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Mircea Grecu, William S. Olson, Chung-Lin Shie, Tristan S. L’Ecuyer, and Wei-Kuo Tao

, Olson et al. (1999 , 2006) and Grecu and Olson (2006 , hereafter GO06) developed methods that directly interpreted satellite passive microwave signatures in terms of heating vertical structure. The first two of these studies utilized cloud-resolving model simulations to synthesize microwave radiances; the model relationships between radiances and heating profiles were then employed in a Bayesian methodology for inferring heating profiles from satellite microwave sensor observations. These

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Christopher W. O’Dell, Frank J. Wentz, and Ralf Bennartz

). Observations from the Special Sensor Microwave Imager (SSM/I) sensor aboard multiple polar-orbiting platforms indicate increases in global water vapor from the record’s inception in 1987 ( Wentz and Schabel 2000 ; Trenberth et al. 2005 ). Both sets of observations are consistent with predictions of anthropogenic climate change. It appears that issues associated with calibration, orbital drift, and other effects have been addressed to the degree necessary to reveal long-term global trends. New results

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Cheng-Zhi Zou, Mei Gao, and Mitchell D. Goldberg

the temperature trend of the earth’s atmosphere and its spatial structure remains a challenge. Temperature trends derived from conventional radiosonde observations are questionable because they are subject to large regional and temporal errors due to varying observational practices in different countries. In addition, radiosonde stations are too sparse for determining spatial trend patterns. The Microwave Sounding Unit (MSU) on board the NOAA polar-orbiting satellites is uniquely positioned to

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Sungwook Hong and Inchul Shin

ice concentration are missed when a 15% “cutoff” is applied ( Ozsoy-Cicek et al. 2009 ). In addition, passive microwave satellite data for a sea ice edge show poor agreement during the melting (summer) season, although passive microwave satellite data for a sea ice edge agree well with the ship observations for the ice growth (winter) season ( Worby and Comiso 2004 ). However, the trend of the averaged roughness and refractive index may not change because of the missing data because the missing

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Xiping Zeng, Gail Skofronick-Jackson, Lin Tian, Amber E. Emory, William S. Olson, and Rachael A. Kroodsma

thick clouds depolarize the upwelling background (polarized) microwaves ( Adams et al. 2008 ). Fig . 7. As in Fig. 6 , but for the radiation at 89 GHz. Since the microwave radiation from the underlying surface is highly polarized at 89 GHz, it is difficult to infer the contribution of ice clouds to 89-GHz PD based on the GMI observations. However, light rain would make the liquid precipitation layer almost opaque and unpolarized at 89 GHz. It is estimated with physics modeling that the PD over a

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Daniel Watters, Alessandro Battaglia, and Richard P. Allan

global climate models, with CMIP3 and CMIP5 simulations of diurnal precipitation amplitudes generally identified to be realistic, while their precipitation typically peaks several hours earlier than surface and satellite observations ( Dai 2006 ; Randall et al. 2007 ; Rosa and Collins 2013 ; Flato et al. 2014 ; Covey et al. 2016 ). The latest CMIP6 multidecade model simulations are yet to be analyzed. Satellite constellations are capable of observing the diurnal cycle of precipitation across the

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Frank J. Wentz

radiometers . J. Atmos. Oceanic Technol. , 22 , 1340 – 1352 , doi: 10.1175/JTECH1769.1 . Chelton , D. B. , and F. J. Wentz , 2005 : Global microwave satellite observations of sea surface temperature for numerical weather prediction and climate research . Bull. Amer. Meteor. Soc. , 86 , 1097 – 1115 , doi: 10.1175/BAMS-86-8-1097 . Draper , D. W. , D. Newell , F. J. Wentz , S. Krimchansky , and G. Skofronick-Jackson , 2015 : The Global Precipitation Measurement (GPM) Microwave

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Viva F. Banzon and Richard W. Reynolds

) ( Woodruff et al. 2011 ) and infrared satellite data from the Advanced Very High Resolution Radiometer (AVHRR), which is referred to here as “AVHRR only.” The other analysis includes additional microwave satellite data from the Advanced Microwave Scanning Radiometer (AMSR). The short-lived Advanced Earth Observing Satellite-II ( ADEOS-II ) carried the first AMSR instrument, but in this paper AMSR is used to refer only to its successor, AMSR-E [i.e., AMSR for the Earth Observing System (EOS) on board

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Mitchell D. Goldberg and Larry M. McMillin

1. Introduction Deep-layer mean temperatures derived from observations made by the National Oceanic and Atmospheric Administration (NOAA) polar orbiting Microwave Sounding Unit (MSU) have become an important data source for monitoring temperature trends between 1979 and the present ( Spencer et al. 1990 ; Spencer and Christy 1992a , b , 1993 ). The MSU measures outgoing radiation within spectral bandwidths centered at 50.31, 53.73, 54.96, and 57.95 GHz. In this spectral region the

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Mitchell D. Goldberg and Henry E. Fleming

MAY 1995 GOLDBERG AND FLEMING 993An Algorithm to Generate Deep-Layer Temperatures from Microwave Satellite Observations for the Purpose of Monitoring Climate Change MITCHELL D. GOLDBERG AND HENRY E. FLEMING*NOAA ?National Environmental Satellite, Data, and Information Service, Satellite Research Laboratory, Washington, DC(Manuscript received 31 January 1994, in final form 6 September 1994

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