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Aina Taniguchi, Shoichi Shige, Munehisa K. Yamamoto, Tomoaki Mega, Satoshi Kida, Takuji Kubota, Misako Kachi, Tomoo Ushio, and Kazumasa Aonashi

product ( Iguchi et al. 2009 ) and the TMI 2A12 version 6 product derived from the GPROF algorithm ( Kummerow et al. 2001 ; McCollum and Ferraro 2003 ; Olson et al. 2006 ; Wang et al. 2009 ). Observations derived from other microwave imagers, such as the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), the Special Sensor Microwave Imager (SSM/I), and Special Sensor Microwave Imager/Sounder (SSMIS) as well as the Advanced Microwave Sounding Unit (AMSU) microwave

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Li Fang, Xiwu Zhan, Jifu Yin, Jicheng Liu, Mitchell Schull, Jeffrey P. Walker, Jun Wen, Michael H. Cosh, Tarendra Lakhankar, Chandra Holifield Collins, David D. Bosch, and Patrick J. Starks

observations from Landsat]. However, these quantities are either too sensitive to nonsoil moisture factors (such as radar backscatter to surface roughness), or not directly related to the soil moisture content (e.g., LST, A , and VI). Consequently, SM estimates based on these finer-scale satellite observations are less reliable than the coarser-scale microwave radiometer observations, but with the trade-off of being higher spatial resolution. While the coarse-resolution SMAP radiometer observations may be

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

1. Introduction Observations of clouds and precipitation processes in the microwave (MW) domain from space have been performed since the late 1980s ( Spencer et al. 1989 ). The launch of the Tropical Rainfall Measurement Mission (TRMM) satellite in 1997, carrying a Precipitation Radar (PR) along with the passive TRMM Microwave Imager (TMI), allowed an unprecedented amount of collocated MW multispectral atmospheric signatures and radar-derived vertical profiles of hydrometeor type and density

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Chuntao Liu

1. Introduction As one of major components in the water cycle and closely related to our daily life, precipitation has been a focus of study and observation since early human history. However, only in recent decades have rain observations become near-globally available thanks to the technologies of space-borne passive microwave radiometers and radar ( Wilheit 1986 ; Kummerow et al. 1998 ; Iguchi et al. 2000 ). The major advantage of space-borne radar and microwave radiometers is that they

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Robert M. Parinussa, Thomas R. H. Holmes, Niko Wanders, Wouter A. Dorigo, and Richard A. M. de Jeu

provide consistent geophysical parameters of our atmosphere, oceans, and/or land surfaces. The absence of a common observation period makes consistent radiometer calibration increasingly difficult, thus directly impacting consistency in the retrieved geophysical parameters. A possible solution is to use observations from other passive microwave radiometers, such as the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The TMI is a multifrequency microwave radiometer on board the TRMM

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F. Joseph Turk, Ziad S. Haddad, and Yalei You

1. Introduction The joint National Aeronautics and Space Administration (NASA) and Japanese Aerospace Exploration Agency (JAXA) Global Precipitation Measurement (GPM) core satellite will provide considerably more overland observations over complex terrain, high-elevation river basins, and cold surfaces, which are problematic for existing Tropical Rainfall Measuring Mission (TRMM) radar and radiometer precipitation algorithms ( Fu and Liu 2007 ). Current passive microwave (PMW) overland

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Konstantinos M. Andreadis and Dennis P. Lettenmaier

strongly alter microwave emissivity and thus complicate retrieval algorithms. An alternative approach to using satellite observations alone is to merge them with physically based model predictions to constrain retrieval algorithms and potentially account for the uncertainties (e.g., through data assimilation; see, e.g., Durand and Margulis 2006 ; Pulliainen 2006 ). Such an approach usually requires coupling a large-scale snow hydrology model and a microwave emission model. Results from previous

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F. Joseph Turk, Sarah E. Ringerud, Yalei You, Andrea Camplani, Daniele Casella, Giulia Panegrossi, Paolo Sanò, Ardeshir Ebtehaj, Clement Guilloteau, Nobuyuki Utsumi, Catherine Prigent, and Christa Peters-Lidard

of global precipitation products ( Skofronick-Jackson et al. 2018 ). The GPM Microwave Imager (GMI) observations are taken near coincidentally with the DPR on the core satellite, but the other constellation members have passive MW-only capabilities. There are two radar-based precipitation products produced from the GPM core spacecraft, the Combined Radar–Radiometer Algorithm (CORRA) ( Grecu et al. 2016 ), and the DPR radar-only algorithm ( Seto et al. 2013 ), both of which have a single

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Yonghwan Kwon, Zong-Liang Yang, Timothy J. Hoar, and Ally M. Toure

Hemisphere (e.g., Stewart et al. 2004 ). The climate and hydrological research communities are therefore invested in improving the estimation of spatial and temporal variation in snowpack. One approach to improving these estimates is the use of snow radiance data assimilation [hereafter, radiance assimilation (RA)] methods, in which microwave brightness temperature T B observations are directly assimilated into a land surface model (LSM). Previous studies have made significant progress in using this

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Sujay V. Kumar, Christa D. Peters-Lidard, Kristi R. Arsenault, Augusto Getirana, David Mocko, and Yuqiong Liu

spatially and temporally consistent estimates of snow conditions. Primarily, there are two types of spaceborne remotely sensed measurements of snow processes: 1) snow cover area (SCA) is typically measured using visible or infrared satellite sensors, exploiting the high reflectance of snow-covered areas compared to areas with no snow cover; and 2) passive microwave (PM)-based measurements of snow depth and snow water equivalent (SWE). Measurements made in the visible spectrum provide observations at

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