improvement of gfs land surface skin temperature and its impact on satellite data assimilation
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
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Improvement of GFS Land Surface Skin Temperature and its Impact on Satellite Data Assimilation
Zheng et al (2012) JGR Atmospheres DOI: 10.1029/2011JD015901
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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BackgroundGFS/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
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Solution• Revised vegetation-related land surface thermal roughness parameterization for improved LST prediction
(Xubin Zeng et al)
• Revised land surface emissivity calculation(Fuzhong Weng et al)
Since the GFS 2011 implementation• improved LST prediction• more satellite data of land surface sensitive channels assimilated in GDAS
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LST [K] Verification at SURFRAD Sites, July 2007
FPK TBL
DRA
Large cold bias during
daytime SURFRAD Network
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Monthly Mean 18Z LST [K] July 2007o
GOES
GDAS GFS
GLDAS
GDAS/GFS/GLDAS have a large cold bias over western CONUS (Arid area).
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GLDAS-GOES
Difference: Monthly Mean 18Z LST [K] July 2007
GDAS-GOES
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GFS-GOES
Cold bias reaches -12C over western CONUS
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GDAS-GOES
GLDAS-GOES
Still cold bias over western CONUS in August
Difference: Monthly Mean 18Z LST [K] Aug. 2007
GFS-GOES
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Difference: Monthly Mean 18Z LST [K] July 2007
GLDAS-GOES
GFS-GOES
bare soil
shrubs
grass
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Obs. Tb
d Tb (w/b) d Tb (w/b) (Used)
Guess Tb Tb observed and simulated by GSI/CRTM
cold bias
NOAA 18 CH15 (89GHz)
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18Z, July 1, 2007
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PDF distribution: Water & Land; Vegetation Type
All Sky
All Sky
Land
Clear Sky
Clear Sky Land
CRTM_Driver
15Z-21Z, July 1-6, 2007
Veg_7: Ground Cover Only; Veg_8: Broad leaf Shrubs w/ Ground Cover
Domain: CONUS
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Clear Sky Land All
15Z-21Z, July 1-6, 2007
CRTM_Driver
Tb Bias for Various Vegetation Types
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Veg_7: Ground Cover Only; Veg_8: Broad leaf Shrubs w/ Ground Cover
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Large cold bias July 2010
LST and T2m
Verification at SURFRAD sites
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Operational Monitoring Plots: NOAA-18 AMSU-A, Ch1
06Z
18Z 00Z
12Z
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● 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 and earth 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.
Satellite Brightness Temperatures (Tb)
α = atmospheric absorption
Kenneth Mitchell
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LST in NCEP NWP models
Sensible heat flux: SH = ρ Cp Ch (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
LST is land surface skin temperature
– Errors in Ch and LST can offset each other to still yield
reasonable sensible heat flux.
– But CRTM surface emission module cannot tolerate large
error in LST.
Kenneth Mitchell
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New formula:
effective z’0m
ln(z’0m
) = (1 − GVF) 2 ln(z0g
) + [1 − (1 − GVF) 2] ln(z0m
)
z0t
ln(z’0m / z0t ) = (1 − GVF)2 Czil 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,
Xubin Zeng et al. (U. Arizona)
Surface Thermal roughness z0t = z
0m (GFS 2007)
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3-Day Mean: July 1-3, 2007
Improved significantly during daytime!
LSTCh
Aerodynamic conductance: CTR vs Zot
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GFS experiments for revised Z0t
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3-Day Mean: July 1-3, 2007
GFS-GOES: CTR GFS-GOES: New Zot
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GFS experiments for revised Z0t
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NOAA-18 AMSU-A CH15 (89 GHz) Tb assimilated in GSI
Control (26% assimilated)
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NOAA-18 AMSU-A CH15 (89 GHz) Tb assimilated in GSI
Z0t (42% assimilated)
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NOAA-18 AMSU-A CH15 (89 GHz) Tb assimilated in GSI
Z0t + ε (49% assimilated)
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control Z0t + ε
NOAA-18 AMSU-A CH15 (89 GHz) Tb assimilated in GSI
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control Z0t + ε
NOAA-18 AMSU-A CH1 (23.8 GHz) Tb assimilated in GSI
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Tb bias/rmse and the number of obs assimilated in GSI
Red: CTLBlue: SEN
Channel 24
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Large cold bias July 2010
LST and T2m
Verification at SURFRAD sites
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Large cold bias Improved !July 2010 July 2011
LST and T2m
Verification at SURFRAD sites
New zot implemented in NCEP Ops GFS on May 9, 2011.
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SummarySupported by JCSDA, research projects have been conducted to
• improve the GFS predicted land surface skin temperature(Xubin Zeng et al, U. of Arizona);
• improve the CRTM land surface emissivity(Fuzhong Weng et al, NESDIS/JCSDA).
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Results from those research projects have been tested and implemented in the operational GFS/GDAS(Mike Ek et al, EMC Land Team).
Since the operational implementation in 2011, GFS/GDAS is now• predicting the land surface skin temperature with better accuracy;• assimilating more satellite data of the land surface sensitive channels.
R
R2O
Advances