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Ming Liu, Jason E. Nachamkin, and Douglas L. Westphal

1. Introduction Solar and thermal infrared radiation is a fundamental mechanism for driving the energy exchange among air mass, clouds, aerosols, and land surface to maintain the thermal and dynamic systems in the atmosphere. The accurate prediction of atmospheric radiative processes, particularly cloud–radiation interaction, highly depends on the accurate calculation of radiative transfer fluxes (i.e., radiative transfer parameterizations). It has been well recognized that radiation modeling

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Robert P. d’Entremont, Richard Lynch, Gennadi Uymin, Jean-Luc Moncet, Ryan B. Aschbrenner, Mark Conner, and Gary B. Gustafson

indexed using NCEP GDAS model output ( Platnick et al. 2003 ; Frey et al. 2008 ). Stowe et al. (1999) employ a radiative transfer model to generate a set of theoretically expected brightness temperatures in the three infrared Advanced Very High Resolution Radiometer (AVHRR) channels as a function of cloud radiative, optical, and microphysical properties. Observed brightness temperatures are then used as indices into these tables to retrieve the various cloud parameters. The operational GOES

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W. F. Feltz, K. M. Bedka, J. A. Otkin, T. Greenwald, and S. A. Ackerman

order to gain new insight into the underlying dynamics and mesoscale moisture variability associated with mountain waves. WRF model-simulated temperature and moisture profiles are used to produce synthetic satellite water vapor imagery at high spatial and temporal resolutions using an infrared radiative transfer model. This provides the direct link between the model dynamic fields and the synthetic satellite imagery required to perform this study. This event was chosen because it exhibited a well

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Chi–Sann Liou, Jen–Her Chen, Chuen–Teyr Terng, Feng–Ju Wang, Chin–Tzu Fong, Thomas E. Rosmond, Hong–Chi Kuo, Chih–Hui Shiao, and Ming–Dean Cheng

-pass filter and Lanczos’s filter once each to a silhouette profile derived from a 10-min resolution terrain dataset. 4. Physical parameterization The global forecast model includes schemes to parameterize the physical processes of surface fluxes, vertical turbulence mixing, shortwave and longwave radiative transfer, cumulus convection, grid-scale condensation, and gravity wave drag. The surface fluxes are calculated with Louis (1979) formulas that are empirical approximations of Monin and Obukhov (1954

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Jörg Bendix, Boris Thies, Jan Cermak, and Thomas Nauß

for the estimation of low stratus geometrical thickness for Germany based on MODIS daytime data and radiative transfer calulations ( section 3 ). The results will be compared with other approaches ( sections 2 and 4 ). Finally, two case studies of different stratus–fog situations shall highlight the potential of the methodology for the discrimination between low stratus and ground fog by using satellite data ( section 4 ). 2. Current methods for the retrieval of stratus cloud geometrical

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Wayne F. Feltz and John R. Mecikalski

collection and the processing of convective indices. The radiance spectra measured by the AERI contain (vertical) temperature and water vapor profile information, as documented in Feltz et al. (1998) . By inverting the radiative transfer equation, these profiles can be retrieved. However, the retrieval of water vapor and temperature from radiance data is an ill-defined problem. Smith et al. (1999) have developed an iterative technique that makes use of a first-guess profile when performing a physical

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Dan Bikos, Daniel T. Lindsey, Jason Otkin, Justin Sieglaff, Louie Grasso, Chris Siewert, James Correia Jr., Michael Coniglio, Robert Rabin, John S. Kain, and Scott Dembek

that described in Grasso et al. (2008) and Otkin et al. (2009) . The imagery is generated by passing output from an NWP model through a forward radiative transfer model capable of computing realistic radiances for different spectral bands. Synthetic imagery has existed for some time (e.g., Chevallier et al. 2001 , Chevallier and Kelly 2002 , Raymond and Aune 2003 ; Otkin and Greenwald 2008 ) and has been used by operational forecasters [e.g., the Cooperative Institute for Meteorological

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Giovanni Leoncini, Roger A. Pielke Sr., and Philip Gabriel

character of the radiative transfer. Although all the input variables for the HS scheme (i.e., pressure, water vapor mixing ratio, and temperature profiles, upwelling longwave radiation, and albedo) have been used in the computation of the EOFs, mixing ratio alone works better than any other input variable or combination of variables, even when the combination includes the mixing ratio itself. If the weights computed as described are multiplied by the fractional explained variance and then normalized

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Young-Joon Kim, William Campbell, and Benjamin Ruston

assumptions made by the forecast model and the radiative transfer model ( Daley 1991 ; Lorenc 1986 ). Observation error for any data, including satellite radiances, consists of both instrument error and the error of representativeness, which can result from, for example, interpolation or radiative transfer model error. Moreover, if the background errors are estimated suboptimally (e.g., overestimated), then the observation errors have to be adjusted (e.g., inflated) to give an optimal analysis. In the

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Christian Keil, Arnold Tafferner, Hermann Mannstein, and Ulrich Schättler

rate is complicated because of the use of various reflectivity–rainfall rate relationships, different scanning procedures, and calibration methods used within a network of radars ( Hagen et al. 2000 ). A second approach is the “model-to-observation” method. With respect to satellite data, forward radiative transfer models (RTMs) are used to transform model control variables to parameters observed by satellites. Such a forward RTM is used at the European Centre for Medium-Range Weather Forecasts

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