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Korean Meteorological Society (한국기상학회)
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Impact of Assimilation of New York State Mesonet Doppler Wind Lidar on High Impact Weather Predictions in New York State

뉴욕 주의 악기상 현상에 대한 뉴욕 주 Mesonet 도플러 윈드 라이다의 자료동화 효과

  • Junkyung Kay (National Center for Atmospheric Research) ;
  • Tammy M. Weckwerth (National Center for Atmospheric Research)
  • 계준경 (미국 국립 대기연구센터) ;
  • Received : 2024.11.02
  • Accepted : 2024.11.06
  • Published : 2024.11.30
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Abstract

The New York State (NYS) Mesonet consists of 126 surface weather stations across the state with 17 of the sites also instrumented with active and passive profiler systems. The NYS Mesonet (NYSM) is the first and only state-run network in the USA, that includes a combination of surface stations, Doppler wind lidars (DWL) and thermodynamic profiles from Microwave Radiometers (MWR). NYSM's continuous and extensive observations from the surface to the lower atmosphere have a wide range of applications in air quality and human health, forecasting of severe storms, and predicting renewable energy production. This study provides results of assimilating the NYSM surface station data and the DWL wind profiles. The impact of NYSM observations on predictive skill is evaluated for one tornadic supercell case that has large uncertainties in analysis with respect to low-level temperature, moisture, and wind variability. Compared to forecasts assimilating solely conventional observations except NYSM, the additional assimilation of NYSM observations effectively corrects the cold and dry biases in central New York State, resulting in a more accurate representation of surface conditions. Notably, the assimilation of NYSM DWL wind profiles improves the prediction of the location and intensity of convective systems, thereby creating an environment that increases the likelihood of supercell and tornado formation.

Keywords

Acknowledgement

본 논문의 개선을 위해 좋은 의견을 제시해 주신 두 분의 심사위원께 감사 드립니다. 본 연구 자료는 협력 계약 번호 1852977에 따라 미국 National Science Foundation (NSF)의 지원을 받는 주요 시설인 National Center for Atmospheric Research (NCAR)의 지원을 받아 수행된 연구에 기반합니다. 또한 NSF가 후원하는 NCAR의 Computational and Information Systems Laboratory (CISL)에서 제공하는 Cheyenne (doi:10.5065/D6RX99HX)의 고성능 컴퓨팅 지원에 감사드립니다. 본 연구는 미국 국립해양대기청의 Weather Program Office의 지원사업 (NOAA Award No. NA23OAR4590399)의 일환으로 수행되었습니다. 본 연구 결과의 분석에 도움을 주신NSF NCAR의 Dr. James O. Pinto와 Dr. Matthew B. Wilson 께 감사드립니다.

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