J5.1 Community Radiative Transfer Model for Satellite Data Simulation/Assimilation

Tuesday, 8 January 2013: 3:30 PM
Ballroom A (Austin Convention Center)
Quanhua (Mark) Liu, ESSIC, University of Maryland, College Park, MD; and P. vanDelst, Y. Chen, D. Groff, Y. Han, A. Collard, F. Weng, S. A. Boukabara, and J. Derber

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

The CRTM development is contributed to by multiple U.S. government agencies, universities as well as private companies. This paper will present the latest 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 highlights of the CRTM version:

1. Dual Transmittance models, ODAS and ODPS, 2. Sensor Specific Transmittance models: Fast Transmittance Model for Stratospheric Sounding Unit to take account for CO2 cell pressure variation, Fast Transmittance Model for SSMIS Upper Atmospheric Sounding (UAS) Channels including Zeeman-splitting. 3. Non-local Thermodynamic Equilibrium (NLTE) Radiative Transfer 4. Surface Emissivity/Reflectivity Models 5. Aerosol, Cloud, and Molecular Scattering Models Pre-computed look-up tables for extinction, scattering coefficients and phase functions 6. Dual Radiative Transfer Solver, Adding Double-Adding method [1][2], Adding Matrix Operator method, and SOI method.

The CRTM is flexible for users' applications, for example one can simulate aircraft measurements, turn scattering off for fast calculations, use an AOD module for aerosol optical depth calculation, use an emissivity interface to input your own emissivity data base, and use a channel selection function for specified channel radiance calculations.

The CRTM has been made available for VIIRS [3], CrIS and ATMS for the America's newest polar-orbiting satellite, the NPOESS Preparatory Project (NPP), which was successfully launched from Vandenberg Air Force, Calif., at 2:48 a.m, 28th October 2011. The CRTM is also available for AMSR-2.

We will show the CRTM applications in the NCEP GSI for satellite radiance assimilation [4] including bias and standard deviation monitoring. The monitoring system is also helpful to instrument calibration, because anomaly in bias and standard deviation usually reflect the change of sensor/satellite characteristics. We will also present the CRTM application for satellite products, for example in one-dimensional retrieval algorithm [5]. In addition, we are going to present various applications using the CRTM including using CRTM SSU transmittance to study long-term temperature trend in the stratosphere, including the Zeeman splitting effect in the CRTM to improve microwave brightness temperature computation accuracy [6], and using the NLTE module to get much better agreement between simulations and observations, and using CRTM AOD module in aerosol optical depth assimilation to improve air quality forecasting [7].


[1] Liu, Q., and F. Weng, 2006: Advanced Doubling-Adding Method for Radiative Transfer in Planetary Atmosphere, J. Atmos. Sci., Vol. 63, No. 12, pages 3459–3465.

[2] 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.

[3] 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.

[4] 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, 31–36.

[5] 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.

[6] 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.

[7] 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.

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