An investigation on how inner‐core structures obtained through radar data assimilation affect track forecasting of typhoon Jangmi (2008) near Taiwan Island

Abstract

The impacts of radar data assimilation (DA) on the westward track deflection of Typhoon Jangmi (2008) near Taiwan Island and the deflection mechanism are investigated. Initial conditions from two data assimilation experiments with significant track forecast differences are analyzed and compared. The environmental, axisymmetric, wave number 1 to 3 asymmetric fields of the typhoon are decomposed by using vortex separation and Fourier decomposition methods. The components are selectively recomposed into new initial conditions that include different vortex‐scale components to examine the impact of individual components on the track prediction. The wave number 1 asymmetric structure is found to play a dominant role in the westward deflection of Typhoon Jangmi, and the accurate analysis of this component with radar DA helps to improve the track forecast. The wave number 1 asymmetric circulation is manifested as a pair of cyclonic and anticyclonic gyres with well‐defined ventilation flows through the inner‐core region, which provides additional steering of the typhoon vortex. The layer‐mean environmental steering flow and ventilation flow associated with the wave number 1 gyres are further calculated to quantitatively evaluate the impact of ventilation flow. The ventilation flow is shown to be responsible for most of the westward motion component, suggesting again its role in causing the westward track deflection of Typhoon Jangmi. The results also suggest the importance of analyzing vortex‐scale asymmetric structures for accurate tropical cyclone track forecasting, especially when there is a significant track deflection.

Convective-Scale Sampling Error and Its Impact on the Ensemble Radar Data Assimilation System: A Case Study of a Heavy Rainfall Event on 16 June 2008 in Taiwan

“…research works (e.g., Gao and Xue 2008;Dowell et al 2011;Chang et al 2014;Wheatley et al 2015;Wang et al 2016;Miyoshi et al 2016). EnRDA has shown benefits of providing probabilistic predictions for convective storms (Snook et al 2015;Yussouf et al 2016;Putnam et al 2017).…”