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Skills of different mesoscale models over Indian region during monsoon season: Forecast errors

Das, S and Ashrit, R and Iyengar, GR and Mohandas, S and Gupta, MD and George, JP and Rajagopal, EN and Dutta, SK (2008) Skills of different mesoscale models over Indian region during monsoon season: Forecast errors. Journal of Earth System Science, 117 (5). pp. 603-620.

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Abstract

Performance of four mesoscale models namely, the MM5, ETA, RSM and WRF, run at NCMRWF for short range weather forecasting has been examined during monsoon-2006. Evaluation is carried out based upon comparisons between observations and day-1 and day-3 forecasts of wind, temperature, speci.c humidity, geopotential height, rainfall, systematic errors, root mean square errors and specific events like the monsoon depressions. It is very difficult to address the question of which model performs best over the Indian region? An honest answer is 'none'. Perhaps an ensemble approach would be the best. However, if we must make a final verdict, it can be stated that in general, (i) the WRF is able to produce best All India rainfall prediction compared to observations in the day-1 forecast and, the MM5 is able to produce best All India rainfall forecasts in day-3, but ETA and RSM are able to depict the best distribution of rainfall maxima along the west coast of India, (ii) the MM5 is able to produce least RMSE of wind and geopotential fields at most of the time, and (iii) the RSM is able to produce least errors in the day-1 forecasts of the tracks, while the ETA model produces least errors in the day-3 forecasts.

Item Type: Article
Additional Information: Copyright of this article belongs to Indian Academy of Sciences
Uncontrolled Keywords: climate prediction; mesoscale meteorology; modeling; monsoon; weather forecasting, Asia; Eurasia; India; South Asia
Subjects: Meteorology and Climatology
Depositing User: Administrator ePrints@MoES
Date Deposited: 21 Jul 2015 07:24
Last Modified: 21 Jul 2015 07:24
URI: http://moeseprints.incois.gov.in/id/eprint/2031

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