Numerical investigation of the super typhoon Mangkhut based on the coupled air-sea model

doi:10.3969/j.issn.1001-909X.2023.04.003

Abstract

Abstract:

Based on the mesoscale atmospheric model WRF and the regional ocean model ROMS, a two-way coupled WRF-ROMS air-sea model was constructed to simulate the super typhoon Mangkhut in 2018. The results showed that the simulation results of the coupled air-sea model were better than those of the only atmospheric or ocean model, and the error of the typhoon track obtained from the coupled model was within 60 km, which was in good agreement with the best track. Compared with the observation results, the simulation results of wind speed and sea level pressure in the coupled model were better than others model. Based on the simulation results of the coupled air-sea model, the spatial and temporal distribution of the wind field, pressure field, sea surface flow field, and storm surge under the super typhoon Mangkhut were further analyzed. The results showed that: (1) In terms of spatial distribution, after the typhoon entered the South China Sea, the radius of the seven-level wind circle was larger behind the right side of the typhoon; the cyclonic flow field showed a significant Ekman effect with the typhoon wind field, and the flow direction was 45° from the wind direction. The wind field, pressure field, wind-generated flow field and water gain distribution all had obvious asymmetry, and the typhoon intensity, flow velocity and water gain were greater on the right side of the typhoon path than on the left side. (2) In terms of time distribution, the distribution of the wind field and the pressure field were similar and synchronized with the typhoon center, while the wind-driven flow field and storm surge were three hours behind the typhoon track.

Key words: coupled air-sea model, storm surge, numerical simulation, the South China Sea, super typhoon Mangkhut

CLC Number:

P731

LÜ Zhao, WU Zhiyuan, JIANG Changbo, ZHANG Haojian, GAO Kai, YAN Ren. Numerical investigation of the super typhoon Mangkhut based on the coupled air-sea model[J]. Journal of Marine Sciences, 2023, 41(4): 21-31.

Figures/Tables 14

Tab.1 Configuration of the physical parameterization schemes in the WRF model

物理选项	参数化方案
云微物理方案(mp_physics)	WSM 6-class graupel方案
长波辐射(ra_lw_physics)	RRTMG方案
短波辐射(ra_sw_physics)	RRTMG方案
表面层物理选项(sf_sfclay_physics)	Monin-Obukhov方案
地表陆面方案(sf_surface_physics)	Noah地表模型
行星边界层(bl_pbl_physics)	YSU方案
积云参数(cu_physics)	Kain-Fritsch方案

Fig.1 The simulation domain and nested grids of the coupled air-sea model (The blue solid line represents the WRF nested grid,and the red dashed line represents the ROMS nested grid.)

Fig.2 Comparison between the simulated track and the best track of super typhoon Mangkhut in different models

Fig.3 Comparison of typhoon central pressure and maximum wind speed from the models with those from the best-track dataset (Vertical dashed line represents the time of typhoon landfall.)

Fig.4 Comparison of the bias of the track simulated by two different models with that from the best-track dataset (Vertical dashed line represents the time of typhoon landfall.)

Fig.5 Distribution diagram of wind speed, sea level pressure and tide stations during the influence of super typhoon Mangkhut

Fig.6 Time series comparison between models’ wind speed and observed wind speed

Fig.7 Time series comparison between sea level pressure by models with that by buoy stations

Fig.8 Spatial distribution of the pressure and wind field in coupled model during super typhoon Mangkhut

Fig.9 Spatial distribution of typhoon wind fields and wind-generated current fields ( represents the typhoon center; represents the center of the flow field.)

Fig.10 Comparison of wind speed and current velocity as well as wind direction and current direction at the stations (The black dotted line indicates the time when the typhoon passes through the stations.)

Fig.11 Validation of results for astronomical and storm tide levels at nearshore tide gauge stations

Fig.12 Spatial distribution of sea surface current fields and surge caused by super typhoon Mangkhut ( represents the typhoon center; represents the center of the flow field.)

Fig.13 Time series of wind speed, pressure, current velocity and storm surge at the stations (The black dotted line indicates the time when the typhoon passes through the stations.)

References 26

[1]	MUIS S, VERLAAN M, WINSEMIUS H C, et al. A global reanalysis of storm surges and extreme sea levels[J]. Nature Communications, 2016, 7: 11969. DOI PMID
[2]	WANG Y B, LIU J Q, DU X, et al. Temporal-spatial characteristics of storm surges and rough seas in coastal areas of China’s Mainland from 2000 to 2019[J]. Natural Hazards, 2021, 107(2): 1273-1285. DOI
[3]	SHI X W, HAN Z Q, FANG J Y, et al. Assessment and zonation of storm surge hazards in the coastal areas of China[J]. Natural Hazards, 2020, 100(1): 39-48. DOI
[4]	段自强, 李永平, 于润玲, 等. 海洋飞沫方案改进对台风“威马逊”强度预报的影响[J]. 海洋与湖沼, 2016, 47(6):1075-1090.
	DUAN Z Q, LI Y P, YU R L, et al. On tropical cyclone intensity forecast using improved sea spray scheme in regional atmosphere-wave coupled model[J]. Oceanologia et Limnologia Sinica, 2016, 47(6): 1075-1090.
[5]	丁瑞, 朱良生. 条件变化对海口湾风暴增水的影响分析:以海鸥台风为例[J]. 海洋工程, 2018, 36(4):147-154.
	DING R, ZHU L S. Impact of condition on storm surge in the Haikou Bay[J]. The Ocean Engineering, 2018, 36(4): 147-154.
[6]	ZHANG W Z, LIN S, JIANG X M. Influence of tropical cyclones in the western North Pacific[M]//LUPO A R. Recent developments in tropical cyclone dynamics, prediction, and detection. InTech, 2016: 3-24.
[7]	EZER T. On the interaction between a hurricane, the Gulf Stream and coastal sea level[J]. Ocean Dynamics, 2018, 68(10): 1259-1272. DOI
[8]	张浩键, 伍志元, 刘晓建, 等. 台风“天鸽”影响下珠江口水动力过程数值模拟研究[J]. 长沙理工大学学报:自然科学版, 2023, 20(4):142-152.
	ZHANG H J, WU Z Y, LIU X J, et al. Numerical simulation of hydrodynamic processes in the Pearl River Estuary influenced by Typhoon “Hato”[J]. Journal of Changsha University of Science & Technology: Natural Science, 2023, 20(4): 142-152.
[9]	LIN S, ZHANG W Z, SHANG S P, et al. Ocean response to typhoons in the western North Pacific: Composite results from Argo data[J]. Deep Sea Research Part I: Oceanographic Research Papers, 2017, 123: 62-74. DOI URL
[10]	JANSSEN P. The interaction of ocean waves and wind[M]. Cambridge, UK: Cambridge University Press, 2004.
[11]	叶芳, 刘磊, 马占宏, 等. 不同环境风场条件下冷尾流对台风强度的影响[J]. 热带气象学报, 2017, 33(3):368-374.
	YE F, LIU L, MA Z H, et al. Effect of cold wake on typhoon intensity in different environmental wind fields[J]. Journal of Tropical Meteorology, 2017, 33(3): 368-374.
[12]	ZHAO X H, CHAN J C L. Changes in tropical cyclone intensity with translation speed and mixed-layer depth: Idealized WRF-ROMS coupled model simulations[J]. Quarterly Journal of the Royal Meteorological Society, 2017, 143(702): 152-163. DOI URL
[13]	SHEN W X, GINIS I. Effects of surface heat flux-induced sea surface temperature changes on tropical cyclone intensity[J]. Geophysical Research Letters, 2003, 30(18): 1933.
[14]	CHEN S E, CAMPBELL T J, JIN H, et al. Effect of two-way air-sea coupling in high and low wind speed regimes[J]. Monthly Weather Review, 2010, 138(9): 3579-3602. DOI URL
[15]	MOHANTY S, NADIMPALLI R, OSURI K K, et al. Role of sea surface temperature in modulating life cycle of tropical cyclones over bay of Bengal[J]. Tropical Cyclone Research and Review, 2019, 8(2): 68-83. DOI URL
[16]	蒋小平, 刘春霞, 齐义泉. 利用一个海气耦合模式对台风Krovanh的模拟[J]. 大气科学, 2009, 33(1):99-108.
	JIANG X P, LIU C X, QI Y Q. The simulation of typhoon Krovanh using a coupled air-sea model[J]. Chinese Journal of Atmospheric Sciences, 2009, 33(1): 99-108.
[17]	WARNER J C, ARMSTRONG B, HE R Y, et al. Development of a coupled ocean-atmosphere-wave-sediment transport (COAWST) modeling system[J]. Ocean Modelling, 2010, 35(3): 230-244. DOI URL
[18]	伍志元, 蒋昌波, 邓斌, 等. 基于海气耦合模式的南中国海北部风暴潮模拟[J]. 科学通报, 2018, 63(33):3494-3504.
	WU Z Y, JIANG C B, DENG B, et al. Simulation of the storm surge in the South China Sea based on the coupled sea-air model[J]. Chinese Science Bulletin, 2018, 63(33): 3494-3504.
[19]	MOONEY P A, MULLIGAN F J, BRUYÈRE C L, et al. Investigating the performance of coupled WRF-ROMS simulations of Hurricane Irene (2011) in a regional climate modeling framework[J]. Atmospheric Research, 2019, 215: 57-74. DOI URL
[20]	WU Z Y, JIANG C B, DENG B, et al. Numerical investigation of Typhoon Kai-tak (1213) using a mesoscale coupled WRF-ROMS model[J]. Ocean Engineering, 2019, 175: 1-15. DOI URL
[21]	WU Z Y, JIANG C B, DENG B, et al. Numerical investi-gation of Typhoon Kai-tak (1213) using a mesoscale coupled WRF-ROMS model— Part Ⅱ: Wave effects[J]. Ocean Engineering, 2020, 196: 106805. DOI URL
[22]	李心雨, 杨昀, 李自如, 等. WRF大气模式与台风经验模型在超强台风“山竹”过程重构中的比较分析[J]. 海洋工程, 2022, 40(4):53-64,111.
	LI X Y, YANG Y, LI Z R, et al. Comparative analyses on reconstructed processes of super typhoon Mangkhut using the WRF atmospheric model and typhoon empirical models[J]. The Ocean Engineering, 2022, 40(4): 53-64, 111.
[23]	陈晓斐, 齐琳琳, 何尽解, 等. 海洋垂直混合对中尺度海气浪耦合模式预报效果的敏感性试验[J]. 热带气象学报, 2018, 34(6):845-855.
	CHEN X F, QI L L, HE J J, et al. A study of sensitivity to the choices of vertical mixing parameterizations in a coupled atmosphere-ocean-wave model[J]. Journal of Tropical Meteorology, 2018, 34(6): 845-855.
[24]	LI Z N, TAM C Y, LI Y B, et al. How does air-sea wave interaction affect tropical cyclone intensity? An atmosphere-wave-ocean coupled model study based on super typhoon Mangkhut (2018)[J]. Earth and Space Science, 2022, 9(3): e2021EA002136.
[25]	于玲玲, 麦健华, 程正泉, 等. 热带气旋大风风圈半径非对称性特征及成因简析[J]. 气象学报, 2022, 80(6):896-908.
	YU L L, MAI J H, CHENG Z Q, et al. Analysis on the asymmetric characteristics and causes of the wind circle radius of tropical cyclones[J]. Acta Meteorologica Sinica, 2022, 80(6): 896-908.
[26]	LIU J L, ZHANG H, ZHONG R, et al. Impacts of wave feedbacks and planetary boundary layer parameterization schemes on air-sea coupled simulations: A case study for Typhoon Maria in 2018[J]. Atmospheric Research, 2022, 278: 106344. DOI URL