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Frontiers of Earth Science

ISSN 2095-0195

ISSN 2095-0209(Online)

CN 11-5982/P

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2018 Impact Factor: 1.205

Front. Earth Sci.    2022, Vol. 16 Issue (1) : 158-174    https://doi.org/10.1007/s11707-021-0923-8
RESEARCH ARTICLE
The impact of vertical resolution on the simulation of Typhoon Lekima (2019) by a cloud-permitting model
Mengjuan LIU1,2(), Lin DENG1,2, Wei HUANG1,2, Wanchen WU1,2
1. Shanghai Typhoon Institute, China Meteorological Administration, Shanghai 200030, China
2. Key Laboratory of Numerical Modeling for Tropical Cyclone of China Meteorological Administration, Shanghai 200030, China
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Abstract

The impact of vertical resolution on the simulation of Typhoon Lekima (2019) is investigated using the Weather Research and Forecasting (WRF) model version 3.8.1. Results show that decreasing vertical grid spacing from approximately 1000 m to 100 m above 1 km height barely influences the simulated track. However, significant differences are found in the simulated tropical cyclone (TC) structure. The simulation with the coarsest vertical resolution shows a clear double warm-core structure. The upper warm core weakens and even disappears with the increase of vertical resolution. A broader eye and a more slantwise eyewall are observed with the increase of vertical resolution due to the vertically extended lower-level and upper-level outflow, which likely results in a weaker subsidence. Vertical grid convergence is evaluated with the simulated kinetic energy (KE) spectra. As the vertical grid spacing becomes finer than 200 m, convergent KE spectra are found in both the free atmosphere and the outer core of the TC. However, sensitivity tests reveal that the grid convergence is sensitive to the choice of the planetary boundary layer scheme.

Keywords vertical resolution      tropical cyclone      warm core      kinetic energy spectra     
Corresponding Author(s): Mengjuan LIU   
Online First Date: 17 August 2021    Issue Date: 04 March 2022
 Cite this article:   
Mengjuan LIU,Lin DENG,Wei HUANG, et al. The impact of vertical resolution on the simulation of Typhoon Lekima (2019) by a cloud-permitting model[J]. Front. Earth Sci., 2022, 16(1): 158-174.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-021-0923-8
https://academic.hep.com.cn/fesci/EN/Y2022/V16/I1/158
Fig.1  Vertical distribution of model levels for all experiments.
Fig.2  (a) Tracks, (b) minimum sea level pressure, (c) maximum azimuthally averaged tangential wind at 10 m, and (d) 200–850 hPa shear of horizontal wind (m·s−1) for V40, V70, V161, and V311. Black lines in (a) and (b) indicate the best track data provided by CMA.
Fig.3  Snapshots of the instantaneous streamlines and horizontal distribution of the relative humidity (color shading) at 36 h and 16 km height for (a) V40, (b) V70, (c) V161, and (d) V311. The front-like and trough-like patterns are denoted by red and blue lines, respectively.
Fig.4  Time-height Hovmöller diagrams of the perturbation temperature in the eye region (r = 0–30 km) for (a) V40, (b) V70, (c) V161, (d) V311, (e) V46L, and (f) V113U. Data are plotted every 3 h.
Fig.5  Radial-height cross sections of the azimuthally averaged equivalent potential temperature on the left panel, and radial wind (color shading) and upward motion (contours) on the right panel at 60 h of the simulation. Contours only include vertical velocity greater than 0.5 m·s−1.
Fig.6  Left panel (a, c, e, g): radial-height cross sections of the azimuthally averaged radial wind (color shading) and upward motion (contours, vertical velocity greater than 0.5 m·s−1); right panel (b, d, f, h): diabatic heating rates (color shading) and tangential winds (contours) at 36 h for all experiments.
Fig.7  Outgoing longwave radiation at the top of the atmosphere at 36 h for (a) V40, (b) V70, (c) V161, and (d) V311.
Fig.8  Compensated horizontal KE (KE × k5/3) spectra at z = 5 km, 16 km, and 24 km. For clarity, the z = 16 km and z = 24 km spectra are shifted two and four decades up, respectively.
Fig.9  Azimuthal KE spectra at z = 5 and 16 km for V40, V70, V161, and V311. For clarity, the z = 16 km spectra are shifted six decades up.
Fig.10  Compensated horizontal KE (KE × k5/3) spectra at z = 16 km for V40_MYJ, V70_MYJ, V161_MYJ, and V311_MYJ.
Fig.11  Azimuthal KE spectra at z = 16 km for V40_MYJ, V70_MYJ, V161_MYJ, and V311_MYJ.
Study Case Model Model domain and grid spacing
Δx/km Ptop/hPa nz
ZW03 Hurricane Andrew (1992) MM5 54, 18, 6 50 23, 32, 35, 46, 69
KD06 idealized MM5 15, 5 100 24, 35
M12 Typhoon Talim (2005) WRF 3.2 30, 10 50 34, 36, 53, 61
Z15 idealized HWRF 27, 9, 3 50 21, 32, 43, 64
This study Typhoon Lekima (2019) WRF 3.8.1 3 10 40, 70, 161, 311
Tab.1  Summary of key model parameters
Fig.12  (a) Minimum sea level pressure and (b) maximum azimuthally averaged tangential wind at 10 m height for V46L and V113U in comparison with V40 and V161.
Fig.13  As in Fig. 5 but for V113U.
Fig.14  Time series of the average relative humidity (%) in the outer region of the TC at 9 km for V40, V113U and V161. The values are averaged within the annulus of 150–300 km from the TC center.
1 G H Bryan, R Rotunno (2009). The maximum intensity of tropical cyclones in axisymmetric numerical model simulations. Mon Weather Rev, 137(6): 1770–1789
https://doi.org/10.1175/2008MWR2709.1
2 J G Charney (1949). On a physical basis for numerical prediction of large-scale motions in the atmosphere. J Meteorol, 6(6): 371–385
https://doi.org/10.1175/1520-0469(1949)006<0372:OAPBFN>2.0.CO;2
3 P Chen, H Yu, M Xu, X Lei, F Zeng (2019). A simplified index to assess the combined impact of tropical cyclone precipitation and wind on China. Front Earth Sci, 13(4): 672–681
https://doi.org/10.1007/s11707-019-0793-5
4 M J P Cullen (2017). The impact of high vertical resolution in the Met Office Unified Model. Q J R Meteorol Soc, 143(702): 278–287
https://doi.org/10.1002/qj.2920
5 M DeMaria, C R Sampson, J A Knaff, K D Musgrave (2014). Is tropical cyclone intensity guidance improving? Bull Am Meteorol Soc, 95(3): 387–398
https://doi.org/10.1175/BAMS-D-12-00240.1
6 S G Gopalakrishnan, F Marks Jr, J A Zhang, X Zhang, J W Bao, V Tallapragada (2013). A study of the impacts of vertical diffusion on the structure and intensity of the tropical cyclones using the high-resolution HWRF system. J Atmos Sci, 70(2): 524–541
https://doi.org/10.1175/JAS-D-11-0340.1
7 B W Green, F Zhang (2015). Numerical simulations of Hurricane Katrina (2005) in the turbulent gray zone. J Adv Model Earth Syst, 7(1): 142–161
https://doi.org/10.1002/2014MS000399
8 K Hamilton, R J Wilson, R S Hemler (1999). Middle atmosphere simulated with high vertical and horizontal resolution versions of a GCM: improvements in the cold pole bias and generation of a QBO-like oscillation in the tropics. J Atmos Sci, 56(22): 3829–3846
https://doi.org/10.1175/1520-0469(1999)056<3829:MASWHV>2.0.CO;2
9 S Y Hong, Y Noh, J Dudhia (2006). A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Weather Rev, 134(9): 2318–2341
https://doi.org/10.1175/MWR3199.1
10 R A Houze Jr (2010). Clouds in tropical cyclones. Mon Weather Rev, 138(2): 293–344
https://doi.org/10.1175/2009MWR2989.1
11 M J Iacono, J S Delamere, E J Mlawer, M W Shephard, S A Clough, W D Collins (2008). Radiative forcing by long-lived greenhouse gases: calculations with the AER radiative transfer models. J Geophys Res D Atmospheres, 113(D13): D13103
https://doi.org/10.1029/2008JD009944
12 J Ito, T Oizumi, H Niino (2017). Near-surface coherent structures explored by large eddy simulation of entire tropical cyclones. Sci Rep, 7(1): 3798
https://doi.org/10.1038/s41598-017-03848-w pmid: 28630481
13 Z I Janić (2001). Nonsingular implementation of the Mellor-Yamada level 2.5 scheme in the NCEP Meso model
14 J Kaplan, M DeMaria, J A Knaff (2010). A revised tropical cyclone rapid intensification index for the Atlantic and eastern North Pacific Basins. Weather Forecast, 25(1): 220–241
https://doi.org/10.1175/2009WAF2222280.1
15 S K Kimball, F C Dougherty (2006). The sensitivity of idealized hurricane structure and development to the distribution of vertical levels in MM5. Mon Weather Rev, 134(7): 1987–2008
https://doi.org/10.1175/MWR3171.1
16 D E Lane, R C J Somerville, S F Iacobellis (2000). Sensitivity of cloud and radiation parameterizations to changes in vertical resolution. J Clim, 13(5): 915–922
https://doi.org/10.1175/1520-0442(2000)013<0915:SOCARP>2.0.CO;2
17 J Li, B Chen, W Huang, X Zhang (2018). Investigation of the impact of cloud initialization on numerical prediction of a convective system. J Trop Meteorol, 34: 198–208
18 R S Lindzen, M Foxrabinovitz (1989). Consistent vertical and horizontal resolution. Mon Weather Rev, 117(11): 2575–2583
https://doi.org/10.1175/1520-0493(1989)117<2575:CVAHR>2.0.CO;2
19 X Lu, H Yu, M Ying, B Zhao, S Zhang, L Lin, L Bai, R Wan (2021). Western North Pacific Tropical Cyclone Database created by the China Meteorological Administration. Adv Atmos Sci, 38: 690– 699
20 Z Ma, J Fei, X Huang, X Cheng (2012). Sensitivity of tropical cyclone intensity and structure to vertical resolution in WRF. Asia-Pac J Atmospheric Sci, 48(1): 67–81
https://doi.org/10.1007/s13143-012-0007-5
21 Z Ma, J Fei, X Huang, X Cheng (2014). Impacts of the lowest model level height on tropical cyclone intensity and structure. Adv Atmos Sci, 31(2): 421–434
https://doi.org/10.1007/s00376-013-3044-9
22 G L Mellor, T Yamada (1982). Development of a turbulence closure-model for geophysical fluid problems. Rev Geophys, 20(4): 851–875
https://doi.org/10.1029/RG020i004p00851
23 D S Nolan, D P Stern, J A Zhang (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 II: inner-core boundary layer and eyewall structure. Mon Weather Rev, 137(11): 3675–3698
https://doi.org/10.1175/2009MWR2786.1
24 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). 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
25 M J Pecnick, D Keyser (1989). The effect of spatial-resolution on the simulation of upper-tropospheric frontogenesis using a sigma-coordinate primitive equation model. Meteorol Atmos Phys, 40(4): 137–149
https://doi.org/10.1007/BF01032454
26 V D Pope, J A Pamment, D R Jackson, A Slingo (2001). The representation of water vapor and its dependence on vertical resolution in the Hadley Centre Climate Model. J Clim, 14(14): 3065–3085
https://doi.org/10.1175/1520-0442(2001)014<3065:TROWVA>2.0.CO;2
27 R Rogers, S Aberson, M Black, P Black, J Cione, P Dodge, J Dunion, J Gamache, J Kaplan, M Powell, N Shay, N Surgi, E Uhlhorn (2006). The intensity forecasting experiment: a NOAA multiyear field program for improving Tropical Cyclone intensity forecasts. Bull Am Meteorol Soc, 87(11): 1523–1538
https://doi.org/10.1175/BAMS-87-11-1523
28 R Rotunno, Y Chen, W Wang, C Davis, J Dudhia, G J Holland (2009). Large-eddy simulation of an idealized Tropical Cyclone. Bull Am Meteorol Soc, 90(12): 1783–1788
https://doi.org/10.1175/2009BAMS2884.1
29 C M Rozoff, C S Velden, J Kaplan, J P Kossin, A J Wimmers (2015). Improvements in the probabilistic prediction of tropical cyclone rapid intensification with passive microwave observations. Wea. Forecasting, 30, 1016–1038
https://doi.org/10.1175/WAF-D-14-00109.1
30 D R Ryglicki, J D Doyle, D Hodyss, J H Cossuth, Y Jin, K C Viner, J M Schmidt (2019). The unexpected rapid intensification of Tropical Cyclones in moderate vertical wind shear. Part III: outflow-environment interaction. Mon Weather Rev, 147(8): 2919–2940
https://doi.org/10.1175/MWR-D-18-0370.1
31 W C Skamarock (2004). Evaluating mesoscale NWP models using kinetic energy spectra. Mon Weather Rev, 132(12): 3019–3032
https://doi.org/10.1175/MWR2830.1
32 W C Skamarock, J Klemp, J Dudhia, D O Gill, D Barker, W Wang, J G Powers (2008). A Description of the Advanced Research WRF Version 3. NCAR Tech Note, NCAR/TN-475+STR, 1–113
33 W C Skamarock, C Snyder, J B Klemp, S Park (2019). Vertical resolution requirements in atmospheric simulation. Mon Weather Rev, 147(7): 2641–2656
https://doi.org/10.1175/MWR-D-19-0043.1
34 R K Smith, S Vogl, (2008). A simple model of the hurricane boundary layer revisited. Quarterly Journal of the Royal Meteorological Society, 134, 337–351
https://doi.org/10.1002/qj.216
35 D P Stern, D S Nolan (2012). On the height of the warm core in tropical cyclones. J Atmos Sci, 69(5): 1657–1680
https://doi.org/10.1175/JAS-D-11-010.1
36 D P Stern, F Zhang (2013). How does the eye warm? Part I: a potential temperature budget analysis of an idealized Tropical Cyclone. J Atmos Sci, 70(1): 73–90
https://doi.org/10.1175/JAS-D-11-0329.1
37 G Thompson, R M Rasmussen, K Manning (2004). Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: description and sensitivity analysis. Mon Weather Rev, 132(2): 519–542
https://doi.org/10.1175/1520-0493(2004)132<0519:EFOWPU>2.0.CO;2
38 M L Waite (2016). Dependence of model energy spectra on vertical resolution. Mon Weather Rev, 144(4): 1407–1421
https://doi.org/10.1175/MWR-D-15-0316.1
39 H Wang, Y Wang, J Xu, Y Duan (2019). Evolution of the warm-core structure during the eyewall replacement cycle in a numerically simulated Tropical Cyclone. J Atmos Sci, 76(8): 2559–2573
https://doi.org/10.1175/JAS-D-19-0017.1
40 S Wang, Q Jiang (2017). Impact of vertical wind shear on roll structure in idealized hurricane boundary layers. Atmos Chem Phys, 17(5): 3507–3524
https://doi.org/10.5194/acp-17-3507-2017
41 S Watanabe, K Sato, Y Kawatani, M Takahashi (2015). Vertical resolution dependence of gravity wave momentum flux simulated by an atmospheric general circulation model. Geosci Model Dev, 8(6): 1637–1644
https://doi.org/10.5194/gmd-8-1637-2015
42 L Wu, Q Liu, Y Li (2019). Tornado-scale vortices in the tropical cyclone boundary layer: numerical simulation with the WRF–LES framework. Atmos Chem Phys, 19(4): 2477–2487
https://doi.org/10.5194/acp-19-2477-2019
43 H Yu, L Chen (2019). Impact assessment of landfalling tropical cyclones: introduction to the special issue. Front Earth Sci, 13(4): 669–671
https://doi.org/10.1007/s11707-019-0809-1
44 B Zhang, R S Lindzen, V Tallapragada, F Weng, Q Liu, J A Sippel, Z Ma, M A Bender (2016). Increasing vertical resolution in US models to improve track forecasts of Hurricane Joaquin with HWRF as an example. Proc Natl Acad Sci USA, 113(42): 11765–11769
https://doi.org/10.1073/pnas.1613800113 pmid: 27698121
45 D Zhang, Y Liu, M Yau (2002). A multiscale numerical study of Hurricane Andrew (1992). Part V: inner-core thermodynamics. Mon Weather Rev, 130(11): 2745–2763
https://doi.org/10.1175/1520-0493(2002)130<2745:AMNSOH>2.0.CO;2
46 D Zhang, X Wang (2003). Dependence of hurricane intensity and structures on vertical resolution and time-step size. Adv Atmos Sci, 20(5): 711–725
https://doi.org/10.1007/BF02915397
47 D Zhang, L Zhu, X Zhang, V Tallapragada (2015). Sensitivity of idealized hurricane intensity and structures under varying background flows and initial vortex intensities to different vertical resolutions in HWRF. Mon Weather Rev, 143(3): 914–932
https://doi.org/10.1175/MWR-D-14-00102.1
48 J A Zhang, W M Drennan (2012). An observational study of vertical eddy diffusivity in the hurricane boundary layer. J Atmos Sci, 69(11): 3223–3236
https://doi.org/10.1175/JAS-D-11-0348.1
49 P Zhu (2008). Simulation and parameterization of the turbulent transport in the hurricane boundary layer by large eddies. J Geophys Res, 113: D17104
https://doi.org/10.1029/2007JD009643
50 P Zhu, B Tyner, J A Zhang, E Aligo, S Gopalakrishnan, F D Marks, A Mehra, V Tallapragada (2019). Role of eyewall and rainband eddy forcing in tropical cyclone intensification. Atmos Chem Phys, 19(22): 14289–14310
https://doi.org/10.5194/acp-19-14289-2019
51 C Zhou, P Chen, S Yang, F Zheng, H Yu, J Tang, Y Lu, G Chen, X Lu, X Zhang, J Sun (2021). The impact of Typhoon Lekima (2019) on East China: a postevent survey in Wenzhou city and Taizhou city. Front Earth Sci
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