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Improvement of GFS Land Surface Skin Temperature and its Impact on Satellite Data Assimilation

Improvement of GFS Land Surface Skin Temperature and its Impact on Satellite Data Assimilation. Jesse Meng , Weizhong Zheng , Helin Wei, Michael Ek , John Derber NOAA/NCEP/EMC Xubin Zeng and Zhuo Wang University of Arizona.

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Improvement of GFS Land Surface Skin Temperature and its Impact on Satellite Data Assimilation

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  1. Improvement of GFS Land Surface Skin Temperature and its Impact on Satellite Data Assimilation Jesse Meng, WeizhongZheng, Helin Wei, Michael Ek, John Derber NOAA/NCEP/EMC XubinZeng and ZhuoWang University of Arizona Zheng et al (2012) JGR AtmospheresDOI: 10.1029/2011JD015901

  2. Background GFS/GDAS 2007-2011 • substantial cold bias in the GFS predicted Land Surface Skin Temperature (LST) for the western US arid region • problematic land surface emissivity calculated for arid region in CRTM • problematic brightness temperature calculated for land surface sensitive channels for arid region in CRTM • most satellite data of land surface sensitive channels for arid region not assimilated in GDAS

  3. Solution • Revised vegetation-related land surface thermal roughness parameterization for improved LST prediction(XubinZeng et al) • Revised land surface emissivity calculation(FuzhongWeng et al) Since the GFS 2011 implementation • improved LST prediction • more satellite data of land surface sensitive channels assimilated in GDAS

  4. LST [K] Verification at SURFRAD Sites, July 2007 TBL FPK DRA Large cold bias during daytime SURFRAD Network

  5. Monthly Mean 18Z LST [K] July 2007o GLDAS GOES GFS GDAS GDAS/GFS/GLDAS have a large cold bias over western CONUS (Arid area).

  6. Difference: Monthly Mean 18Z LST [K] July 2007 GLDAS-GOES GFS-GOES GDAS-GOES Cold bias reaches -12C over western CONUS

  7. Difference: Monthly Mean 18Z LST [K] Aug. 2007 GLDAS-GOES GDAS-GOES GFS-GOES Still cold bias over western CONUS in August

  8. Difference: Monthly Mean 18Z LST [K] July 2007 GLDAS-GOES grass bare soil shrubs GFS-GOES

  9. Tb observed and simulated by GSI/CRTM 18Z, July 1, 2007 Guess Tb Obs. Tb NOAA 18 CH15 (89GHz) d Tb (w/b) (Used) d Tb (w/b) cold bias

  10. PDF distribution: Water & Land; Vegetation Type 15Z-21Z, July 1-6, 2007 Domain: CONUS All Sky Clear Sky All Sky Land Clear Sky Land CRTM_Driver Veg_8: Broad leaf Shrubs w/ Ground Cover Veg_7: Ground Cover Only;

  11. 15Z-21Z, July 1-6, 2007 Tb Bias for Various Vegetation Types Clear Sky Land All Veg_8: Broad leaf Shrubs w/ Ground Cover Veg_7: Ground Cover Only; CRTM_Driver

  12. LST and T2mVerification at SURFRAD sites Large cold bias July 2010

  13. Operational Monitoring Plots: NOAA-18 AMSU-A, Ch1 06Z 12Z 18Z 00Z

  14. α = atmospheric absorption Satellite Brightness Temperatures (Tb) • GSI analysis assimilates satellite Tb obs in various IR and MW channels • Analysis increment: derived from difference of observed and simulated Tb • Simulated Tb is product of CRTM radiative transfer applied to GFS forecast of atmospheric states andearth surface states (land, ice, sea) Example expression for simulated Tb for a microwave channel: For surface sensitive channels (aka “window channels”): atmospheric absorption (α) is weak, so Tsurf(LST) & surface emissivity (ε) are key factors Surface emissivity (ε) is strong function of land surface states: snow cover/density, vegetation cover/density, soil moisture amount, soil moisture phase (frozen vs. liquid) If LST has a large error, Tb would have a large error too. Kenneth Mitchell

  15. LST in NCEP NWP models Sensible heat flux: SH = ρ CpCh (LST– Tair) Ch (m/sec) = (Ch*) x lVl= aerodynamic conductance Ch* is non-dimensional surface exchange coefficient lVl is the wind speed at same level as Tair LSTis land surface skin temperature • Errors in Chand LSTcan offset each other to still yield reasonable sensible heat flux. • But CRTM surface emission module cannot tolerate large error in LST. Kenneth Mitchell

  16. Surface Thermal roughness z0t= z0m (GFS 2007) Newformula: effective z’0m ln(z’0m) = (1 − GVF) 2ln(z0g) + [1 − (1 − GVF) 2] ln(z0m) z0t ln(z’0m/ 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, z0g: the bare soil roughness length for momentum (0.01 m), 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, XubinZeng et al. (U. Arizona)

  17. GFS experiments for revised Z0t 3-Day Mean: July 1-3, 2007 LST Ch Improved significantly during daytime! Aerodynamic conductance: CTR vsZot

  18. 3-Day Mean: July 1-3, 2007 GFS experiments for revised Z0t GFS-GOES: New Zot GFS-GOES: CTR

  19. Control (26% assimilated) NOAA-18 AMSU-A CH15 (89 GHz) Tb assimilated in GSI

  20. NOAA-18 AMSU-A CH15 (89 GHz) Tb assimilated in GSI Z0t(42% assimilated)

  21. NOAA-18 AMSU-A CH15 (89 GHz) Tb assimilated in GSI Z0t + ε(49% assimilated)

  22. NOAA-18 AMSU-A CH15 (89 GHz) Tb assimilated in GSI control Z0t + ε

  23. NOAA-18 AMSU-A CH1 (23.8 GHz) Tb assimilated in GSI control Z0t + ε

  24. Tb bias/rmseand the number of obs assimilated in GSI Red: CTL Blue: SEN Channel

  25. LST and T2mVerification at SURFRAD sites Large cold bias July 2010

  26. LST and T2mVerification at SURFRAD sites Large cold bias July 2010 Improved ! July 2011 New zotimplementedin NCEP Ops GFS on May 9, 2011.

  27. Summary R Supported by JCSDA, research projects have been conducted to • improve the GFS predicted land surface skin temperature(XubinZeng et al, U. of Arizona); • improve the CRTM land surface emissivity(FuzhongWeng et al, NESDIS/JCSDA). R2O Results from those research projects have been tested and implemented in the operational GFS/GDAS(Mike Ek et al, EMC Land Team). Advances • Since the operational implementation in 2011, GFS/GDAS is now • predicting the land surface skin temperature with betteraccuracy; • assimilating more satellite data of the land surface sensitive channels.

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