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<p>This study investigates a multi-day extreme rainfall event (MERE) that occurred on 8–9 July 2023 in the Indian Himalayan mountainous region (IHMR). This catastrophic MERE, accompanied by exceptionally heavy rainfall reaching up to 424&#xa0;mm over two days in Ropar, with 286&#xa0;mm recorded in a single day in Chandigarh triggered flash floods, landslides, and debris flows, resulting in severe devastation, significant loss of life (91 fatalities), and extensive economic damage. Given that the skill of the Weather Research and Forecasting (WRF) model in simulating heavy rainfall is highly dependent on the choice of cloud microphysics (CMP) in a convective-permitting modelling framework, sensitivity analysis has been conducted using five CMP schemes to assess their performance to identify the best suitable CMP for this event in the study region. The WRF model was configured with three nested two-way-interacting domains (18&#xa0;km, 6&#xa0;km, and 2&#xa0;km) to assess the performance of these CMP schemes in a convective-permitting simulation framework. Our study demonstrates the skillfulness of WRF to forecast the MERE of July 2023 over IHMR at 2&#xa0;km resolution domain and also resolve the local and synoptic-scale interactions. These results show that all the CMP configurations (with 2-km resolution) were able to reproduce heavy rainfall; however, among the five CMP schemes, the Aerosol-Aware Thompson (AA-Thompson) scheme demonstrated the best agreement with IMERG observations, exhibiting the most reliable spatial and temporal distribution of rainfall, minimal bias, and a lower root mean square error compared to the other schemes. Furthermore, with the best CMP configuration, we evaluate the predictive capability of the WRF-based convective modelling framework (2-km horizontal grid spacing) for forecasting extreme rainfall at different lead times (72&#xa0;h, 48&#xa0;h and 24&#xa0;h ahead). The analysis reveals that the 24-hour lead time prediction of extreme rainfall aligns most closely with IMERG observations, while discrepancies in precipitation amount and location become more pronounced at 48- and 72-hour lead times. Consequently, the index of agreement decreases from 0.59 to 0.45 against ERA5 and from 0.45 to 0.41 against IMERG. Overall, the study highlights the predictive capability of mountain MEREs and the complex interaction of synoptic features, moisture dynamics, and large-scale circulations in generating extreme rainfall over the study region.</p>

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Cloud microphysics sensitivity and predictability of a multi-day extreme rainfall event over the Indian Himalayas in convective-permitting simulations

  • 期刊论文

  • 发表日期：

    2026年6月30日

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  • Rohtash Saini,
  • Raju Attada,
  • Nikhil Hale,
  • Prashant Kumar,
  • Akhil Srivastava,
  • V. Yesubabu

摘要

This study investigates a multi-day extreme rainfall event (MERE) that occurred on 8–9 July 2023 in the Indian Himalayan mountainous region (IHMR). This catastrophic MERE, accompanied by exceptionally heavy rainfall reaching up to 424 mm over two days in Ropar, with 286 mm recorded in a single day in Chandigarh triggered flash floods, landslides, and debris flows, resulting in severe devastation, significant loss of life (91 fatalities), and extensive economic damage. Given that the skill of the Weather Research and Forecasting (WRF) model in simulating heavy rainfall is highly dependent on the choice of cloud microphysics (CMP) in a convective-permitting modelling framework, sensitivity analysis has been conducted using five CMP schemes to assess their performance to identify the best suitable CMP for this event in the study region. The WRF model was configured with three nested two-way-interacting domains (18 km, 6 km, and 2 km) to assess the performance of these CMP schemes in a convective-permitting simulation framework. Our study demonstrates the skillfulness of WRF to forecast the MERE of July 2023 over IHMR at 2 km resolution domain and also resolve the local and synoptic-scale interactions. These results show that all the CMP configurations (with 2-km resolution) were able to reproduce heavy rainfall; however, among the five CMP schemes, the Aerosol-Aware Thompson (AA-Thompson) scheme demonstrated the best agreement with IMERG observations, exhibiting the most reliable spatial and temporal distribution of rainfall, minimal bias, and a lower root mean square error compared to the other schemes. Furthermore, with the best CMP configuration, we evaluate the predictive capability of the WRF-based convective modelling framework (2-km horizontal grid spacing) for forecasting extreme rainfall at different lead times (72 h, 48 h and 24 h ahead). The analysis reveals that the 24-hour lead time prediction of extreme rainfall aligns most closely with IMERG observations, while discrepancies in precipitation amount and location become more pronounced at 48- and 72-hour lead times. Consequently, the index of agreement decreases from 0.59 to 0.45 against ERA5 and from 0.45 to 0.41 against IMERG. Overall, the study highlights the predictive capability of mountain MEREs and the complex interaction of synoptic features, moisture dynamics, and large-scale circulations in generating extreme rainfall over the study region.