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Simulating SEVIRI observations of RACMO

Simulating SEVIRI observations of RACMO. Bastiaan Jonkheid Erik van Meijgaard Rob Roebeling. Goals. Explore weak spots in the CPP retrieval algorithm by testing against known input Compare RACMO output with satellite observations Simulated vs observed reflectances

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Simulating SEVIRI observations of RACMO

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  1. Simulating SEVIRI observations of RACMO Bastiaan Jonkheid Erik van Meijgaard Rob Roebeling

  2. Goals • Explore weak spots in the CPP retrieval algorithm by testing against known input • Compare RACMO output with satellite observations • Simulated vs observed reflectances • Model vs retrieved liquid water path RACMO output LWP simulator Reflectance SEVIRI observation retrieval algorithm

  3. SEVIRI • Spinning Enhanced Visible & InfraRed Imager aboard MSG • 12 channel image every 15 minutes • For cloud retrieval 3 channels are used: • Visible at 0.6 µm • Near IR at 1.6 µm • Thermal IR

  4. The CPP retrieval algorithm cf Nakajima & King (1990)

  5. Procedure • Get RACMO output • Obtain cloud optical thickness (COT), effective radius (both liquid droplets and ice crystals) • Determine satellite geometry • Calculate reflectances at 0.6, 1.6 µm using DAK (or from table) • Perform CPP retrievals

  6. RACMO output • # model_origin HIRLAM_FMT • # reference_dtg 2009051412 • # forecast_length 24 • # verifying_dtg 2009051512 • # receptor_point 1 • # ilon/ilat_point 1 1 • # geographic_coord. -25.06 27.93 • # height_surface 0.0 m • # surface_pressure 1022.63 hPa • # land_sea_mask 0.00 • # surface_temperature 20.78 C • # actual_surf._albedo 0.060 • # surface_roughness 0.00009 • # snow_cover 0.00000 m water equiv. • # total_cloud_cover 100.0 [%] • # water_vapour_column 21.626 kg/m^2 • # liquid_water_column 25.662 g/m^2 • # ice_water_column 26.973 g/m^2 • # 2D_cloud_cover 100.0 100.0 100.0 100.0 [%] (max,upw,dwnw,random) • # • #lev hght pres Th T qv cl ql qi • # [m] [hPa] [K] [C] [g/kg] [%] [mg/kg][mg/kg] • 1 30539.4 10.90 837.38 -42.84 0.003 0. 0.0 0.0 • 2 23330.0 32.60 579.31 -55.12 0.003 0. 0.0 0.0 • 3 20123.9 54.40 484.06 -62.36 0.003 0. 0.0 0.0 • 4 18070.0 76.10 430.27 -66.89 0.003 0. 0.0 0.0 • 5 16696.0 95.60 401.73 -67.62 0.003 0. 0.0 0.0 • 6 15666.6 113.40 383.75 -67.01 0.004 0. 0.0 0.0 • 7 14714.7 132.80 367.71 -66.54 0.006 0. 0.0 0.0 • 8 13812.5 154.10 354.92 -65.08 0.012 18. 0.0 0.2 • 9 12952.2 177.30 345.89 -62.09 0.016 69. 0.0 1.3 • 10 12128.5 202.30 338.16 -58.87 0.021 36. 0.0 1.4 • 11 11338.2 229.20 332.43 -54.85 0.048 44. 0.0 1.5 • 12 10574.0 257.90 330.24 -48.86 0.105 100. 0.0 11.6 • 13 9828.1 288.50 330.23 -41.60 0.226 100. 0.0 20.8 • 14 9100.3 320.70 328.54 -35.70 0.397 100. 0.0 26.5 • 15 8394.4 354.60 326.00 -30.68 0.521 92. 0.0 19.0

  7. Converting to DAK input (1) • Obtain reff: • Simple assumption: fixed sizes (8 µm for liquid, 12 µm for ice) • Not yet implemented: use RACMO’s sizes • 10 µm droplets over land • 13 µm droplets over sea • 30-60 µm ice crystals, depending on temperature

  8. Converting to DAK input (2) • Cloud fraction: • Stochastic overlap: divide into independent subcolumns, in each subcolumn a layer is either cloudy or not

  9. Converting to DAK input (3) • Cloud phase: • Separate liquid (2 km) and ice (6 km) layers

  10. Running DAK • Running DAK for each model grid point is very expensive (several weeks on a workstation) • Use lookup table (22x10x3x44x44x91) for COT, ice/liquid ratio, albedo, θ, θ0 and φ • Need more dimensions for ice crystal and water droplet effective radii

  11. Comparing reflectances

  12. Reflectances • Most structures seen by SEVIRI are represented by RACMO • Simulated reflectances are too high • Run again with larger particle sizes

  13. Retrieval (1)

  14. Retrieval (2) • Retrieval of effective radius is pretty good • Retrieved COT is too high • Simulator has positive bias in the 0.6 µm reflectance due to ignored ozone absorption

  15. Retrieval (3)

  16. Future work • Look at retrieved vs model LWP • Look at effects of diurnal cycle • Implement larger particle sizes • Fix ozone absorption

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