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Lei Meng, Yijun He, Jinnian Chen, and Yumei Wu

1. Introduction The Special Sensor Microwave Imager (SSM/I) was first flown on the Defense Meteorological Satellite Program (DMSP) F8 satellite in June 1987 ( Hollinger et al. 1987 ). Since then, six SSM/I sensors have been launched successfully, and currently there are three sensors onboard the DMSP F13 – 15 . Our analysis is based on the observations by the SSM/I onboard the DMSP F14 between 1997 and 2002. The scan direction of the SSM/I on DMSP F14 is from left to right with a spatial

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Randall J. Alliss, Sethu Raman, and Simon W. Chang

DECEMBER 1992 ALLISS ET AL. 2723Special Sensor Microwave/Imager (SSM/I) Observations of Hurricane Hugo (1989) RANDALL J. ALLISS AND SETHU RAMANDepartment of Marine, Earth and Atmospheric Sciences, North Carolina State University, Raleigh, North Carolina SIMON W. CHANGNaval Research Laboratory, Washington, D.C.(Manuscript received 21 November 1991, in final form 25 March

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Yaping Li, Edward J. Zipser, Steven K. Krueger, and Mike A. Zulauf

the CRM or assumptions employed in satellite retrievals. Therefore, direct comparisons between simulated radiances and satellite observations have some advantages for a systematic evaluation of atmospheric models. The sensitivity of passive microwave radiances to precipitating ice particles makes it possible to quantitatively assess the ice content generated by the cloud model. However desirable, it is not necessary to obtain corresponding satellite observations for every special case to evaluate

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Massimo Bonavita, Alan J. Geer, and Mats Hamrud

1. Introduction The assimilation of cloud- and precipitation-affected satellite radiances in the microwave part of the spectrum is one of the main success stories of global numerical weather prediction (NWP) of the past 10 years (all-sky assimilation; Geer et al. 2018 ). Using observations affected by cloud and precipitation allows NWP centers to greatly increase the use of available satellite radiances and to consistently and significantly improve analysis accuracy and forecast skill ( Geer

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Seyed Hamed Alemohammad, Dennis B. McLaughlin, and Dara Entekhabi

Observing Laboratory (EOL). Prior precipitation retrievals. Precipitation measurements from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) (hereafter referred to as TMI) are used as training images to generate prior replicates, as described in section 4 . The TRMM 2A12 product (version 6) that is used here is produced on an orbital grid, and we map it into a 0.25° × 0.25° grid using nearest-neighbor sampling. Cloud observations. Cloud-top temperature observations from the

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Filipe Aires, Francis Marquisseau, Catherine Prigent, and Geneviève Sèze

Polarization (CALIOP) instrument lidar on board the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) platform of the A-Train constellation have been used to evaluate the VIS and IR passive measurements algorithms ( Holz et al. 2008 ; Sèze et al. 2009 ). Microwave observations are less sensitive to thin clouds than visible or infrared measurements. However, contrarily to visible and infrared observations, which only sense radiation scattered or emitted from the top of the clouds

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Eric W. Uhlhorn, Peter G. Black, James L. Franklin, Mark Goodberlet, James Carswell, and Alan S. Goldstein

demonstrated the ability to provide in situ measurements of hurricane surface wind velocities, most importantly in the inner core, and recent work utilizing these measurements has improved the accuracy of extrapolations ( Franklin et al. 2003 ). Since 1984, the National Oceanic and Atmospheric Administration/Hurricane Research Division’s (NOAA/ HRD) Stepped Frequency Microwave Radiometer (HSFMR) has flown on one of two NOAA WP-3D research aircraft to estimate hurricane surface wind speeds ( Uhlhorn and

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Herschel L. Mitchell, P. L. Houtekamer, and Sylvain Heilliette

1. Introduction As the number of satellite observations available for assimilation has increased over the past several decades, radiances from the Advanced Microwave Sounding Unit-A (AMSU-A) instruments have been critically important over oceanic areas and in the stratosphere ( Gelaro et al. 2010 ; Todling 2013 ; Joo et al. 2013 ). However, despite the global coverage they afford and their abundance, there are a number of reasons why assimilating AMSU-A radiance data is more difficult than

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Zachary S. Bruick, Kristen L. Rasmussen, and Daniel J. Cecil

Argentina. With time, this will be a promising avenue to explore hail within this region, but currently the data record is not extensive enough for a thorough analysis. Fig . 1. Southern South America with topography shaded and the study area outlined. As a result, the most comprehensive way to examine the climatology of hail in subtropical South America and compare these results to other parts of the world is to use passive microwave satellite observations of ice hydrometeors. These measurements have

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Kozo Okamoto, Kazumasa Aonashi, Takuji Kubota, and Tomoko Tashima

background error covariance in EnVA. Despite the limited usage of DPR, the impact extended to as high as 14 km as a result of the background vertical error correlation. The combined use of GMI and KuPR (or KaPR) resulted in the best balanced analysis and forecast with respect to the agreement of observations and position errors of TC forecasts. The importance of the synergetic use of the microwave radiance and radar Ze is one of the significant findings of this study. One advantage of DPR Ze over

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