OUTLINE

1. Overview of the Advances in CRTM: - Applications to Support JPSS Sensors Cal/Val and AssimilationActivities Quanhua (Mark) Liu1,4, Paul van Delst1,2, Yong Chen1,4, David Groff1,2, Ming Chen1, Andrew Collard2, Fuzhong Weng3, John Derber2, Sid-Ahmed Boukabara1,3 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 11th JCSDA Science Workshop on Satellite Data Assimilation, College Park, MD June 5-7, 2013

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 Satellite Radiance Radiative Transfer (CRTM) forward adjoint Sensor monitoring Physical retrieved satellite products Radiance assimilation Reanalysis 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 include • 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.gov

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).

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) CRTM simulations compared with SSU observations for SSU noaa-14. CO2 cell pressure variations, which causes SSU SRF variations.

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. Without using Earth magnetic field

9. CRTM NLTE simulation vs observation, Solar zenith angle = 30o, sensor zenith angle = 0.7o AIRS Tm1 (0.005-0.2hPa) , Tm2 (0.2-52hPa)

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)

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

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. CRTM Support to JPSS Radiance Validation and Monitoring

15. ATMS Weighting Function

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

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. 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 M1, M4, and M11 measured (R-Rm)/Rm *100 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.

20. Detailed CRTM Calculation for the striping A B

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

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.

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