Physical processes associated with movement of maximum wind of Typhoon Rammasun (2014)

Abstract

In this study, the movement of the maximum wind of Typhoon Rammasun (2014) was measured by the radial movement of the maximum symmetric rotational kinetic energy. The weather research and forecasting (WRF) model was used to simulate Typhoon Rammasun, and validated simulation data for the lower troposphere were analyzed to examine the physical processes responsible for the radial movement of the maximum wind. The radii, where maximum symmetric rotational kinetic energy and its maximum tendency were located, were compared to explain radial movement. The tendency in the lower troposphere is controlled by the flux convergence of symmetric rotational kinetic energy and the conversion from symmetric divergent kinetic energy to symmetric rotational kinetic energy, as well as frictional dissipation in the symmetric rotational kinetic energy budget. The inward movement before rapid intensification (RI) resulted from radial flux convergence; cyclonic circulation develops while moving inward. Stationary maximum symmetric rotational kinetic energy and RI were caused by the conversion, which was observed to be proportional to the symmetric rotational kinetic energy. Landfall increased terrain-induced friction dissipation, which led to outward movement and ended the RI.

Graphical abstract

Keywords

typhoon / radial movement of maximum wind / symmetric rotational kinetic energy / rapid intensification / kinetic energy budget / flux convergence / conversion

References

[1] Bougeault P, Lacarrere P (1989). Parameterization of orography-induced turbulence in a mesobeta-scale model.Mon Weather Rev, 117(8): 1872–1890

[2] Dudhia J (1989). Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model.J Atmos Sci, 46(20): 3077–3107

[3] Ek M B, Mitchell K E, Lin Y, Rogers E, Grunmann P, Koren V, Gayno G, Tarpley J D (2003). Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model.J Geophys Res, 108(D22): 8851

[4] Hong S Y, Lim J O J (2006). The WRF Single-Moment 6-Class Microphysics Scheme (WSM6).J Korean Meteor Soc, 42(2): 129–151

[5] Kain J S (2004). The Kain-Fritsch convective parameterization: an update.J Appl Meteorol, 43(1): 170–181

[6] Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo K C, Ropelewski C, Wang J, Leetmaa A, Reynolds R, Jenne R, Joseph D (1996). The NCEP/NCAR reanalysis 40-year project.Bull Amer Meteo Soc, 77(3): 437–471

[7] Kieu C Q (2012). An investigation into the contraction of the hurricane radius of maximum wind.Meteorol Atmos Phys, 115(1–2): 47–56

[8] Kurihara Y, Bender M A, Ross R J (1993). An initialization scheme of hurricane models by vortex specification.Mon Weather Rev, 121(7): 2030–2045

[9] Kurihara Y, Bender M A, Tuleya R E, Ross R J (1995). Improvements in the GFDL Hurricane Prediction System.Mon Weather Rev, 123(9): 2791–2801

[10] Li A, Zhang L, Zang Z, Zhang Y (2012). Computation of stream function and velocity potential in a limited area and its application to typhoon. J Meteorol Sci, 32(1): 1−8 (in Chinese)

[11] Li Y, Wang Y, Lin Y (2019). Revisiting the dynamics of eyewall contraction of tropical cyclones.J Atmos Sci, 76(10): 3229–3245

[12] Li Y, Wang Y, Lin Y, Wang X (2021). Why does rapid contraction of the radius of maximum wind precede rapid intensification in tropical cyclones? J Atmos Sci, 78(11): 3441–3453

[13] Liang X S, Anderson D G M (2007). Multiscale window transform.Multiscale Model Simul, 6(2): 437–467

[14] Mlawer E J, Taubman S J, Brown P D, Iacono M J, Clough S A (1997). Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave.J Geophys Res, 102(D14): 16663–16682

[15] Moon I J, Ginis I, Hara T, Thomas B (2007). A physics-based parameterization of air–sea momentum flux at high wind speeds and its impact on hurricane intensity predictions.Mon Weather Rev, 135(8): 2869–2878

[16] Qin N, Wu L (2021). Possible environmental inflence on eyewall expansion during the rapid intensification of hurricane Helene (2006).Front Earth Sci, 9(7): 715012

[17] Qin N, Zhang D L, Li Y (2016). A statistical analysis of steady eyewall sides associated with rapidly intensifying hurricanes.Weather Forecast, 31(3): 737–742

[18] Skamarock W C, Klemp J B, Dudhia J, Gill D O, Liu Z, Berner J, Wang W, Powers J G, Duda M G, Barker D M, Huang X (2019), A description of the advanced research WRF Model Version 4, NCAR Tech, Note NCAR/TN-556+STR

[19] Stern D P, Vigh J L, Nolan D S, Zhang F (2015). Revisiting the relationship between eyewall contraction and intensification.J Atmos Sci, 72(4): 1283–1306

[20] Sun Y, Zhong Z, Yi L, Ha Y, Sun Y (2014). The opposite effects of inner and outer sea surface temperature on tropical cyclone intensity.J Geophys Res Atmos, 119(5): 2193–2208

[21] Willoughby H E, Clos J A, Shoreibah M G (1982). Concentric eye walls, secondary wind maxima, and the evolution of the hurricane vortex.J Atmos Sci, 39(2): 395–411

[22] Zhao H, Wu L, Liang X (2015). Preliminary application of the MWT method to separate tropical cyclone circulation. Adv Meteor Sci Technol, 5(6): 31−36 (in Chinese)

Acknowledgments

The authors thank the Training Center of Atmospheric Sciences of Zhejiang University and the two anonymous reviewers for their constructive comments and suggestions. We also thank Huiyan Xu for deriving kinetic energy equations, Liguang Wu and Huarui Zhao for providing the MWT filter codes, Xinyong Shen for improving the simulation of Typhoon Rammasun, and Liangliang Li and Chi Zhang for valuable discussions. This study was supported by the National Natural Science Foundation of China (Grant No. 41930967).

RIGHTS & PERMISSIONS

2022 Higher Education Press 2022