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Chinnawat Surussavadee and David H. Staelin

radiometric background provided by oceanic reflections of cosmic radio waves originally near 3 K; fourth, emission from colder nonscattering precipitating hydrometeor layers (e.g., warm rain) can often be seen against the warmer background of microwave-opaque air below. The stochastic link between hydrometeors aloft and those reaching the ground varies with climate and terrain. This relationship can be revealed by faithful cloud-resolving numerical weather prediction models such as the fifth

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Annakaisa von Lerber, Dmitri Moisseev, David A. Marks, Walter Petersen, Ari-Matti Harri, and V. Chandrasekar

. 10.1029/2012JD017979 Behrangi , A. , G. Stephens , R. F. Adler , G. J. Huffman , B. Lambrigtsen , and M. Lebsock , 2014 : An update on the oceanic precipitation rate and its zonal distribution in light of advanced observations from space . J. Climate , 27 , 3957 – 3965 , . 10.1175/JCLI-D-13-00679.1 Bennartz , R. , and P. Bauer , 2003 : Sensitivity of microwave radiances at 85–183 GHz to precipitating ice particles . Radio Sci

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Kotaro Bessho, Tetsuo Nakazawa, Shuji Nishimura, and Koji Kato

estimation for tropical cyclones in the developing and mature stages. Microwave scatterometers can also estimate the sea surface wind distribution in and around tropical cyclones ( Katsaros et al. 2001 ). By way of example, Sharp et al. (2002) and Gierach et al. (2007) inferred tropical cyclone genesis using the vorticity retrieved from observational data produced by the scatterometer of the Quick Scatterometer (QuikSCAT). Unfortunately, however, QuikSCAT observations were only available at most

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A. T. C. Chang, A. Barnes, M. Glass, R. Kakar, and T. T. Wilheit

JUNE 1993 CHANG ET AL. 1083Aircraft Observations of the Vertical Structure of Stratiform Precipitation Relevant to Microwave Radiative Transfer A. T. C. CHANGHydrological Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, Maryland A. BARNES AND M. GLASSPhillips Laboratory, Hanscom Air Force Base, Massachusetts R. KAKARO/rice of Space

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Robert M. Parinussa, Thomas R. H. Holmes, Niko Wanders, Wouter A. Dorigo, and Richard A. M. de Jeu

provide consistent geophysical parameters of our atmosphere, oceans, and/or land surfaces. The absence of a common observation period makes consistent radiometer calibration increasingly difficult, thus directly impacting consistency in the retrieved geophysical parameters. A possible solution is to use observations from other passive microwave radiometers, such as the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The TMI is a multifrequency microwave radiometer on board the TRMM

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Karl W. Hoppel, Stephen D. Eckermann, Lawrence Coy, Gerald E. Nedoluha, Douglas R. Allen, Steven D. Swadley, and Nancy L. Baker

extended to ~100 km and assimilated observations, initially up to ~80 km ( Hoppel et al. 2008 ) and later up to ~92 km ( Eckermann et al. 2009 ). For these studies, mesospheric temperatures from two research satellite instruments were assimilated: the Sounding of the Atmosphere Using Broadband Emission Radiometry (SABER) limb sensor on the National Aeronautics and Space Administration (NASA) Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite, and the Microwave Limb Sounder

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Kwo-Sen Kuo, William S. Olson, Benjamin T. Johnson, Mircea Grecu, Lin Tian, Thomas L. Clune, Bruce H. van Aartsen, Andrew J. Heymsfield, Liang Liao, and Robert Meneghini

1. Introduction In recent satellite missions, spaceborne radar observations, sometimes in combination with passive microwave radiometer measurements, are being used to quantify the precipitation rates of liquid, ice-phase, and mixed-phase hydrometeors. The Tropical Rainfall Measuring Mission (TRMM; see Kummerow et al. 1998 ) satellite observatory featured a 13.8-GHz (Ku band) radar and a microwave radiometer with nine channels ranging from 10 to 85 GHz. TRMM was recently succeeded by the

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Alan J. Geer, Peter Bauer, and Christopher W. O’Dell

satellite observations (e.g., Kummerow 1998 ). Here, even when two fields of view contain the same mass of rain or cloud, variations in fractional cloudiness can cause large differences in measured radiances. Rain- and cloud-affected microwave radiances are assimilated at the European Centre for Medium-Range Weather Forecasts (ECMWF; Bauer et al. 2006a , b ), improving forecasts of tropical moisture and wind ( Kelly et al. 2008 ). However, large biases between simulated and observed brightness

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Robert F. Adler and Ida M. Hakkarinen

VOL. 8, NO. 2 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY APRIL1991Aircraft Multifrequency Passive Microwave Observations of Light Precipitation over the Ocean ROBERT F. ADLERLaboratory for Atmospheres, NASA /Goddard Space Flight Center, Greenbelt, Maryland IDA M. HAKKARINENGeneral Sciences Corporation, Laurel, Maryland(Manuscript received 15 June 1990, in final form 8 December 1990

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Anne G. O’Carroll, John R. Eyre, and Roger W. Saunders

long enough periods to start being able to detect long-term changes in SST. However, it is important to understand the error characteristics of these data. This study investigates the errors in SST observations from three different sources: infrared SSTs from the Advanced Along-Track Scanning Radiometer (AATSR), microwave SST observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; AMSR-E), and in situ SST observations from drifting and moored buoys. A three

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