대기 (Atmosphere)

  • 제33권5호
  • /
  • Pages.441-456
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  • 2023
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  • 1598-3560(pISSN)
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  • 2288-3266(eISSN)
한국기상학회 (Korean Meteorological Society)
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기상드론 바람관측자료의 정확도 확보를 통한 대기하층 시공간 관측공백 해소 연구

A Study on Filling the Spatio-temporal Observation Gaps in the Lower Atmosphere by Guaranteeing the Accuracy of Wind Observation Data from a Meteorological Drone

  • 이승협 (국립기상과학원 예보연구부) ;
  • 박미은 (국립기상과학원 예보연구부) ;
  • 전혜림 (국립기상과학원 예보연구부) ;
  • 박미르 (국립기상과학원 예보연구부)
  • Seung-Hyeop Lee (Forecast Research Department, National Institute of Meteorological Sciences) ;
  • Mi Eun Park (Forecast Research Department, National Institute of Meteorological Sciences) ;
  • Hye-Rim Jeon (Forecast Research Department, National Institute of Meteorological Sciences) ;
  • Mir Park (Forecast Research Department, National Institute of Meteorological Sciences)
  • 투고 : 2023.07.04
  • 심사 : 2023.10.18
  • 발행 : 2023.11.30
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초록

The mobile observation method, in which a meteorological drone observes while ascending, can observe the vertical profile of wind at 1 m-interval. In addition, since continuous flights are possible at time intervals of less than 30 minutes, high-resolution observation data can be obtained both spatially and temporally. In this study, we verify the accuracy of mobile observation data from meteorological drone (drone) and fill the spatio-temporal observation gaps in the lower atmosphere. To verify the accuracy of mobile observation data observed by drone, it was compared with rawinsonde observation data. The correlation coefficients between two equipment for a wind speed and direction were 0.89 and 0.91, and the root mean square errors were 0.7 m s-1 and 20.93°. Therefore, it was judged that the drone was suitable for observing vertical profile of the wind using mobile observation method. In addition, we attempted to resolve the observation gaps in the lower atmosphere. First, the vertical observation gaps of the wind profiler between the ground and the 150 m altitude could be resolved by wind observation data using the drone. Secondly, the temporal observation gaps between 3-hour interval in the rawinsonde was resolved through a drone observation case conducted in Taean-gun, Chungcheongnam-do on October 13, 2022. In this case, the drone mobile observation data every 30-minute intervals could observe the low-level jet more detail than the rawinsonde observation data. These results show that the mobile observation data of the drone can be used to fill the spatio-temporal observation gaps in the lower atmosphere.

키워드

과제정보

이 연구는 기상청 국립기상과학원 「기상업무지원 기술개발연구」 "관측기술 지원 및 활용연구(KMA2018-00123)"의 지원으로 수행되었습니다.

참고문헌

  1. Adkins, K. A., C. J. Swinford, P. D. Wambolt, and G. Bease, 2020: Development of a sensor suite for atmospheric boundary layer measurement with a small multirotor unmanned aerial system. Int. J. Aviation Aeronautics. Aerospace, 7, 1-14, doi:10.15394/IJAAA. 2020.1433.
  2. Bell, T. M., B. R. Greene, P. M. Klein, M. Carney, and P. B. Chilson, 2020: Confronting the boundary layer data gap: evaluating new and existing methodologies of probing the lower atmosphere. Atmos. Measurement Techniques, 13, 3855-3872, doi:10.5194/amt13-3855-2020.
  3. Cho, Y.-J., T.-Y. Kwon, and B.-C. Choi, 2015: Characteristics of meteorological variables in the leeward side associated with the downslope windstorm over the yeongdong region. J. Korean Earth Sci. Soc., 36, 315-329, doi:10.5194/amt-13-3855-2020.
  4. Chong, J. H., S. H. Lee, S. S. Shin, S. E. Hwang, Y.-T. Lee, J. Y. Kim, and S. B. Kim, 2019: Research on the meteorological technology development using drones in the fourth industrial revolution. J. Korea Contents Assoc., 19, 12-21, doi:10.5392/JKCA.2019.19.11.012.
  5. Chong, J. H., S. S. Shin, S. E. Hwang, S. H. Lee, S.-H. Lee, B.-J. Kim, and S. B. Kim, 2020: Vertical measurement and analysis of meteorological factors over boseong region using meteorological drones. J. Korean Earth Sci. Soc., 41, 575-587, doi:10.5467/JKESS.2020.41.6.575.
  6. Greene, B. R., A. R. Segales, T. M. Bell, E. A. Pillar-Little, and P. B. Chilson, 2019: Environmental and sensor integration influences on temperature measurements by rotary-wing unmanned aircraft systems. MDPI Sensors, 19, 1470, doi:10.3390/s19061470.
  7. Hedworth, H., J. Page, J. Sohl, and T. Saad, 2022: Investigating errors observed during UAV-based vertical measurements using computational fluid dynamics. Drones, 6, 253, doi:10.3390/drones6090253.
  8. Hemingway, B. L., A. E. Frazier, B. R. Elbing, and J. D. Jacob, 2017: Vertical sampling scales for atmospheric boundary layer measurements from small unmanned aircraft systems (sUAS). Atmosphere, 8, 176, doi:10.3390/atmos8090176.
  9. Jo, W.-G., B.-H. Kwon, and H.-J. Yoon, 2019: Clutter fence effect on data quality of ultra high frequency radar. J. Korea Institute of electronic Commun. Sci., 14, 275-282, doi:10.13067/JKIECS.2019.14.2.275.
  10. Jo, W.-G., 2020: Quality Control of Wind Profiler Doppler Spectrum Data. Master thesis, Pukyong National University. 110 pp [Available online at https://repository.pknu.ac.kr:8443/bitstream/2021.oak/23751/2/Quaility%20Control%20of%20Wind%20Profiler%20Doppler%20Spectrum%20Data.pdf].
  11. Kim, K.-H., M.-S. Kim, S.-W. Seo, P.-S. Kim, D. H. Kang, and B. H. Kwon, 2015: Quality evaluation of wind vectors from UHF wind profiler using radiosonde measurements. J. Environ. Sci. Int., 24, 133-150, doi:10.5322/JESI.2015.24.1.133.
  12. Kim, Y.-I., S. K. Ku, and C. H. Park, 2018: Flow analysis and flight experiment for optimum height of weather data sensor. J. Advanced Navigation Technol., 22, 551-556, doi:10.12673/jant.2018.22.6.551.
  13. Kwak, K.-H., J.-J. Baik, S.-H. Lee, and Y.-H. Ryu, 2011: Computational fluid dynamics modelling of the diurnal variation of flow in a street canyon. Bound.-Layer Meteor., 141, 77-92, doi:10.1007/s10546-011-9630-4.
  14. Laroche, S., and R. Sarrazin, 2013: Impact of radiosonde balloon drift on numerical weather prediction and verification. Wea. Forecasting, 28, 772-782, doi:10.1175/WAF-D-12-00114.1.
  15. Neumann, P. P., and M. Bartholmai, 2015: Real-time wind estimation on a micro unmanned aerial vehicle using its inertial measurement unit. Sensors and Actuators A: Physical, 235, 300-310, doi:10.1016/j.sna.2015.09.036.
  16. Ng, E., C. Yuan, L. Chen, C. Ren, and J. C. H. Fung, 2011: Improving the wind environment in high-density cities by understanding urban morphology and surface roughness: A study in Hong Kong. Landscape and Urban Planning, 101, 59-74, doi:10.1016/j.landurbplan.2011.01.004.
  17. Oude Nijhuis, A. C. P., and Coauthors, 2018: Wind hazard and turbulence monitoring at airports with lidar, radar, and mode-S downlinks: The UFO project. Bull. Amer. Meteor. Soc., 99, 2275-2293, doi:10.1175/BAMS-D-15-00295.1.
  18. Palomaki, R. T., N. T. Rose, M. V. D. Bossche, T. J. Sherman, and S. F. J. De Wekker, 2017: Wind estimation in the lower atmosphere using multirotor aircraft. J. Atmos. Oceanic Technol., 34, 1183-1191, doi:10.1175/JTECH-D-16-0177.1.
  19. Samad, A., D. A. Florez, I. Chourdakis, and U. Vogt, 2022: Concept of using an unmanned aerial vehicle (UAV) for 3D investigation of air quality in the atmosphere-example of measurements near a roadside. MDPI Atmosphere, 13, 663, doi:10.3390/atmos13050663.
  20. Schiano, F., J. Alonso-Mora, K. Rudin, P. Beardsley, R. Siegwart, and B. Sicilianok, 2014: Towards estimation and correction of wind effects on a quadrotor UAV. ETH Zurich. doi:10.3929/ETHZ-A-010286793.
  21. Segales, A. R., B. R. Greene, T. M. Bell, W. Doyle, J. J. Martin, E. A. Pillar-Little, and P. B. Chilson, 2020: The CopterSonde: an insight into the development of a smart unmanned aircraft system for atmospheric boundary layer research. Atmos. Measurement Techniques, 13, 2833-2848, doi:10.5194/amt-13-2833-2020.
  22. Shimura, T., M. Inoue, H. Tsujimoto, K. Sasaki, and M. Iguchi, 2018: Estimation of wind vector profile using a hexarotor unmanned aerial vehicle and its application to meteorological observation up to 1000 m above surface. J. Atmos. Oceanic Technol., 35, 1621-1631, doi:10.1175/JTECH-D-17-0186.1.
  23. Varentsov, M., V. Stepanenko, I. Repina, A. Artamonov, V. Bogomolov, N. Kuksova, E. Marchuk, A. Pashkin, A., and Varentsov, 2021: Balloons and quadcopters: intercomparison of two low-cost wind profiling methods. Atmosphere, 12, 380, doi:10.3390/ATMOS12030380.
  24. Watkins, S., M. Thompson, B. Loxton, and M. Abdulrahim, 2010: On low altitude flight through the atmospheric boundary layer. Int. J. Micro Air Vehicles, 2, 55-68, doi:10.1260/1756-8293.2.2.55.
  25. Weide Luiz, E., and S. Fiedler, 2022: Spatiotemporal observations of nocturnal low-level jets and impacts on wind power production. Wind Energy Science, 7, 1575-1591, doi:10.5194/wes-2022-26.
  26. Wetz, T., N. Wildmann, and F. Beyrich, 2021: Distributed wind measurements with multiple quadrotor unmanned aerial vehicles in the atmospheric boundary layer. Atmos. Meas. Tech., 14, 3795-3814, doi:10.5194/amt14-3795-2021.
  27. WMO, 2015: Manual on the Global Observing System. World Meteorological Organizaion, 25 pp.
  28. Ye, X., B. Wu, and H. Zhang, 2015: The turbulent structure and transport in fog layers observed over the Tianjin area. Atmos. Res., 153, 217-234, doi:10.1016/j.atmosres.2014.08.003.
  29. Ziemann, A., A. Galvez Arboleda, and A. Lampert, 2020: Comparison of wind lidar data and numerical simulations of the low-level jet at a grassland site. MDPI Energies, 13, 6264, doi:10.3390/en13236264.