了解和预测2022年11月24日创纪录的吉达极端降雨事件

IF 2.5 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Hari Prasad Dasari, Karumuri Ashok, Md Saquib Saharwardi, Thang M. Luong, Sateesh Masabathini, Koteswararao Vankayalapati, Harikishan Gandham, Rakesh Thiruridathil, Arjan Zamreeq, Ayman Ghulam, Yasser Abulnaja, Ibrahim Hoteit
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引用次数: 0

摘要

吉达是沙特阿拉伯王国的第二大城市,在2022年11月24日经历了前所未有的220毫米降雨。这场极端的降雨是11月气候月平均降雨量的四倍,造成了严重的洪水和对基础设施的严重破坏。本研究利用现场观测和卫星观测以及数值模拟研究了导致这一极端事件的潜在物理机制及其可预测性。我们的分析表明,该事件最初是通过来自中纬度的冷空气和来自东南部的暖空气的相互作用,在红海西北部地区发展成一个锋面系统。它于11月24日世界时0600到达吉达,并伴有强烈的地面辐合,这是吉达冬季降雨的典型特征。在阿拉伯反气旋东南运动的推动下,地中海和南红海持续的湿气侵入进一步推动了这一系统。我们评估了天气研究与预报(WRF)模型在不同提前期预测这一极端事件的预测能力,利用1公里的云分辨配置。由国家环境预测中心运营的全球预报驱动的世界气象基金会模式,成功地提前5天再现了极端降雨事件。即使提前5天,该模式也捕捉到了风暴从西北到东南的运动和降雨的定性空间分布,与卫星观测和雷达反射率一致。此外,预测的总可降水量分布与气象卫星的亮度温度密切相关。这表明,WRF模型的高预测技能是由于其高分辨率配置、仔细选择领域和物理参数化。通过解决物理机制和模型的性能,这项工作为极端降雨预报提供了有价值的见解,并强调了减轻吉达地区极端事件影响的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Understanding and Predicting the November 24, 2022, Record-Breaking Jeddah Extreme Rainfall Event

Understanding and Predicting the November 24, 2022, Record-Breaking Jeddah Extreme Rainfall Event

Understanding and Predicting the November 24, 2022, Record-Breaking Jeddah Extreme Rainfall Event

Understanding and Predicting the November 24, 2022, Record-Breaking Jeddah Extreme Rainfall Event

Jeddah, the second-largest city in the Kingdom of Saudi Arabia, experienced an unprecedented 220 mm of rainfall on November 24, 2022. This extreme rainfall, which was four times the climatological monthly mean rainfall for November, resulted in severe flooding and significant damage to infrastructure. This study investigates the underlying physical mechanisms contributing to this extreme event and its predictability using in situ and satellite observations and numerical modeling. Our analysis reveals the event initially developed as a frontal system over the northwest regions of the Red Sea through interactions between cold air from mid-latitudes and warm air from the southeast. It reached Jeddah at 0600 UTC, November 24, accompanied by strong surface convergence, which is typical of winter rainfall in Jeddah. The system was further fueled by persistent moisture intrusion from the Mediterranean and the southern Red Sea, driven by the southeast movement of the Arabian Anticyclone. We evaluated the predictive capability of the Weather Research and Forecasting (WRF) model to forecast this extreme event at different lead times, utilizing a cloud-resolving 1-km configuration. The WRF model, driven by the National Centers for Environmental Prediction operational Global Forecasts, successfully reproduced the extreme rainfall event up to 5 days in advance. Even at a 5-day lead time, the model captured the storm's movement from northwest to southeast and the qualitative spatial distribution of rainfall, consistent with satellite observations and radar reflectivity. Additionally, the predicted distribution of total precipitable water vapor aligned closely with Meteosat brightness temperatures. This demonstrates that the high predictive skill of the WRF model is due to its high-resolution configuration, careful selection of the domain, and physical parameterizations. By addressing both the physical mechanisms and the model's performance, this work provides valuable insights into extreme rainfall forecasting and highlights the potential for mitigating the impacts of such extreme events in the Jeddah region.

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来源期刊
Meteorological Applications
Meteorological Applications 地学-气象与大气科学
CiteScore
5.70
自引率
3.70%
发文量
62
审稿时长
>12 weeks
期刊介绍: The aim of Meteorological Applications is to serve the needs of applied meteorologists, forecasters and users of meteorological services by publishing papers on all aspects of meteorological science, including: applications of meteorological, climatological, analytical and forecasting data, and their socio-economic benefits; forecasting, warning and service delivery techniques and methods; weather hazards, their analysis and prediction; performance, verification and value of numerical models and forecasting services; practical applications of ocean and climate models; education and training.
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