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Pingping Xie, Robert Joyce, Shaorong Wu, Soo-Hyun Yoo, Yelena Yarosh, Fengying Sun, and Roger Lin

North American Land Data Assimilation System (NLDAS) project . J. Geophys. Res. , 108 , 8842 , doi: 10.1029/2002JD003118 . Ebert , E. E. , J. E. Janowiak , and C. Kidd , 2007 : Comparison of near-real-time precipitation estimates from satellite observations and numerical models . Bull. Amer. Meteor. Soc. , 88 , 47 – 64 , doi: 10.1175/BAMS-88-1-47 . 10.1175/BAMS-88-1-47 Ferraro , R. , and Coauthors , 2005 : NOAA operational hydrological products derived from the Advanced Microwave

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Renu Joseph, Thomas M. Smith, Mathew R. P. Sapiano, and Ralph R. Ferraro

section 4 . 2. Methodology for the creation of CHOMPS Four satellite precipitation estimates are used in our analysis: 1) the SSM/I, with at least two satellites in orbit at all times; 2) the AMSU-B, on the National Oceanic and Atmospheric Administration’s (NOAA) operational satellites; 3) the Advanced Microwave Scanning Radiometer (AMSR) on Aqua [AMSR for Earth Observing System (AMSR-E)]; and 4) the TRMM Microwave Imager (TMI). The complete set of satellite observations, their periods of operation

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Zhong Liu

-real-time and research products available from 1998 (2000 for the near–real time) onward. Launched on 27 February 2014, the Global Precipitation Measurement (GPM) mission ( Hou et al. 2014 ) comprises an international network of satellites to provide the next generation of global observations of rain and snow. Built upon the success of the widely used TMPA products, the newly released Integrated Multisatellite Retrievals for GPM (IMERG) products ( Huffman et al. 2015a , b , c ) continue to make improvements

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N. Carr, P.-E. Kirstetter, Y. Hong, J. J. Gourley, M. Schwaller, W. Petersen, Nai-Yu Wang, Ralph R. Ferraro, and Xianwu Xue

passive microwave observations . J. Appl. Meteor. , 40 , 1367 – 1380 , doi: 10.1175/1520-0450(2001)040<1367:OPEFTP>2.0.CO;2 . Hou, A. Y. , Zhang S. Q. , da Silva A. M. , Olson W. S. , Kummerow C. D. , and Simpson J. , 2001 : Improving global analysis and short-range forecast using rainfall and moisture observations derived from TRMM and SSM/I passive microwave sensors . Bull. Amer. Meteor. Soc. , 82 , 659 – 680 , doi: 10.1175/1520-0477(2001)082<0659:IGAASF>2.3.CO;2 . Kirstetter, P

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Peter J. Shellito, Sujay V. Kumar, Joseph A. Santanello Jr., Patricia Lawston-Parker, John D. Bolten, Michael H. Cosh, David D. Bosch, Chandra D. Holifield Collins, Stan Livingston, John Prueger, Mark Seyfried, and Patrick J. Starks

direct observation of soil moisture, but rather an estimate based on observed microwave brightness temperature (Tb) and radiometric theory. It contains its own parameterizations and biases. Fortunately, observations and LSMs can provide scientific insights despite systematic errors and LSM-specific parameters ( Kumar et al. 2018 ). Prior to DA, it is common to rescale observations such that they match the first and second (or higher) statistical moments of the LSM time series. This can be done via

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Alberto Ortolani, Francesca Caparrini, Samantha Melani, Luca Baldini, and Filippo Giannetti

from multiple instruments are presented in Haese et al. (2017) . They use a stochastic approach called random mixing to generate precipitation fields from a set of rain gauge observations and path-averaged rain rates estimated using commercial microwave links. They apply their method to both synthetic (generated via the COSMO model) and real data in a study area in Germany, adopting an hourly time step. Bianchi et al. (2013) also present a technique to combine measurements from rain gauges

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Andrea Camplani, Daniele Casella, Paolo Sanò, and Giulia Panegrossi

to the five different 10° latitude bins indicated in the legend. The extremely variable snow-cover extent and snow radiative properties in the MW are one of the main issues in the detection and quantification of snowfall by passive microwave observations, which remain among the most challenging tasks in global precipitation retrieval ( Bennartz and Bauer 2003 ; Skofronick-Jackson et al. 2004 , 2019 ; Noh et al. 2009 ; Levizzani et al. 2011 ; Kongoli and Helfrich 2015 ; Chen et al. 2016

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Daniel Vila, Cecilia Hernandez, Ralph Ferraro, and Hilawe Semunegus

by optimally merging estimates computed from microwave, infrared, and sounder data and precipitation gauge analyses; 2) the Global Precipitation Climatology Centre (GPCC; Rudolf 1993 ) based on rain gauge analysis only; and 3) NOAA’s Precipitation Reconstruction over Land (PREC/L; Chen et al. 2002 ), which is also based on rain gauges observations from the Global Historical Climatology Network (GHCN), version 2, and the Climate Anomaly Monitoring System (CAMS) datasets. b. Algorithms A

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Seyed Hamed Alemohammad, Dara Entekhabi, and Dennis B. McLaughlin

. The FT measurements are obtained from the National Snow and Ice Data Center (NSIDC) as part of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Record of Daily Landscape Freeze/Thaw Status, version 2 ( Kim et al. 2011 , 2012 ). This product is a global daily record of FT status derived using microwave observations from SSM/I and Scanning Multichannel Microwave Radiometer (SMMR). The data are provided on a Climate Modeling Grid (CMG) at 25-km grids. We

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Abebe Sine Gebregiorgis, Pierre-Emmanuel Kirstetter, Yang E. Hong, Nicholas J. Carr, Jonathan J. Gourley, Walt Petersen, and Yaoyao Zheng

/IR data processing first discriminates the brightness of the cloud in the visible spectrum and/or the low temperature of the cloud top as seen in the thermal spectrum ( Arkin and Meisner 1987 ). Then it evaluates further criteria such as cloud area extent, time history or evolutionary information, and textural features to correlate with the rainfall estimates ( Adler and Negri 1988 ). Microwave instruments can provide observations of cloud and precipitation properties, beyond simple cloud

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