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Improvement of land surface skin temperature (LST) in GFS

Improvement of Satellite Data Utilization over Desert and Arid Regions in NCEP Operational NWP Modeling and Data Assimilation Systems Michael Ek, Weizhong Zheng, Helin Wei, Jesse Meng and John Deber (NOAA/NCEP/EMC) Banghua Yan and Fuzhong Weng (NOAA/NESDIS/STAR)

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Improvement of land surface skin temperature (LST) in GFS

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  1. Improvement of Satellite Data Utilization over Desert and Arid Regions in NCEP Operational NWP Modeling and Data Assimilation Systems Michael Ek, Weizhong Zheng, Helin Wei, Jesse Meng and John Deber (NOAA/NCEP/EMC) Banghua Yan and Fuzhong Weng (NOAA/NESDIS/STAR) JCSDA 7th Workshop on Satellite Data Assimilation UMBC, May 12-13, 2009

  2. Motivation • Problems: Satellite data (IR/MW) is rarely used over desert/arid regions in GSI/CRTM (e.g. W. CONUS and N. Africa) • Substantial cold bias of land surface skin temperature (LST) in GFS. • Inaccurate emissivity calculation for MW in GSI/CRTM • Improvement of land surface skin temperature (LST) in GFS • New formula for momentum andthermal roughness lengths (Zom,Zot) (X. Zeng et al) • New emissivity calculation for MW in GSI/CRTM • Empirical emissivity model over desert region (B. Yan and F. Weng). Tb Simulation in GSI: IR NOAA-17 HIRS3: Ch8: 11-micron MW NOAA-18 AMSU_A: Ch1: 23.8 Ghz ; Ch15: 89.0 Ghz ;

  3. Xubin Zeng et al. (U. Arizona) New z0tformula in GFS: ln(z0m / z0t) = (1 − GVF) 2Czil k (u*z0g/ν)0.5 z0m : the momentum roughness length specified for each grid, z0t : the roughness length for heat, GVF: the green vegetation fraction, Czil : a coefficient to be determined and takes 0.8 in this study, k: the Von Karman constant (0.4), ν: the molecular viscosity (1.5×10-5 m2 s-1), u* : the friction velocity, z0g : the bare soil roughness length for momentum (0.01 m). Effective z0m is used as follows: ln(z0m) = (1 − GVF) 2 ln(z0g) + [1 − (1 − GVF) 2] ln(z0m) OPS: z0t = z0m

  4. Surface Emissivity Module in JCSDA Community Radiative Transfer Model: CRTM (Sfc Emissivity as function of satellite sensor channel & incidence angle) IR EM module over land IR EM module over ocean IR EM module over snow IR EM module over Ice Surface Emissivity Module MW EM module over bare soil MW EM module over land MW EM module over ocean MW EM module over snow MW EM module over Ice Based on Fuzhong Weng et al., NOAA/NESDIS Specified surface IR emissivity via look-up tables Specified surface MW emissivityvia physical OR empirical models, depending on spectral band and ocean / land / snow / sea-ice presence.

  5. LST [K] Verification with GOES and SURFRAD 3-Day Mean: July 1-3, 2007 GFS-GOES: New Zom,t GFS-GOES: New Zot GFS-GOES: CTR (a) (b) Large cold bias (c) (d) LST Ch Aerodynamic conductance: CTR vs Zom,t Improved significantly during daytime!

  6. Used in GSI Tb Simulation in GSI: NOAA-17 HIRS3 Ch8: 11-micron IR CTR Zom,t More data used in GSI CTR Zom,t Clear Sky Land: Bias-2.626 -0.546 rmse5.570 3.080

  7. Tb Simulation in GSI: NOAA-18 AMSU_A Ch15 MW CTR Zom,t Zom,t + ε Less data used in GSI (CTR)

  8. Ch 15 MW PDF distribution, Bias and RMSE: Land, Compacted Soil & Scrub CTR Zom,t Zom,t + ε Cold bias for Scrub Improved for Scrub W. CONUS CTR Zom,t Zom,t+ ε Scrub: Bias-3.445 2.260 1.420 rmse8.039 5.6664.784 Most part of W. CONUS is covered by “Scrub”.

  9. Case: 12Z, 20080801 Vegetation type, soil type and Green Vegetation Fraction (GVF) Veg type Soil type Soil type GVF Vegetation Types GFS CRTM Veg_7: Ground Cover Only (Perennial) (Scrub) Veg_8: Broad leaf Shrubs w/ Ground Cover (Scrub) Veg_9: Broadleaf Shrubs with Bare Soil (Scrub-Soil) Veg_11: Bare Soil (Compacted Soil) Unlike W. CONUS, most part of N. Africa is desert.

  10. Tb Simulation in GSI: NOAA-18 AMSU_A PHY_CTR vs EMP_LST MW CTR Zom,t + ε Ch 1 Improved! Zom,t + ε CTR Ch 15

  11. Ch 15 MW PDF distribution, Bias and RMSE: Land, Compacted Soil & Scrub-Soil CTR ε only Zom,t + ε N. Africa CTR εonly Zom,t+ ε Land:Bias2.846 0.890 1.196 rmse8.180 9.4286.520 Improved significantly

  12. Summary • New formula for momentum and thermal roughness lengths (Zom,Zot) as a function of green vegetation fraction was implemented in the NCEP GFS model to reduce a substantial cold bias of land surface skin temperature over arid and semi-arid regions during daytime in warm seasons. • The new empirical MW emissivity model, developed by B. Yan and F. Weng at NESDIS, corrected unreasonable MW surface emissivity calculation over desert regions in CRTM . • With new roughness changes and new emissivity MW model together, obvious reduction of large bias of the calculated brightness temperatures was found for infrared or microwave satellite sensors at window or near window channels, so that much more satellite measurements can be utilized in GSI data assimilation system.

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