A TC-targeted Ensemble Data Assimilation System: A Case Study of Typhoon Fanapi (2010)

Kuan Jen Lin(1)* and Shu-Chih Yang(1), Shuyi S. Chen(2)
(1) Department of Atmospheric Sciences, National Central University, Taiwan
(2) Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida

In this study, a TC-targeted regional ensemble data assimilation is constructed to study the scientific issues related to TC assimilation and prediction. The issues examined in this study include the impact of TC position uncertainty and the effect of initial TC structure, which is usually generated by vortex initialization techniques.

The impact of TC position uncertainty is first demonstrated under the idealized framework and investigated with a real case study of typhoon Fanapi (2010). For the real case study, a TC-centered ensemble assimilation method (TCC-EDA), proposed by Navarro and Hakim (2014) is implemented in the WRF-LETKF system to reduced the impact of TC position uncertainty. Results show that the TCC-EDA is able to establish a better background mean TC strcuture and reasonable background error statistic. Therefore, observations from Impact of Typhoons on the Ocean in the Pacific(ITOP) field experiment can be more effectively assimilated. The TC structure derived from TCC-EDA is in better agreement with Stepped Frequency Microwave Radiometer (SFMR) surface wind observation. The TC intensity forecast intialized from the TCC analysis is improved although the intensitfication is too rapid in the first two-day forecast.

The effect of initial TC structure is examined by comparing experiments using different vortex initialization techniques. A less realistic symmeric component of the TC structure introduced by the bogus data assimilation can last after several analysis cycles and affect the performance of the EDA. By using a more realistic vortex generated by the vortex relocation technique, the TC forecast can better represent the intensity under the TCC-EDA framework. For this case, we also found that the track forecast is less sensitive to the assimilation strategies.

*email: kjlin7@gmail.com
*Preference: Oral