Improving the spin-up of regional EnKF for typhoon assimilation and forecasting with Typhoon Sinlaku (2008)

Shu-Chih Yang; Kuan-Jen Lin; Takemasa Miyoshi; Eugenia Kalnay

doi:10.3402/tellusa.v65i0.20804

Improving the spin-up of regional EnKF for typhoon assimilation and forecasting with Typhoon Sinlaku (2008)

Tellus

Volume 65 (2013): Issue 1

By: Shu-Chih Yang, Kuan-Jen Lin, Takemasa Miyoshi and Eugenia Kalnay

Open Access

|Dec 2013

Abstract

The Running-In-Place (RIP) method is implemented in the framework of the Local Ensemble Transform Kalman Filter (LETKF) coupled with the Weather Research and Forecasting (WRF) model. RIP aims at accelerating the spin-up of the regional LETKF system when the WRF ensemble is initialised from a global analysis, which is obtained at a coarser resolution and lacks features related to the underlying mesoscale evolution. The RIP method is further proposed as an outer-loop scheme to improve the nonlinear evolution of the ensemble when the characteristics of the error statistics change rapidly owing to strong nonlinear dynamics. The impact of using RIP as an outer-loop for the WRF-LETKF system is evaluated for typhoon assimilation and prediction with Typhoon Sinlaku (2008) as a case study. For forecasts beyond one day, the typhoon track prediction is significantly improved after RIP is applied, especially during the spin-up period of the LETKF assimilation when Sinlaku is developing rapidly from a severe tropical storm to a typhoon. The impact of the dropsondes is significantly increased by RIP at early assimilation cycles. Results suggest that these improvements are because of the positive impact on the environmental condition of the typhoon. Results also suggest that using the RIP scheme adaptively allows RIP to be used as an outer-loop for the WRF-LETKF with further improvements.

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Articles in this issue

DOI: https://doi.org/10.3402/tellusa.v65i0.20804 | Journal eISSN: 3035-9554

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Language: English

Page range: 20804 - 20804

Submitted on: Mar 8, 2013

Accepted on: Aug 14, 2013

Published on: Dec 1, 2013

Published by: Stockholm University Press

In partnership with: Paradigm Publishing Services

Keywords:

regional data assimilation,

ensemble Kalman Filter (EnKF),

typhoon prediction,

nonlinearity,

observation impact

© 2013 Shu-Chih Yang, Kuan-Jen Lin, Takemasa Miyoshi, Eugenia Kalnay, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 65 (2013): Issue 1