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Kenneth D. Leppert II and Daniel J. Cecil

found that channels near 85 and 150 GHz appear useful for the detection of snow, and the response to snow and/or graupel increases with increasing frequency. Hong et al. (2005) found that these high-frequency channels are most sensitive to the presence of graupel, followed by cloud ice and snow. The assumption of spherical particles greatly simplifies radiative transfer simulations but is not realistic for many frozen particles. Olson et al. (2016) were able to match 165-GHz BT measurements

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Randy J. Chase, Stephen W. Nesbitt, and Greg M. McFarquhar

, 2016 : Full access the microwave radiative properties of falling snow derived from nonspherical ice particle models. Part I: An extensive database of simulated pristine crystals and aggregate particles, and their scattering properties . J. Appl. Meteor. Climatol. , 55 , 691 – 708 , . 10.1175/JAMC-D-15-0130.1 Langille , R. C. , and R. S. Thain , 1951 : Some quantitative measurements of three-centimeter radar echoes from falling snow . Can. J. Phys

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Zhaoxia Pu, Chaulam Yu, Vijay Tallapragada, Jianjun Jin, and Will McCarty

1. Introduction Since the 1990s, satellite observations have become a major source of observations for numerical weather prediction (NWP), owing to the rapid development of radiative transfer models, data assimilation technologies, and launches of numerous major satellites. Specifically, data assimilation allows the incorporation of observational information into the NWP system if the transformation of analysis variables into the form of observations is achievable. For satellite radiance

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Wesley Berg, Stephen Bilanow, Ruiyao Chen, Saswati Datta, David Draper, Hamideh Ebrahimi, Spencer Farrar, W. Linwood Jones, Rachael Kroodsma, Darren McKague, Vivienne Payne, James Wang, Thomas Wilheit, and John Xun Yang

understanding of Earth’s water and energy cycles ( Hou et al. 2014 ). To provide unified estimates of precipitation from microwave radiometers built and launched by different space agencies with widely varying specifications and capabilities requires that the input brightness temperatures (Tb) be physically consistent between sensors. This means that differences in the observed Tb between sensors should agree with the expected differences based on radiative transfer model simulations that account for

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Sara Q. Zhang, T. Matsui, S. Cheung, M. Zupanski, and C. Peters-Lidard

to be simulated from model states. An all-sky radiative transfer model is applied as the observation operator based on the Goddard Satellite Data Simulator Units (G-SDSU; Matsui et al. 2014 ). The microwave simulator consists of two-stream radiative transfer calculations with Eddington’s second approximation along the slant radiance path ( Kummerow et al. 2001 ; Olson et al. 2006 ), the TELSEM (Tool to Estimate Land-Surface Emissivities at Microwave Frequencies) atlas ( Aires et al. 2011 ) for

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H. Dong and X. Zou

radiative transfer model (RTM) using the WindSat ocean retrievals collocated with the GMI observations as input. The root-mean-square error of the differences between the GMI measured brightness temperature and the WindSat simulations was estimated to be about 0.25 K for all GMI channels using 13 months of global data. The two water vapor sounding channels at frequencies 183.31 3 and 183 7 GHz are among the four new high-frequency channels that are added to the GMI in order to provide additional

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Jiaying Zhang, Liao-Fan Lin, and Rafael L. Bras

extending from the surface to 50 hPa. The physics option follows the one outlined in Lin et al. (2015) , including the WRF single-moment 3-class microphysics scheme ( Hong et al. 2004 ), the Rapid Radiative Transfer Model for longwave radiation ( Mlawer et al. 1997 ), the Dudhia shortwave radiation ( Dudhia 1989 ), the unified Noah land surface model ( Chen and Dudhia 2001 ), the revised MM5 Monin–Obukhov surface layer scheme, the Yonsei University (YSU) planetary boundary layer ( Hong et al. 2006

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Clément Guilloteau, Efi Foufoula-Georgiou, Christian D. Kummerow, and Veljko Petković

). The final GPROF estimate is a Bayesian weighted average of the precipitation rates associated with each of the retained dictionary vectors. The dictionary consists of a large number (several million) of observed DPR reflectivity profiles and associated microwave TBs simulated by a physical radiative transfer model. In practice, 14 different dictionaries are used over oceans and over different types of land surface. Because the observed vector of microwave TBs depends on the density, size

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E. F. Stocker, F. Alquaied, S. Bilanow, Y. Ji, and L. Jones

/validation period by comparing measured and theoretical T b values using a theoretical Radiative Transfer Model (RTM), which was subsequently updated ( Wentz 2015 ). However, for version 8, the Central Florida Remote Sensing Laboratory (CFRSL) independently derived the channel emissivity values based on its analysis of a deep-space maneuver (DSM) that was performed in 2015 ( Alquaied and Jones 2017 ). This more rigorous approach is solely based on radiometric measurements of the known cosmic microwave

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Rachael Kroodsma, Stephen Bilanow, and Darren McKague

. Procedure A complication with updating the TMI dataset is that some corrections are dependent on each other. Corrections were derived in sequence; that is, we first updated two aspects of alignment (cone and azimuth) that impact geolocation and earth incidence angle (EIA). These values are needed to process our radiative transfer model for the along-scan correction. However, after analyzing the along-scan correction, we determined that we needed to further update other aspects of the alignment (pitch

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