1. Development and Implementation Progress of Community Radiative Transfer Model (CRTM) Yong Han JCSDA/NESDIS P. van Delst, Q. Liu, F. Weng, Y. Chen, D. Groff, B. Yan, N. Nalli, R. Treadon, J. Derber and Y. Han at JCSDA JCSDA Workshop, May 31, 2006 Greenbelt Marriott Hotel

2. Community Contributions Community Research: Radiative transfer science  AER. Inc: Optimal Spectral Sampling (OSS) Method  NRL – Improving Microwave Emissivity Model (MEM) in deserts  NOAA/ETL – Fully polarmetric surface models and microwave radiative transfer model  UCLA – Delta 4 stream vector radiative transfer model  UMBC – aerosol scattering  UWisc – Successive Order of Iteration  CIRA/CU – SHDOMPPDA  UMBC SARTA  Princeton Univ – snow emissivity model improvement  NESDIS/ORA – Snow, sea ice, microwave land emissivity models, vector discrete ordinate radiative transfer (VDISORT), advanced double/adding (ADA), ocean polarimetric, scattering models for all wavelengths Core team (ORA/EMC): Smooth transition from research to operation  Maintenance of CRTM (OPTRAN/OSS coeff., Emissivity upgrade)  CRTM interface  Benchmark tests for model selection  Integration of new science into CRTM

3. Outline Major progress CRTM-v1 implementation Ongoing projects

4. Major Progress CRTM has been integrated into the GSI at NCEP/EMC (Dec. 2005) Beta version CRTM has been released to the public CRTM with OSS (Optimal Spectral Sampling) has been preliminarily implemented and is being evaluated and improved. New postdoc Yong Chen has recently joined CRTM development team.

5. CRTM-v1 implementation Four main components – Atmospheric gaseous absorption (AtmAbsorption) – Scattering and absorption by clouds and aerosols (AtmScatter) – Surface optics; emissivity and reflectivity (SfcOptics) – Radiative transfer solution (RTSolution) Four models – Forward  used operationally – Tangent-linear – Adjoint – K-Matrix  used operationally

6. CRTM Major Modules Forward CRTM SfcOptics (Surface Emissivity Reflectivity Models) AerosolScatter (Aerosol Absorption Scattering Model) AtmAbsorption (Gaseous Absorption Model) CloudScatter (Cloud Absorption Scattering Model) RTSolution (RT Solver) Source Functions public interfaces CRTM InitializationCRTM DestructionJacobian CRTM still developing

7. Gaseous Transmittance Model (AtmAbsorption) Compact OPTRAN Surface A1A1 AnAn A n-1 A0A0 K – absorption coefficient of an absorber A – integrated absorber amount P j – predictors a j – constants obtained from regression Level 0 Level n-1 Level n Level 1 Currently water vapor and ozone are the only variable trace gases and other trace gases are “fixed”. The model provides good Jacobians and is very efficient in using computer memory estimate layer transmittance – spectral response function Channel transmittance definition

8. Radiance errors due to transmittance model uncertainty Radiance Jacobians with respect to water vapor, compared with LBLRTM

9. Surface Emissivity/ Reflectivity Module IR EM module over land IR EM module over ocean IR EM module over Snow IR EM module over Ice MW EM module over land MW EM module over ocean MW EM module over Snow MW EM module over Ice Surface Emissivity/Reflectivity Module and Sub-modules

10. IR Sea Surface Emission Model (IRSSE) c 0 – c 4 are regression coefficients, obtained through regression against Wu-Smith model (1997). The IRSSE model is a parameterized Wu-Smith model for rough sea surface emissivity

11. IR emissivity database for land surfaces Surface Type Compacted soilGrass scrub Tilled soilOil grass SandUrban concrete RockPine brush Irrigated low vegetationBroadleaf brush Meadow grassWet soil ScrubScrub soil Broadleaf forestBroadleaf(70)/Pine(30) Pine forestWater TundraOld snow Grass soilFresh snow Broadleaf/Pine forestNew ice Surface types included in the IR emissivity database (Carter et al., 2002):

12. NESDIS Microwave Land Emission Model (LandEM) (1) Three layer medium: desert, canopy, … (2) Emissivity derived from a two-stream radiative transfer solution and modified Fresnel equations for reflection and transmission at layer interfaces: Conditions using LandEM: over land: f = 80 GHz, e_v = e_h = 0.95 over snow: f = 80 GHz, e_v = e_h = 0.90 Weng, et al, 2001

13. (1) Emissivity Database: (2) Snow type discriminators are used to pick up snow type and emissivity: Microwave empirical snow and ice surface emissivity model T b,j – e.g. AMSU window channel measurements (3) Supported sensors: AMSU, AMSRE, SSMI, MSU, SSMIS

14. Microwave Ocean Emissivity Model Model inputs: satellite zenith angle, water temperature, surface wind speed, and frequency Model outputs: emissivity (Vertical polarization) and emissivity (horizontal polarization) FASTEM-1 (English and Hewison, 1998):

15. Cloud Absorption/Scattering LUT Six cloud types: water, ice, rain, snow, graupel and hail NESDIS/ORA lookup table (Liu et al., 2005): mass extinction coefficient, single scattering albedo, asymmetric factor and Legendre phase coefficients. Sources:  IR: spherical water cloud droplets (Simmer, 1994); non-spherical ice cloud particles (Liou and Yang, 1995; Macke, Mishenko et al.; Baum et al., 2001).  MW: spherical cloud, rain and ice particles (Simmer, 1994).

16. RTSolution: Advanced Doubling-Adding Method (ADA) AtmOptics Optical depth, single scattering Albedo, asymmetry factor, Legendre coefficients for phase matrix Planck functions Planck_Atmosphere Planck_Surface SfcOptics Surface emissivity reflectivity Compute the emitted radiance and reflectance at the surface (without atmosphere) Compute layer transmittance, reflectance matrices by doubling method. Combine (transmittance, reflectance, upwelling source) current level and added layers to new level Output radiance Loop from bottom to top layers (New algorithm) compute layer sources from above layer transmittance and Reflectance analytically. Liu and Weng, 2006 1.7 times faster then VDISORT; 61 times faster than DA Maximum differences between ADA,VDISORT and DA are less than 0.01 K.

17. Ongoing Development

18. Weighting function (km -1 ) Height (km) Ch20 Ch19 Ch21 Ch22 Ch23 Ch24 Zeeman Effect SSMIS upper-air sounding Channel weighting functions Fast RT algorithm for SSMIS upper-Air sounding channels affected by Zeeman-splitting

19. Predictors for estimating absorption coefficients : ChannelsPredictors 19, 20 , cos  B, cos 2  B,  cos 2  B, B -1, B -2, cos 2  B /B 2 21 , cos 2  B, B -1, B -2, B -3, B -4, cos2  B /B 2 22, 23, 24 ,  2, cos 2  B, B -1  = 300./T, B – Earth magnetic field magnitude  B – angle between magnetic field and propagation direction. RMS errors, compared with LBL model:

20. Nick Nalli's Ensemble IR Ocean Surface Emissivity Model Properly accounts for reflected downwelling radiance. Conventional approach to modeling IR surface-leaving radiance results in systematic underestimation of surface leaving radiance. The approach shows good agreement with M-AERI from CSP and AEROSE. Amounts to a 0.15-0.3% correction in emissivity; 0.1-0.2K correction in bias. Work beginning on integration into the CRTM.

21. Ongoing Development (Cont.) CRTM-OSS improvement; OSS LUT-generation software transfer from AER to JCSDA. UMBC SARTA forward algorithm implementation; SARTA TL and AD model development (Dr. Yong Chen) RTTOV transmittance module integration (Dr. Roger Saunders) OPTRAN-v7 improvement and integration Aerosol component development Visible component development CRTM test and validation

22. Summary CRTM has been successfully integrated in the NCEP/EMC GSI. CRTM-v1 is implemented with the following models: OPTRAN, IRSSE, LandE, NESDIS MW snow/ice empirical surface emissivity models and ADA radiative transfer solver. CRTM-OSS has been preliminarily implemented, tested and evaluated. Several areas have been identified for improvement. The OSS LUT software is being transferred to JCSDA. Ongoing development projects also include: fast RT algorithm for SSMIS Zeeman-affected channels, ensemble IR ocean surface emissivity model, integrations of OPTRAN-v7, SARTA and RTTOV and developments of aerosol and visible components.