791
PROGRESS IN COMMUNITY RADIATIVE TRANSFER MODEL FOR SATELLITE RADIANCE ASSIMILATION

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
Wednesday, 5 February 2014
Hall C3 (The Georgia World Congress Center )
Quanhua (Mark) Liu, ESSIC, University of Maryland, College Park, MD; and P. vanDelst, D. Groff, M. Chen, A. Collard, S. A. Boukabara, F. Weng, and J. C. Derber

The Community Radiative Transfer Model (CRTM), developed at the Joint Center for Satellite Data Assimilation, has being operationally supporting satellite radiance assimilation for weather forecasting in NOAA and NASA. The CRTM is also supporting the MODIS, GOES-R and JPSS/NPP missions for instrument calibration, validation, monitoring long-term trends, and satellite products using a retrieval approach.

The CRTM development includes contributions from multiple U.S. government agencies, universities as well as private companies. This paper will present the updates associate with the CRTM version 2.1, which is applicable for passive microwave, infrared and visible sensors. It supports all NOAA satellite instruments, NASA MODIS, and many foreign meteorological satellites. In this study, we will describe the CRTM functionalities and capabilities in the new release of version 2.1. The following are the progress highlights in the CRTM since 2013: 1. Microwave sea surface emissivity (FASTEM-5) verification in GSI 2. Improved microwave land surface emissivity model 3. Aircraft radiance simulations 4. Add CMAQ aerosol module 5. ScatteringIndicator development and testing 6. Sensitivity of the CRTM jacobian to input vertical coordinates 7. Transmittance coefficients for new sensors including Suomi NPP sensors

In this presentation, we focus on the CRTM development in supporting the Suomi NPP sensor validation and verification, new microwave surface emissivity impact in the GSI, CRTM aerosol module (AOD) for air quality forecasting. The CRTM AOD has been used in the WRF-chem to assimilate the MODIS AOD, which improved aerosol concentration prediction. The AOD module is based on the global GOCART aerosol classification. We are including the CMAQ aerosol types for applications in regional model. The CMAQ has more aerosol types for chemical species.

References

Boukabara, S., Kevin Garrett, Wanchun Chen, Flavio Iturbide-Sanchez, Christopher Grassotti, Cezar Kongoli, Ruiyue Chen, Quanhua (Mark) Liu, Banghua Yan, Fuzhong Weng, Ralph Ferraro, Thomas J. Kleespies, Huan Meng, 2011: MiRS: An All-Weather 1DVAR Satellite Data Assimilation and Retrieval System. IEEE T. Geoscience and Remote Sensing 49(9): 3249-3272.

Collard, A., F. Hilton, M. Forsythe, B. Candy, 2011: From Observations to Forecasts Part 8: The use of satellite observations in numerical weather prediction, Weather, 66, 3136.

Han, Y., F. Weng, Q. Liu and P. van Delst, 2007: A fast radiative transfer model for SSMIS upper atmosphere sounding channel, J. Geophys. Res., 112, D11121, doi:10.1029/2006JD008208.

Heidinger, A. K., O. Christopher, R. Bennartz, T. Greenwald, The Successive-Order-of-Interaction Radiative Transfer Model. Part I: Model Development, J. Appl. Meteorol., Vol. 45, No. 10, pages 1388-1402.

Liu, Q., and F. Weng, 2006: Advanced Doubling-Adding Method for Radiative Transfer in Planetary Atmosphere, J. Atmos. Sci., 63, 12, 34593465.

Liu, Z., Q. Liu, H.-C. Lin, C. S. Schwartz, Y.-H. Lee, and T. Wang (2011), Three-dimensional variational assimilation of MODIS aerosol optical depth: Implementation and application to a dust storm over East Asia, J. Geophys. Res., 116, D23206, doi:10.1029/2011JD016159.

Visible/Infrared Imager Radiometer Suite (VIIRS) Sensor Data Record (SDR) User's Guide Version 1.0, 2011, Changyong Cao, Jack Xiong, Frank DeLuccia, Quanhua (Mark) Liu, Slawomir Blonski, and Dave Pogorzala, NOAA technical report.