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A new ensemble-based data assimilation algorithm to improve track prediction of tropical cyclones

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  • Deepak Subramani
  • R. Chandrasekar
  • K. Ramanujam
  • C. Balaji

Abstract

This paper proposes a new ensemble-based algorithm that assimilates the vertical rain structure retrieved from microwave radiometer and radar measurements in a regional weather forecast model, by employing a Bayesian framework. The goal of the study is to evaluate the capability of the proposed technique to improve track prediction of tropical cyclones that originate in the North Indian Ocean. For this purpose, the tropical cyclone Jal has been analyzed by the community mesoscale weather model, weather research and forecasting (WRF). The ensembles of prognostic variables such as perturbation potential temperature (θ, k), perturbation geopotential (ϕ, m 2 /s 2 ), meridional (U) and zonal velocities (V) and water vapor mixing ratio (q v , kg/kg) are generated by the empirical orthogonal function technique. An over pass of the tropical rainfall-measuring mission (TRMM) satellite occurred on 06th NOV 0730 UTC over the system, and the observations from the radiometer and radar on board the satellite(1B11 data products) are inverted using a combined in-home radiometer-radar retrieval technique to estimate the vertical rain structure, namely the cloud liquid water, cloud ice, precipitation water and precipitation ice. Each ensemble is input as a possible set of initial conditions to the WRF model from 00 UTC which was marched in time till 06th NOV 0730 UTC. The above-mentioned hydrometeors from the cloud water and rain water mixing ratios are then estimated for all the ensembles. The Bayesian filter framework technique is then used to determine the conditional probabilities of all the candidates in the ensemble by comparing the retrieved hydrometeors through measured TRMM radiances with the model simulated hydrometeors. Based on the posterior probability density function, the initial conditions at 06 00 UTC are then corrected using a linear weighted average of initial ensembles for the all prognostic variables. With these weighted average initial conditions, the WRF model has been run up to 08th Nov 06 UTC and the predictions are then compared with observations and the control run. An ensemble independence study was conducted on the basis of which, an optimum of 25 ensembles is arrived at. With the optimum ensemble size, the sensitivity of prognostic variables was also analyzed. The model simulated track when compared with that obtained with the corrected set of initial conditions gives better results than the control run. The algorithm can improve track prediction up to 35 % for a 24 h forecast and up to 12 % for a 54 h forecast. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Deepak Subramani & R. Chandrasekar & K. Ramanujam & C. Balaji, 2014. "A new ensemble-based data assimilation algorithm to improve track prediction of tropical cyclones," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 71(1), pages 659-682, March.
  • Handle: RePEc:spr:nathaz:v:71:y:2014:i:1:p:659-682
    DOI: 10.1007/s11069-013-0942-1
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    References listed on IDEAS

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    1. P. Raju & Jayaraman Potty & U. Mohanty, 2012. "Prediction of severe tropical cyclones over the Bay of Bengal during 2007–2010 using high-resolution mesoscale model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 63(3), pages 1361-1374, September.
    2. S. Sandeep & A. Chandrasekar & S. Dash, 2007. "Impact of modification of initial cyclonic structure on the prediction of a cyclone over the Arabian Sea," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 41(3), pages 487-499, June.
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    1. Tanvir Islam & Prashant K. Srivastava & Dinesh Kumar & George P. Petropoulos & Qiang Dai & Lu Zhuo, 2016. "Satellite radiance assimilation using a 3DVAR assimilation system for hurricane Sandy forecasts," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 82(2), pages 845-855, June.
    2. Tanvir Islam & Prashant Srivastava & Miguel Rico-Ramirez & Qiang Dai & Manika Gupta & Sudhir Singh, 2015. "Tracking a tropical cyclone through WRF–ARW simulation and sensitivity of model physics," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 76(3), pages 1473-1495, April.

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