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Michael Peterson, Chuntao Liu, Douglas Mach, Wiebke Deierling, and Christina Kalb

the GEC on subseasonal time scales. The goal of this study is to develop a method for directly estimating electric fields above individual electrified clouds from common 37- and 85-GHz passive microwave observations. By coalescing the long record of satellite passive microwave observations at or near these frequencies taken by the Special Sensor Microwave Imager (SSM/I; Hollinger et al. 1990 ), Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI; Kummerow et al. 1998 ), and Global

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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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Ingo Meirold-Mautner, Catherine Prigent, Eric Defer, Juan R. Pardo, Jean-Pierre Chaboureau, Jean-Pierre Pinty, Mario Mech, and Susanne Crewell

1. Introduction A strong need is emerging to have accurate radiative transfer simulations from realistic cloudy and rainy scenes at high microwave frequencies. First, efforts are made to assimilate satellite microwave radiation from cloudy and rainy atmospheres within numerical weather prediction (NWP) models. As a first step, precipitation affected satellite observations at microwave frequencies up to 22 GHz are assimilated in the European Centre for Medium-Range Weather Forecasts (ECMWF

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Michael Peterson, Wiebke Deierling, Chuntao Liu, Douglas Mach, and Christina Kalb

regridding is to use the large volume of AMPR measurements rather than a small number of coincident TRMM overpasses to determine coefficients in the Peterson et al. (2015) algorithm that are compatible with the TMI scan geometry. b. The Peterson et al. (2015) ER-2 algorithm The passive microwave electric field retrieval algorithm from Peterson et al. (2015) uses the AMPR 37- or 85-GHz observations of clouds below the aircraft to estimate the electric field that is measured by LIP at the ER-2

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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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Satya Prakash, Hamid Norouzi, Marzi Azarderakhsh, Reginald Blake, Catherine Prigent, and Reza Khanbilvardi

over the ocean, microwave emissivity over land is highly variable because of a plethora of surface characteristics that include soil moisture, soil texture, surface roughness, land-cover type, and vegetation optical depth. During the last three decades, substantial progress has been made in retrieving LSE from passive microwave (PMW) sensors. Retrieval algorithms are broadly based on land surface models and direct satellite observations ( Ferraro et al. 2013 ; Turk et al. 2014 ; Ringerud et al

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Paul A. Hwang, Nicolas Reul, Thomas Meissner, and Simon H. Yueh

aircraft in the scene to make photographic or video observations, and tower-based operations are suspended during inclement weather. Microwave radiometer data represent another source of whitecap information. As in ocean surface optical images, microwave brightness temperature T bp increases sharply in the presence of whitecaps (surface foams); subscript p is polarization and is either vertical (V) or horizontal (H) in this paper. Several investigations of whitecap retrieval from T bp data have

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Eui-Seok Chung, Brian J. Soden, and Viju O. John

homogenized upper-tropospheric water vapor dataset with a long-term stability. The rectification and intersatellite calibration of SSM/T-2 measurements are deferred because of the lack of calibration information in the 2000s. In addition, the AMSU-B and MHS 183.31 ± 3 GHz channel observations will be analyzed to produce a continuous dataset of midtropospheric water vapor suitable for climate studies. 2. Satellite-based microwave radiometer observations Satellite-based radiance measurements of the 183-GHz

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Lidia Cucurull and Richard A. Anthes

system that already is tuned to many different observational systems ( English et al. 2013 ). Since the expected end of the lifetime of Suomi-NPP is 2016, and the launch of the first JPSS satellite has been delayed from 2016 to at least early 2017, a gap or significant reduction in the U.S. microwave satellite data stream is possible. However, because there are other MW observations besides the ones on the NOAA satellites, as well as a number of infrared (IR) sensors on various satellites and radio

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