Publication Type:

Journal Article

Source:

IEEE Transactions on Geoscience and Remote Sensing, IEEE, Volume 52, Number 12, p.7721-7726 (2014)

Accession Number:

14379552

URL:

http://ieeexplore.ieee.org/abstract/document/6813672/

Keywords:

Analytical models, Predictive models, Rain, Sea measurements, Sea surface, Wind forecasting

Abstract:

This paper describes the improvements in the simulation of a heavy rainfall event due to the assimilation of surface wind observations from the Oceansat-2 scatterometer using ensemble Kalman filter (EnKF) technique. A heavy rainfall event over the southern peninsular region of India during the northeast Indian monsoon season is investigated in this paper using the Advanced Research Weather Research and Forecasting model. A control (CTRL) run where no surface wind observations are assimilated, as well as a 3-D variational (3DVar) run and an EnKF run wherein surface wind observations are assimilated using the 3DVar and EnKF techniques, is performed. Results indicate that the EnKF assimilation run simulates various meteorological fields, including precipitation fields during the rainfall event, better than the CTRL and the 3DVar runs. Qualitative and quantitative comparisons with Tropical Rainfall Measurement Mission precipitation observations indicate that the rainfall simulation shows improvement due to EnKF assimilation as compared with the other two model runs. Vertical profiles of area-averaged and time-averaged relative vorticities and temperature anomalies around the low-pressure system are also better reproduced in the EnKF experiment. Considering the importance of accurate real time simulations of heavy rainfall events associated with the Indian monsoon season, this paper provides encouraging results on the utility of EnKF technique as applied over the Indian region.

Cite this Research Publication

Dr. Dhanya M. and Chandrasekar, A., “Improved Rainfall Simulation by Assimilating Oceansat-2 Surface Winds Using Ensemble Kalman Filter for a Heavy Rainfall Event over South India”, IEEE Transactions on Geoscience and Remote Sensing, vol. 52, pp. 7721-7726, 2014.

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