Meteorology and Atmospheric Physics

, Volume 116, Issue 3–4, pp 81–94 | Cite as

Disaggregation of screen-level variables in a numerical weather prediction model with an explicit simulation of subgrid-scale land-surface heterogeneity

Original Paper

Abstract

The earth’s surface is characterized by small-scale heterogeneity attributable to variability in land cover, soil characteristics and orography. In atmospheric models, this small-scale variability can be partially accounted for by the so-called mosaic approach, i.e., by computing the land-surface processes on a grid with an explicit higher horizontal resolution than the atmosphere. The mosaic approach does, however, not account for the subgrid-scale variability in the screen-level atmospheric parameters, part of which might be related to land-surface heterogeneity itself. In this study, simulations with the numerical weather prediction model COSMO are shown, employing the mosaic approach together with a spatial disaggregation of the atmospheric forcing by the screen-level variables to the subgrid-scale. The atmospheric model is run with a 2.8 km horizontal grid resolution while the land surface processes are computed on a 400-m horizontal grid. The disaggregation of the driving atmospheric variables at screen-level is achieved by a three-step statistical downscaling with rules learnt from high-resolution fully coupled COSMO simulations, where both, atmosphere and surface, were simulated on a 400-m grid. The steps encompass spline interpolation of the grid scale variables, conditional regression based on the high-resolution runs, and an optional stochastic noise generator which restores the variability of the downscaled variables. Simulations for a number of case studies have been carried out, with or without mosaic surface representation and with or without atmospheric disaggregation, and evaluated with respect to the surface state variables and the turbulent surface exchange fluxes of sensible and latent heat. The results are compared with the high-resolution fully coupled COSMO simulations. The results clearly demonstrate the high importance of accounting for subgrid-scale surface heterogeneity. It is shown that the atmospheric disaggregation leads to clear additional improvements in the structures of the two-dimensional surface state variable fields, but to only marginally impacts on the simulation of the turbulent surface exchange fluxes. A detailed analysis of these results identifies strongly correlated errors in atmospheric and surface variables in the mosaic approach as the main reason for the latter. The effects of these errors largely cancel out in the flux parameterization, and thus explain the comparably good results for the fluxes in the mosaic approach without atmospheric disaggregation despite inferior performance for the surface state variables themselves. Inserting noise in the disaggregation scheme leads to a deterioration of the results.

Notes

Acknowledgements

The authors gratefully acknowledge financial support by the SFB/TR 32 “Pattern in Soil-Vegetation-Atmosphere Systems: Monitoring, Modelling, and Data Assimilation” funded by the Deutsche Forschungsgemeinschaft (DFG), in which framework this work has been carried out. Moreover, the authors thank the German Meteorological Service (DWD) for the access to the COSMO-analyses data archive and the COSMO model code.

References

  1. Abramowitz G, Leung R, Clark M, Pitman A (2008) Evaluating the performance of land surface models. J Climate 21: 5468–5480CrossRefGoogle Scholar
  2. Ament F, Simmer C (2006) Improved representation of land-surface heterogeneity in a non-hydrostatic numerical weather prediction model. Bound Layer Meteorol 121(1):153–174CrossRefGoogle Scholar
  3. Arola A (1999) Parameterization of turbulent and mesoscale fluxes for heterogeneous surfaces. J Atmos Sci 56:584–598CrossRefGoogle Scholar
  4. Avissar R (1991) A statistical-dynamical approach to parameterize subgrid-scale land-surface heterogeneity in climate models. Surv Geophys 12:155–178CrossRefGoogle Scholar
  5. Avissar R (1992) Conceptual aspects of a statistical-dynamical approach to represent landscape subgrid-scale heterogeneities in atmospheric models. J Geophys Res 97:2729–2741CrossRefGoogle Scholar
  6. Avissar R (1998) Which type of soil-vegetation-atmosphere transfer scheme is needed for general circulation models: a proposal for a higher-order scheme. J Hydrology 212–213:136–154CrossRefGoogle Scholar
  7. Avissar R, Pielke RA (1989) A parameterization of heterogeneous land surfaces for atmospheric numerical models and its impact on regional meteorology. Mon Wea Rev 117:2113–2136CrossRefGoogle Scholar
  8. Avissar R, Schmidt T (1998) An evaluation of the scale at which ground-surface heat flux patchiness affects the convective boundary layer using large-eddy simulations. J Atmos Sci 55:2666–2689CrossRefGoogle Scholar
  9. Baldauf M, Förstner J, Klink S, Reinhardt T, Schraff C, Seifert A, Stephan K (2011) Kurze Beschreibung des Lokal-Modells Kürzestfrist COSMO-DE (LMK) und seiner Datenbanken auf dem Datenserver des DWD, Deutscher Wetterdienst, Geschäftsbereich Forschung und Entwicklung, Offenbach, Germany, http://www.dwd.de
  10. Belušić D, Güttler I (2010) Can mesoscale models reproduce meandering motions?. Q J R Meteorol Soc 136:553–565Google Scholar
  11. Beyrich F, Mengelkamp HT (2006) Evaporation over a heterogeneous land surface: EVA_GRIPS and the LITFASS-experiment: an overview. Bound Layer Meteorol 121:5–32CrossRefGoogle Scholar
  12. Beyrich F, Leps J, Mauder M, Bange J, Foken T, Huneke S, Lohse H, Lüdi A, Meijninger W, Mironov D, Wesensee U, Zittel P (2006) Area-averaged surface fluxes over the LITFASS region based on eddy-coveriance measurements. Bound Layer Meteorol 121:33–65CrossRefGoogle Scholar
  13. Bonan GB, Pollard D, Thompson SL (1993) Influence of subgrid-scale heterogeneity in leaf area index, stomatal resistance, and soil moisture on grid-scale land-atmosphere interactions. J Climate 6:1882–1896CrossRefGoogle Scholar
  14. Chow FK, Weigel AP, Street R, Rotach MW, Xue M (2006) High-resolution large-eddy simulations of flow in a steep Alpine valley. part I: Methodology, verification, and sensitivity experiments. J Appl Meteor Climatol 45:63–86CrossRefGoogle Scholar
  15. Dimri A (2009) Impact of subgrid scale scheme on topography and landuse for better regional scale simulation of meteorological variables over the western Himalayas. Clim Dyn 32:565–574CrossRefGoogle Scholar
  16. Doms G, Förstner J, Heise E, Herzog HJ, Mironov D, Raschendorfer M, Reinhardt T, Ritter B, Schrodin R, Schulz JP, Vogel G (2011) A description of the nonhydrostatic regional model LM, Part II: Physical parameterization. Deutscher Wetterdienst, http://www.cosmo-model.org
  17. EEA (2000) Corine land cover (CLC90). European Environment Agency, Copenhagen, http://dataservice.eea.eu.int/dataservice/
  18. Essery R, Best M, Betts R, Cox P (2003) Explicit representation of subgrid heterogeneity in a GCM land surface scheme. J Hydrometeorol 4:530–543CrossRefGoogle Scholar
  19. Foken T (2008) The energy balance closure problem: an overview. Ecol Appl 18:1351–1367 doi: 10.1890/06-0922.1 CrossRefGoogle Scholar
  20. Gao Y, Chen F, Barlage M, Liu W, Cheng G, Li X, Yu Y, Ran Y, Li H, Peng H, Ma M (2008) Enhancement of land surface information and its impact on atmospheric modeling in the Heihe River Basin, northwest China. J Geophys Res 113: D20S90CrossRefGoogle Scholar
  21. Giorgi F, Francisco R, Pal J (2003) Effects of a subgrid-scale topography and land use scheme on the simulation of surface climate and hydrology. Part I: Effects of temperature and water vapor disaggregation. J Hydrology 4(2):317–333Google Scholar
  22. Górska M, de Arellano JVG, LeMone MA, van Heerwaarden C (2008) Mean and flux horizontal variability of virtual potential temperature, moisture, and carbon dioxide: aircraft observations and LES study. Mon Wea Rev 136:4435–4451. doi:  10.1175/2008MWR2230.1 CrossRefGoogle Scholar
  23. Heinemann G, Kerschgens M (2005) Comparison of methods for area-averaging surface energy fluxes over heterogeneous land surfaces using high-resolution non-hydrostatic simulations. Int J Clim 25:379–403CrossRefGoogle Scholar
  24. Heinemann G, Kerschgens M (2006) Simulation of surface energy fluxes using high-resolution non-hydrostatic simulations and comparisons with measurements for the LITFASS-2003 experiment. Bound Layer Meteorol 121:195–220CrossRefGoogle Scholar
  25. Henderson-Sellers A, Pitman AJ (1992) Land-surface schemes for future climate models: specification, aggregation, and heterogeneity. J Geophys Res 97:2687–2696CrossRefGoogle Scholar
  26. Hendricks Franssen H, Stöckli R, Lehner I, Rotenberg E, Seneviratne S (2010) Energy balance closure of eddy-covariance data: a multisite analysis for European FLUXNET stations. Agr Forest Meteorol 150:1553–1567. doi: 10.1016/j.agrformet.2010.08.005 CrossRefGoogle Scholar
  27. Klemp JB, Wilhelmson RB (1978) The simulation of three-dimensional convective storm dynamics. J Atmos Sci 35:1070–1096CrossRefGoogle Scholar
  28. Kollet S, Maxwell R (2008) Capturing the influence of groundwater dynamics on land surface processes using an integrated, distributed watershed model. Water Resour Res 44:W02,402. doi: 10.1029/2007WR006004 CrossRefGoogle Scholar
  29. Koster RD, Suarez MJ (1992) Modeling the land surface boundary in climate models as a composite of independent vegetation stands. J Geophys Res 97:2697–2715CrossRefGoogle Scholar
  30. Lengfeld K, Ament F (2011) Observing local scale variability of near surface temperature and humidity using a wireless sensor network. J Appl Meteor Climatol. doi: 10.1175/JAMC-D-11-025.1 Google Scholar
  31. Maxwell RM, Chow FK, Kollet SJ (2007) The groundwater-land-surface-atmosphere connection: soil moisture effects on the atmospheric boundary layer in fully-coupled simulations. Adv Water Resour 30:2447–2466CrossRefGoogle Scholar
  32. Mengelkamp HT, Beyrich F, Heinemann G, Ament F, Bange J, Berger F, B senberg J, Foken T, Hennemuth B, Heret C, Huneke S, Johnsen KP, Kohsiek W, Leps JP, Liebethal C, Lohse H, Mauder M, Meijninger W, Raasch S, Simmer C, Spiess T, Tittebrand A, Uhlenbrock J, Zittel P (2006) Evaporation over a heterogeneous land surface: the EVA_GRIPS project. Bull Am Meteorol Soc 87:775–786. doi: 10.1175/BAMS-87-6-775 CrossRefGoogle Scholar
  33. Molod A, Salmun H, Waugh DW (2003) A new look at modeling surface heterogeneity: extending its influence in the vertical. J Hydrology 4:810–825Google Scholar
  34. Myoung B, Choi YS, Park SK (2011) A review on vegetation models and applicability to climate simulations at regional scale. Asia-Pacific J Atmos Sci 47(5):463–475. doi: 10.1007/s13143-011-0031-x CrossRefGoogle Scholar
  35. Rihani J, Maxwell R, Chow F (2010) Coupling groundwater and land surface processes: Idealized simulations to identify effects of terrain and subsurface heterogeneity on land surface energy fluxes. Water Resour Res 46:W1252. doi: 10.1029/2010WR009111 CrossRefGoogle Scholar
  36. Ritter B, Geleyn JF (1992) A comprehensive radiation scheme for numerical weather prediction models with potential applications in climate simulations. Mon Wea Rev 120:303–325CrossRefGoogle Scholar
  37. Schlünzen KH, Katzfey JJ (2003) Relevance of sub-grid-scale land-use effects for mesoscale models. Tellus 55(3): 232–246CrossRefGoogle Scholar
  38. Schmidli J, Poulus G, Daniels M, Chow F (2009) External influences on nocturnal thermally driven flows in a deep valley. J Appl Meteor Climatol 48:3–23CrossRefGoogle Scholar
  39. Schomburg A, Venema V, Lindau R, Ament F, Simmer C (2010) A downscaling scheme for atmospheric variables to drive soil-vegetation-atmosphere transfer models. Tellus 62B:242–258Google Scholar
  40. Schomburg A, Venema V, Ament F, Simmer C (2012) Application of an adaptive radiative transfer parameterisation in a mesoscale numerical weather prediction model. Q J R Meteorol Soc 138:91–102. doi: 10.1002/qj.890 CrossRefGoogle Scholar
  41. Seifert A, Beheng KD (2001) A double-moment parameterization for simulating autoconversion, accretion and selfcollection. Atmos Res 59-60:265–281CrossRefGoogle Scholar
  42. Seth A, Giorgi F, Dickinson R (1994) Simulating fluxes from heterogeneous land surfaces: explicit subgrid method employing the biosphere-atmosphere transfer scheme (BATS). J Geophys Res 99(D9): 18,651–18,667CrossRefGoogle Scholar
  43. Seuffert G, Gross P, an EF Wood CS (2002) The influence of hydrologic modeling on the predicted local weather: two-way coupling of a mesoscale weather prediction model and a land surface hydrologic model. J Hydrometeo 3(5):505–523CrossRefGoogle Scholar
  44. Steppeler J, Doms G, Schättler U, Bitzer H, Gassmann A, Damrath U (2003) Meso-gamma scale forecasts using the nonhydrostatic model LM. Meteor Atmos Phys 82: 75–96. doi: 10.1007/s00703-001-0592-9 CrossRefGoogle Scholar
  45. Stull RB (1988) An Introduction to Boundary Layer Meteorology. Kluwer Academic Publishers, DordrechtGoogle Scholar
  46. Tiedtke M (1989) A comprehensive mass flux scheme for cumulus parameterisation in large-scale models. Mon Wea Rev 117:1779–1799CrossRefGoogle Scholar
  47. Vereecken H, Kollet S, Simmer C (2010) Patterns in soil-vegetation-atmosphere systems: Monitoring, modeling, and data assimilation. Vadose Zone J 9 (4):821–827. doi: 10.2136/vzj2010.0122 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.University of BonnBonnGermany
  2. 2.Deutscher WetterdienstOffenbachGermany
  3. 3.Meteorological InstituteUniversity of BonnBonnGermany
  4. 4.ZMAW, University of HamburgHamburgGermany

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