Affiliation:
1. College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang 524088, China
Abstract
High-impact weather (HIW) events, such as typhoons, usually have sensitive regions where additional observations can be deployed and sensitive observations assimilated, which can improve forecasting accuracy. The ensemble transform sensitivity (ETS) method was employed to estimate the sensitive regions in the “Chaba” case in order to explore the impact of observation data in sensitive areas on typhoon forecasting during the rapid intensification phase. A set of observation system simulation experiments were conducted, with assimilations of sensitive observations (SEN), randomly selected observations (RAN), whole domain observations (ALL), and no assimilation (CTRL). The results show that (1) the sensitive areas of Typhoon “Chaba” are primarily located in the southwest of the typhoon center and are associated with the distribution of the wind field structure; (2) the typhoon intensity and tracks simulated by the SEN and RAN experiments are closer to the truth than the CTRL; (3) the SEN experiment, with only 3.6% of assimilated data observations, is comparable with the ALL experiment during the rapid intensification phase of the typhoon; (4) the uncertainty of the mesoscale model can be improved by capturing large-scale vertical wind shear and vorticity features from the GEFS data and then using the data assimilation method, which makes the vertical shear and vorticity field more reasonable.
Funder
National Natural Science Foundation of China
National Key R&D Program of China
Guangdong Ocean University
Reference42 articles.
1. Global trends in tropical cyclone risk;Peduzzi;Nat. Clim. Chang.,2012
2. Progress on the experiment of a multi-platform collaborative field campaign on offshore typhoon;Zhao;Sci. China Adv. Earth Sci.,2022
3. Accuracy of Atla ntic and eastern North Pacific tropical cyclone intensity forecast guidance;Elsberry;Weather Forecast.,2007
4. Methods, current status, and prospect of targeted observation;Mu;Sci. China Earth Sci.,2013
5. Kalnay, E. (2002). Data Assimilation and Predictability, Cambridge University Press.