1. Overview of the Advances in CRTM: - Applications to Support JPSS Sensors Cal/Val and Assimilation Activities Quanhua (Mark) Liu 1,4, Paul van Delst 1,2, Yong Chen 1,4, David Groff 1,2, Ming Chen 1, Andrew Collard 2, Fuzhong Weng 3, John Derber 2, Sid-Ahmed Boukabara 1,3 1 Joint Center for Satellite Data Assimilation 2 NOAA/NCEP 3 NOAA/NESDIS Center for Satellite Applications and Research 4 ESSIC, University of Maryland, College Park, MD 11 th JCSDA Science Workshop on Satellite Data Assimilation, College Park, MD June 5-7, 2013 1

2. OUTLINE CRTM – Radiance Interpreter CRTM Functionalities CRTM Achievements CRTM 2.1.1 Release SNPP Measurements CRTM Support to SNPP Validation and Monitoring Discussion and Summary Future Plan

3. What is CRTM? --- Radiance interpreter 3 Satellite Radiance Sensor monitoring Radiance assimilation Reanalysis Radiative Transfer (CRTM) forward adjoint Physical retrieved satellite products Geophysical Parameters

4. Areas CRTM may apply Satellite radiance data assimilations for NWP Radiometric data impact assessment in Observing System Simulation Experiments (OSSEs) Radiometric instrument design, calibration and monitoring Physical retrievals of atmospheric and surface state variables Air-quality monitoring and forecast Reanalysis and climate studies Aircraft campaign Scientific research and education

5. CRTM 2.1.1 Release CRTM 2.1.1 was released on Dec. 06, 2012 and can be downloaded from ftp.emc.ncep.noaa.gov. New features includeftp.emc.ncep.noaa.gov –Non-LTE for hyperspectral infrared sensors –Successive Order of Interaction (SOI) radiative transfer algorithm –Updated microwave sea surface emissivity model –Updated microwave land surface emissivity model –Aerosol optical depth functions –Channel subseting –Number of streams option for scattering atmospheres –Scattering switch option for clouds and aerosols –Aircraft instrument capability –Option structure I/O Contact the CRTM team at ncep.list.emc.jcsda_crtm.support@noaa.govncep.list.emc.jcsda_crtm.support@noaa.gov 5

6. Transmittance Models Transmittance module –ODAS: Optical Depth Absorber Space (O3, H2O, good performance for water vapor absorption) –ODPS: Optical Depth Pressure Space (H2O, CO2, O3, N2O, CO, CH4) –SSU model –Fast Zeeman model for SSMIS UAS channels –NLTE CRTM simulated brightness temperature spectra for hyper- spectral infrared sensors IASI (black), AIRS (red) and CrIS (blue). 6

7. Fast Transmittance Model for Stratospheric Sounding Unit (SSU) The SSU channel spectral response function (SRF) is a combination of the instrument filter function and the transmittance of a CO2 cell. The SRF varies due to the cell CO2 leaking problem. CRTM-v2 includes schemes to take the SRF variations into account (Liu and Weng, 2009; Chen et al. 2011) CO2 cell pressure variations, which causes SSU SRF variations. CRTM simulations compared with SSU observations for SSU noaa-14. 7

8. Fast Transmittance Model for SSMIS Upper Atmospheric Sounding (UAS) Channels Zeeman-splitting can have an effect up to 10 K on SSMIS UAS channels. The fast transmittance model is implemented to take both effects into account (Han et al., JGR 2007). Zeeman effect: The O2 transition lines are split into many sublines and the radiation is polarized. 8 Without using Earth magnetic field

9. CRTM NLTE simulation vs observation, Solar zenith angle = 30 o, sensor zenith angle = 0.7 o AIRS T m1 (0.005-0.2hPa), T m2 (0.2-52hPa) 9

10. Clouds Liquid MW, IR, VIS: Mie Rain MW, IR, VIS: Mie, Spheroid Ice MW: Mie, IR, VIS: ( non-spherical particle Yang et al., 2005) Snow: MW, IR, VIS: Mie Graupel MW, IR, VIS: Mie Hail: ( non-spherical particle Yang et al., 2005) 10

11. Aerosol Models Global Model, Goddard Chemistry Aerosol Radiation and Transport (GOCART) Dust Sea Salt ( dry (hydrophobic), wet (hydrophilic) ) Organic carbon Black carbon Sulfate To be considered: Regional Model WRF-NMM, Community Multiscale Air Quality (CMAQ) Sulfate mass Ammonium mass Nitrate mass Organic mass Unspecified anthropogenic mass Elemental carbon mass Marine mass Soil derived mass CRTM Model for GOES-R Applications (preliminary ) Continental Urban Generic l Heavy smoke l Dust 5 Coarse mode aerosol 4 Fine mode aerosol 11

12. Surface emissivity/reflectivity model The surface is categorized as Land IR: ASTER spectral library (NPOESS LUT ) MW: Physical model (EMC land group and STAR are working on improvement) UV/VIS: ASTER spectral library (NPOESS LUT ) Ocean IR: Wu-Smith, Nalli MW: Fastem-1+low frequency model, Fastem-5 UV/VIS: ASTER spectral library (NPOESS LUT ) BRDF model Ice IR: ASTER spectral library (NPOESS LUT ) MW: from sensor data derived UV/VIS: ASTER spectral library (NPOESS LUT ) Snow IR: ASTER spectral library (NPOESS LUT ) MW: from sensor data derived UV/VIS: ASTER spectral library (NPOESS LUT )

13. FASTEM-5

14. 14 CRTM Support to JPSS Radiance Validation and Monitoring

15. 15 ATMS Weighting Function

16. 16 ATMS Striping Courtesy of Ninghai Sun in JPSS ATMS SDR Team

17. 17 CrIS -1 Red: all ocean cases; green uses ch. 3 homogeneity (0.7 K); black also with Ch. 3 one sigma central points.

18. 18 CrIS -2 The nine FOV to FOV (FOV-2-FOV) relative radiometric variability by removing the mean bias between observations and CRTM simulations.

19. VIIRS and CRTM Modeling for M12 Striping Investigation 19 The STAR team applied the CRTM to simulate the VIIRS SDR data. It is found that the M12 striping reported by the SST EDR team is caused by the difference in VIIRS azimuth angles among detectors. M1, M4, and M11 measured (R-Rm)/Rm *100

20. Detector #BRDFABR Brightness temperature 10.7368580.3680.042530.510550.105900.61645302.666 20.7364980.5430.043090.509230.107170.61641302.648 30.7370080.7170.043650.510220.108730.61894302.738 40.7364580.8920.044220.509640.109990.61962302.769 50.7370581.0660.044790.511140.111590.62273302.871 60.7362881.2410.045370.511470.112800.62427302.931 70.7370181.4150.045960.511640.114480.62612302.987 80.7359681.5890.046560.510740.115660.62640303.020 90.7367381.7640.047150.511750.117390.62914303.115 100.7355781.9380.047760.511240.118550.62978303.153 110.7364182.1130.048370.511200.120360.63157303.230 120.7350982.2870.049010.511340.121550.63289303.316 130.7356282.4610.049620.511800.123250.63505303.396 140.7348682.6360.050260.510570.124610.63518303.417 150.7352682.8100.050890.509930.126290.63622303.439 160.7356582.9850.051540.509980.128120.63810303.560 AB Detailed CRTM Calculation for the striping 20

21. Blackbody Temperature Warm Up and Cool Down Objective: To test non-linaerity and stability. Result: NEdT depends on BB temperature (Solid and dashed line), as our model predicted (red line). Black solid and dashed lines are for measured values at HAM A and B sides. Lines in red are predicted based on single operational BB temperature (see green triangle).

22. 22 Scene-dependent NEdT Cao et al., 2013)

23. 23 CRTM capability for OMPS application OMPS nadir mapper and profiler radiance between observations (black line) and CRTM-uvspec calculations (red line). The ECMWF forecasting profiles including ozone is used.

24. Discussion and Summary CRTM is a fast and accurate model to compute satellite radiance and radiance derivatives for IR, MW, Visible and UV sensors. It includes advanced RT components to compute absorption, emission and scattering from various gases, clouds, aerosols and surfaces. It has been extensively validated against its base models and observations. The user interface and program structure are designed for easy use and future expansion. CRTM has been applied in data assimilation in supporting of weather forecast, satellite product retrieval, air quality analysis, climate studies, and sensor monitoring and calibration. Scene-dependent measurement error needs be further investigated. 24

25. Requests Highlights Improve computation efficiency for cloud and aerosol radiance assimilation. MonoRTM, MW sensor response function data Integrate advances in the surface emissivity and reflectivity models, integrated snow/ice empirical model, BRDF, ocean-bio-optic model New aerosol models, CMAQ, GOES-R (MODIS, VIIRS) Limb-scan simulation, no zenith angle Extend the CRTM capability for radiation energy calculations for satellite radiation flux measurements (CERES) Polarimetric (full Stokes) RT model Parallel computation in the CRTM 25