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Alan J. Geer, Peter Bauer, and Christopher W. O’Dell

1. Introduction The small-scale variability of cloud and precipitation must be carefully modeled in order to get accurate simulations of atmospheric radiative transfer. For example, the amount of overlap between different cloud layers can strongly affect quantities such as heating rates and the earth’s albedo (e.g., Morcrette and Fouquart 1986 ; Morcrette and Jakob 2000 ). At microwave frequencies, the nonlinear dependence of radiance on hydrometeor amount causes a “beamfilling effect” in

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Song Yang, Fuzhong Weng, Banghua Yan, Ninghai Sun, and Mitch Goldberg

the spacecraft and the glare suppression system was successfully corrected (e.g., Colton and Poe 1999 ). The SSM/I measurements are calibrated with respect to the radiative transfer simulations over oceans ( Hilburn and Wentz 2008 ) and the well-calibrated SSM/I data reduce the discrepancy between the observed precipitation trend and the climate model prediction ( Wentz et al. 2007 ). Recently, the observational anomalies of the first SSMIS on board the F-16 satellite were investigated and

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Frank S. Marzano, Domenico Cimini, Tommaso Rossi, Daniele Mortari, Sabatino Di Michele, and Peter Bauer

, which denotes p ( x | y ), the a posteriori probability density function (pdf) of x when y is observed, in terms of the likelihood pdf p ( y | x ) and a priori pdf p ( x ). In our context the link between x and y is described by the radiative transfer observation operator, H , which may be strongly nonlinear and characterized by an error distribution ε , summarizing both observation errors and forward modeling errors with an overall error covariance matrix 𝗖 ε . For linear problems the

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Cristian Mitrescu, Tristan L’Ecuyer, John Haynes, Steven Miller, and Joseph Turk

freezing level. For layers with temperatures below −20°C, ice is the only phase allowed in the current algorithm. b. 94-GHz reflectivity model Described here is the radiative model (i.e., radar model) used to describe the scattering processes observed by CloudSat . We represent the radar equation in terms of modeled reflectivity Z m as follows: where Z is the total backscatter reflectivity (from clouds, or the surface), MS is the multiple-scattering contribution, and PIA is the path integrated

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J. J. Shi, W-K. Tao, T. Matsui, R. Cifelli, A. Hou, S. Lang, A. Tokay, N-Y. Wang, C. Peters-Lidard, G. Skofronick-Jackson, S. Rutledge, and W. Petersen

-frequency channels on the GMI to support snowfall retrievals over land at mid- and high latitudes. This makes the simulation and evaluation of high-frequency Tb from WRF an important focus for supporting the GPM mission. AMSU-B-consistent Tb were computed from the WRF simulations through a passive microwave simulator in the SDSU (using delta-Eddington two-stream radiative transfer with slant path view; Kummerow 1993 ; Olson and Kummerow 1996 ). AMSU-B Tb (within the 30° sensor-viewing angle) and corresponding

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Long S. Chiu and Roongroj Chokngamwong

monthly oceanic rain rates is based on a relationship between brightness temperature and rain rate ( T b – R ) that is derived from radiative transfer calculation using a cloud model ( Wilheit et al. 1991 ). In the cloud model, a Marshall–Palmer distribution ( Marshall and Palmer 1948 ) of raindrops as a function of rain rate is assumed from the ocean surface to the freezing level (FL; 0°C) in the atmosphere. In addition, a constant lapse rate of 6.5°C km −1 and a relative humidity that increases

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Meike Kühnlein, Boris Thies, Thomas Nauß, and Jörg Bendix

; Arkin and Meisner 1987 ), which uses a cloud-top temperature of 235 K as a threshold to delineate precipitating clouds. A constant rainfall rate is assigned to these raining pixels. Kerrache and Schmetz (1988) transferred the GPI to Meteosat. Menz and Zock (1997) , Ba and Nicholson (1998) , and Todd et al. (1999) used the GPI successfully over eastern Africa. The autoestimator technique ( Vicente et al. 1998 ) uses the GOES 10.7- μ m band to compute real-time precipitation amounts based on a

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Axel Andersson, Christian Klepp, Karsten Fennig, Stephan Bakan, Hartmut Grassl, and Jörg Schulz

surface. Additionally, regionally limited measurements acquired by rawinsondes and the radiative transfer calculations underlying the satellite retrieval algorithms as well as the reanalyses lead to locally different results in the wind speed. The large differences over the monsoon regions of the Bay of Bengal and the Arabian Sea are likely to originate from lack of input data representing the specific atmospheric and sea surface properties in these regions due to atmospheric advection and oceanic

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