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Ju-Hye Kim, Dong-Bin Shin, and Christian Kummerow

-orbiting satellite observations owing to difficulties in matching temporal and spatial scales and the intervention of uncertainties in the retrieved products. Other studies have discussed the shortcomings in cloud microphysics parameterizations of CRMs from the perspective of passive microwave rainfall estimations. Shin and Kummerow (2003) highlighted that when the liquid portion of the profile is matched for the model and observation combined a priori databases, the CRMs consistently specify ice particles of

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Nobuyuki Utsumi, F. Joseph Turk, Ziad S. Haddad, Pierre-Emmanuel Kirstetter, and Hyungjun Kim

1. Introduction Global precipitation products capitalize upon the long period of record of satellite-based passive microwave (MW) radiometer observations ( Aonashi and Ferraro 2020 ). The passive MW brightness temperature (TB) represents the net top-of-atmosphere upwelling radiation, after taking into consideration the emission and scattering properties of hydrometeors within the top-to-bottom profile, including the contribution from the surface emissivity. The surface precipitation represents

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Alessandro Battaglia, Pablo Saavedra, Thomas Rose, and Clemens Simmer

frequencies mirror those that are/will be present in many spaceborne radiometers [e.g., Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Special Sensor Microwave Imager (SSM/I), and Global Precipitation Measurement (GPM) Microwave Imager (GMI)]. Thus this setup offers an important contribution to the ground-based observations that can be used to validate passive microwave spaceborne rain-rate retrieval algorithms. The general radiometer configuration is illustrated in Fig. 2 . For each

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Damian J. Barrett and Luigi J. Renzullo

approaches to hydrometeorological problems include better estimation of initial soil moisture and temperature in mesoscale climatological models ( Jones et al. 2004 ; Huang et al. 2008 ), improved energy partitioning between latent and sensible heat fluxes ( Pipunic et al. 2008 ), and a concomitant higher skill in quantitative precipitation forecasts ( Koster et al. 2000 ). For example, it has been shown that updating soil moisture in a numerical weather model using passive microwave observations at

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Fengge Su, Huilin Gao, George J. Huffman, and Dennis P. Lettenmaier

basins globally ( Hong et al. 2007 ; Hossain et al. 2007 ; Hossain and Lettenmaier 2006 ). Current Tropical Rainfall Measuring Mission (TRMM)-era and future Global Precipitation Measurement (GPM) mission combined precipitation products generally merge geostationary infrared data and polar-orbiting microwave data to take advantage of the frequent sampling of the infrared and the superior quality of the microwave. With respect to hydrometeorological prediction, GPM is intended to improve

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Christian Kummerow and Louis Giglio

FEBRUARY 1995 KUMMEROW AND GIGLIO 33A Method for Combining Passive Microwave and Infrared RainfaI~ Observations CHRISTIAN KUMMEROWLaboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, Maryland LOUIS GIGLIOScience Systems and Applications, Inc., Lanham, Maryland(Manuscript received 14 January 1994, in final form 8 July 1994)ABSTRACT Passive microwave

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Yalei You, F. Joseph Turk, Ziad S. Haddad, Li Li, and Guosheng Liu

1. Introduction Microwave land surface emissivity (MLSE) is a fundamental parameter in physical overland rainfall retrieval algorithm development involving space-based passive microwave (PMW) radiometer observations since it influences the thermal emission and scattering of radiation at the surface. In general, MLSE retrieved from brightness temperature (TB) differs from the soil emissivity in a way that MLSE is an effective emissivity that includes the effects of vegetation. Despite its

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Don Cline, Simon Yueh, Bruce Chapman, Boba Stankov, Al Gasiewski, Dallas Masters, Kelly Elder, Richard Kelly, Thomas H. Painter, Steve Miller, Steve Katzberg, and Larry Mahrt

February 2002, IOP2 from 24 to 30 March 2002, IOP3 from 17 to 25 February 2003, and IOP4 from 25 March through 1 April 2003. In this paper, we summarize the CLPX airborne remote sensing datasets from four categories that span three spectral regions: gamma radiation observations, multi- and hyperspectral optical imaging and optical altimetry, and passive and active microwave. 2. Gamma radiation snow and soil moisture surveys Natural terrestrial gamma radiation is emitted from the potassium, uranium, and

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Chuntao Liu and Edward Zipser

: Why are there large differences in the precipitation estimates from radar and passive microwave observations over different regions? Would these differences vary in precipitation systems with different properties? If so, how? How can we improve precipitation retrievals based on what we learn from these differences? Though these questions have been addressed in various ways in the past (e.g., Nesbitt et al. 2004 ; Shige et al. 2006 ; Seo et al. 2007 ; Wang et al. 2009 ), there is still lack of

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Christian D. Kummerow, Sarah Ringerud, Jody Crook, David Randel, and Wesley Berg

methodology for passive microwave precipitation retrieval algorithms. J. Atmos. Sci. , 55 , 1583 – 1600 . 10.1175/1520-0469(1998)055<1583:ASMFPM>2.0.CO;2 Grecu, M. , and Olson W. S. , 2006 : Bayesian estimation of precipitation from satellite passive microwave observations using combined radar–radiometer retrievals. J. Appl. Meteor. Climatol. , 45 , 416 – 433 . 10.1175/JAM2360.1 Griffith, C. G. , Woodley W. L. , Grube P. G. , Martin D. W. , Stout J. , and Sikdar D. N. , 1978

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