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Liang Liao and Robert Meneghini

time ( Grecu et al. 2011 ; Seto and Iguchi 2015 ; Liao and Meneghini 2019 ). Adjustment factors at each gate/profile are determined by optimizing predefined cost functions that constrain radar measurements. Early versions of the DPR algorithms, such as version-3 algorithms, rested on the DFR-based technique, which is, in principle, capable of fully characterizing spatial and temporal DSD variations. However, because of the fact that dual solutions exist in estimating mass-weighted diameter ( D m

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W.-K. Tao, T. Iguchi, and S. Lang

-resolving models (CRMs) are regularly used to build lookup tables (LUTs) for LH retrieval algorithms (see the review papers by Tao et al. 2006 , 2016a ). Currently, two different LH algorithms, the Goddard convective–stratiform heating (CSH) and the Japanese spectral latent heating (SLH), are being used to produce standard LH products for both the TRMM and GPM periods. Table 1 shows the key references, inputs, CRM-simulated cases, and the LUTs used in the CSH and SLH algorithms. The CSH- and SLH

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Stephen E. Lang and Wei-Kuo Tao

great importance, LH is hard to measure directly. However, the launch of the Tropical Rainfall Measuring Mission satellite (TRMM; Simpson et al. 1996 ; Kummerow et al. 2000 ) in November of 1997 made it possible to obtain quantitative precipitation measurements over the global tropics and, as a consequence of their close connection, estimates of tropical LH as well. In support of this effort, five different LH algorithms were developed to retrieve profiles of LH using TRMM rainfall products

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Liang Liao and Robert Meneghini

stepping forward or backward along the radar beam ( Meneghini et al. 1992 , 1997 ; Mardiana et al. 2004 ; Liao and Meneghini 2005 ; Rose and Chandrasekar 2005 ; Seto et al. 2013 ; Seto and Iguchi 2015 ; Liao et al. 2016 ). To improve the robustness of the dual-wavelength retrieval, several optimal estimation methods have been recently proposed, including the GPM DPR (version 5) algorithm in which one or more adjustment factors are used to modify nominal relationships between the parameters of

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Minda Le and V. Chandrasekar

melting-layer detection ( Le and Chandrasekar 2013 ). Using a similar philosophy, Le et al. (2017) developed an algorithm to identify surface snowfall at each DPR matched footprint. It uses vertical features of dual-frequency reflectivity profiles of different precipitation types. These features include reflectivity amplitude, dual-frequency ratio, slope with respect to height, and storm-top height. An effective “snow index” is then built that can separate surface snowfall from rain at a 97% success

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Zeinab Takbiri, Ardeshir Ebtehaj, Efi Foufoula-Georgiou, Pierre-Emmanuel Kirstetter, and F. Joseph Turk

variations of surface emissivity ( Kulie et al. 2010 ; Skofronick-Jackson and Johnson 2011 ; Gong and Wu 2017 ; You et al. 2017 ). Among high-frequency channels, Bennartz and Bauer (2003) found that frequencies around and above 150 GHz provide a strong polarization signal for snowfall detection ( Gong and Wu 2017 ; You et al. 2017 ; Panegrossi et al. 2017 ). Remote sensing of snowfall is among the most challenging tasks in precipitation retrieval algorithms ( Bennartz and Bauer 2003 ; Skofronick

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Toshio Iguchi, Nozomi Kawamoto, and Riko Oki

spaceborne radiometer relies on the scattering signal at high-frequency channels. Combining microwave radiometer data with CloudSat data has accelerated the improvement of ice retrieval algorithms for microwave radiometers substantially ( Liu and Seo 2013 ). Nevertheless, there still remains large uncertainty in the retrieval algorithms for both radar and radiometer to detect and quantify ice precipitation with sufficient accuracy. A major part of uncertainty originates in large variation of the

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Jackson Tan, Walter A. Petersen, and Ali Tokay

Precipitation Analysis (TMPA; Huffman et al. 2007 ). These gridded precipitation datasets differ in their sources of data and in their algorithms, as well as in their resolutions and coverage. They have been extensively compared to ground-based gauges and radars over different regions ( AghaKouchak et al. 2012 ; Bharti and Singh 2015 ; Chen et al. 2013a , b ; Ebert et al. 2007 ; Gottschalck et al. 2005 ; Gourley et al. 2010 ; Habib et al. 2009 , 2012 ; Hossain and Huffman 2008 ; Krajewski et al

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Thomas Stanley, Dalia B. Kirschbaum, George J. Huffman, and Robert F. Adler

satellite information and an improved morphing scheme, and a final run uses a global gauge network to calibrate the observations. Because of the use of different sensors, algorithms, and calibrations, the IMERG and TMPA products differ considerably. A comparison of percentiles from 7 March 2015 to 6 March 2016 for the GIS-formatted IMERG Version 3 Late Run (IMERG-L) and Real-Time TMPA, version 7 (TMPA-RT), daily products ( Huffman 2016a , b ) revealed some of the characteristics of these differences

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Clément Guilloteau and Efi Foufoula-Georgiou

1. Introduction Since the first experimental algorithms developed for the SMMR (see appendix A for all acronyms used in this article) imager in the 1980s, the algorithms performing the retrieval of precipitation from passive microwave imagers in orbit have been continuously evolving and improving ( Wilheit and Chang 1980 ; Spencer 1986 ; Spencer et al. 1989 ; Wilheit et al. 1991 ; Liu and Curry 1992 ; Kummerow and Giglio 1994 ; Petty 1994 ; Ferraro and Marks 1995 ; Kummerow et al

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