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Yanjun Guo, Fuzhong Weng, Guofu Wang, and Wenhui Xu

1. Introduction Upper-air temperature is one of the essential climate variables measuring the atmospheric state and its long-term trend is an important indicator for climate change. Generally, upper-air temperature data are obtained from satellite remote sensing and in situ radiosonde observations. Since 1978, NOAA has launched a series of satellites with the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit (AMSU) onboard. MSU and AMSU measure the radiance of at the top of

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Chinnawat Surussavadee and David H. Staelin

1. Introduction To assimilate passive microwave precipitation observations or retrievals into numerical weather prediction (NWP) models, the modeled radiances must be consistent with those observed. This paper tests the sensitivity of that consistency to assumptions in a particular radiative transfer model (RTM), and in a cloud-resolving NWP model that predicts hydrometeor habits and profiles. The precipitation and water path retrieval accuracies are shown to be less sensitive to the physical

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H. Dong and X. Zou

1. Introduction Satellite microwave temperature sounders, humidity sounders, and imagers have provided complementary global observations of the global atmospheric and Earth surface variables for several decades. It is important to ensure the highest possible accuracy and precision of these observations before they are assimilated into numerical weather prediction (NWP) models. Although the Advanced Technology Microwave Sounder (ATMS) on board the Suomi–National Polar-Orbiting Partnership

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Xin Lin and Arthur Y. Hou

spaceborne active microwave rainfall retrieval, with a horizontal footprint of 5 km over a swath of 247 km (after the orbit boost in August 2001). Not only have PR data been undergoing rigorous internal and external calibrations, they have also been evaluated favorably against surface observations over different oceanic and land validation sites (e.g., Schumacher and Houze 2000 ; Liao et al. 2001 ). Considering the PR’s stable and active microwave sensing features (especially its theoretical

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Ruiyao Chen and Ralf Bennartz

scattering properties of nonspherical precipitation-sized ice particles and their effects on radar and passive microwave observations of clouds and precipitation has drawn significant attention in the last years. Tremendous progress has been made in understanding the scattering properties of nonspherical ice particles ( Ekelund and Eriksson 2019 ; Eriksson et al. 2015 ; Hogan and Westbrook 2014 ; Hogan et al. 2017 ; Hong 2007 ; Hong et al. 2009 ; Kim 2006 ; Kuo et al. 2016 ; Leinonen et al. 2018

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Stephen R. Guimond, Gerald M. Heymsfield, and F. Joseph Turk

1. Introduction a. Instruments for tropical cyclone observation Advancements in the field of atmospheric science (and science in general) often arise because of new and innovative observations of the entity being studied. Such is the case with the problem of tropical cyclone (TC) intensification. In recent years, the plethora of instruments (e.g., dropsondes, aircraft Doppler radars, microwave satellite imagers and sounders) has led to an increase in the frequency and quality of TC inner core

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William S. Olson, Lin Tian, Mircea Grecu, Kwo-Sen Kuo, Benjamin T. Johnson, Andrew J. Heymsfield, Aaron Bansemer, Gerald M. Heymsfield, James R. Wang, and Robert Meneghini

of an “effective” spherical particle density for the purpose of parameterizing the nonspherical snow particle scattering properties would not lead to consistent scattering properties across the spectrum of DPR and GMI channel frequencies. The validity of different snow particle models has been tested using coincident radar and passive microwave observations by Kulie et al. (2010) . They interpreted W-band (94 GHz) radar observations from CloudSat in terms of snow particle size distributions

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Haobo Tan, Jietai Mao, Huanhuan Chen, P. W. Chan, Dui Wu, Fei Li, and Tao Deng

verification and comparison of results obtained by four radiative transfer models using ground-based, remote sensing observations of brightness temperatures, and it is found that MonoRTM simulates the brightness temperatures of microwave radiations very well. b. Principal component analysis and stepwise regression When the regression method is used in forecasting, there could be a number of predictors that may not be mutually independent but rather are related to each other to a certain extent. In such

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Thiago S. Biscaro and Carlos A. Morales

Global Precipitation Measurement (GPM) mission ( Adams et al. 2002 ) will improve the temporal resolution of microwave-based observations by launching a constellation of combined equatorial and polar orbit satellites to produce an observation over the same spot every 3 h (information about the GPM mission is available online at http://gpm.gsfc.nasa.gov ). Unlike IR radiation, MW radiation penetrates clouds and can interact with hydrometeors, depending on the wavelength and particle sizes. Low

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David B. Wolff and Brad L. Fisher

designed around validating the TRMM Microwave Imager (TMI), Precipitation Radar (PR), and Combined (COM) standard rain products on monthly scales over the regional GV sites. Prior to launch, instantaneous validation was still considered somewhat intractable because of statistical uncertainties stemming from the spatiotemporal measuring characteristics of the satellite and GV observations. Direct instantaneous comparisons between coincident measurements are difficult to achieve without a sufficient

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