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<p>Tropical Cyclone Heat Potential (TCHP) is a critical parameter for understanding the ocean’s energy contribution to tropical cyclone intensification, especially in the Indian Ocean, where accurate forecasting is essential for disaster preparedness. In this study, we use the global ocean model based on NEMO, set up at the National Centre for Medium Range Weather Forecasting (NCMRWF), to calculate the upper ocean heat content up to the 26&#xa0;°C isotherm depth, known as TCHP. NCMRWF has real-time daily TCHP monitoring capability, produced using a global ocean forecast system that provides forecasts up to 15 days with a coupled atmosphere-ocean model, primarily for tropical cyclone (TC) research. This study also examines two pre-monsoon TCs stronger than cyclonic storms: Cyclone Biparjoy (06–19 June 2023) in the Arabian Sea (AS) and Cyclone Mocha (09–14 May 2023) in the Bay of Bengal (BoB). It can be seen that the TCHP anomaly over AS is higher than the BoB during May and June 2023. TCHP provides energy for the cyclone to persist and maintain its strength. The model successfully simulates the spatial structure and magnitude of TCHP, particularly in the early forecast, although biases increase with increased forecast lead times. The model captures higher TCHP values along the cyclone tracks, which are associated with the stronger potential for intensification. During the Biparjoy event, there is a significant positive correlation between TCHP and Sea Surface Height (SSH). The coupled model successfully maintains the physical relationship between SSH and TCHP in both forecast and analysis fields. It is found that errors in simulation of SSH could explain a large part of biases in TCHP. These results highlight that coupled modeling systems can help make better predictions of TC through accurate simulation of subsurface ocean heat content.</p>

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Tropical cyclone heat potential monitoring and forecasting over the Indian ocean using the UM-based coupled model

  • 期刊论文

  • 发表日期：

    2025年9月9日

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  • Lokesh Kumar Pandey,
  • Ankur Gupta,
  • Imranali M. Momin,
  • Akhilesh Mishra

摘要

Tropical Cyclone Heat Potential (TCHP) is a critical parameter for understanding the ocean’s energy contribution to tropical cyclone intensification, especially in the Indian Ocean, where accurate forecasting is essential for disaster preparedness. In this study, we use the global ocean model based on NEMO, set up at the National Centre for Medium Range Weather Forecasting (NCMRWF), to calculate the upper ocean heat content up to the 26 °C isotherm depth, known as TCHP. NCMRWF has real-time daily TCHP monitoring capability, produced using a global ocean forecast system that provides forecasts up to 15 days with a coupled atmosphere-ocean model, primarily for tropical cyclone (TC) research. This study also examines two pre-monsoon TCs stronger than cyclonic storms: Cyclone Biparjoy (06–19 June 2023) in the Arabian Sea (AS) and Cyclone Mocha (09–14 May 2023) in the Bay of Bengal (BoB). It can be seen that the TCHP anomaly over AS is higher than the BoB during May and June 2023. TCHP provides energy for the cyclone to persist and maintain its strength. The model successfully simulates the spatial structure and magnitude of TCHP, particularly in the early forecast, although biases increase with increased forecast lead times. The model captures higher TCHP values along the cyclone tracks, which are associated with the stronger potential for intensification. During the Biparjoy event, there is a significant positive correlation between TCHP and Sea Surface Height (SSH). The coupled model successfully maintains the physical relationship between SSH and TCHP in both forecast and analysis fields. It is found that errors in simulation of SSH could explain a large part of biases in TCHP. These results highlight that coupled modeling systems can help make better predictions of TC through accurate simulation of subsurface ocean heat content.