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Tropical cyclone forecasting with model‐constrained 3D‐Var. II: Improved cyclone track forecasting using AMSU‐A, QuikSCAT and cloud‐drift wind data
Author(s) -
Liang Xudong,
Wang Bin,
Chan Johnny CL,
Duan Yihong,
Wang Dongliang,
Zeng Zhihua,
Leiming Mab
Publication year - 2007
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.10
Subject(s) - mm5 , data assimilation , meteorology , mesoscale meteorology , tropical cyclone , environmental science , typhoon , tropical cyclone forecast model , satellite , numerical weather prediction , atmospheric model , track (disk drive) , wind speed , climatology , computer science , geology , aerospace engineering , geography , engineering , operating system
Generally, the three‐dimensional variational (3D‐Var) data assimilation technique does not impose constraints on the numerical model such as the dynamics and physics, which are used in a 4D‐Var technique. On the other hand, adopting the 4D‐Var technique requires a large amount of computer resources, which limits its practical application. In Part I, a 3D‐Var method was proposed by minimizing the distance between observations and model variables, and the time tendency of model variables which makes the optimized initial conditions satisfy the constraints of full dynamics and physics in the numerical model. The forward and adjoint models used in this method are the same as those in a 4D‐Var method but are only integrated one time step to calculate the time tendency and gradient. Because a numerical model is adopted as weak constraint, this technique is labelled as MC‐3DVar. Here the National Center for Atmospheric Research/Penn State Mesoscale Model 5 (MM5) is used. In this paper (Part II), AMSU‐A retrieved air temperatures are assimilated into 32 tropical cyclone (TC) cases using this framework. The results show a significant decrease in track forecast errors. Meanwhile, one case‐study of assimilating AMSU‐A temperature, QuikSCAT sea‐level winds, and cloud‐drift winds gives dramatic track error decreases. The study shows that the assimilation of these data with MC‐3DVar improves TC forecasts, and more satellite data give better performances. Copyright © 2007 Royal Meteorological Society