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Implementation of the Land Surface Processes into a Vector Vorticity Equation Model (VVM) to Study its Impact on Afternoon Thunderstorms over Complex Topography in Taiwan

  • Chien-Ming WuEmail author
  • Hsiao-Chun Lin
  • Fang-Yi Cheng
  • Mu-Hua Chien
Original Article
  • 25 Downloads

Abstract

In this study, we aim to evaluate the impact of fast land-atmosphere interactions on the afternoon thunderstorm in Taiwan through high-resolution meteorological simulations. For this purpose, the Noah land surface model (LSM) is implemented into the vector vorticity equation cloud-resolving model (VVM) with corresponding realistic land surface data of Taiwan into the coupling system, called TaiwanVVM. Two idealized experiments are conducted by giving the same surface forcing but one with direct land-atmosphere coupling from Noah LSM (called Coupled experiment) and the other with prescribed surface fluxes (called Prescribed experiment). Our results show that the fast land-atmosphere interaction over complex topography has a significant influence on rainfall intensity, especially in the heavy precipitating region where the interaction is strong. Without direct coupling between the land surface and the atmosphere in the Prescribed experiment, the diurnal intensity is suppressed by 50% over whole Taiwan and 70% for East Taiwan. Our findings demonstrate that the intensity of the afternoon thunderstorm is sensitive to fast land-atmosphere interactions by modifying local circulation in the mountainous region of Taiwan.

Keywords

Land-atmosphere interactions Afternoon thunderstorms Cloud-resolving model TaiwanVVM 

Notes

Acknowledgements

We thank TIMS for providing computation resources and data storages. The authors are supported by Taiwan’s MoST through grant 107-2111-M-002 -010 -MY4 to National Taiwan University.

References

  1. Albertson, J.D., Kustas, W.P., Scanlon, T.M.: Large-eddy simulation over heterogeneous terrain with remotely sensed land surface conditions. Water Resour. Res. 37(7), 1939–1953 (2001).  https://doi.org/10.1029/2000WR900339 CrossRefGoogle Scholar
  2. Anthes, R.A.: Enhancement of convective precipitation by mesoscale variation in vegetative covering in semiarid regions. J. Clim. Appl. Meteorol. 23, 541–554 (1984).  https://doi.org/10.1175/1520-0450(1984)023<0541:EOCPBM>2.0.CO;2 CrossRefGoogle Scholar
  3. Arakawa, A., Wu, C.-M.: A unified representation of deep moist convection in numerical modeling of the atmosphere. Part I. J. Atmos. Sci. 70, 1977–1992 (2013).  https://doi.org/10.1175/JAS-D-13-0163.1 CrossRefGoogle Scholar
  4. Arnold, D., et al.: High resolution modelling in complex terrain. Report on the HiRCoT 2012 Workshop, Vienna, Austria, 21–23 February 2012, BOKU-MET Rep. 21, 42 pp. (2012). Available online at http://www.boku.ac.at/met/report/BOKU-Met_Report_21_online.pdf. Accessed 24 Mar 2019
  5. Arnold, D., Morton, D., Schicker, I., Seibert, P., Rotach, M.W., Horvath, K., Dudhia, T., Satomura, T., Müller, M., Zängl, G., Takemi, T., Serafin, S., Schmidli, J., Schneider, S.: Issues in high-resolution atmospheric modeling in complex terrain - the HiRCoT workshop. Croatian Meteorol. J. 47(47), 3–11 (2014)Google Scholar
  6. Avissar, R., Liu, Y.: Three-dimensional numerical study of shallow convective clouds and precipitation induced by land surface forcing. J. Geophys. Res. 101(D3), 7499–7518 (1996).  https://doi.org/10.1029/95JD03031 CrossRefGoogle Scholar
  7. Banta, R. M.: The Role of Mountain Flows in Making Clouds, in: Atmospheric Processes over Complex Terrain, Meteor. Monogr., edited by W. Blumen, Amer. Meteor. Soc., Boston, Massachusetts, the United States, 299–283, (1990)Google Scholar
  8. Benjamin, S.G., Grell, G.A., Brown, J.M., Smirnova, T.G.: Mesoscale weather prediction with the RUC hybrid isentropic-terrain-following coordinate model. Mon. Weather Rev. 132, 473–494 (2004)CrossRefGoogle Scholar
  9. Betts, A.K., Ball, J.H., Beljaars, A.C.M., Miller, M.J., Viterbo, P.A.: The land surface-atmosphere interaction: a review based on observational and global modeling perspectives. J. Geophys. Res. 101, 7209–7225 (1996).  https://doi.org/10.1029/95JD02135 CrossRefGoogle Scholar
  10. Bonan, G.B., Pollard, D., Thompson, S.L.: Influence of subgrid-scale heterogeneity in leaf area index, stomatal resistance, and soil moisture on grid-scale land–atmosphere interactions. J. Clim. 6, 1882–1897 (1993)CrossRefGoogle Scholar
  11. Carleton, A.M., Arnold, D.L., Travis, D.J., Curran, S., Adegoke, J.O.: Synoptic circulation and land surface influences on convection in the Midwest U.S. “Corn Belt” during the summers of 1999 and 2000. Part I: composite synoptic environments. J. Clim. 21, 3389–3415 (2008).  https://doi.org/10.1175/2007JCLI1578.1 CrossRefGoogle Scholar
  12. Chen, C.-S., Chen, Y.-L.: The rainfall characteristics of Taiwan. Mon. Weather Rev. 131, 1323–1341 (2003).  https://doi.org/10.1175/1520-0493(2003)131<1323:TRCOT>2.0.CO;2 CrossRefGoogle Scholar
  13. Chen, F., Dudhia, J.: 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, 569–585 (2001a).  https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2 CrossRefGoogle Scholar
  14. Chen, F., Dudhia, J.: Coupling an advanced land surface– hydrology model with the Penn State–NCAR MM5 modeling system. Part II: preliminary model validation. Mon. Weather Rev. 129, 587–604 (2001b)CrossRefGoogle Scholar
  15. Chen, F., Mitchell, K., Schaake, J., Xue, Y., Pan, H., Koren, V., Duan, Y., Ek, M., Betts, A.: Modeling of land surface evaporation by four schemes and comparison with FIFE observations. J. Geophys. Res. 101(D3), 7251–7268 (1996).  https://doi.org/10.1029/95JD02165 CrossRefGoogle Scholar
  16. Chen, F., Janjic, Z.I., Mitchell, K.: Impact of atmospheric surface-layer parameterizations in the new land-surface scheme of the NCEP mesoscale eta model. Bound.-Layer Meteorol. 85, 391–421 (1997)CrossRefGoogle Scholar
  17. Chen, C.-S., Chen, W.-C., Tao, W.-K.: Characteristics of heavy summer rainfall in southwestern Taiwan in relation to orographic effects. J. Meteor. Soc. Japan. 82, 1521–1543 (2004).  https://doi.org/10.2151/jmsj.82.1521 CrossRefGoogle Scholar
  18. Chen, C.-S., Liu, C.-L., Yen, M.-C., Chen, C.-Y., Lin, P.-L., Huang, C.-Y.: Terrain effects on an afternoon heavy rainfall event, observed over northern Taiwan on 20 June 2000 during monsoon break. J. Meteor. Soc. Japan. 88, 649–671 (2010).  https://doi.org/10.2151/jmsj.2010-403 CrossRefGoogle Scholar
  19. Cheng, F.-Y., Hsu, Y.-C., Lin, P.-L., Lin, T.-H.: Investigation of the effects of different land use and land cover patterns on mesoscale meteorological simulations in the Taiwan area. J. Appl. Meteorol. Climatol. 52, 570–587 (2013).  https://doi.org/10.1175/JAMC-D-12-0109.1 CrossRefGoogle Scholar
  20. Chien, M.-H., Wu, C.-M.: Representation of topography by partial steps using the immersed boundary method in a vector vorticity equation model (VVM). J. Adv. Model. Earth Syst. 8, 212–223 (2016).  https://doi.org/10.1002/2015MS000514 CrossRefGoogle Scholar
  21. Cosby, B.J., Hornberger, G.M., Clapp, R.B., Ginn, T.R.: A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils. Water Resour. Res. 20(6), 682–690 (1984).  https://doi.org/10.1029/WR020i006p00682 CrossRefGoogle Scholar
  22. Deardorff, J.W.: Parameterization of the planetary boundary layer for use in general circulation models. Mon. Weather Rev. 100, 93–106 (1972).  https://doi.org/10.1175/1520-0493(1972)100<0093:POTPBL>2.3.CO;2 CrossRefGoogle Scholar
  23. Ek, M.B., Mitchell, K.E., Lin, Y., Rogers, E., Grunmann, P., Koren, V., Gayno, G., Tarpley, J.D.: Implementation of Noah land surface model advances in the National Centers for environmental prediction operational mesoscale eta model. J. Geophys. Res. 108, (2003).  https://doi.org/10.1029/2002JD003296
  24. Erlingis, J.M., Barros, A.P.: A study of the role of daytime land-atmosphere interactions on nocturnal convective activity in the southern Great Plains during CLASIC. J. Hydrometeorol. 15, 1932–1953 (2014).  https://doi.org/10.1175/JHM-D-14-0016.1 CrossRefGoogle Scholar
  25. Feng, Z., Hagos, S., Rowe, A.K., Burleyson, C.D., Martini, M.N., de Szoeke, S.P.: Mechanisms of convective cloud organization by cold pools over tropical warm ocean during the AMIE/DYNAMO field campaign. J. Adv. Model. Earth Syst. 7, 357–381 (2015).  https://doi.org/10.1002/2014MS000384 CrossRefGoogle Scholar
  26. Gentine, P., Garelli, A., Park, S., Nie, J., Torri, G., Kuang, Z.: Role of surface heat fluxes underneath cold pools. Geophys. Res. Lett. 43, 874–883 (2016)CrossRefGoogle Scholar
  27. Hechtel, L., Moeng, C.-H., Stull, R.: Temperature profiling of the atmospheric boundary layer with rotational Raman lidar during the HD(CP)2 observational prototype experiment. J. Atmos. Sci. 47, 1721–1741 (1990).  https://doi.org/10.1175/1520-0469(1990)047,1721:TEONSF.2.0.CO;2 CrossRefGoogle Scholar
  28. Houze Jr., R.A.: Orographic effects on precipitating clouds. Rev. Geophys. 50, RG1001 (2012).  https://doi.org/10.1029/2001RG000365 CrossRefGoogle Scholar
  29. Huang, H.-Y., Margulis, S.A.: On the impact of surface heterogeneity on a realistic convective boundary layer. Water Resour. Res. 45, W04425 (2009).  https://doi.org/10.1029/2008WR007175 CrossRefGoogle Scholar
  30. Jarvis, A., H.I. Reuter, A. Nelson, and E. Guevara: Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90m Database (http://www.srtm.csi.cgiar.org), (2008)
  31. Jung, J.H., Arakawa, A.: A three-dimensional anelastic model based on the vorticity equation. Mon. Weather Rev. 136(1), 276–294 (2008).  https://doi.org/10.1175/2007MWR2095.1 CrossRefGoogle Scholar
  32. Kerns, B.W.J., Chen, Y.-L., Chang, M.-Y.: The diurnal cycle of winds, rain, and clouds over Taiwan during Mei-Yu, summer, and autumn rainfall regimes. Mon. Weather Rev. 138, 497–516 (2010).  https://doi.org/10.1175/2009MWR3031.1 CrossRefGoogle Scholar
  33. Krueger, S.K.: Numerical simulation of tropical cumulus clouds and their interaction with the subcloud layer. J. Atmos. Sci. 45, 2221–2250 (1988).  https://doi.org/10.1175/1520-0469(1988)045<2221:NSOTCC>2.0.CO;2 CrossRefGoogle Scholar
  34. Krueger, S.K., Fu, Q., Liou, K.N., Chin, H.-N.S.: Improvements of an ice-phase microphysics parameterization for use in numerical simulations of tropical convection. J. Appl. Meteorol. 34(1), 281–287 (1995).  https://doi.org/10.1175/1520-0450-34.1.281 CrossRefGoogle Scholar
  35. Kumar, A., Chen, F., Barlage, M., Ek, M.B., Niyogi, D.: Assessing impacts of integrating MODIS vegetation data in the weather research and forecasting (WRF) model coupled to two different canopy-resistance approaches. J. Appl. Meteorol. Climatol. 53, 1362–1380 (2014).  https://doi.org/10.1175/JAMC-D-13-0247.1 CrossRefGoogle Scholar
  36. Lam, J.S.L., Lau, A.K.H., Fung, J.C.H.: Application of refined land-use categories for high resolution mesoscale atmospheric modeling. Bound.-Layer Meteor. 119(2), 263–288 (2006).  https://doi.org/10.1007/s10546-005-9027-3 CrossRefGoogle Scholar
  37. Lawrence, D.M., Oleson, K.W., Flanner, M.G., Thornton, P.E., Swenson, S.C., Lawrence, P.J., Zeng, X., Yang, Z.L., Levis, S., Sakaguchi, K., Bonan, G.B., Slater, A.G.: Parameterization improvements and functional and structural advances in version 4 of the community land model. J. Adv. Model. Earth Syst. 3, M03001 (2011)Google Scholar
  38. Leung, K.-W., Chen, T.-T.: Soils of Taiwan. J. Agric. Assoc. China New Series. 20, 1–25 (1957)Google Scholar
  39. Leung, L.R., Ghan, S.J.: A subgrid parameterization of orographic precipitation. Theor. Appl. Climatol. 52(1–2), 95–118 (1995).  https://doi.org/10.1007/BF00865510 CrossRefGoogle Scholar
  40. Lin, T.-S., Cheng, F.-Y.: Impact of soil moisture initialization and soil texture on simulated land-atmosphere interaction in Taiwan. J. Hydrometeorol. 17, 1337–1355 (2016).  https://doi.org/10.1175/JHM-D-15-0024.1 CrossRefGoogle Scholar
  41. Lin, P.-F., Chang, P.-L., Jou, B.J.-D., Wilson, J.W., Roberts, R.D.: Warm season afternoon thunderstorm characteristics under weak synoptic-scale forcing over Taiwan Island. Weath. Forecasting. 26, 44–60 (2011).  https://doi.org/10.1175/2010WAF2222386.1 CrossRefGoogle Scholar
  42. Mahrt, L., Ek, M.: The influence of atmospheric stability on potential evaporation. J. Climate Appl. Meteor. 23, 222–234 (1984).  https://doi.org/10.1175/1520-0450(1984)023<0222:TIOASO>2.0.CO;2 CrossRefGoogle Scholar
  43. Manabe, S.: Climate and the ocean circulation. The atmospheric circulation and hydrology of the Earth's surface. Mon. Weather Rev. 97, 739–774 (1969).  https://doi.org/10.1175/1520-0493(1969)097<0739:CATOC>2.3.CO;2 CrossRefGoogle Scholar
  44. Monin, A.S., Obukhov, A.M.: Basic laws of turbulent mixing in the surface layer of the atmosphere. Contrib. Geophys. Inst. Acad. Sci. USSR. 151, 163–187 (1954)Google Scholar
  45. Niu, G.-Y., Yang, Z.L., Mitchell, K.E., Chen, F., Ek, M.B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. J. Geophys. Res. 116, D12109 (2011).  https://doi.org/10.1029/2010JD015139 CrossRefGoogle Scholar
  46. Pielke Sr., R.A., Avissar, R., Raupach, M., Dolman, H., Zeng, X., Denning, S.: Interactions between the atmosphere and terrestrial ecosystems: influence on weather and climate. Glob. Chang. Biol. 4, 101–115 (1998)CrossRefGoogle Scholar
  47. Raasch, S., Harbusch, G.: An analysis of secondary circulations and their effects caused by small-scale surface inhomogeneities using large-eddy simulation. Bound.-Layer Meteor. 101(1), 31–59 (2001).  https://doi.org/10.1023/A:1019297504109 CrossRefGoogle Scholar
  48. Rodell, M., Houser, P.R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J.K., Walker, J.P., Lohmann, D., Toll, D.: The global land data assimilation system. Bull. Amer. Meteor. Soc. 85(3), 381–394 (2004).  https://doi.org/10.1175/BAMS-85-3-381 CrossRefGoogle Scholar
  49. Santanello, J.P., Dirmeyer, P.A., Ferguson, C.R., Findell, K.L., Tawfik, A.B., Berg, A., Ek, M., Gentine, P., Guillod, B.P., van Heerwarden, C., et al.: Land-atmosphere interactions: the LoCo perspective. Bull. Am. Meteorol. Soc. (2017)Google Scholar
  50. Sellers, P.J., Mintz, Y., Sud, Y.C., Dalcher, A.: A simple biosphere model (SIB) for use within general circulation models. J. Atmos. Sci. 43, 505–531 (1986)CrossRefGoogle Scholar
  51. Steeneveld, G.J., van de Wiel, B.J.H., Holtslag, A.A.M.: Modeling the evolution of the atmospheric boundary layer coupled to the land surface for three contrasting nights in CASES-99. J. Atmos. Sci. 63, 920–935 (2006).  https://doi.org/10.1175/JAS3654.1 CrossRefGoogle Scholar
  52. Stein, U., Alpert, P.: Factor separation in numerical simulations. J. Atmos. Sci. 50, 2107–2115 (1993)CrossRefGoogle Scholar
  53. Tsai, W.-M., Wu, C.-M.: The environment of aggregated deep convection. J. Adv. Model. Earth Sy. 9, 2061–2078 (2017).  https://doi.org/10.1002/2017MS000967 CrossRefGoogle Scholar
  54. Tucker, D.F., Crook, N.A.: Flow over heated terrain. Part II: generation of convective precipitation. Mon. Weather Rev. 133, 2565–2582 (2005).  https://doi.org/10.1175/MWR2965.1 CrossRefGoogle Scholar
  55. Wu, C.-M., Arakawa, A.: Inclusion of surface topography into the vector vorticity equation model (VVM). J. Adv. Model. Earth Syst. 3(2), M04002 (2011).  https://doi.org/10.1029/2011MS000061 CrossRefGoogle Scholar
  56. Wu, C.-M., Arakawa, A.: A unified representation of deep moist convection in numerical modeling of the atmosphere. Part II. J. Atmos. Sci. 71, 2089–2103 (2014).  https://doi.org/10.1175/JAS-D-13-0382.1 CrossRefGoogle Scholar
  57. Wu, C.-M., Lo, M.-H., Chen, W.-T., Lu, C.-T.: The impacts of heterogeneous land surface fluxes on the diurnal cycle precipitation: a framework for improving the GCM representation of land-atmosphere interactions. J. Geophys. Res. Atmos. 120, 3714–3727 (2015).  https://doi.org/10.1002/2014JD023030 CrossRefGoogle Scholar
  58. Xiu, A., Pleim, J.E.: Development of a land surface model. Part I: application in a mesoscale meteorological model. J. Appl. Meteorol. 40, 192–209 (2001)CrossRefGoogle Scholar
  59. Xue, Y., Shukla, J.: The influence of land surface properties on Sahel climate. Part I: desertification. J. Clim. 6, 2232–2246 (1993)CrossRefGoogle Scholar
  60. Xue, Y., Sellers, P.J., Kinter, J.L., Shukla, J.: A simplified biosphere model for global climate studies. J. Clim. 4, 345–364 (1991)CrossRefGoogle Scholar
  61. Yang, L.-G.: Analysis of afternoon convective precipitation over Taiwan (in Chinese). M.S. thesis, Institute of Atmospheric Physics, National Central University, Taiwan, 120 pp, (2000). Available from Institute of Atmospheric Physics, National Central University, Chung-Li, TaiwanGoogle Scholar
  62. Yu, C.-K., Cheng, L.-W.: Distribution and mechanisms of orographic precipitation associated with typhoon Morakot (2009). J. Atmos. Sci. 70, 2894–2915 (2013).  https://doi.org/10.1175/JAS-D-12-0340.1 CrossRefGoogle Scholar
  63. Zhao, W., Li, A.: A review on land surface processes modelling over complex terrain. Adv. Meteorol. 2015(3), 1–17 (2015).  https://doi.org/10.1155/2015/607181 Google Scholar
  64. Zobler, L.: A World Soil File for Global Climate Modeling, NASA Technical Memorandum 87802. NASA Goddard Institute for Space Studies, New York (1986)Google Scholar

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© Korean Meteorological Society and Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Atmospheric SciencesNational Taiwan UniversityTaipeiTaiwan
  2. 2.Department of Atmospheric SciencesNational Central UniversityTaoyuanTaiwan
  3. 3.Department of Atmospheric and Environmental SciencesUniversity at Albany, State University of New YorkAlbanyUSA
  4. 4.Center for Atmosphere Ocean Science, Courant Institute of Mathematical SciencesNew York UniversityNew YorkUSA

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