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Toshio Iguchi, Nozomi Kawamoto, and Riko Oki

estimate the particle size more accurately than a single-frequency radar so that we can improve the estimates of rainfall rate and identify snow precipitation regions. In fact, by using the difference in the scattering and attenuation properties of liquid and solid water particles between Ku- and Ka-band electromagnetic (EM) waves, it is possible to estimate the mean diameter of precipitation particles once an appropriate particle size distribution (PSD) model is chosen. Since the mean particle size

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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

calibration differences corresponding to the coldest observable temperatures but also does not rely on the limited availability and regional distribution of coincident observations. c. Double differences over unpolarized vegetated land (window channel warm scenes) To determine sensor calibration differences for warm scenes, double differences were computed for depolarized areas in highly vegetated regions, such as the Amazon rain forest, using variations of the approach developed by Brown and Ruf (2005

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Clément Guilloteau and Efi Foufoula-Georgiou

profiles associated with collocated GMI radiometric measurements. The first database contains only profiles over vegetated land surfaces, excluding, in particular, coastal areas and snow-covered areas. For this, we rely on the surface type classification used in the current operational implementation (V05) of the GPROF algorithm ( Aires et al. 2011 ). The vegetated surface classes account for 70% of all land surfaces at the latitudes covered by the GPM Core Observatory . The second database contains

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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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Rachael Kroodsma, Stephen Bilanow, and Darren McKague

will generally be different for the forward look of the instrument than for the backward look. There is a large contrast between land and ocean TAs at microwave imager frequencies, so small offsets in geolocation cause coastlines to appear highlighted when taking the difference between the forward- and backward-looking maps of observed TAs. This method has been used with great success for various sun-synchronous orbiters by taking the difference between ascending and descending passes (e.g., Berg

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

1. Introduction The National Aeronautics and Space Administration (NASA), as a research-driven engineering and science innovator, has always included the reprocessing of mission data products as part of all mission phases. Within NASA missions, reprocessing allows for all the data collected during a mission to be reprocessed using the latest algorithms, ensuring that consistent data products are generated for the entire mission. During the course of its more than 17 yr of operations, the

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Liang Liao and Robert Meneghini

( Feingold and Levin 1986 ), and several dual-wavelength radar retrieval techniques have been developed ( Meneghini et al. 1992 ; Mardiana et al. 2004 ; Liao and Meneghini 2005 ; Rose and Chandrasekar 2005 ; Seto et al. 2013 ; Seto and Iguchi 2015 ; Liao et al. 2016 ; Liao and Meneghini 2019 ). Some of them are based on an analytic technique that uses the dual-frequency ratio (DFR), that is, the difference of radar reflectivities between two wavelengths, to estimate a characteristic size parameter

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