Tropical cyclone (TC) is one of the major meteorology disasters, as they lead to deaths, destroy the infrastructure and the environment. Therefore, how to improve the predictability of TC’s activities, such as formation, track, and intensity, is very important and is considered an important task for current operational predicting TC centers in many countries. However, predicting TC’s activities has remained a big challenge for meteorologists due to our incomplete understanding of the multiscale interaction of TCs with the ambient environment and the limitation of numerical weather forecast tools. Hence, this chapter will exhibit some techniques to improve the ability to predict the formation and track of TCs using an ensemble prediction system. Particularly, the Local Ensemble Transform Kalman Filter (LETKF) scheme and its implementation in the WRF Model, as well as the Vortex tracking method that has been applied for the forecast of TCs formation, will be presented in subSection 1. Application of Breeding Ensemble to Tropical Cyclone Track Forecasts using the Regional Atmospheric Modeling System (RAMS) model will be introduced in subSection 2.
Abstract:In this research, several experiments using WRF to research the geneses of tropical cyclonesby data assimilation of coupled 3DVAR (3-dimensional variation) – LETKF (Local Ensemble Transform Kalman filter) have been processed. The analysis field after data assimilation procedure with observational information from synop, METAR, ships, soundings,... database produced from 36h before the recorded tropical cyclogeneses from recorded database with 72-days forecasts. The resulted analysis sea level pressure (SLP) fields were compared to the inital fields from control forecast (CTL), which shown the anomalous high pressure of +0.4mb in the area surrounding the actual genesis locations. The SLP and wind fields at 10-meter level in the WUTIP 2013 case has been using as an example of the divergence and diversity of multiple members’ tropical cyclone development. Key words: tropical cyclones, tropical cyclogenesis, WRF, data assimilation, 3DVAR, LETKF
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