Abstract

Thunderstorms, associated with lightning and heavy rain, are a weather hazard causing human deaths, urban floods and damage to crops. Current work attempted to study the thunderstorms over Andhra Pradesh, coastal state in southeast India, using multiple satellite datasets, gridded rainfall, Doppler Radar Images and Advanced Research Weather Research and Forecasting (ARW) model simulations during the pre-monsoon seasons of 2017 and 2018. Thermodynamic stability indices computed using INSAT-3D/3DR satellite data were used to identify precursors and lead time of prediction. India Meteorological Department (IMD) daily gridded rainfall data were used to identify the thunderstorm occurrence days, and Doppler Radar Images and INSAT imagery were conjointly used to fix the location. Eight severe thunderstorm cases were analyzed to assess the precursors and the predictability. Further, ARW model predictions for two thunderstorm cases were performed and stability indices computed using model output were compared with satellite-based indices for evaluation. Statistical metrics had shown good agreement of ARW model-based stability indices with satellite-based stability indices. Model had simulated rainfall and cloud properties associated with thunderstorm activity. The results illustrated the predictability of the location and intensity of thunderstorms with 3–4 hrs lead time, which would find usefulness in the real-time prediction of thunderstorms.

Research Highlights

1. 1.

   Occurrence of thunderstorms using satellite and radar imagery.

2. 2.

   Identification of thunderstorm occurrences using daily rainfall data.

3. 3.

   Emphasizing the use of thermodynamic stability indices in the prediction of thunderstorms.

4. 4.

   Numerical model predictability of thunderstorms.

5. 5.

   Predictability of thunderstorms collocating satellite and model experiments.

中文翻译：

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基于统计和动力学的印度东南部雷暴预报

摘要

雷雨与雷电和大雨有关，是一种天气隐患，可导致人类死亡，城市洪灾和农作物受损。当前工作试图使用2017年和2018年季风前季节的多个卫星数据集，栅格化降雨，多普勒雷达图像和高级研究天气研究与预报（ARW）模型模拟​​研究印度东南沿海邦安得拉邦的雷暴。使用INSAT-3D / 3DR卫星数据计算出的热力学稳定性指标用于识别前兆和预测的提前期。印度气象部门（IMD）的每日栅格降雨数据被用于识别雷暴发生的日子，多普勒雷达图像和INSAT图像被联合用于确定位置。分析了八例严重的雷暴案例，以评估前兆和可预测性。此外，针对两个雷暴案例进行了ARW模型预测，并将使用模型输出计算的稳定性指标与基于卫星的指标进行了比较，以进行评估。统计指标表明，基于ARW模型的稳定性指标与基于卫星的稳定性指标具有很好的一致性。该模型模拟了与雷暴活动有关的降雨和云特性。结果表明，在提前3到4个小时的时间内，雷暴的位置和强度是可预测的，这将对实时预测雷暴有用。对两个雷暴案例进行了ARW模型预测，并将使用模型输出计算的稳定性指标与基于卫星的指标进行了比较，以进行评估。统计指标表明，基于ARW模型的稳定性指标与基于卫星的稳定性指标具有很好的一致性。该模型模拟了与雷暴活动有关的降雨和云特性。结果表明，在提前3到4个小时的时间内，雷暴的位置和强度是可预测的，这将对实时预测雷暴有用。对两个雷暴案例进行了ARW模型预测，并将使用模型输出计算的稳定性指标与基于卫星的指标进行了比较，以进行评估。统计指标表明，基于ARW模型的稳定性指标与基于卫星的稳定性指标具有很好的一致性。该模型模拟了与雷暴活动有关的降雨和云特性。结果表明，在提前3到4个小时的时间内，雷暴的位置和强度是可预测的，这将对实时预测雷暴有用。

研究重点

1. 1。

   使用卫星和雷达图像发生雷暴。

2. 2。

   使用每日降雨量数据识别雷暴发生。

3. 3。

   强调在预测雷暴天气中使用热力学稳定性指标。

4. 4，

   雷暴的数值模型可预测性。

5. 5，

   雷暴并置卫星和模型实验的可预测性。