Christa D. Peters-Lidard, Ken Harrison, Yudong TianHydrological Sciences Branch, Code 614.3

NASA Goddard Space Flight CenterGreenbelt, MD 20771Greenbelt, MD 20771

Email: christa.peters@nasa.gov

PMM Science Team Land Surface Characterization Working Group : R l h F ** (WG C Ch i ) Ch i t P t Lid d (WG C Ch i ) G il Sk f i k

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Ralph Ferraro** (WG Co-Chair), Christa Peters-Lidard (WG Co-Chair) Gail Skofronick-Jackson** (WG Co-Chair), Ken Harrison**, Xin Lin**, Joe Turk**, Catherine Prigent, Fatima Karbou, Chuntao Liu, Sid Boukabara, Fuzhong Weng, Nai-Yu Wang, Banghua Yan, Li Li, Sarah Ringerud

1. Motivation

2. LIS-CRTM Forward LSMEM Modeling

M d l d I SSM/I E i i iti3. Modeled vs. Inverse SSM/I Emissivities

4 Modeled vs Inverse soil moisture sensitivities4. Modeled vs. Inverse soil moisture sensitivities

5. Conclusions

•Pg. 2

•Surface•Hydrometeors•Relative h idithumidity•Cloud water + cloud ice + cosmiccosmic background (~1%)•Atmospheric poxygen and nitrogen

•Pg. 3G. Skofronick Jackson and B. Johnson, submitted to JGR, 2010

Land Information System (LIS) JCSDA Community RadiativeLand Information System (LIS)http://lis.gsfc.nasa.gov

yTransfer Model (CRTM)

http://www.star.nesdis.noaa.gov/smcd/spb/CRTM/

Ocean Sea Ice Snow Canopy/Soil Desert

•Microwave land emissivity model (LandEM, Weng et al 2001) and desert emissivity dataWeng et al., 2001) and desert emissivity data base•NPOESS Infrared emissivity data base

•Empirical snow and sea ice microwave emissivity data base (Yan and Weng 2003; 2008)emissivity data base (Yan and Weng, 2003; 2008)•New two layer snow emissivity model (Yan, 2008)

•FASTEM microwave emissivity model from (English and Hewison, 1998)•IR emissivity model (Wu and Smith, 1991; van Delst et al., 2001)IR emissivity model (Wu and Smith, 1991; van Delst et al., 2001)

<-Sensitive to soil i lmoisture at low

frequencies (~ 0.2% decrease per % smdecrease per % smincrease)

Sensitive to LAI ->at all frequencies

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q

LIS‐Catchment‐RTM Tb simulationStd‐error (linear regression) 6.00KStd‐error (linear regression)‐‐no snow 6.75K

LIS‐Noah‐CRTM Tb simulationStd‐error (linear regression) 5.68KStd‐error (linear regression)‐‐no snow 6.49K

Std‐error (linear regression)‐‐snow 6.69K

Correlation 0.86Correlation‐‐no snow 0.86Correlation‐‐snow 0.38

Std‐error (linear regression)‐‐snow 5.68K

Correlation 0.87Correlation‐‐no snow 0.86Correlation‐‐snow ‐0.70

•7

CRTM v 1 2 2 CRTM v. 1.2.2 Three-yr period (2004-2007) LSM forcing: GDAS with CMAP precip Land classification: UMD -25km Atmospheric profile: 26-layer GDAS Domain: ½ degree box; ¼ degree running resolution Domain: ½ degree box; ¼ degree running resolution Only observations with lat/lon falling close (within ½

width cell) to grid cell center are accepted In results shown, screening out observations if LSMs

indicated presence of snow

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Method of Prigent et al. (1997, 2006) for cloud-free conditions and Aires et al. (2001) for cloudy conditions. Contributions from the atmosphere clouds and rain removed usingatmosphere, clouds, and rain removed using ISCCP data and NCEP analyses

Three-yr period (2004-2007) Three yr period (2004 2007) Forward-modeled emissivity is normalized to

benchmark surface temperature (ISCCP Tsurf)

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C3VP SGP HMT-E

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C3VP SGP HMT-E

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C3VP SGP HMT-E

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RMSE: ~0.01 emissivity in

C3VP

lowest SSMI frequency (19 GHz), increasing to ~0.04 in hi h t (85 GH )highest (85 GHz)

Larger uncertainties in

SGP

higher frequencies is expected due to increased

t ib ti fcontribution from the atmosphere, i.e., atm profile errors

HMT-E

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LIS/CRTMPrigent Prigent LIS/CRTM

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Both retrieved and forward-modeled emissivity show considerable dynamic rangeconsiderable dynamic range

As expected from theory and shown by retrieved and modeled emissivity, emissivity is sensitive to a range of land surface states. E.g., retrieved and mean g ,emissivity shown to be considerably and similarly sensitive to soil moisture at one of the three sites (SGP).Using inversion based estimates as a benchmark the Using inversion-based estimates as a benchmark, the standard error in emissivity evaluation using LIS/CRTM was just over 0.01 for 19 GHz and increasing with frequency to roughly 0.04, for allincreasing with frequency to roughly 0.04, for all three sites

Due to significant biases, decreases in these differences expected with LIS/CRTM calibration

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(1) Three layer medium:•Atmosphere120RI )1)(,( 210 RI

0I1,1 Layer

)(,,2 2 TBLayer )1( 120 RI ),( 1 I

231 ),( RI

0

•Canopy, snow

3,3 Layer)(31 TBeI 1

•(2) Emissivity derived from a two-stream radiative transfer solution and modified

•Soil

(2) Emissivity derived from a two stream radiative transfer solution and modified• Fresnel equations for reflection and transmission at layer interfaces:

)(212

)(2 0101 ])[1(]1)[1()1(

kk eReRRe )(2

2121

122112 01)()1(

)1(

keRRRRe

•Weng, et al, 2001Weng, et al, 2001

Geometric optics is applied Geometric optics is applied because the leaf size is typically larger than wavelength◦ Wegmuller et al ’s derivation •H

Wegmuller et al. s derivation◦ Canopy leaves are oriented ◦ Matzler’s dielectric constant

H

0.25

0.30

0.35

0.40

•d - leaf thickness•H - canopy height•LAI - leaf area index

0.05

0.10

0.15

0.20 •md - dry matter content leaf orientation angle incident angle of EM wave•LAI = 2

•md = 0.5

0.006 10 20 30 40 50 60 70 80 90 100

.m.)m..( dswdveg •Frequency (GHz)

•Effective dielectric constant• (Dobson et al 1985):

•h

vwvss

bm mm

• (Dobson et al., 1985):

•Reflectivity (Choudhury et al. 1979):

•mv - volumatric moisture - dielectric constant of soil solidsb - density of soil

d it f lid )coshexp(r)q(qrr vh

'h

)coshexp(r)q(qrr'

s - density of solids•S - sand fraction•C - clay fraction•h - roughness height )coshexp(r)q(qrr hvv •h - roughness height•q- cross-polarization factor