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Impact of Representing Model Error in a Hybrid Ensemble-Variational Data Assimilation System for Track Forecast of Tropical Cyclones over the Bay of Bengal

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Abstract

Uncertainties in the numerical weather prediction models are generally not well-represented in ensemble-based data assimilation (DA) systems. The performance of an ensemble-based DA system becomes suboptimal, if the sources of error are undersampled in the forecast system. The present study examines the effect of accounting for model error treatments in the hybrid ensemble transform Kalman filter—three-dimensional variational (3DVAR) DA system (hybrid) in the track forecast of two tropical cyclones viz. Hudhud and Thane, formed over the Bay of Bengal, using Advanced Research Weather Research and Forecasting (ARW-WRF) model. We investigated the effect of two types of model error treatment schemes and their combination on the hybrid DA system; (i) multiphysics approach, which uses different combination of cumulus, microphysics and planetary boundary layer schemes, (ii) stochastic kinetic energy backscatter (SKEB) scheme, which perturbs the horizontal wind and potential temperature tendencies, (iii) a combination of both multiphysics and SKEB scheme. Substantial improvements are noticed in the track positions of both the cyclones, when flow-dependent ensemble covariance is used in 3DVAR framework. Explicit model error representation is found to be beneficial in treating the underdispersive ensembles. Among the model error schemes used in this study, a combination of multiphysics and SKEB schemes has outperformed the other two schemes with improved track forecast for both the tropical cyclones.

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Acknowledgements

The authors thank the supercomputing facility of Indian Institute of Tropical Meteorology, Pune for providing computational resources and NCAR for WRF model.

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Authors and Affiliations

  1. Department of Earth and Space Sciences, Indian Institute of Space Science and Technology, Thiruvananthapuram, 695 547, India

    Govindan Kutty & Rohit Muraleedharan

  2. National Atmospheric Research Laboratory, Gadanki, Chittoor District, Andhra Pradesh, India

    Amit P. Kesarkar

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  1. Govindan Kutty
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  2. Rohit Muraleedharan
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  3. Amit P. Kesarkar
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Correspondence to Govindan Kutty.

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Kutty, G., Muraleedharan, R. & Kesarkar, A.P. Impact of Representing Model Error in a Hybrid Ensemble-Variational Data Assimilation System for Track Forecast of Tropical Cyclones over the Bay of Bengal. Pure Appl. Geophys. 175, 1155–1167 (2018). https://doi.org/10.1007/s00024-017-1747-z

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  • DOI: https://doi.org/10.1007/s00024-017-1747-z

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