Impact of physical representations in CALMET on the simulated wind field over land during Super Typhoon Meranti (2016)

Abstract

A WRF (Weather Research and Forecasting Model)/CALMET (California Meteorological Model) coupled system is used to investigate the impact of physical representations in CALMET on simulations of the near-surface wind field of Super Typhoon Meranti (2016). The coupled system is configured with a horizontal grid spacing of 3 km in WRF and 500 m in CALMET, respectively. The model performance of the coupled WRF/CALMET system is evaluated by comparing the results of simulations with observational data from 981 automatic surface stations in Fujian Province. The root mean square error (RMSE) of the wind speed at 10 m in all CALMET simulations is significantly less than the WRF simulation by 20%–30%, suggesting that the coupled WRF/CALMET system is capable of representing more realistic simulated wind speed than the mesoscale model only. The impacts of three physical representations including blocking effects, kinematic effects of terrain and slope flows in CALMET are examined in a specified local region called Shishe Mountain. The results show that before the typhoon landfall in Xiamen, a net downslope flow that is tangent to the terrain is generated in the west of Shishe Mountain due to blocking effects with magnitude exceeding 10 m/s. However, the blocking effects seem to take no effect in the strong wind area after typhoon landfall. Whether being affected by the typhoon strong wind or not, the slope flows move downslope at night and upslope in the daytime due to the diurnal variability of the local heat flux with magnitude smaller than 3 m/s. The kinematic effects of terrain, which are speculated to play a significant role in the typhoon strong wind area, can only be applied to atmospheric flows in stable conditions when the wind field is quasi-nondivergent.

Keywords

physical representations / CALMET / wind field / Super Typhoon Meranti

References

[1] Al-Yahyai S, Charabi Y, Gastli A (2010). Review of the use of Numerical Weather Prediction (NWP) models for wind energy assessment. Renew Sustain Energy Rev, 14(9): 3192–3198

[2] Allwine K J, Whiteman C D (1985). MELSAR: A mesoscale air quality model for complex terrain. Volume 1—Overview, technical description and user’s guide. Pacific Northwest Laboratory: Richland, Washington

[3] Arakawa A, Jung J H, Wu C M (2011). Toward unification of the multiscale modeling of the atmosphere. Atmos Chem Phys, 11(8): 3731–3742

[4] Black T L (1994). The new NMC mesoscale Eta model: description and forecast examples. Weather Forecast, 9(2): 265–278

[5] Chen F, Dudhia J (2001). Coupling an advanced land surface hydrology model with the Penn State-NCAR MM5 modeling system. Part I: model implementation and sensitivity. Mon Weather Rev, 129(4): 569–585

[6] GB/T 35237–2017. Specifications for Surface Meteorological Observation-automatic Observatrion. Beijing: China Standards Press, 2017 (in Chinese)

[7] Gioli B, Gualtieri G, Busillo C, Calastrini F, Gozzini B, Miglietta F (2014). Aircraft wind measurements to assess a coupled WRF-CALMET mesoscale system. Meteorol Appl, 21(1): 117–128

[8] González J A, Hernández-Garcés A, Rodriguez A, Saavedra S, Casares J J (2015). Surface and upper-air WRF-CALMET simulations assessment over a coastal and complex terrain area. Int J Environ Pollut, 57(3–4): 249–260

[9] Goodin W R, Mcrae G J, Seinfeld J H (1980). An objective analysis technique for constructing three-dimensional urban-scale wind fields. J Appl Meteorol, 19(1): 98–108

[10] Grell G A, Dudhia J, Stauffer D R (1995). A description of the fifth generation Penn State/NCAR mesoscale model (MM5). Tech Note NCAR/TN-398+STR. NCAR: Boulder, CO

[11] Holtslag A A M, Van Ulden A P (1983). A Simple scheme for daytime estimates of the surface fluxes from routine weather data. J Appl Meteorol, 22(4): 517–529

[12] Horst T W, Doran J C (1986). Nocturnal drainage flow on simple slopes. Boundary-Layer Meteorol, 34(3): 263–286

[13] Iacono M J, Delamere J S, Mlawer E J, Shephard M W, Clough S A, Collins W D (2008). Radiative forcing by long–lived greenhouse gases: calculations with the AER radiative transfer models. J Geophy Res-atmos, 113(D13)

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

[15] Lam H, Kok M H, Shum K K Y (2012). Benefits from typhoons–the Hong Kong perspective. Weather, 67(1): 16–21

[16] Lin Y L, Farley R D, Orville H D (1983). Bulk parameterization of the snow field in a cloud model. J Clim Appl Meteorol, 22(6): 1065–1092

[17] Liu M K, Yocke M A (1980). Siting of wind turbine generators in complex terrain. J Energy, 4(1): 10–16

[18] Lu Y X, Tang J P, Wang Y, Song L L (2012). Validation of near-surface winds obtained by a hybrid WRF/CALMET modeling system over a coastal island with complex terrain. J Trop Meteorol, 18(3): 284–296

[19] Ludwig F L, Miller D K, Gallaher S G (2006). Evaluating a hybrid prognostic-diagnostic model that improves wind forecast resolution in complex coastal topography. J Appl Meteorol Climatol, 45(1): 155–177

[20] Mahrt L (1982). Momentum balance of gravity flows. J Atmos Sci, 39(12): 2701–2711

[21] Mortensen N G, Landberg L (1993). Wind altas analysis and application program (WAsP) user’s guide. Riso National Laboratory: Roskilde, Denmark

[22] Pielke R A, Cotton W R, Walko R L, Tremback C J, Lyons W A, Grasso L D, Nicholls M E, Moran M D, Wesley D A, Lee T J, Copeland J H (1992). A comprehensive meteorological modeling system—RAMS. Meteorol Atmos Phys, 49(1–4): 69–91

[23] Scire J S, Robe F R, Fernau M E, Yamartino R J (1998). A user’s guide for the CALMET meteorological model (Version 5). Earth Tech Inc: Concord, MA

[24] Skamarock W C, Klemp J B, Dudhia J, Gill D O, Barker D M, Duda M G, Huang X Y, Wang W, Powers J G (2008). A description of the advanced research WRF version 3. Tech Note NCAR/TN-475+STR. NCAR: Boulder, CO.

[25] Sukoriansky S, Galperin B, Perov V (2005). Application of a new spectral theory of stably stratified turbulence to the atmospheric boundary layer over sea ice. Boundary-Layer Meteorol, 117(2): 231–257

[26] US EPA (2004). User's Guide for the AERMOD meteorological preprocessor (AERMET). EPA–454/B–03e002. U.S. Environmental Protection Agency: Research Triangle Park, NC

[27] Wang W, Shaw W J, Seiple T E, Rishel J P, Xie Y (2008). An evaluation of a diagnostic wind model (CALMET). J Appl Meteorol Climatol, 47(6): 1739–1756

[28] Wyngaard J C (2004). Toward numerical modeling in the “terra incognita”. J Atmos Sci, 61(14): 1816–1826

[29] Yim S H L, Fung J C H, Lau A K H, Kot S C (2007). Developing a high-resolution wind map for a complex terrain with a coupled MM5/CALMET system. J Geophy Res-atmos, 112 (D5)

[30] Ying M, Zhang W, Yu H, Lu X Q, Feng J X, Fan Y X, Zhu Y T, Chen D Q (2014). An overview of the China Meteorological Administration tropical cyclone database. J Atmos Ocean Technol, 31(2): 287–301

[31] Zhao Y, Wang Y, Chen J, Huang H (2018). Numerical investigation on detailed structure of typhoon“Meranti”(2016) and extreme heavy rainfall event induced by it before and after landfall in Fujian. Torrential Rain and Disa, 37(2): 135–148

Acknowledgement

This research was supported by the National Basic Research Program of China (No. 2015CB452806), the National Natural Science Foundation of China (Nos. 41805088, 41875080), Natural Science Foundation of Shanghai (No. 18ZR1449100), and Fundamental Research Foundation of Shanghai Typhoon Institute of the China Meteorological Administration (Nos. 2018JB05, 2019JB06).

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