Hybrid assimilation on a parameter-calibrated model to improve the prediction of heavy rainfall events during the Indian summer monsoon

Chinta, S and Prasad, VS and Balaji, C (2023) Hybrid assimilation on a parameter-calibrated model to improve the prediction of heavy rainfall events during the Indian summer monsoon. In: Current Science, 124 (6). pp. 693-703.

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Abstract

Heavy rainfall events during the Indian summer mon-soon cause landslides and flash floods resulting in a significant loss of life and property every year. The ex-actness 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 accu-racy 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 signi-ficantly reduced the model prediction error of the varia-bles: 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 para-meters without assimilation

Item Type:	Journal Article
Publication:	Current Science
Publisher:	Indian Academy of Sciences
Additional Information:	The copyright for this article belongs to Indian Academy of Sciences.
Keywords:	Heavy rainfall events; hybrid assimilation; numerical weather model; parameter calibration; summer monsoon
Department/Centre:	Division of Mechanical Sciences > Divecha Centre for Climate Change
Date Deposited:	25 May 2023 03:18
Last Modified:	25 May 2023 03:18
URI:	https://eprints.iisc.ac.in/id/eprint/81511

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