Abstract Extreme dust episodes have been noticeably increasing in recent years. Dust burden during such events imparts several threats to the environment and human health. Therefore, forecast of such extreme events becomes of utmost importance to minimise the adverse impact on various socio-economy sectors. In the present study model experiments have been carried out for the simulation of an extreme dust episode that emanated over the western Indian region that coupled with previously transported dust from the West Asia region and resulted in the highly degraded air quality over the Indian region. For this purpose four ensemble members, using Weather Research and Forecasting model fully coupled with chemistry (WRF-CHEM), have been generated for identical model configuration but for the perturbed initial conditions. Model performance has been evaluated with respect to the available ground based measurements from Central Pollution Control Board (CPCB) and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) data sets using standard statistical measures. Ensemble spread is noted amongst the four ensemble members generated based on 4 varying initial conditions, therefore it is suggested to use ensemble mean forecast for the dust storm monitoring and analysis. It is revealed from the analysis based on the categorical validation scheme that the WRF-CHEM simulated dust load is able to capture the locational accuracy with respect to the reanalysis/observational data sets over Indian subcontinent fairly well with 0% false alarm ratio, 78–92% probability of detection and 79–90% accuracy. It is noted that thick blanket of dust load enveloped Northern part of India subsequent to the dust storm which resulted in substantially dropped ground reaching solar radiation (~100–150 W/m2) and a consequential reduction in the surface temperature (2 °C). Further to this enhanced temperature in association with reduced relative humidity from 800 to 600 hPa has also been noted in response to the enhanced dust loading in the model simulations. Results obtained in the present study suggests that WRF-CHEM is able to simulate both the direct and semi-direct effects of dust aerosols reasonably well. Present work has implications for improved prediction skills of WRF-CHEM in simulating extreme dust episodes and investigating the impact of dust aerosols on weather and climate.

中文翻译：

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使用基于最优集合方法的 WRF-CHEM 模拟极端沙尘事件

摘要 近年来，极端沙尘事件显着增加。此类事件期间的灰尘负担会给环境和人类健康带来多种威胁。因此，对此类极端事件的预测对于尽量减少对各个社会经济部门的不利影响至关重要。在本研究中，已经进行了模型实验，以模拟在印度西部地区散发的极端沙尘事件，再加上先前从西亚地区输送的灰尘，导致印度地区空气质量严重下降。为此，使用与化学完全耦合的天气研究和预测模型 (WRF-CHEM) 为相同的模型配置生成了四个集合成员，但初始条件受到扰动。模型性能已根据来自中央污染控制委员会 (CPCB) 的可用地面测量和使用标准统计测量的现代研究与应用回顾性分析第 2 版 (MERRA2) 数据集进行了评估。在基于 4 个不同初始条件生成的四个集合成员之间注意到集合传播，因此建议使用集合平均预报进行沙尘暴监测和分析。从基于分类验证方案的分析中可以看出，WRF-CHEM 模拟粉尘负荷能够很好地捕获印度次大陆再分析/观测数据集的定位精度，误报率为 0%，78– 92% 的检测概率和 79-90% 的准确度。值得注意的是，沙尘暴过后，印度北部被厚厚的沙尘覆盖，导致地面大幅下降，达到太阳辐射（~100-150 W/m2），并因此导致地表温度降低（2°C） . 此外，随着模型模拟中灰尘负载的增加，温度升高与相对湿度从 800 到 600 hPa 降低相关。本研究中获得的结果表明，WRF-CHEM 能够相当好地模拟粉尘气溶胶的直接和半直接影响。目前的工作对改进 WRF-CHEM 在模拟极端沙尘事件和调查沙尘气溶胶对天气和气候的影响方面的预测技能有影响。