LARGE-SCALE WRF-SIMULATED PROXY ATMOSPHERIC PROFILE DATASETS USED TO SUPPORT GOES-R RESEARCH ACTIVITIES

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1 LARGE-SCALE WRF-SIMULATED PROXY ATMOSPHERIC PROFILE DATASETS USED TO SUPPORT GOES-R RESEARCH ACTIVITIES Jason Otkin, Hung-Lung Huang, Tom Greenwald, Erik Olson, and Justin Sieglaff Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison Madison, Wisconsin, U.S.A. Abstract The Cooperative Institute for Meteorological Satellite Studies (CIMSS) at the University of Wisconsin-Madison is heavily involved in GOES-R Advanced Baseline Imager (ABI) algorithm development, risk reduction, data processing, and measurement capability demonstration activities. In support of this work, an end-to-end processing system that utilizes proxy top of atmosphere (TOA) radiance datasets derived from numerical weather prediction model output has been developed. The availability of model-derived proxy datasets with temporal and spatial resolutions comparable to the anticipated ABI sensor specifications supports a realistic demonstration of its measurements and capabilities. 1. METHODOLOGY The first step in the end-to-end processing system is to use the Weather Research and Forecasting (WRF) model (Skamarock et al. 2005) to generate a synthetic atmosphere containing high spatial and temporal resolution. Numerical model output, including the surface skin temperature, atmospheric temperature, water vapor mixing ratio, and the mixing ratios and effective particle diameters for five hydrometeor species (cloud water, rain water, ice, snow, and graupel) are subsequently passed through the Successive Order of Interaction (SOI) forward radiative transfer model (Heidinger et al. 2006) in order to generate simulated ABI infrared radiances and visible reflectances. Gas optical depths are calculated for each infrared band using the Community Radiative Transfer Model (CRTM). Ice cloud absorption and scattering properties are obtained from Baum et al. (2006), whereas the liquid cloud properties are based on Lorenz-Mie calculations. Noise and other instrument effects can also be added to the data in order to fully characterize the performance of the GOES-R algorithms. 2. CURRENT SIMULATION ACTIVITIES In order to provide high-quality proxy datasets that realistically represent ABI measurement capabilities, a large, memory-intensive WRF model simulation was recently performed on a supercomputer at the National Center for Supercomputing Applications (NCSA) at the University of Illinois in Urbana-Champaign. The simulation contains 3 nested domains configured to represent the anticipated GOES-R scanning regions (i.e. full disk, continental United States (CONUS), and a special mesoscale domain). The outermost domain covers the entire GOES-R viewing area between 56º S and 56º N with 6-km horizontal resolution, while the inner domains cover the CONUS and mesoscale regions with 2-km and 667-m resolution, respectively (Fig. 1). The simulation employed the Thompson et al. (2006) cloud microphysics scheme, the Eta planetary boundary layer scheme (e.g. Mellor and Yamada 1982), the Rapid Radiative Transfer Model longwave (Mlawer et al. 1997) and Dudhia (1989) shortwave radiation schemes, and the Noah land surface model. No cumulus parameterization scheme was used; therefore, only explicitly resolved convection occurred during the simulation. The simulation was initialized at 00 UTC on 04 June 2005 with 1º Global Data Assimilation System analyses and then integrated for 30 hours, with the last 24 hours used to construct the proxy ABI radiance and reflectance datasets. Representative examples of the simulated ABI and observed GOES-12 brightness temperatures for the full disk and CONUS domains are shown in Figs. 2-5.

2 Figure 1. WRF model domain configuration. D1 contains 6-km horizontal resolution, D2 contains 2-km horizontal resolution, and D3 contains km horizontal resolution. 3. ACKNOWLEDGEMENTS This work was supported by NOAA grant NAO7EC0676 and NCSA grant ATM The simulation was performed on the cobalt supercomputer at the NCSA. 4. REFERENCES Baum, B. A., P. Yang, A. J. Heymsfield, S. Platnick, M. D. King, Y.-X. Hu, and S. T. Bedka, 2006: Bulk scattering properties for the remote sensing of ice clouds. Part II: Narrowband models. J. Appl. Meteor., 44, Dudhia, J. 1989: Numerical Study of Convection Observed During the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model. J. Atmos. Sci., 46, Heidinger, A. K., C. O Dell, R. Bennartz, and T. Greenwald, 2006: The succesive-order-ofinteraction radiative transfer model. J. Appl. Meteor. Clim., 45, Mellor, G. L., and T. Yamada, 1982: Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys. Space Phys., 20, Mlawer, E. J., S. J. Taubman, P. D. Brown, and M. J. Iacono, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k Model for the longwave. J. Geophys. Res., 102, Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers, 2005: A description of the Advanced Research WRF Version 2. NCAR Tech. Note/TN-468+STR, 88 pp. Thompson, G., P. R. Field, W. D. Hall, and R. M. Rasmussen, 2006: A new bulk microphysical parameterization for WRF & MM5. Preprints, Seventh Weather Research and Forecasting User s Workshop, Boulder, CO, NCAR, CD-ROM.

3 Figure 2. GOES μm brightness temperatures (K) valid at 1745 UTC on 04 June 2005.

4 Figure 3. Simulated GOES-R ABI 6.95 μm brightness temperatures (K) valid at 1800 UTC on 04 June 2005.

5 Figure 4. GOES μm brightness temperatures (K) valid at 1745 UTC on 04 June Figure 5. Simulated GOES-R ABI 10.4 μm brightness temperatures (K) valid at 1800 UTC on 04 June 2005.