Advertisement

Boundary-Layer Meteorology

, Volume 165, Issue 3, pp 475–496 | Cite as

The Impact of Land-Surface Parameter Properties and Resolution on the Simulated Cloud-Topped Atmospheric Boundary Layer

  • Leonhard GantnerEmail author
  • Vera Maurer
  • Norbert Kalthoff
  • Olga Kiseleva
Research Article
  • 300 Downloads

Abstract

Sensitivity tests using the ‘Consortium for Small Scale Modeling’ model in large-eddy simulation mode with a grid spacing of 100 m are performed to investigate the impact of the resolution of soil- and vegetation-related parameters on a cloud-topped boundary layer in a real-data environment. The reference simulation uses the highest land-surface parameter resolution available for operational purposes (300 m). The sensitivity experiments were conducted using spatial averaging of about \(2.5\,\hbox {km}\times 2.5\,\hbox {km}\) and \(10\,\hbox {km} \times 10\,\hbox {km}\) for the land-surface parameters and a completely homogeneous distribution for the whole model domain of about \(70\,\hbox {km} \times 70\,\hbox {km}\). Additionally, one experiment with a higher mean soil moisture and another with six mesoscale patches of enhanced or reduced soil moisture are performed. Boundary-layer clouds developed in all simulations. To assess the deviations of cloud cover on different scales within the model domain, we calculated the root-mean-square deviation (RMSD) between the sensitivity experiments and the reference simulation. The RMSD depends strongly on the spatial resolution at which cloud fields are compared. Different spatial resolutions of the cloud fields were generated by applying a low-pass filter. For all sensitivity experiments, large RMSD values occur for cut-off wavelengths \({<}1\) km, reflecting the stochastic nature of convection, but they decrease rapidly for wavelengths between 1 and 5 km. For cut-off wavelengths \({>}5\,\hbox {km}\), the RMSD is still pronounced for the simulation with higher mean soil moisture. Additionally, for cut-off wavelengths between 5 and 30 km, considerable differences can be found for the experiment with mesoscale patches and for that with homogeneous land-surface parameters. Spatial averaging of land-surface parameters for areas of \(2.5\,\hbox {km} \times 2.5\,\hbox {km}\) and \(10\,\hbox {km} \times 10\,\hbox {km}\) results in larger patch sizes but simultaneously in reduced amplitudes of land-surface parameter anomalies and shows the lowest RMSD for all cut-off wavelengths.

Keywords

High-definition clouds and precipitation for advancing climate prediction \((\hbox {HD}(\hbox {CP})^{2})\) High resolution modelling Real-data large-eddy simulation Soil moisture 

Notes

Acknowledgements

This work was funded by the Federal Ministry of Education and Research in Germany (BMBF) as part of the research program ‘High Definition Clouds and Precipitation for Climate Prediction—HD(CP)\(^{2}\)’ (FKZ: 01LK1205A). We thank the DWD-colleagues Jürgen Helmert for providing us with the version of the TERRA model that contains the use of the pedotransfer functions and Ulrich Blahak for his support concerning the LES version of the COSMO model. We thank the three anonymous reviewers for their constructive comments.

References

  1. Adler B, Kalthoff N, Gantner L (2011) Initiation of deep convection caused by land-surface inhomogeneities in West Africa: a modelled case study. Meteorol Atmos Phys 112:15–27CrossRefGoogle Scholar
  2. Baldauf M, Seifert A, Förstner J, Majewski D, Raschendorfer M, Reinhardt T (2011) Operational convective-scale numerical weather prediction with the COSMO model: description and sensitivities. Mon Weather Rev 139(12):3887–3905CrossRefGoogle Scholar
  3. Barthlott C, Hoose C (2015) Spatial and temporal variability of clouds and precipitation over Germany: multiscale simulations across the “gray zone”. Atmos Chem Phys 15:12361–12384Google Scholar
  4. Catalano F, Moeng CH (2010) Large-eddy simulation of the daytime boundary layer in an idealized valley using the weather research and forecasting numerical model. Boundary-Layer Meteorol 137(1):49–75. doi: 10.1007/s10546-010-9518-8 CrossRefGoogle Scholar
  5. Caughey SJ (1982) Observed characteristics of the atmospheric boundary layer. In: Nieuwstadt FT, Van Dop H (eds) Atmospheric turbulence and air pollution modelling, Springer, Netherlands, pp 107–158Google Scholar
  6. Courault D, Drobinski P, Brunet Y, Lacarrere P, Talbot C (2007) Impact of surface heterogeneity on a buoyancy-driven convective boundary layer in light winds. Boundary-Layer Meteorol 124(3):383–403. doi: 10.1007/s10546-007-9172-y CrossRefGoogle Scholar
  7. Dixon NS, Parker DJ, Taylor CM, Garcia-Carreras L, Harris PP, Marsham JH, Polcher J, Woolley A (2013) The effect of background wind on mesoscale circulations above variable soil moisture in the Sahel. Q J R Meteorol Soc 139(673):1009–1024. doi: 10.1002/qj.2012 CrossRefGoogle Scholar
  8. Fiori E, Parodi A, Siccardi F (2010) Turbulence closure parameterization and grid spacing effects in simulated supercell storms. J Atmos Sci 67(12):3870–3890. doi: 10.1175/2010JAS3359.1 CrossRefGoogle Scholar
  9. Gantner L, Kalthoff N (2010) Sensitivity of a modelled life cycle of a mesoscale convective system to soil conditions over West Africa. Q J R Meteorol Soc 136(S1):471–482. doi: 10.1002/qj.425 CrossRefGoogle Scholar
  10. Garcia-Carreras L, Parker DJ, Marsham JH (2011) What is the mechanism for the modification of convective cloud distributions by land surface-induced flows? J Atmos Sci 68(3):619–634CrossRefGoogle Scholar
  11. Garratt JR (1994) The atmospheric boundary layer. Cambridge University Press, Cambridge, 316 ppGoogle Scholar
  12. Hanley KE, Plant RS, Stein THM, Hogan RJ, Nicol JC, Lean HW, Halliwell C, Clark PA (2015) Mixing-length controls on high-resolution simulations of convective storms. Q J R Meteorol Soc 141(686):272–284. doi: 10.1002/qj.2356 CrossRefGoogle Scholar
  13. Heinze R, Dipankar A, Carbajal Henken C, Moseley C, Sourdeval O, Trömel S, Xie X, Adamidis P, Ament F, Baars H, Barthlott C, Behrendt A, Blahak U, Bley S, Brdar S, Brueck M, Crewell S, Deneke H, Di Girolamo P, Evaristo R, Fischer J, Frank C, Friederichs P, Göcke T, Gorges K, Hande L, Hanke M, Hansen A, Hege HC, Hoose C, Jahns T, Kalthoff N, Klocke D, Kneifel S, Knippertz P, Kuhn A, van Laar T, Macke A, Maurer V, Mayer B, Meyer CI, Muppa SK, Neggers RAJ, Orlandi E, Pantillon F, Pospichal B, Röber N, Scheck L, Seifert A, Seifert P, Senf F, Siligam P, Simmer C, Steinke S, Stevens B, Wapler K, Weniger M, Wulfmeyer V, Zängl G, Zhang D, Quaas J (2017) Large-eddy simulations over germany using icon: a comprehensive evaluation. Q J R Meteorol Soc 143(702):69–100. doi: 10.1002/qj.2947 CrossRefGoogle Scholar
  14. Heise E (2002) Die neue Modellkette des DWD I, 4: Parametrisierungen. promet - Fortbildungszeitschrift des DWD 27(3/4):130–141Google Scholar
  15. Herzog HJ, Schubert U, Vogel G, Fielder A, Kirchner R (2002a) LLM - the high-resolving nonhydrostatic simulation model in the DWD project LITFASS Part I, modelling technique and simulation method. DWD Forschung und Entwicklung: Arbeitsergebnisse 67:75 SGoogle Scholar
  16. Herzog HJ, Vogel G, Schubert U (2002b) LLM: a nonhydrostatic model applied to high-resolving simulations of turbulent fluxes over heterogeneous terrain. Theor Appl Climatol 73(1–2):67–86. doi: 10.1007/s00704-002-0694-4 CrossRefGoogle Scholar
  17. Honnert R, Masson V, Couvreux F (2011) A diagnostic for evaluating the representation of turbulence in atmospheric models at the kilometric scale. J Atmos Sci 68:31123131. doi: 10.1175/JAS-D-11-061.1 CrossRefGoogle Scholar
  18. Hozumi K, Harimaya T, Magono C (1982) The size distribution of cumulus clouds as a function of cloud amount. J Meteorol Soc Jpn 60:691–699CrossRefGoogle Scholar
  19. Huang HY, Margulis SA (2013) Impact of soil moisture heterogeneity length scale and gradients on daytime coupled land-cloudy boundary layer interactions. Hydrol Process 27(14):1988–2003CrossRefGoogle Scholar
  20. Khodayar S, Kalthoff N, Schädler G (2013) The impact of soil moisture variability on seasonal convective precipitation simulations: Part 1: validation, feedbacks, and realistic initialisation. Meteorol Z 22(4):489–505CrossRefGoogle Scholar
  21. Kohler M, Kalthoff N, Kottmeier C (2010) The impact of soil moisture modifications on CBL characteristics in West Africa: A case study from the AMMA campaign. Q J R Meteorol Soc 136(S1):442–455. doi: 10.1002/qj.430 CrossRefGoogle Scholar
  22. Kohler M, Schädler G, Gantner L, Kalthoff N, Königer F, Kottmeier C (2012) Validation of two SVAT models for different periods during the West African monsoon. Meteorol Z 21(5):509–524CrossRefGoogle Scholar
  23. Langhans W, Schmidli J, Schär C (2012) Bulk convergence of cloud-resolving simulations of moist convection over complex terrain. J Atmos Sci 69(7):2207–2228CrossRefGoogle Scholar
  24. Larson VE, Schanen DP, Wang M, Ovchinnikov M, Ghan S (2012) PDF parameterization of boundary layer clouds in models with horizontal grid spacings from 2 to 16 km. Mon Weather Rev 140(1):285–306CrossRefGoogle Scholar
  25. Lenschow DH, Wyngaard JC, Pennell WT (1980) Mean-field and second-moment budgets in a baroclinic, convective boundary layer. J Atmos Sci 37(6):1313–1326CrossRefGoogle Scholar
  26. Lenschow DH, Lothon M, Mayor SD, Sullivan PP, Canut G (2012) A comparison of higher-order vertical velocity moments in the convective boundary layer from lidar with in situ measurements and large-eddy simulation. Boundary-Layer Meteorol 143(1):107–123CrossRefGoogle Scholar
  27. Lohou F, Patton EG (2014) Surface energy balance and buoyancy response to shallow cumulus shading. J Atmos Sci 71(2):665–682CrossRefGoogle Scholar
  28. Macke A, Seifert P, Baars H, Beekmans C, Behrendt A, Bohn B, Bühl J, Crewell S, Damian T, Deneke H, Düsing S, Foth A, Girolamo PD, Hammann E, Heinze R, Hirsikko A, Kalisch J, Kalthoff N, Kinne S, Kohler M, Löhnert U, Madhavan BL, Maurer V, Muppa SK, Schween J, Serikov I, Siebert H, Simmer C, Späth F, Steinke S, Träumner K, Wehner B, Wieser A, Wulfmeyer V, Xie X (2017) The HD(CP)\(^{2}\) observational prototype experiment (HOPE) - an overview. Atmos Chem Phys 17(7):4887–4914. doi: 10.5194/acp-17-4887-2017 CrossRefGoogle Scholar
  29. Maronga B, Raasch S (2013) Large-eddy simulations of surface heterogeneity effects on the convective boundary layer during the LITFASS-2003 experiment. Boundary-Layer Meteorol 146(1):17–44. doi: 10.1007/s10546-012-9748-z CrossRefGoogle Scholar
  30. Mason P (1994) Large-eddy simulation: a critical review of the technique. Q J R Meteorol Soc 120:1–26CrossRefGoogle Scholar
  31. Mason P, Brown A (1999) Large-eddy simulation: On subgrid models and filter operations in large eddy simulations. J Atmos Sci 56:2101–2114CrossRefGoogle Scholar
  32. Maurer V, Kalthoff N, Wieser A, Kohler M, Mauder M (2016) Observed spatial variability of boundary-layer turbulence over flat, heterogeneous terrain. Atmos Chem Phys 16:1377–1400CrossRefGoogle Scholar
  33. Nachtergaele F, van Velthuizen H, Verelst L, Wiberg D (2012) Harmonized World Soil Database, Version 1.2. Technical ReportGoogle Scholar
  34. Ookouchi Y, Segal M, Kessler R, Pielke R (1984) Evaluation of soil moisture effects on the generation and modification of mesoscale circulations. Mon Weather Rev 112(11):2281–2292CrossRefGoogle Scholar
  35. Petch JC, Brown AR, Gray MEB (2002) The impact of horizontal resolution on the simulations of convective development over land. Q J R Meteorol Soc 128(584):2031–2044. doi: 10.1256/003590002320603511 CrossRefGoogle Scholar
  36. Pielke RA (2001) Influence of the spatial distribution of vegetation and soils on the prediction of cumulus convective rainfall. Rev Geophys 39(2):151–177. doi: 10.1029/1999RG000072 CrossRefGoogle Scholar
  37. Plank VG (1969) The size distribution of cumulus clouds in representative florida populations. J Appl Meteorol 8(1):46–67CrossRefGoogle Scholar
  38. Raasch S, Harbusch G (2001) An analysis of secondary circulations and their effects caused by small-scale surface inhomogeneities using large-eddy simulation. Boundary-Layer Meteorol 101(1):31–59. doi: 10.1023/A:1019297504109 CrossRefGoogle Scholar
  39. Ricard D, Lac C, Riette S, Legrand R, Mary A (2013) Kinetic energy spectra characteristics of two convection-permitting limited-area models AROME and Meso-NH. Q J R Meteorol Soc 139(674):1327–1341. doi: 10.1002/qj.2025 CrossRefGoogle Scholar
  40. Rieck M, Hohenegger C, van Heerwaarden CC (2014) The influence of land surface heterogeneities on cloud size development. Mon Weather Rev 142(10):3830–3846CrossRefGoogle Scholar
  41. Rieck M, Hohenegger C, Gentine P (2015) The effect of moist convection on thermally induced mesoscale circulations. Q J R Meteorol Soc 141:2418–2428. doi: 10.1002/qj.2532 CrossRefGoogle Scholar
  42. Ritter B, Geleyn JF (1992) A comprehensive radiation scheme for numerical weather prediction models with potential applications in climate simulations. Mon Weather Rev 120(2):303–325. doi: 10.1175/1520-0493(1992)120<0303:ACRSFN>2.0.CO;2 CrossRefGoogle Scholar
  43. Schättler U, Doms G, Schraff C (2014) A description of the nonhydrostatic regional COSMO-model, Part VII: User’s Guide. Technical Report, Deutscher Wetterdienst, Offenbach, GermanyGoogle Scholar
  44. Schwendike J, Kalthoff N, Kohler M (2010) The impact of mesoscale convective systems on the surface and boundary-layer structure in West Africa: Case-studies from the AMMA campaign 2006. Q J R Meteorol Soc 136(648):566–582. doi: 10.1002/qj.599 Google Scholar
  45. Segal M, Arritt R (1992) Nonclassical mesoscale circulations caused by surface sensible heat-flux gradients. Bull Am Meteorol Soc 73(10):1593–1604CrossRefGoogle Scholar
  46. Shen S, Leclerc MY (1995) How large must surface inhomogeneities be before they influence the convective boundary layer structure? A case study. Q J R Meteorol Soc 121(526):1209–1228. doi: 10.1002/qj.49712152603 CrossRefGoogle Scholar
  47. Shin HH, Hong SY (2013) Analysis of resolved and parameterized vertical transports in convective boundary layers at gray-zone resolutions. J Atmos Sci 70(10):3248–3261CrossRefGoogle Scholar
  48. Shuttleworth WJ (1991) Evaporation models in hydrology. In: Schmugge TJ, André J-C (eds) Land surface evaporation. Springer, New York, pp 93–120Google Scholar
  49. Siebert J, Sievers U, Zdunkowski W (1992) A one-dimensional simulation of the interaction between land surface processes and the atmosphere. Boundary-Layer Meteorol 59(1–2):1–34. doi: 10.1007/BF00120684 CrossRefGoogle Scholar
  50. Smiatek G, Helmert J, Gerstner EM (2016) Impact of land use and soil data specifications on COSMO-CLM simulations in the CORDEX-MED area. Meteorol Z 25(2):215–230. doi: 10.1127/metz/2015/0594 CrossRefGoogle Scholar
  51. Stein T, Hogan RJ, Clark PA, Halliwell CE, Hanley KE, Lean HW, Nicol JC, Plant RS (2015) The DYMECS project: a statistical approach for the evaluation of convective storms in high-resolution NWP models. Bull Am Meteorol Soc 96:939–951. doi: 10.1175/BAMS-D-13-00279.1 CrossRefGoogle Scholar
  52. Sun WY, Bosilovich M (1996) Planetary boundary layer and surface layer sensitivity to land surface parameters. Boundary-Layer Meteorol 77(3–4):353–378. doi: 10.1007/BF00123532 CrossRefGoogle Scholar
  53. Talbot C, Bou-Zeid E, Smith J (2012) Nested mesoscale large-eddy simulations with WRF: performance in real test cases. J Hydrometeorol 13(5):1421–1441CrossRefGoogle Scholar
  54. Taylor CM, Parker DJ, Harris PP (2007) An observational case study of mesoscale atmospheric circulations induced by soil moisture. Geophys Res Lett 34(L15):801. doi: 10.1029/2007GL030572 Google Scholar
  55. van Genuchten MT (1980) A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. J Appl Meteorol 54(1):189–206. doi: 10.1175/JAMC-D-14-0140.1 Google Scholar
  56. van Heerwaarden CC, Mellado JP, De Lozar A (2014) Scaling laws for the heterogeneously heated free convective boundary layer. J Atmos Sci 71(11):3975–4000. doi: 10.1175/JAS-D-13-0383.1 CrossRefGoogle Scholar
  57. Wetzel PJ, Chang JT (1988) Evapotranspiration from nonuniform surfaces: a first approach for short-term numerical weather prediction. Mon Weather Rev 116(3):600–621CrossRefGoogle Scholar
  58. Wösten JHM, Lilly A, Nemes A, Le Ba C (1999) Development and use of a database of hydraulic properties of European soils. Geoderma 90:169–185CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.Institute of Meteorology and Climate ResearchKarlsruhe Institute of Technology (KIT)KarlsruheGermany
  2. 2.Deutscher WetterdienstOffenbachGermany

Personalised recommendations