteria for blunt ballistic impacts. J. Trauma, 57, 12181224, https://doi.org/10.1097/01.TA.0000114066.77967.DE.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bir, C., D. C. Viano, and A. King, 2004: Development of biomechanical response corridors of the thorax to blunt ballistic impacts. J. Biomech., 37, 7379, https://doi.org/10.1016/S0021-9290(03)00238-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bonin, T., P. Chilson, B. Zielke, and E. Fedorovich, 2013: Observations of the early evening boundary-layer transition using a small unmanned aerial system. Bound.-Layer Meteor., 146, 119132, https://doi.org/10.1007/s10546-012-9760-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bradford, S., 2018: FAA UTM concept of operations—V1.0. Federal Aviation Administration Tech. Rep., 44 pp., https://utm.arc.nasa.gov/docs/2018-UTM-ConOps-v1.0.pdf.

  • Breunig, J., J. Forman, S. Sayed, L. Audenaerd, A. Branch, and M. Hadjimichael, 2018: Modeling risk-based approach for small unmanned aircraft systems. 2018 Aviation Technology, Integration, and Operations Conf., Atlanta, GA, American Institute of Aeronautics and Astronautics, AIAA 2018-3349, https://doi.org/10.2514/6.2018-3349.

    • Crossref
    • Export Citation
  • Bristol, T., 2019: Safety management system manual. Federal Aviation Administration Air Traffic Organization Tech. Rep., 128 pp., https://www.faa.gov/air-traffic/publications/media/ATO-SMS-Manual.pdf.

  • Byun, S., J. Park, W. A. Appiah, M.-H. Ryou, and Y. M. Lee, 2017: The effects of humidity on the self-discharge properties of Li(Ni1/3Co1/3Mn1/3)O2/graphite and LiCoO2/graphite lithium-ion batteries during storage. RSC Adv., 7, 10 91510 921, https://doi.org/10.1039/C6RA28516C.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Campbell, S. E., D. A. Clark, and J. E. Evans, 2017a: Preliminary weather information gap analysis for UAS operations. MIT Lincoln Laboratory Tech. Rep., 126 pp., https://www.ll.mit.edu/sites/default/files/publication/doc/2018-05/Campbell_2017_ATC-437.pdf.

  • Campbell, S. E., D. A. Clark, and J. E. Evans, 2017b: Preliminary UAS weather research roadmap. MIT Lincoln Laboratory Tech. Rep., 62 pp., https://www.ll.mit.edu/sites/default/files/publication/doc/2018-05/Campbell_2017_ATC-438.pdf.

  • Castagno, J., C. Ochoa, and E. Atkins, 2018: Comprehensive risk-based planning for small unmanned aircraft system rooftop landing. Int. Conf. on Unmanned Aircraft Systems, Dallas, TX, Institute of Electrical and Electronics Engineers, 1031–1040, https://doi.org/10.1109/ICUAS.2018.8453483.

    • Crossref
    • Export Citation
  • Center for International Earth Science Information Network, 2018: Documentation for the Gridded Population of the World, version 4 (GPWv4), revision 11. NASA Socioeconomic Data and Applications Center, accessed 1 August 2019, https://doi.org/10.7927/H49C6VHW.

    • Crossref
    • Export Citation
  • Clare, V. R., J. H. Lewis, A. P. Mickiewicz, and L. M. Sturdivan, 1975: Blunt trauma data correlation. Edgewood Arsenal Tech. Rep., 54 pp., https://apps.dtic.mil/docs/citations/ADA012761.

  • Cornman, L. B., and W. N. Chan, 2017: Summary of a workshop on integrating weather into unmanned aerial system traffic management. Bull. Amer. Meteor. Soc., 98, ES257ED259, https://doi.org/10.1175/BAMS-D-16-0284.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dalamagkidis, K., K. P. Valavanis, and L. A. Piegl, 2008: Evaluating the risk of unmanned aircraft ground impacts. 16th Mediterranean Conf. on Control and Automation, Ajaccio, France, Institute of Electrical and Electronics Engineers, 709–716, https://doi.org/10.1109/MED.2008.4602249.

    • Crossref
    • Export Citation
  • de Boer, G., and et al. , 2020: Development of community, capabilities and understanding through unmanned aircraft-based atmospheric research: The LAPSE-RATE campaign. Bull. Amer. Meteor. Soc., 101, E684E699, https://doi.org/10.1175/BAMS-D-19-0050.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Denney, E., G. Pai, and M. Johnson, 2018: Towards a rigorous basis for specific operations risk assessment of UAS. IEEE/AIAA 37th Digital Avionics Systems Conf., London, United Kingdom, Institute of Electrical and Electronics Engineers, https://doi.org/10.1109/DASC.2018.8569475.

    • Crossref
    • Export Citation
  • Dowell, D., C. Alexander, T. Alcott, and T. Ladwig, 2018: HRRR Ensemble (HRRRE) guidance 2018 HWT Spring Experiment. Earth System Research Laboratory Tech. Rep., 6 pp., https://rapidrefresh.noaa.gov/hrrr/HRRRE/.

  • Duncan, J. S., 2016: Small unmanned aircraft systems (sUAS). U.S. Department of Transportation Rep. AC 107-2, 52 pp., https://www.faa.gov/documentlibrary/media/advisory_circular/ac_107-2.pdf.

  • Elston, J. S., J. Roadman, M. Stachura, B. Argrow, A. Houston, and E. Frew, 2011: The tempest unmanned aircraft system for in situ observations of tornadic supercells: Design and VORTEX2 flight results. J. Field Rob., 28, 461483, https://doi.org/10.1002/rob.20394.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elston, J. S., B. Argrow, M. Stachura, D. Weibel, D. Lawrence, and D. Pope, 2015: Overview of small fixed-wing unmanned aircraft for meteorological sampling. J. Atmos. Oceanic Technol., 32, 97115, https://doi.org/10.1175/JTECH-D-13-00236.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Erdelj, M., E. Natalizio, K. R. Chowdhury, and I. F. Akyildiz, 2017: Help from the sky: Leveraging UAVs for disaster management. IEEE Pervasive Comput., 16, 2432, https://doi.org/10.1109/MPRV.2017.11.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fenton, N., and M. Neil, 2013: Risk Assessment and Decision Analysis with Bayesian Networks. CRC Press, 660 pp.

    • Crossref
    • Export Citation
  • Geipel, J., J. Link, W. Claupein, 2014: Combined spectral and spatial modeling of corn yield based on aerial images and crop surface models acquired with an unmanned aircraft system. Remote Sens., 6, 10335, https://doi.org/10.3390/RS61110335.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Glasheen, K., J. Pinto, M. Steiner, and E. Frew, 2019: Experimental assessment of local weather forecasts for small unmanned aircraft flight. Scitech 2019 Forum, Reston, VA, American Institute of Aeronautics and Astronautics, AIAA 2019-1193, https://doi.org/10.2514/6.2019-1193.

    • Crossref
    • Export Citation
  • Guglieri, G., A. Lombardi, and G. Ristorto, 2015: Operation oriented path planning strategies for RPAS. Amer. J. Sci. Technol., 2, 321328, http://www.aascit.org/journal/archive2?journalId=902&paperId=3358.

    • Search Google Scholar
    • Export Citation
  • Haddal, C. C., and J. Gertler, 2010: Homeland Security: Unmanned aerial vehicles and border surveillance. Congressional Research Service Tech. Rep., 10 pp., www.crs.gov.

  • Holtslag, A. A. M., and et al. , 2013: Stable atmospheric boundary layers and diurnal cycles: Challenges for weather and climate models. Bull. Amer. Meteor. Soc., 94, 16911706, https://doi.org/10.1175/BAMS-D-11-00187.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houston, A. L., R. J. Laurence, T. W. Nichols, S. Waugh, B. Argrow, and C. L. Ziegler, 2016: Intercomparison of unmanned aircraftborne and mobile mesonet atmospheric sensors. J. Atmos. Oceanic Technol., 33, 15691582, https://doi.org/10.1175/JTECH-D-15-0178.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huerta, M. P., 2012: Safety risk management policy. Federal Aviation Administration Tech. Rep., 33 pp., https://www.faa.gov/documentLibrary/media/Order/FAA_Order_8040.4B.pdf.

  • Hunt, E. R., W. D. Hively, S. J. Fujikawa, D. S. Linden, C. S. T. Daughtry, and G. W. McCarty, 2010: Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring. Remote Sens., 2, 290305, https://doi.org/10.3390/rs2010290.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, S. H., 2019: Third-party risk of mid-air collision between small unmanned aircraft systems. AIAA Aviation Forum, Dallas, TX, American Institute of Aeronautics and Astronautics, AIAA 2019-3052, https://doi.org/10.2514/6.2019-3052.

    • Crossref
    • Export Citation
  • Koch, S. E., M. Fengler, P. B. Chilson, K. L. Elmore, B. Argrow, D. L. Andra, 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krishnakumar, K., P. Kopardekar, C. Ippolito, J. E. Melton, V. Stepanyan, S. Sankararaman, and B. Nikaido, 2017: Safe autonomous flight environment (SAFE50) for the notional last “50 ft” of operation of “55 lb” class of UAS. AIAA Information Systems–AIAA Infotech @ Aerospace, Grapevine, TX, American Institute of Aeronautics and Astronautics, AIAA 2017-0445, https://doi.org/10.2514/6.2017-0445.

    • Crossref
    • Export Citation
  • La Cour-Harbo, A., 2019: Quantifying risk of ground impact fatalities for small unmanned aircraft. J. Intell. Rob. Syst., 93, 367384, https://doi.org/10.1007/s10846-018-0853-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, G.-J., D. Muñoz-Esparza, C. Yi, and H. J. Choe, 2019: Application of the cell perturbation method to large-eddy simulations of a real urban area. J. Appl. Meteor. Climatol., 58, 11251139, https://doi.org/10.1175/JAMC-D-18-0185.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, X.-X., D. Y. C. Leung, C.-H. Liu, and K. M. Lam, 2008: Physical modeling of flow field inside urban street canyons. J. Appl. Meteor. Climatol., 47, 20582067, https://doi.org/10.1175/2007JAMC1815.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lukacs, M., and D. Bhadra, 2017: FAA Aerospace Forecast: Fiscal years 2018-2038. Federal Aviation Administration Tech. Rep., 101 pp., https://www.faa.gov/data_research/aviation/aerospace_forecasts/media/FY2018-38_FAA_Aerospace_Forecast.pdf.

  • Lundby, T., M. P. Christiansen, and K. Jensen, 2019: Towards a weather analysis software framework to improve UAS operational safety. Int. Conf. on Unmanned Aircraft Systems, Atlanta, GA, Institute of Electrical and Electronics Engineers, 1372–1380, https://doi.org/10.1109/icuas.2019.8798271.

    • Crossref
    • Export Citation
  • Luxhøj, J. T., 2013: Predictive analytics for modeling UAS safety risk. SAE Int. J. Aerosp., 6, 128138, https://doi.org/10.4271/2013-01-2104.

  • Luxhøj, J. T., 2015: A socio-technical model for analyzing safety risk of unmanned aircraft systems (UAS): An application to precision agriculture. Procedia Manuf., 3, 928935, https://doi.org/10.1016/j.promfg.2015.07.140.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luxhøj, J. T., and A. Öztekin, 2009: A regulatory-based approach to safety analysis of unmanned aircraft systems. Engineering Psychology and Cognitive Ergonomics, D. Harris, Ed., Vol. 5639, Springer, 564–573, https://doi.org/10.1007/978-3-642-02728-4-60.

    • Crossref
    • Export Citation
  • Magister, T., 2010: The small unmanned aircraft blunt criterion based injury potential estimation. Saf. Sci., 48, 13131320, https://doi.org/10.1016/j.ssci.2010.04.012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahrt, L., 2014: Stably stratified atmospheric boundary layers. Annu. Rev. Fluid Mech., 46, 2345, https://doi.org/10.1146/annurev-fluid-010313-141354.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martin, G., 2019: Press release—U.S. Transportation Secretary Elaine L. Chao announces FAA certification of commercial package delivery. Federal Aviation Administration, https://www.faa.gov/news/press_releases/news_story.cfm?newsId=23554.

  • Mathew, N., S. L. Smith, and S. L. Waslander, 2015: Planning paths for package delivery in heterogeneous multirobot teams. IEEE Trans. Autom. Sci. Eng., 12, 12981308, https://doi.org/10.1109/TASE.2015.2461213.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murray, C. C., and A. G. Chu, 2015: The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery. Transp. Res., 54, 86109, https://doi.org/10.1016/j.trc.2015.03.005.

    • Search Google Scholar
    • Export Citation
  • National Academies of Sciences, Engineering, and Medicine, 2018: Assessing the Risks of Integrating Unmanned Aircraft Systems (UAS) into the National Airspace System. National Academies Press, 78 pp., https://www.nap.edu/catalog/25143/assessing-the-risks-of-integrating-unmanned-aircraft-systems-uas-into-the-national-airspace-system.

  • National Centers for Environmental Information, 2018: State of the climate: Synoptic discussion for June 2018. National Oceanic and Atmospheric Administration, https://www.ncdc.noaa.gov/sotc/synoptic/201806.

  • Nolan, P. J., and et al. , 2018: Coordinated unmanned aircraft system (UAS) and ground-based weather measurements to predict Lagrangian coherent structures (LCSs). Sensors, 18, 4448, https://doi.org/10.3390/s18124448.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Öztekin, A., and J. Luxhøj, 2008: Hazard, safety risk and uncertainty modeling of the integration of unmanned aircraft systems into the National Airspace. 26th Int. Congress of the Aeronautical Sciences, Anchorage, AK, International Council of the Aeronautical Sciences, 347–356, http://icas.org/icas-archive/icas2008/papers/062.pdf.

  • Primatesta, S., G. Guglieri, and A. Rizzo, 2019: A risk-aware path planning strategy for UAVs in urban environments. J. Intell. Rob. Syst., 95, 629643, https://doi.org/10.1007/s10846-018-0924-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Primatesta, S., A. Rizzo, and A. La Cour-Harbo, 2020: Ground risk map for unmanned aircraft in urban environments. J. Intell. Rob. Syst., 97, 489509, https://doi.org/10.1007/S10846-019-01015-Z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ranquist, E., M. Steiner, and B. Argrow, 2017: Exploring the range of weather impacts on UAS operations. 18th Conf. on Aviation, Range, and Aerospace Meteorology, Seattle, WA, Amer. Meteor. Soc., J3.1, https://ams.confex.com/ams/97Annual/webprogram/Paper309274.html.

  • Roseman, C. A., B. M. Argrow, and J. O. Pinto, 2019: Targeted weather forecasts for small unmanned aircraft systems. 19th Conf. on Aviation, Range, and Aerospace Meteorology, Phoenix, AZ, Amer. Meteor. Soc., 1.4, https://ams.confex.com/ams/2019Annual/webprogram/Paper351492.html.

  • Schultz, P., and M. K. Politovich, 1992: Toward the improvement of aircraft-icing forecasts for the continental United States. Wea. Forecasting, 7, 491500, https://doi.org/10.1175/1520-0434(1992)007<0491:TTIOAI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sheng, R., and et al. , 2018: Does hot weather affect work-related injury? A case-crossover study in Guangzhou, China. Int. J. Hyg. Environ. Health, 221, 423428, https://doi.org/10.1016/j.ijheh.2018.01.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shim, J., R. Kostecki, T. Richardson, X. Song, and K. A. Striebel, 2002: Electrochemical analysis for cycle performance and capacity fading of a lithium-ion battery cycled at elevated temperature. J. Power Sources, 112, 222230, https://doi.org/10.1016/S0378-7753(02)00363-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and et al. , 2019: A description of the Advanced Research WRF Model version 4. NCAR Tech. Rep. NCAR/TN-556+STR, 162 pp., https://doi.org/10.5065/1dfh-6p97.

    • Crossref
    • Export Citation
  • Sturdivan, L. M., D. C. Viano, and H. R. Champion, 2004: Analysis of injury criteria to assess chest and abdominal injury risks in blunt and ballistic impacts. J. Trauma, 56, 651663, https://doi.org/10.1097/01.TA.0000074108.36517.D4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thiels, C. A., J. M. Aho, S. P. Zietlow, and D. H. Jenkins, 2015: Use of unmanned aerial vehicles for medical product transport. Air Med. J., 34, 104108, https://doi.org/10.1016/j.amj.2014.10.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tuna, G., B. Nefzi, and G. Conte, 2014: Unmanned aerial vehicle-aided communications system for disaster recovery. J. Network Comput. Appl., 41, 2736, https://doi.org/10.1016/j.jnca.2013.10.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wackwitz, K., and H. Boedecker, 2015: Safety risk assessment for UAV operation. Drone Industry Insights Tech. Rep., 16 pp., https://miningquiz.com/pdf/Drone_Safety/Safety-Risk-Assessment-for-UAV-Operation-Rev.-1.1.compressed.pdf.

  • Wang, P., C. Huang, E. C. Brown de Colstoun, J. C. Tilton, and B. Tan, 2017: Documentation for the Global Human Built-Up and Settlement Extent (HBASE) dataset from Landsat. NASA Socioeconomic Data and Applications Center, accessed 14 August 2019, https://doi.org/10.7927/H4DN434S.

    • Crossref
    • Export Citation
  • Washington, A., R. A. Clothier, and J. Silva, 2017a: A review of unmanned aircraft system ground risk models. Prog. Aerosp. Sci., 95, 2444, https://doi.org/10.1016/j.paerosci.2017.10.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Washington, A., R. A. Clothier, and B. P. Williams, 2017b: A Bayesian approach to system safety assessment and compliance assessment for unmanned aircraft systems. J. Air Transp. Manage., 62, 1833, https://doi.org/10.1016/j.jairtraman.2017.02.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Washington, A., R. A. Clothier, N. Neogi, J. Silva, K. Hayhurst, and B. Williams, 2019: Adoption of a Bayesian belief network for the system safety assessment of remotely piloted aircraft systems. Saf. Sci., 118, 654673, https://doi.org/10.1016/j.ssci.2019.04.040.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, C., J. M. Kovacs, C. Zhang, and J. M. Kovacs, 2012: The application of small unmanned aerial systems for precision agriculture: A review. Precis. Agric., 13, 693712, https://doi.org/10.1007/s11119-012-9274-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Weather Hazard Risk Quantification for sUAS Safety Risk Management

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Abstract

As the number of applications for small unmanned (i.e., remotely operated) aircraft systems (sUAS) continues to grow, comprehensive safety risk assessment studies are required to ensure their safe integration into the National Airspace System. One source of hazards for sUAS that has not been extensively addressed is adverse weather. A framework is presented for analyzing weather forecast data to provide sUAS operators with risk assessment information that they can use for making risk-aware decisions. The sUAS Weather Risk Model (sWRM) framework quantifies weather hazard risk for sUAS operations in rural to urban environments using weather forecast, population density, structure density, and sUAS data. sWRM is developed by following the safety risk management guidelines from the U.S. Federal Aviation Administration. Development of sWRM highlights a number of aerospace and meteorological research areas that must be addressed prior to weather risk models for sUAS becoming operational. Primary among these research areas is developing widely available finescale (<1 km) weather forecasts and conducting extensive sUAS flight-report studies to accurately estimate parameters of Bayesian belief network conditional probability tables used in the proposed framework. As a proof of concept, sWRM was applied over Boulder, Colorado, using the High-Resolution Rapid Refresh weather product. This initial demonstration of sWRM highlights the potential effectiveness of a detailed risk assessment model that takes into account high-resolution weather and environmental data.

Corresponding author: Christopher A. Roseman, christopher.roseman@colorado.edu

Abstract

As the number of applications for small unmanned (i.e., remotely operated) aircraft systems (sUAS) continues to grow, comprehensive safety risk assessment studies are required to ensure their safe integration into the National Airspace System. One source of hazards for sUAS that has not been extensively addressed is adverse weather. A framework is presented for analyzing weather forecast data to provide sUAS operators with risk assessment information that they can use for making risk-aware decisions. The sUAS Weather Risk Model (sWRM) framework quantifies weather hazard risk for sUAS operations in rural to urban environments using weather forecast, population density, structure density, and sUAS data. sWRM is developed by following the safety risk management guidelines from the U.S. Federal Aviation Administration. Development of sWRM highlights a number of aerospace and meteorological research areas that must be addressed prior to weather risk models for sUAS becoming operational. Primary among these research areas is developing widely available finescale (<1 km) weather forecasts and conducting extensive sUAS flight-report studies to accurately estimate parameters of Bayesian belief network conditional probability tables used in the proposed framework. As a proof of concept, sWRM was applied over Boulder, Colorado, using the High-Resolution Rapid Refresh weather product. This initial demonstration of sWRM highlights the potential effectiveness of a detailed risk assessment model that takes into account high-resolution weather and environmental data.

Corresponding author: Christopher A. Roseman, christopher.roseman@colorado.edu
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