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<p>Thunderstorms are one of the most disastrous weather phenomena that have resulted in numerous casualties in India in recent years. Thermodynamic indices have been extensively used as predictors of thunderstorms. This study aims to understand the long-term climatology and trends of thermodynamic indices across various stations in India and to identify the most suitable indices for thunderstorm prediction. We utilised a dataset of thermodynamic indices from 32 IMD (India Meteorological Department) stations, which was prepared using 00 UTC radiosonde data from 1981 to 2020 (40&#xa0;years). The dataset includes the Showalter index (SHOW), SWEAT index, K index (KI), Lifted index (LI), Total totals index (TTI), Vertical totals index (VTI), Cross totals index (CTI), and CAPE. In addition, thunderstorm occurrence data for these stations from IMD were also utilised. Our findings reveal that the climatology of only a few indices aligns with the pattern of the number of thunderstorm days, and they are station-specific. Long-term trends in annual thunderstorm days indicate that 60% of the stations exhibit a declining trend in thunderstorm days during the study period, with 26% showing a statistically significant trend. The seasonal trends in thermodynamic indices reflect the trends in thunderstorm days. More than 75% of the stations demonstrate a decreasing trend in atmospheric instability in all seasons. We identified suitable indices and their threshold values for thunderstorm prediction using optimal values of Normalised Skill Score, which can be used for operational thunderstorm forecasting.</p>

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Characterisation of thermodynamic indices and their performance in thunderstorm prediction over India using radiosonde observations

  • 期刊论文

  • 发表日期：

    2025年7月3日

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  • Nakul M. Krishna,
  • B. L. Sudeepkumar,
  • S. H. Bhagwat,
  • Beena Jain

摘要

Thunderstorms are one of the most disastrous weather phenomena that have resulted in numerous casualties in India in recent years. Thermodynamic indices have been extensively used as predictors of thunderstorms. This study aims to understand the long-term climatology and trends of thermodynamic indices across various stations in India and to identify the most suitable indices for thunderstorm prediction. We utilised a dataset of thermodynamic indices from 32 IMD (India Meteorological Department) stations, which was prepared using 00 UTC radiosonde data from 1981 to 2020 (40 years). The dataset includes the Showalter index (SHOW), SWEAT index, K index (KI), Lifted index (LI), Total totals index (TTI), Vertical totals index (VTI), Cross totals index (CTI), and CAPE. In addition, thunderstorm occurrence data for these stations from IMD were also utilised. Our findings reveal that the climatology of only a few indices aligns with the pattern of the number of thunderstorm days, and they are station-specific. Long-term trends in annual thunderstorm days indicate that 60% of the stations exhibit a declining trend in thunderstorm days during the study period, with 26% showing a statistically significant trend. The seasonal trends in thermodynamic indices reflect the trends in thunderstorm days. More than 75% of the stations demonstrate a decreasing trend in atmospheric instability in all seasons. We identified suitable indices and their threshold values for thunderstorm prediction using optimal values of Normalised Skill Score, which can be used for operational thunderstorm forecasting.