Prediction of Meso-γ-Scale Local Heavy Rain by Ground-Based Cloud Radar Assimilation with Water Vapor Nudging
Prediction of Meso-γ-Scale Local Heavy Rain by Ground-Based Cloud Radar Assimilation with Water Vapor Nudging
复制标题
地基云雷达水汽助推同化预报中γ尺度局地暴雨
DOI：
10.1175/waf-d-22-0017.1
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发表时间：
2022
影响因子：
2.9
通讯作者：
Iwanami Koyuru
中科院分区：
文献类型：
--
作者：
Kato Ryohei;Shimizu Shingo;Ohigashi Tadayasu;Maesaka Takeshi;Shimose Ken-ichi;Iwanami Koyuru
中-γ尺度（2-20 km）局地暴雨（LHR）可通过突然的河水上涨和道路洪水造成人员伤亡。为了帮助防止这种生命损失，我们开发了这些类型的气象灾害的预测方法。我们吸收了地基云雷达（Ka波段雷达）的数据，可以捕捉雨滴形成之前的云滴，并试图预测LHR与云解决数值天气预报（NWP）模式。利用高时程（1分钟间隔）的三维云雷达资料，在一次LHR积雨云的雨前阶段，利用水汽轻推方法进行了同化。虽然没有同化的NWP模式没有预测降雨，但在云雷达数据同化循环结束后约20分钟预测了LHR。结果表明，数值预报结合雨前云雷达资料同化在预报LHR方面具有很大的潜力，并且可以导致早期疏散警报和随后的脆弱人群的疏散。（在几公里内）大雨（LHR）是重要的，因为LHR事件可能会导致死亡，通过河流的突然上涨和洪水的道路，迅速发展（≤30 min）雨云。这项研究的目的是开发一种方法来预测LHR甚至开始下雨之前，这一直是很难的日期。使用一种称为数据同化的技术，它将观测和模拟结合起来，我们开发了一种同化云雷达观测的方法，可以在雨滴形成之前捕获云滴。因此，我们成功地预测LHR开始降雨之前。通过扩展和应用这项研究，在LHR期间尽早疏散弱势人群是可能的。
Meso-γ-scale (2–20 km) local heavy rain (LHR) can cause fatalities through the sudden rise of rivers and flooding of roads. To help prevent this loss of life, we developed prediction methods for these types of meteorological hazards. We assimilated ground-based cloud radar (Ka-band radar) data that can capture cloud droplets before raindrops form and attempted to predict LHR with a cloud resolving numerical weather prediction (NWP) model. High-temporal (1-min interval) three-dimensional cloud radar data obtained through special observation were assimilated using a water vapor nudging method in the pre-rain stage of an LHR-causing cumulonimbus. While rainfall was not predicted by the NWP model without assimilation, LHR was predicted approximately 20 min after the conclusion of cloud radar data assimilation cycling. Results suggest that NWP with cloud radar data assimilation in the pre-rain stage has great potential for predicting LHR, and can lead to an early evacuation warning and subsequent evacuation of vulnerable populations.Significance StatementThe development of prediction methods for local (within several kilometers) heavy rain (LHR) is important because LHR events can cause deaths through the sudden rise of rivers and flooding of roads by rapidly developing (≤30 min) rain clouds. This study aims to develop a method for predicting LHR even before it begins to rain, which has been difficult to date. Using a technique called data assimilation, which integrates observation and simulation, we developed a method for assimilating cloud radar observations that can capture cloud droplets before raindrops form. As a result, we succeeded in predicting LHR before rainfall commenced. By extending and applying this research, early evacuation of vulnerable populations during LHR is possible.