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The Change of Meteorological Parameters with Land Use in MM5

The Change of Meteorological Parameters with Land Use in MM5. Jaemeen Baek Dec. 01. 2003. Overview. It has been widely accepted that land surface processes and their modeling play an important role in meteorology models (F. Chen et al., 2001)

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The Change of Meteorological Parameters with Land Use in MM5

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  1. The Change of Meteorological Parameters with Land Use in MM5 Jaemeen Baek Dec. 01. 2003

  2. Overview • It has been widely accepted that land surface processes and their modeling play an important role in meteorology models (F. Chen et al., 2001) • The change of meteorological parameters that affect air quality, for example, boundary layer depth, vertical diffusivity, etc., was analyzed as the land use and grid size varies in MM5.

  3. Model Domain • Lambert conformal conic projection • Central meridian: 97W, Origin of projection: 40N, 97W, Standard Parallels: 33N, 45N • Pleim-Xiu land-surface model • 24 land use categories • The original domain covers whole Georgia • 3x3 cells for a 36km resolution and 9x9 cells for a 12km resolution were chosen around Lake Lanier • Dates • 2001/07/01-2001/07/10, 2002/01/01-2002/01/10

  4. Cat Land use 12km (%) 36km (%) 1 Urban 960 (3.7) 240 (8.3) 2 Dry land crop 2400 (9.3) 11 Deciduous broadleaf 480 (1.8) 14 Evergreen broadleaf 20640 (79.6) 2640 (91.7) 15 Mixed forest 1200 (4.6) 16 Water 240 (0.9) Land Use Characteristics • Due to the grid size difference, the land use distribution for 12km and 36km resolution grids are different

  5. Expected Results – Direct Interactions of Parameterizations Cloud detrainment Microphysics Cumulus Cloud effects Cloud fraction Radiation PBL Surface fluxes SH, LH Downward SW, LW Surface emission/ albedo Surface T, QV, wind Surface

  6. 12km vs. 36km – Ground Temperature

  7. 12km vs. 36km – Sensible Heat Flux

  8. Variance(water)/mean2(water) Variance(EB)/mean2(EB) Data Analysis • The 1st Analysis • The ratio of normalized variance of water area (12km resolution) to that of evergreen broadleaf (36km resolution) • To screen which parameters could vary significantly with land use • Parameters whose ratios are more than 0.05 are selected • Ground temperature, PBL height, sensible heat flux, latent heat flux, outgoing short wave radiation, winds

  9. Results of 1st Analysis – Jun/01 and Jan/02

  10. Data Analysis • The 2nd Analysis • To prove that different land use would result in significant changes in meteorological parameters • Assumptions • If the land uses are same, differences between outputs from 12km resolution and from 36km resolution can be ignored • Sets of [(Outputs)12km – (Outputs)36km] for same land use(evergreen broadleaf, A), and sets of output differences for different land use area (B) are compared • F-test - The variance of A is same as that of B • T-test - The mean of A is same as that of B

  11. 1 14 1 14 Jan Jun Jan Jun Jan Jun Jan Jun 1 O O 1 O O 2 O O 2 O X 11 X X 11 O X 15 X X 15 X X 16 O O 16 O O Results of F-test Ground temperature PBL Height (O: Rejected)

  12. 1 14 1 14 Jan Jun Jan Jun Jan Jun Jan Jun 1 O O 1 O O 2 O O 2 O O 11 O X 11 X X 15 X X 15 X X 16 O O 16 O O Results of T-test Ground temperature PBL Height (O: Rejected)

  13. Conclusions • We can say that ground temperature, PBL height, sensible heat flux, and friction velocity changes a lot with land use • This cause discontinuity between adjacent cells • In the mixed land use area, meteorological parameters predicted in MM5 could have big errors • The accuracy of land surface classification is also important

  14. Improvement • Calculating averaged physical parameters of each cell during TERRAIN process • The original land-use data has percentages of all land uses in a grid cell • Smoothing results • For some land-use categories, like water, average outputs from MM5 with outputs from the adjacent grid cells

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