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Hybrid Assimilation on a Parameter-Calibrated Model to Improve the Prediction of Heavy Rainfall Events during the Indian Summer Monsoon


Affiliations
1 Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600 036, India
2 National Centre for Medium Range Weather Forecasting, A-50, Sector 62, Noida 201 309, India
3 Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, India
 

Heavy rainfall events during the Indian summer monsoon cause landslides and flash floods resulting in a significant loss of life and property every year. The exactness of the model physics representation and initial conditions is critical for accurately predicting these events using a numerical weather model. The values of parameters in the physics schemes influence the accuracy of model prediction; hence, these parameters are calibrated with respect to observation data. The present study examines the influence of hybrid data assimilation on a parameter-calibrated WRF model. Twelve events during the period 2018–2020 were simulated in this study. Hybrid assimilation on the WRF model significantly reduced the model prediction error of the variables: rainfall (18.04%), surface air temperature (7.91%), surface air pressure (5.90%) and wind speed at 10 m (27.65%) compared to simulations with default parameters without assimilation.

Keywords

Heavy Rainfall Events, Hybrid Assimilation, Numerical Weather Model, Parameter Calibration, Summer Monsoon.
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  • Hybrid Assimilation on a Parameter-Calibrated Model to Improve the Prediction of Heavy Rainfall Events during the Indian Summer Monsoon

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Authors

Sandeep Chinta
Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600 036, India
V. S. Prasad
National Centre for Medium Range Weather Forecasting, A-50, Sector 62, Noida 201 309, India
C. Balaji
Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, India

Abstract


Heavy rainfall events during the Indian summer monsoon cause landslides and flash floods resulting in a significant loss of life and property every year. The exactness of the model physics representation and initial conditions is critical for accurately predicting these events using a numerical weather model. The values of parameters in the physics schemes influence the accuracy of model prediction; hence, these parameters are calibrated with respect to observation data. The present study examines the influence of hybrid data assimilation on a parameter-calibrated WRF model. Twelve events during the period 2018–2020 were simulated in this study. Hybrid assimilation on the WRF model significantly reduced the model prediction error of the variables: rainfall (18.04%), surface air temperature (7.91%), surface air pressure (5.90%) and wind speed at 10 m (27.65%) compared to simulations with default parameters without assimilation.

Keywords


Heavy Rainfall Events, Hybrid Assimilation, Numerical Weather Model, Parameter Calibration, Summer Monsoon.

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DOI: https://doi.org/10.18520/cs%2Fv124%2Fi6%2F693-703