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

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

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

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

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

Tb bias/rmse and the number of obs assimilated in GSI

Red: CTLBlue: SEN

Channel 24

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.

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R2O

Advances