Impact of radiance data assimilation on the prediction performance of cyclonic storm SIDR using WRF-3DVAR modelling system View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-09-15

AUTHORS

K. S. Singh, M. Mandal, Prasad K. Bhaskaran

ABSTRACT

This study attempts to investigate the impact of assimilation of satellite radiances and its role to improve the model initial condition and forecast of Bay of Bengal cyclone ‘Sidr’ by using the weather research and forecasting model and its three-dimensional variational data assimilation system. Results signify that the assimilation of high-resolution satellite radiances of advanced microwave sounding unit B (AMSU-B) data at peak channels has led to significant improvement in the initial fields of the storm structure. It improved the initial condition of moisture profile more significantly than the temperature profile, when radiances are assimilated into the model. In addition, the assimilation of AMSU-B showed a more positive impact on the prediction of the track and intensity of the storm than the assimilation of radiances of advanced microwave sounding unit A (AMSU-A) and high-resolution infra-red sounder (HIRS). The assimilation exercise with all observations (NCEP PREBUFR, AMSU-A, AMSU-B, HIRS, microwave humidity sounder, and atmospheric infra-red sounder) indicate that the track errors are reduced by about 46, 62, 90, and 86%, respectively, at 24, 48, 72, and 96 h forecasts compared to the experiment considering without data assimilation. The landfall, intensity, and structure of storm are well captured when all observations are assimilated into the model. Overall, it is concluded that assimilation of radiances is beneficial for the analysis and forecast of the storm. The results suggest that assimilating of both NCEP PREBUFR and radiance observations into the mesoscale model improves the initial condition and forecast of the storm. More... »

PAGES

11-28

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00703-017-0552-7

DOI

http://dx.doi.org/10.1007/s00703-017-0552-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1091778101


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