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<p>Accurate simulation of the Indian Summer Monsoon (ISM) is frequently constrained by uncertainties in land-surface initialization and the representation of land–atmosphere feedback. This study investigates the impact of high-resolution land surface initialization on ISM characteristics using the Weather Research and Forecasting (WRF) model. Two numerical experiments were conducted for the 2007 monsoon season: a Control run initialized with ERA5 reanalysis data, and an Experimental run (MWF) initialized with soil moisture and temperature states, that are at equilibrium, generated via the NASA Land Information System (LIS). The LIS-based initialization reduced systematic warm and dry biases in the planetary boundary layer, particularly over the arid Northwest and Central India. These improvements are driven by the correction of systematic biases in the antecedent subsurface soil states, which act as a persistent boundary condition regulating the surface energy partition and the bowen ratio throughout the season. Consequently, the MWF simulation demonstrated a distinct 8.0% reduction in the Root Mean Square Error (RMSE) of the large-scale circulation field during the withdrawal phase. While the experimental configuration reduced the false alarm ratio during transitional periods, the intensity of extreme rainfall events remained underestimated, the latter attributed to the limitations of convective parameterization at mesoscale resolutions. These results demonstrate that replacing inconsistent initial land states with spun-up, high-resolution soil moisture and soil temperature fields does enhance land–atmosphere coupling strength, offering a potential pathway for improving regional monsoon predictability.</p>

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Enhanced Land–Atmosphere Coupling via Land Information Systems’ Soil-State Initialization: Improving Weather Research and Forecasting Model of Indian Summer Monsoon Circulation and Rainfall

  • 期刊论文

  • 发表日期：

    2026年5月29日

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  • Srikar Kalyanam,
  • Anantharaman Chandrasekar

摘要

Accurate simulation of the Indian Summer Monsoon (ISM) is frequently constrained by uncertainties in land-surface initialization and the representation of land–atmosphere feedback. This study investigates the impact of high-resolution land surface initialization on ISM characteristics using the Weather Research and Forecasting (WRF) model. Two numerical experiments were conducted for the 2007 monsoon season: a Control run initialized with ERA5 reanalysis data, and an Experimental run (MWF) initialized with soil moisture and temperature states, that are at equilibrium, generated via the NASA Land Information System (LIS). The LIS-based initialization reduced systematic warm and dry biases in the planetary boundary layer, particularly over the arid Northwest and Central India. These improvements are driven by the correction of systematic biases in the antecedent subsurface soil states, which act as a persistent boundary condition regulating the surface energy partition and the bowen ratio throughout the season. Consequently, the MWF simulation demonstrated a distinct 8.0% reduction in the Root Mean Square Error (RMSE) of the large-scale circulation field during the withdrawal phase. While the experimental configuration reduced the false alarm ratio during transitional periods, the intensity of extreme rainfall events remained underestimated, the latter attributed to the limitations of convective parameterization at mesoscale resolutions. These results demonstrate that replacing inconsistent initial land states with spun-up, high-resolution soil moisture and soil temperature fields does enhance land–atmosphere coupling strength, offering a potential pathway for improving regional monsoon predictability.