This study explores the impact of assimilating diverse observational data on forecasting extreme rainfall events (EREs) using a three dimensional variational (3D-Var) assimilation approach. It focuses on 38 EREs across three meteorological divisions in Karnataka, India, using a high-resolution (03-km) Weather Research and Forecasting (WRF) model with three nested domains. Five distinct experiments were conducted, including a Control experiment without assimilation, and subsequent experiments integrating observations from various sources like atmospheric profiles from Atmospheric InfraRed Sounder (AIRS) and Moderate resolution Imaging Spectroradiometer (MODIS) satellites and radiosondes, ocean surface wind observations from Advanced Scatterometer (ASCAT), Special Sensor Microwave Imager (SSMI), and WindSAT satellites and buoys, ground observations from Karnataka State Natural Disaster Monitoring Centre (KSNDMC), as well as a combined assimilation experiment with all available observations. The accuracy of rainfall forecasts is evaluated by comparing model outputs with high-resolution telemetric rain-gauge (TRG; 6480 stations) data and other meteorological parameters against telemetric weather station (TWS; 860 stations) data from KSNDMC. Assimilation experiments show positive improvements over control experiment in predicting rainfall. Results consistently indicate underprediction of rainfall in the intricate topographical region of the Western Ghats (WG) across all experiments, contrasting with overprediction along the coastal areas of Karnataka. The experiment involving Ocean Winds showcased a substantial 40 % reduction in rainfall overprediction (above 2 mm threshold). Both Ocean Winds and Station Data assimilation notably enhanced rainfall prediction accuracy over most of the regions in Karnataka, with Ocean Winds exhibiting the highest improvement (53 %), closely followed by Station Data (50 %). Importantly, assimilating Ocean Winds and Station Data aided in reducing overprediction, while assimilating Satellite Profiles reduced underprediction in the interior part of Karnataka but increased overprediction over the coastal region compared to the control experiment. Frequency of occurrence of rainfall is considerably enhanced along the coastline in all 3D-Var experiments. Bias score indicates maximum improvement in assimilation using Ocean Winds and Station Data. Simulation of basic meteorological parameters also improved with assimilation particularly during the day hours. The results underscore the crucial role of assimilation of satellite and in-situ observations in improving forecast accuracy of EREs during the monsoon season.

中文翻译：

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使用 3D-Var 同化多源观测对印度卡纳塔克邦极端降雨事件模拟的相对影响


本研究探讨了使用三维变分 （3D-Var） 同化方法同化不同观测数据对预测极端降雨事件 （EREs） 的影响。它使用具有三个嵌套域的高分辨率（03 公里）天气研究和预报 （WRF） 模型，重点关注印度卡纳塔克邦三个气象部门的 38 个 ERE。进行了五次不同的实验，包括一项无同化的控制实验，以及随后的实验，整合了来自各种来源的观测结果，如大气红外测深仪 （AIRS） 和中分辨率成像光谱仪 （MODIS） 卫星和无线电探空仪的大气剖面、高级散射计 （ASCAT）、特殊传感器微波成像仪 （SSMI） 和 WindSAT 卫星和浮标的海洋表面风观测，卡纳塔克邦的地面观测国家自然灾害监测中心 （KSNDMC），以及与所有可用观测结果的联合同化实验。通过将模型输出与高分辨率遥测雨量计（TRG;6480 个站点）数据和其他气象参数与 KSNDMC 的遥测气象站（TWS;860 个站点）数据进行比较来评估降雨预报的准确性。同化实验表明，在预测降雨量方面，比对照实验有积极的改善。结果一致表明，在所有实验中，西高止山脉 （WG） 错综复杂的地形区域的降雨量预测过低，这与卡纳塔克邦沿海地区的降雨预测过高形成鲜明对比。涉及 Ocean Winds 的实验表明，降雨量预测超值（阈值超过 2 毫米）大幅减少了 40%。 Ocean Winds 和 Station Data 同化都显着提高了卡纳塔克邦大部分地区的降雨预测准确性，其中 Ocean Winds 的改进最高 （53%），紧随其后的是 Station Data （50%）。重要的是，同化海洋风和站数据有助于减少高预测，而同化卫星剖面减少了卡纳塔克邦内陆的低预测，但与对照实验相比，增加了沿海地区的高预测。在所有 3D-Var 实验中，沿海岸线的降雨频率大大增加。偏差分数表示使用 Ocean Winds 和 Station Data 的同化效果最大。基本气象参数的模拟也随着同化而改进，尤其是在白天。结果强调了卫星和原位观测的同化在提高季风季节 ERE 预报准确性的关键作用。