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<p>For the Research Demonstration Project Paris 2024 Olympics supported by the World Meteorological Organization, two types of experiments: 1) ensemble and 2) sensitivity simulations with and without urban effect, were conducted to assess their prediction skills and understand the mechanisms of a thunderstorm over Paris on 7 May 2022.</p><p>As the first experiment, the ensemble simulations involved data assimilation using a 50-member Local Ensemble Transform Kalman Filter with the Japan Meteorological Agency Non-Hydrostatic Model (JMANHM), followed by 500-m resolution simulations downscaled from a 2-km ensemble. As a result, some predictive skill was observed, linked more to surface temperature and humidity than the lower troposphere.</p><p>As the second, to explore urban impacts on thunderstorm prediction, 500-m JMANHM simulations with an urban canopy model were performed. Comparing a simulation treating Paris as urban (URB) with one replacing it with cropland (NOURB) showed that in URB, precipitation initiated in southern Paris, aligning with radar data, while NOURB did not. This difference is attributed to the urban heat-island effect on surface temperatures near Paris, causing low pressure in URB.</p><p>Findings from these experiments underscore the significance of considering surface conditions in models and the potential role of urbanization in thunderstorm events.</p>

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Simulating a Thunderstorm Using a High-Resolution Ensemble and an Urban Canopy Model: A Test Case for RDP Paris

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月1日

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  • Takuya Kawabata,
  • Akane Saya,
  • Akifumi Nishi,
  • Naoko Seino,
  • Syugo Hayashi,
  • Junshi Ito,
  • Yasutaka Ikuta,
  • Hiromu Seko

摘要

For the Research Demonstration Project Paris 2024 Olympics supported by the World Meteorological Organization, two types of experiments: 1) ensemble and 2) sensitivity simulations with and without urban effect, were conducted to assess their prediction skills and understand the mechanisms of a thunderstorm over Paris on 7 May 2022.

As the first experiment, the ensemble simulations involved data assimilation using a 50-member Local Ensemble Transform Kalman Filter with the Japan Meteorological Agency Non-Hydrostatic Model (JMANHM), followed by 500-m resolution simulations downscaled from a 2-km ensemble. As a result, some predictive skill was observed, linked more to surface temperature and humidity than the lower troposphere.

As the second, to explore urban impacts on thunderstorm prediction, 500-m JMANHM simulations with an urban canopy model were performed. Comparing a simulation treating Paris as urban (URB) with one replacing it with cropland (NOURB) showed that in URB, precipitation initiated in southern Paris, aligning with radar data, while NOURB did not. This difference is attributed to the urban heat-island effect on surface temperatures near Paris, causing low pressure in URB.

Findings from these experiments underscore the significance of considering surface conditions in models and the potential role of urbanization in thunderstorm events.