Effects of local and non-local closure PBL schemes on the simulation of Super Typhoon Mangkhut (2018)

doi:10.1007/s11707-020-0854-9

Front. Earth Sci.

2022, Vol. 16

Issue (2) : 277-290 https://doi.org/10.1007/s11707-020-0854-9

RESEARCH ARTICLE

Effects of local and non-local closure PBL schemes on the simulation of Super Typhoon Mangkhut (2018)

Zixi RUAN^1,², Jiangnan LI^1,²(

), Fangzhou LI¹, Wenshi LIN¹

¹. School of Atmospheric Sciences, and Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Zhuhai 519082, China
². Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China

Download: PDF(16963 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

With the convection-permitting simulation of Super Typhoon Mangkhut (2018) with a 3 km resolution for 10.5 days using mesoscale numerical model, Weather Research and Forecasting Model Version 4.1 (WRFV4.1), the influences of local closure QNSE planetary boundary layer (PBL) scheme and non-local closure GFS planetary boundary layer scheme on super typhoon Mangkhut are mainly discussed. It is found that in terms of either track or intensity of typhoon, the local closure QNSE scheme is better than the non-local closure GFS scheme. Local and non-local closure PBL schemes have a large influence on both the intensity and the structure of typhoon. The maximum intensity difference of the simulated typhoon is 50 hPa. The intensity of typhoon is closely related to its variations in structure. In the rapid intensification stage, the typhoon simulated by the QNSE scheme has a larger friction velocity, stronger surface latent heat flux, sensible heat flux and vapor flux, related to a higher boundary height and stronger vertical mixing. The latent heat flux and sensible heat flux on the surface conveyed energy upward for the typhoon while the water vapor was transported upward through vertical mixing. While the water vapor condensed, the latent heat was released, which further warmed the typhoon eyewall, strengthening the convection. The stronger winds also intensified the vertical mixing and the warm-core structure, further strengthened the typhoon. The differences in surface layer schemes dominated the differences between the two simulations.

Keywords typhoon planetary boundary layer scheme WRF model

Corresponding Author(s): Jiangnan LI

About author: Tongcan Cui and Yizhe Hou contributed equally to this work.

Online First Date: 01 June 2021 Issue Date: 26 August 2022

Cite this article:

Zixi RUAN,Jiangnan LI,Fangzhou LI, et al. Effects of local and non-local closure PBL schemes on the simulation of Super Typhoon Mangkhut (2018)[J]. Front. Earth Sci., 2022, 16(2): 277-290.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-020-0854-9
https://academic.hep.com.cn/fesci/EN/Y2022/V16/I2/277

Fig.1 Comparison of the simulated track of TC with observation (OBS) based on the CMA's TC database.

Fig.2 Simulated distance error (left, unit: km) and angle error (right, unit: °).

Fig.3 Comparison of simulated minimum sea level pressure of TC with observation.

Fig.4 Comparison of simulated maximum surface wind of TC with observation.

Tab.1 Assessment statistics of simulated minimum sea level pressure of TC

Tab.2 Assessment statistics of simulated maximum surface wind of TC

Fig.5 Time variation of simulated regional average (within a radius of 150 km from TC center) (a) surface sensible heat flux (HFX), (b) surface latent heat flux (LH), (c) surface water vapor flux (QFX), (d) surface friction velocity (U*) and (e) boundary layer height (PBLH) during the mature stage (from 00:00 of 10th to 00:00 of 13th, the same below).

Fig.6 Simulated surface sensible heat fluxes (unit: W/m²) of TC at 00:00 on 13 September, 2018.

Fig.7 Simulated surface latent heat fluxes (unit: W/m²) of TC at 00:00 on 13 September, 2018.

Fig.8 Simulated water vapor fluxes (unit: 10⁻⁴kg·m⁻²·s⁻¹) of TC at 00:00 on 13 September, 2018.

Fig.9 Simulated azimuthally-averaged vertical velocity (unit: m/s) of TC inner core at 00:00 on 13 September, 2018 at radius-height.

Fig.10 Simulated azimuthally-averaged tangential wind speed (unit: m/s) of TC inner core at 00:00 on 13 September, 2018 at radius-height.

Fig.11 Simulated azimuthally-averaged radial wind speed (unit: m/s) of TC inner core at 00:00 on 13 September, 2018 at radius-height.

Fig.12 Simulated time variation of vertical-averaged and azimuthally-averaged radar reflectivity factor (unit: dBz) of TC inner core during the mature stage.

Fig.13 Simulated time variation of vertical-averaged and azimuthally-averaged tangential wind speed (unit: m/s) of TC inner core during the mature stage.

Fig.14 Simulated time variation of vertical-averaged and azimuthally-averaged radial wind speed (unit: m/s) of TC inner core during the mature stage.

Fig.15 Simulated time variation of vertical-averaged and azimuthally-averaged vertical velocity (unit: m/s) of TC inner core during the mature stage.

1	S Y Bae, S Y Hong, W K Tao (2019). Development of a single-moment cloud microphysics scheme with prognostic hail for the Weather Research and Forecasting (WRF) model. Asia-Pac J Atmos Sci, 55(2): 233–245 https://doi.org/10.1007/s13143-018-0066-3
2	S A Braun, W K Tao (2000). Sensitivity of high-resolution simulations of hurricane Bob (1991) to planetary boundary layer parameterizations. Mon Weather Rev, 128(12): 3941–3961 https://doi.org/10.1175/1520-0493(2000)129<3941:SOHRSO>2.0.CO;2
3	G Deng, Y S Zhou, J T Li (2005). The experiments of the boundary layer schemes on simulated typhoon Part I: the effect on the structure of typhoon. Chinese J Atmos Sci, 29(3): 417–428 (in Chinese) https://doi.org/10.3878/j.issn.1006-9895.2005.03.09
4	C H Ding, J N Li, Y J Zhao, Y R Feng (2018). The influence of boundary layer parameterization schemes on autumn typhoon Sarika (2016) in South China Sea. J Tropical Meteorology, 34(5): 657–673 (in Chinese) https://doi.org/1004–4965(2018)05–0657–17
5	J W Deardorff (1972). Theoretical expression for the countergradient vertical heat flux. J Geophys Res Atmos, 77(30): 5900–5904 https://doi.org/10.1029/JC077i030p05900
6	J Dudhia (1989). Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two–dimensional model. J Atmos Sci, 46(20): 3077–3107 https://doi.org/10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2
7	G A Efstathiou, N M Zoumakis, D Melas, C J Lolis, P Kassomenos (2013). Sensitivity of WRF to boundary layer parameterizations in simulating a heavy rainfall event using different microphysical schemes. Effect on large-scale processes. Atmos Res, 132(10):125–143 https://doi.org/10.1016/j.atmosres.2013.05.004
8	K A Emanuel (1995). Sensitivity of tropical cyclones to surface exchange coefficients and a revised steady-state model incorporating eye dynamics. J Atmos Sci, 52(22): 3969–3976 https://doi.org/10.1175/1520-0469(1995)052<3969:SOTCTS>2.0.CO;2
9	T Filippos, C Demetris, M Silas, L Jos (2018). Intercomparison of boundary layer parameterizations for summer conditions in the eastern Mediterranean island of Cyprus using the WRF-ARW model. Atmos Sci, 208: 45–59 https://doi.org/10.1016/j.atmosres.2017.09.011
10	A A M Holtslag, B A Boville (1993). Local versus non-local boundary layer diffusion in a global climate model. Climate (Basel), 6(10): 1825–1842 https://doi.org/10.1175/1520-0442(1993)006<1825:LVNBLD>2.0.CO;2
11	A A M Holtslag, C H Moeng (1991). Eddy diffusivity and countergradient transport in the convective atmospheric boundary layer. J Atmos Sci, 48(14): 1690–1698 https://doi.org/10.1175/1520-0469(1991)048<1690:EDACTI>2.0.CO;2
12	S Y Hong, H L Pan (1996). Non-local boundary layer vertical diffusion in a medium-range forecast model. Mon Weather Rev, 124(10): 2322–2339 https://doi.org/10.1175/1520-0493(1996)124<2322:NBLVDI>2.0.CO;2
13	W Y Huang, X Y Shen, W G Wang, W Huang (2014). Comparison of the thermal and dynamic structural characteristics in boundary layer with different boundary layer parameterizations. Chinese J Geophys, 57(5):1399–1414 (in Chinese) https://doi.org/10.6038/cjg20140505
14	Z I Janjić (2002). Nonsingular implementation of the Mellor—Yamada level 2.5 scheme in the NCEP Meso model. NCEP Office Note
15	J Kaplan, M DeMaria (2003). Large-scale characteristics of rapidly intensifying tropical cyclones in the North Atlantic basin. Weather Forecast, 18(6): 1093–1108 https://doi.org/10.1175/1520-0434(2003)018<1093:LCORIT>2.0.CO;2
16	J N Li, C H Ding, F Z Li, Y L Chen (2020). Effects of single- and double-moment microphysics schemes on the intensity of super typhoon Sarika (2016). Atmos Res, 238: 104894 https://doi.org/10.1016/j.atmosres.2020.104894
17	J N Li, X D Huang, G Wang, S K Fong, W B Li (2009). Numerical simulation study of the inner-core structures and the mechanism for inshore strengthening of South China Sea Typhoon Vongfong (0214) during landfall. J Trop Meteorol, 15(1): 45–48 https://doi.org/10.3969/j.issn.1006-8775.2009.01.006
18	J N Li, G Wang, W S Lin, Q H He, Y R Feng, J Y Mao (2013). Cloud-scale simulation study of Typhoon Hagupit (2008) Part II: Impact of cloud microphysical latent heat processes on typhoon intensity. Atmos Res, 120: 202–215 https://doi.org/10.1016/j.atmosres.2012.08.018
19	J J Liu, F M Zhang, Z X Pu (2017). Numerical simulation of the rapid intensification of Hurricane Katrina (2005): Sensitivity to boundary layer parameterization schemes. Advances in Atmos Sci, 34(4): 482–496 https://doi.org/10.1007/s00376-016-6209-5
20	X L Li, Z X Pu (2008). Sensitivity of numerical simulation of early rapid intensification of Hurricane Emily (2005) to cloud microphysical and planetary boundary layer parameterizations. Mon Weather Rev, 136(12): 4819–4838 https://doi.org/10.1175/2008MWR2366.1
21	G L Mellor, T Yamada (1982). Development of a turbulence closure model for geophysical fluid problems. Rev Geophys Space Phys, 20(4): 851–875 https://doi.org/10.1029/RG020i004p00851
22	J Ming, J A Zhang (2016). Effects of surface flux parameterization on the numerically simulated intensity and structure of Typhoon Morakot (2009). Advances in Atmos Sci, 33: 58–72 https://doi.org/10.1007/s00376-015-4202-z
23	E J Mlawer, S J Taubman, P D Brown, M J Iacono, S A Clough (1997). Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J Geophys Res, 102(D14): 16663–16682 https://doi.org/10.1029/97JD00237
24	M S Moss, S L Rosenthal (1975). On the estimation (from bulk data) of boundary layer variables and cloud base mass flux in mature hurricanes. Mon Weather Rev, 20(4): 851–875
25	D S Nolan, J A Zhang, D P Stern (2009a). Evaluation of planetary boundary layer parameterizations in tropical cyclones by comparison of in situ observations and high-resolution simulations of Hurricane Isabel (2003). Part I: initialization, maximum winds, and the outer-core boundary layer. Mon Weather Rev, 137(11): 3651–3674 https://doi.org/10.1175/2009MWR2785.1
26	D S Nolan, J A Zhang, D P Stern (2009b). Evaluation of planetary boundary layer parameterizations in tropical cyclones by comparison of in situ observations and high-resolution simulations of Hurricane Isabel (2003). PartⅡ: inner-core boundary layer and eyewall structure. Mon Weather Rev, 137(11): 3675–3698 https://doi.org/10.1175/2009MWR2786.1
27	J E Pleim, J S Chang (1992). A non-local closure model for vertical mixing in the convective boundary layer. Atmos Environ, 26A(6): 965–981 https://doi.org/10.1016/0960-1686(92)90028-J
28	A Ricchi, M M Miglietta, and et al.. (2017). Sensitivity of a Mediterranean tropical-like cyclone to different model configurations and coupling strategies. Atmosphere, 8(5): 92 https://doi.org/10.3390/atmos8050092
29	R Rotunno, G H Bryan (2012). Effects of parameterized diffusion on simulated hurricanes. J Atmos Sci, 69(7): 2284–2299 https://doi.org/10.1175/JAS-D-11-0204.1
30	R K Smith, G L Thomsen (2010). Dependence of tropical-cyclone intensification on the boundary-layer representation in a numerical model. Quarterly J Royal Meteorol Soc, 136(652): 1671–1685 https://doi.org/10.1029/RG020i004p00851
31	S Sukoriansky, B Galperin, V Perov (2005). Application of a new spectral model of stratified turbulence to the atmospheric boundary layer over sea ice. Boundary-Layer Meteorol, 117(2): 231–257 https://doi.org/10.1007/s10546-004-6848-4
32	W Q Sun, C Y Li (2018). A review of atmospheric boundary layer parameterization schemes in numerical models. J Marine Meteorology, 38(3): 11–19 (in Chinese) https://doi.org/10.19513/j.cnki.issn2096-3599.2018.03.002
33	Y Sun, L Yi, Z Zhong, Y J Hu, Y Ha (2013). Dependence of model convergence on horizontal resolution and convective parameterization in simulations of a tropical cyclone at gray-zone resolutions. J Geophys Res, 118(14): 7715–7732 https://doi.org/10.1002/jgrd.50606
34	M F Tewari, W Chen, J Wang, J Dudhia, R H Cuemca (2004). Implementation and verification of the unified NOAH land surface model in the WRF model. In: 20th Conference on Weather Analysis and Forecasting/16th Conference on Numerical Weather Prediction: 11–15.
35	I B Troen, L Mahrt (1986). A simple model of the atmospheric boundary layer; sensitivity to surface evaporation. Boundary-Layer Meteorol, 37:129–148 https://doi.org/10.1007/BF00122760
36	F Tymvios, C Demetris, M Silas, L Jos (2018). Intercomparison of boundary layer parameterizations for summer conditions in the eastern Mediterranean island of Cyprus using the WRF-ARW model. Atmos Res, 208: 45–59 https://doi.org/10.1016/j.atmosres.2017.09.011
37	Y X Wang, z Zhong, Y Sun, Y J Hu (2017). The mechanism analysis of the track deviation of tropical cyclone Megi (2010) simulated with two planetary boundary layer schemes. Chinese J Geophy, 60(7): 2545–2555 (in Chinese) https://doi.org/10.6038/cjg20170704
38	X P Wen, X Long, S W Zhang, D H Li (2018). Numerical studies of planetary boundary layer parameterization schemes on super typhoon SANBA (2012) during its initial stage. J Trop Meteorol, 24(3): 288–299 https://doi.org/10.16555/j.1006-8775.2018.03.003
39	H Y Xu, Y Q Xu, Z Wang, P J Zhu, X F Li, G Q Zhai (2017). Modification tests for the coefficient of turbulent mixing length scale in QNSE scheme in the WRF model. Chinese J Atmos Sciences, 41(2): 357–371 (in Chinese) https://doi.org/10.3878/j.issn.1006-9895.1606.16108
40	T Yamada, G L Mellor (1975). A simulation of the Wangara atmospheric boundary layer data. J Atmos Sci, 32: 2309–2329 https://doi.org/10.1175/1520-0469(1975)0322.0.CO;2
41	M Ying, W Zhang, H Yu, X Q Lu, J X Feng, Y X Fan, Y T Zhu, D Q Chen (2014). An overview of the China Meteorological Administration tropical cyclone database. J Atmos Ocean Technol, 31(2): 287–301 https://doi.org/10.1175/JTECH-D-12-00119.1
42	Y T Zhang, Y X Jiang, B K Tan (2013). Influences of different PBL schemes on secondary eyewall formation and eyewall replacement cycle in simulated Typhoon Sinlaku (2008). Acta Meteorol Sin, 27(3): 322–334 https://doi.org/10.1007/s13351-013-0312-7
43	J A Zhang, R F Rogers, V Tallapragada (2017). Impact of parameterized boundary layer structure on tropical cyclone rapid intensification forecasts in HWRF. Mon Weather Rev, 145: 1413–1426 https://doi.org/10.1175/MWR-D-16-0129.1

[1]	Siqi CHEN, Feng XU, Yu ZHANG, Guiling YE, Jianjun XU, Chunlei LIU. Sensitivity of Typhoon Lingling (2019) simulations to horizontal mixing length and planetary boundary layer parameterizations[J]. Front. Earth Sci., 2022, 16(2): 304-322.
[2]	Jing ZHU, Yi LU, Fumin REN, John L McBRIDE, Longbin YE. Typhoon disaster risk zoning for China’s coastal area[J]. Front. Earth Sci., 2022, 16(2): 291-303.
[3]	Jiakai ZHU, Jianhua DAI. A rain-type adaptive optical flow method and its application in tropical cyclone rainfall nowcasting[J]. Front. Earth Sci., 2022, 16(2): 248-264.
[4]	Fen XU, X. San LIANG. Drastic change in dynamics as Typhoon Lekima experiences an eyewall replacement cycle[J]. Front. Earth Sci., 2022, 16(1): 121-131.
[5]	Cong ZHOU, Peiyan CHEN, Shifang YANG, Feng ZHENG, Hui YU, Jie TANG, Yi LU, Guoming CHEN, Xiaoqing LU, Xiping ZHANG, Jing SUN. The impact of Typhoon Lekima (2019) on East China: a postevent survey in Wenzhou City and Taizhou City[J]. Front. Earth Sci., 2022, 16(1): 109-120.
[6]	He FANG, William PERRIE, Gaofeng FAN, Zhengquan LI, Juzhen CAI, Yue HE, Jingsong YANG, Tao XIE, Xuesong ZHU. High-resolution sea surface wind speeds of Super Typhoon Lekima (2019) retrieved by Gaofen-3 SAR[J]. Front. Earth Sci., 2022, 16(1): 90-98.
[7]	Shengming TANG, Yun GUO, Xu WANG, Jie TANG, Tiantian LI, Bingke ZHAO, Shuai ZHANG, Yongping LI. Validation of Doppler Wind Lidar during Super Typhoon Lekima (2019)[J]. Front. Earth Sci., 2022, 16(1): 75-89.
[8]	Guomin CHEN, Xiping ZHANG, Qing CAO, Zhihua ZENG. Evaluation of forecast performance for Super Typhoon Lekima in 2019[J]. Front. Earth Sci., 2022, 16(1): 17-33.
[9]	Seung-Woo LEE, Sung Hyun NAM, Duk-Jin KIM. Estimation of marine winds in and around typhoons using multi-platform satellite observations: Application to Typhoon Soulik (2018)[J]. Front. Earth Sci., 2022, 16(1): 175-189.
[10]	Hiroshi TAKAGI, Atsuhei TAKAHASHI. Short-fetch high waves during the passage of 2019 Typhoon Faxai over Tokyo Bay[J]. Front. Earth Sci., 2022, 16(1): 206-219.
[11]	Rong GUO, Hui YU, Zifeng YU, Jie TANG, Lina BAI. Application of the frequency-matching method in the probability forecast of landfalling typhoon rainfall[J]. Front. Earth Sci., 2022, 16(1): 52-63.
[12]	Md. Rezuanul ISLAM, Hiroshi TAKAGI. Typhoon parameter sensitivity of storm surge in the semi-enclosed Tokyo Bay[J]. Front. Earth Sci., 2020, 14(3): 553-567.
[13]	Gefei DENG, Yongming SHEN, Changping LI, Jun TANG. Computational investigation on hydrodynamic and sediment transport responses influenced by reclamation projects in the Meizhou Bay, China[J]. Front. Earth Sci., 2020, 14(3): 493-511.
[14]	Linna ZHAO, Xuemei BAI, Dan QI, Cheng XING. BMA probability quantitative precipitation forecasting of land-falling typhoons in south-east China[J]. Front. Earth Sci., 2019, 13(4): 758-777.
[15]	Jia ZHU, Jiong SHU, Xing YU. Improvement of typhoon rainfall prediction based on optimization of the Kain-Fritsch convection parameterization scheme using a micro-genetic algorithm[J]. Front. Earth Sci., 2019, 13(4): 721-732.

Viewed

Full text

Abstract

Cited

Shared

Discussed