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Development and Improvement of Land Surface Emissivity Model ( LandEM )

Development and Improvement of Land Surface Emissivity Model ( LandEM ) . Ming Chen 1,2 and Fuzhong Weng 1 1.NOAA Center for Satellite Applications and Research 2. Joint Center for Satellite Data Assimilation. CRTM Baseline Surface Emissivity Modules.

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Development and Improvement of Land Surface Emissivity Model ( LandEM )

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  1. Development and Improvement of Land Surface Emissivity Model (LandEM) Ming Chen1,2 and Fuzhong Weng1 1.NOAA Center for Satellite Applications and Research 2. Joint Center for Satellite Data Assimilation

  2. CRTM Baseline Surface Emissivity Modules Ocean Water Sea Ice Snow Canopy Desert • FASTEM 4/5 MW emissivity model (English et al., 1998; Liu, Weng, English, 2010) • IR emissivity model (Wu and Smith, 1991; van Delst et al., 2001) • Physical snow MW emissivity model (Weng et al., 2001) • Empirical & Semi-Empirical snow MW emissivity (Yan and Weng, 2003; 2008) • NPOESS Infrared emissivity data base • Empirical & Semi-Empirical sea ice MW emissivity model (Yan and Weng, 2003; 2008) • IR emissivity model (Wu and Smith, 1991) • Physical canopy and soil MW emissivity model (Weng et al., 2001) • Desert microwave emissivity library (Yan and Weng, 2010) • NPOESS Infrared emissivity data base

  3. Outline Overview of the physical MW LandEM in the latest CRTM release (REL-2.1) Ongoing improvements of the physical MW LandEM Development of the physical Vis-IR LandEM Development of semi-empirical and semi-physical MW snow and sea-ice emissivity model Summary and Future plans

  4. Updates of Microwave LandEM in CRTM REL 2.1 • Instead of using default constants, the LandEM now employs type-based values for the functional parameters of vegetation and soil, which allows the model to account for the variation of the underlying surface characteristics to some extent and to reduce the uncertainty of model inputs. • 13 vegetation types and 9 soil types are used. The mapping of these types at global scale is identical to that in NCEP GDAS. • A modification of the two-stream RT solution is made to account for the impact of temperature difference between surface skin temperature and subsoil temperature.

  5. Vegetation and Soil Types Used in MW LandEM

  6. ATMS O-B vs. Vegetation Types • In comparison with the LandEM in REL 2.0, the LandEM in REL 2.1 has greatly reduced data points with large negative bias, meanwhile bring about more points with large positive bias.

  7. Comparison of AMSUA 23.8GHz TB Obs - CRTM SimAM vs. PM Orbit and NCEP vs. ECMWF NWP Ascending NOAA-18 Descending • Extensive analyses also indicated that there exists fairly different model performance for ascending and descending orbits, and different NWP inputs. • The data points of |O-S|<2.5K are reduced about 30% in ascending orbit in comparison with ascending orbit. • The model tends to have positive bias in ascending orbit, which is not so normal. • To account more physical details, meanwhile keeping the model integrity and consistency on every aspects, the microwave LandEM is under systematical refinement recently. NCEP NCEP ECMWF ECMWF

  8. Ongoing Improvements of Physical Microwave Land Emissivity Model

  9. I(τ0,μ) (1-R21 ) I0 Layer 1 ԑ1 I0R12 τ0 I0(1-R12 ) I(τ0,μ) R21 Bc (Tc) Layer 2 ԑ2 I(τ1,-μ) R23 I(τ1,-μ) τ1 Is (Ts ,i , qs,i ,i) Layer 3 Physical Land Emissivity Model with Non-isothermal Layers • Canopy Physical RT Model (Weng et al , 2001) Where, Solution: • Soil Physical RT Model (Burke et al, 1979) This equation bears very clear physical interpretation of the total canopy upper-welling radiance reaching the air-canopy interface from blow: the sky incidence that is first transmitted into the canopy then scattered by the canopy, the soil emission that is first transmitted into the canopy then scattered by the canopy, and the contribution from the canopy emission. For soil with fine particle size

  10. Ulaby et al., 1981 ip ρp Leaf slab d leaf thickness τp Canopy Model with Improved Volume Scattering Scheme • Leaf-Level Model (Weng & Yan, 2001) • Leaf-Level Model (Chen & Weng, 2013) ρpτp air Leaf slab d leaf thickness • Canopy-Level Model(Chen & Weng, 2013) air • Canopy-Level Model (Weng & Yan , 2001) Ip τpρp Rp Leaf slab 1 Leaf slab 2 … Leaf slab N Tp

  11. Improvement of Leaf Single Scattering This figure shows the single scattering albedo spectra at nadir and 50Deg view angle. The leaf thickness is set to 0.18mm. It is found the old single scattering albedo doesn’t vary with respect to mean LIA, but provides proper mean values. At 50Deg view angle, the single scattering of the old algorithm is much lower than the new one when the mean LIA is 0 (i.e., leaf is horizontal).

  12. Comparison of AMSUA 23.8GHz TB Obs - CRTM SimAM vs. PM Orbit and NCEP vs. ECMWF NWP Ascending NOAA-18 Descending • In comparison with LandEM in REL 2.1, consistent model performance is achieved. Data points that could be possibly assimilated have increased (about 30% more). Error structure is also improved. NCEP NCEP ECMWF ECMWF

  13. Comparison of AMSUA TB Obs - CRTM SimLandEM in REL-2.1 vs. Ongoing 23.8GHz 50.3GHz In comparison with the LandEM in REL-2.1, the ongoing improvements have significantly increased the data points that are possibly assimilated, especially over desert / bare soil regions. Both window-channel and surface sensible sounding channels are improved. REL 2.1 Ongoing Ongoing REL 2.1

  14. Development of Physical VIS-IR Emissivity Model

  15. Physical Canopy VIS-IR Emissivity Model LEAF MODEL (PROSPECT) (ρl , l )=L(N, Cab, Cbp , Cw , Cdm , n(λ) , ki(λ)) NLeaf structural layersCabChlorophyll a+b contentCbp Brown pigment contentCw Water contentCdm Dry matter content n(λ)Refractive index (KK-Analysis mixture model) ki(λ) Absorption coefficient of leaf constituants Chen & Weng, JGR, 2012 Jacquemoud et al, 1990, 2009 CANOPY MODEL (SAIL) (Is , Io , I+ , I-)=RT(LAI, θl, Θ , φ , ρl , l) LAI Leaf Area Index θlMeanleaf angle ΘSolarzenith angle φViewzenith angle ρlLeafreflectancelLeaftransmittance ρsSoilreflectance Kubelka-Munk ,1931; Allen, 1970 ;Suits,1972; Verhoef, 1984

  16. Dry Leaf Fresh Leaf Pure Water Kramers-Kronig Analysis of Leaf Refarctive Index Kramers-Kronig Dispersion Relations Water Dry Mass Water Chlorophyll a+b Dry Mass The specific absorption coefficients of leaf components from the PROSPECT and Gerber et al. [2011] and (left) the leaf refractive index by VariationalKramers_kronig Analysis (Chen and Weng, 2012)

  17. Model Simulation vs. Emissivity LUTs KK-Based Model This spectrum library is generated using the IGBP surface type classifications, where land covers are simulated by the model, soil data corresponding to the land covers are from JPL measurements. (A) The current CRTM IR LUT data and (B) the University of Wisconsin Global Infrared Land Surface Emissivity Database (UWLSED, July 2008), where 10 hinge-point were used to form the curve shapes. The model simulation is similar to UWLSED, but without the prior assumption of the curve shapes.

  18. Development of Semi-empirical and Semi-Physical MW Emissivity Model

  19. Snow MW Emissivity Modeling Based on Spectrum Library Type-based snow emissivity spectrum library is constructed and validated from well-controlled lab measurements and in-situ observations, which may provide more reliable spectral structure than other empirical method. For non-polarized (or mixed-polarization) channels, snow Library: But it is hard to establish a global type map in practice, especially when types vary within small-scale time, e.g, snow. Snow Typing: Use the observations of few window channels to generate “real-time” typing and meanwhile avoid the looping of data usage for data assimilation system Utilize the library spectral structure to keep the emissivity quality of the sounding channels. Polarized Snow Emissivity Library by Yan and Weng H-pol V-pol

  20. Comparison of AMSUA TB O-S Over SnowLibrary-Based Regression Model vs. TELSEM Atlas • Two different CRTM runs were performed for analyzing the CRTM forward performances over snow covers: • 1) run with MW Atlas datasets(TELSEM) • 2) run with snow MW library (Yan) • The results indicated that the snow library-based runs are generally much better than those with the Atlas. TELSEM NOAA-18 23.8 GHz MODEL

  21. Limitation of Regression-Based Snow Typing Algorithm The AMSU snow-typing is based on empirical regression, and not transferable for ATMS channels without dedicated regression parameters. Although it has very good performance for AMSU , but it doesn’t work for ATMS Question: Can we have a typing method which is based on some physical analysis? If we can, the type-based library may be easily applied for different sensors, e.g, GPM imagers and sounders? ATMS AMSUA 23.8 GHz 23.8 GHz ATMS AMSUA 89.0 GHz 88.2 GHz

  22. Development of Physics-Based Snow Typing Algorithm A semi-empirical and semi-physical snow-typing algorithm has been developed. Instead of using pre-established empirical “discriminators”, the new snow-typing algorithm uses a simplified TB equation to diagnose the candidate Library spectrum from window-channel observations, where the atmosphere contribution is assumed to be much smaller than that of surface. Simulation With Regression-based Snow-Typing AMSUA 89.0 GHz Simulation With Diagnosis-based Snow-Typing AMSUA 89.0 GHz

  23. Comparison of ATMS TB Obs - CRTM Sim Over SnowDiagnosis-Based Model vs. TELSEM Atlas TELSEM 50.3GHz 88GHz 183GHz Model

  24. Development of Diagnosis-Based Sea Ice MW Emissivity Model • A library-based sea-ice MW emissivity model has been developed for ATMS. This model is similar to the library-based snow MW emissivity model . • The library consists of 13 sea-ice emissivity spectra. • The physics-based typing algorithm also proved very efficient in practice.

  25. Comparison of ATMS TB Obs - CRTM Sim Over Sea IceDiagnosis-Based Model vs. TELSEM Atlas TELSEM 23.8GHz 50.3GHz 23.8GHz 50.3GHz MODEL

  26. Summary • Physically, it is essential to account the vertical soil temperature variation and the temperature difference between canopy and land surface in the surface emissivity modeling. Yet, the modification in the CRTM Release 2.1 has some inconsistent features. In fact, the original two-stream RT model developed by Weng et al (2001) already provided proper mechanism framework to account for the possible temperature differences among canopy, skin surface and deep soil except the implementation of some mechanism details. A refined model solution which account for the non-isothermal layers has been performed. • The volume scattering by surface vegetation canopy depends on the radiative properties of individual leaves (e.g., the reflectivity and transmissivity of leaf as single scatter) and the morphological configuration of the canopy. Canopy architecture is generally described by two essential parameters − LAI and leaf angle distribution (LAD), the latter of which was not implemented in the previous version of the physical microwave emissivity model. A distribution model (Campbell ,1986) has been implemented. The volume scattering parameters, e.g, single scattering albedo and asymmetric property, are now the function of to both view angle and LIA which varies with respect to land cover types. • A semi physical type-based snow/sea ice emissivity model has been developed. The type-based emissivity model consists of a type-based library and a typing algorithm. The performance of the type-based snow emissivity model is generally much better than those of the retrieved emissivity atlas, which benefits from two aspects: the quality library data as the first guess and the “real-time” adjustment based on the observations. • A physical diagnosis-based snow-typing has been developed. The new snow-typing is applicable for both AMSU and ATMS channels even without modifications, which indicates that the physical diagnosis-based algorithm is more general, and may be applicable for other sensors.

  27. Plans and Further Work • Develop and implement a unified land-cover and soil-type mapping system for use in both MW and VIS-IR physical emissivity models. • Continue to refine the model physical details and optimize the model parameters of MW LandEM. • Continue to develop the physical VIS-IR land surface emissivity model. • Continue to refine the multi-layered soil MW RT model by including scattering mechanism . • Evaluation of the emissivity atlas.

  28. Physical Land Surface Emissivity Model Structure MODEL INPUT PARAMETERS MODEL CORE SURFACE Classification System USGS, IGBP, UMD, MODIS LEAF Parameter Leafthickness, Leafgravimetricmoisture, Mass density of cellmaterials • LEAF Radiative Transfer • ρlLeafreflectance • lLeaftransmittance • CANOPY Radiative Transfer • Canopy structure • Volume Scattering • Effective Emissivity based on radiance Functional TYPED Parameter Translator/Adapter Model INPUT Parameter Transformer/Adapter Soil Classification System STATSGO • SOIL Parameter • Soil textures, Wilting point, Maximum Soil Water content • SOIL RT MODEL • ρs- Soilreflectance • SoilEmittance

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