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Ting-Chi Wu, Milija Zupanski, Lewis D. Grasso, Christian D. Kummerow, and Sid-Ahmed Boukabara

is an advanced microwave sounder on board both the SNPP and NOAA-20 satellites. In this study, ATMS on board the nonoperational SNPP satellite is used. Similar to its two predecessors, AMSU-A and the Microwave Humidity Sounder (MHS), ATMS provides observations of the surface and atmosphere of Earth. ATMS has 22 channels with frequencies ranging from 23.8 to 183.31 GHz. Channels 1–16 have frequencies ranging from 23.8 to 87.9 GHz and are primarily used for temperature soundings. In contrast

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Kozo Okamoto and John C. Derber

on the scattering index from Ferraro (1997) . The thinning distance is set to 160 km, accounting for the observation’s spatial resolution (from 15 km × 13 km at 85.5 GHz to 69 km × 43 km at 19.35 GHz), operational model resolution (approximately 50 km), and the thinning distance of other instruments [145 km for Advanced Microwave Sounding Unit-A (AMSU-A), 240 km for AMSU-B, and 180 km for High Resolution Infrared Radiation Sounder (HIRS)]. In addition, a reduced weight is given to a pixel when

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Rosanne Polkinghorne and Tomislava Vukicevic

the assimilation of precipitation observations because more quantitative precipitation data are available from surface networks and satellite retrieval algorithms. Several works have been undertaken to investigate the impacts of assimilating microwave brightness temperatures versus rainfall ( Moreau et al. 2004 ) as well as to examine the sensitivity of the precipitation to the initial conditions ( Mahfouf and Bilodeau 2007 ). A few studies have been carried out to define the error covariances of

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Randhir Singh, P. C. Joshi, and C. M. Kishtawal

observations from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR), and the Special Sensor Microwave Imager (SSM/I) on board the Defense Meteorological Satellite Program (DMSP) satellite. AVHRR Pathfinder ( Brown et al. 1993 ) data consists of daily fields of gridded SST with a spatial resolution of 54 km 2 with data gaps over cloudy regions. We computed monthly averages of SST fields at 1° × 1° latitude–longitude resolution for the period 1988

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Michael S. Fischer, Robert F. Rogers, and Paul D. Reasor

; Dougherty et al. 2018 ). Motivated by the important operational and research challenges RI, ERCs, and SEF pose, this study examines the evolution of an RI event in Hurricane Irma (2017) that features two rapidly evolving ERC events. The environmental, vortex, and convective-scale evolutions related to the RI event are examined using flight-level and tail Doppler aircraft reconnaissance observations, passive microwave satellite data, and model analyses of the environment. Here, we will show that the two

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Yasu-Masa Kodama, Haruna Okabe, Yukie Tomisaka, Katsuya Kotono, Yoshimi Kondo, and Hideyuki Kasuya

1. Introduction The Tropical Rainfall Measuring Mission (TRMM) satellite carries a precipitation radar (PR; Iguchi et al. 2000 ), a Visible and Infrared Scanner (VIRS), the TRMM Microwave Imager (TMI; Kummerow et al. 1998 , 2000 ), and a Lightning Imaging Sensor (LIS; Christian et al. 1999 ) and therefore can perform satellite-based multisensor observations of precipitation. Multisensor observations from TRMM can evaluate microphysical features and internal structures in precipitating

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Godelieve Deblonde

1. Introduction Over the oceans, the number of conventional humidity observations is extremely limited. A source of humidity measurements with high spatial and temporal resolution is available from satellite observations. These observations however measure humidity indirectly. Three techniques currently exist to extract humidity from these observations. First, a retrieval technique can be used to derive humidity from satellite observations in the same form as conventional

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Daniel J. Cecil, Kevin R. Quinlan, and Douglas M. Mach

below 220 K (to avoid including the eye). (b) Minimum IR TB. (c) Best-track maximum sustained wind (solid) and minimum surface pressure (dashed), with the time of the ER-2 observations marked. Fig . 3. Sequence of 85-GHz horizontal channel imagery [89 GHz for Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E)] from the Navy Research Laboratory (NRL)—Monterey tropical cyclone Web page: (a) 1708 UTC 16 Jul TMI, (b) 1845 UTC 16 Jul AMSR-E, (c

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Hsiao-ming Hsu, Lie-Yauw Oey, Walter Johnson, Clive Dorman, and Richard Hodur

rugged terrain. Quality high-resolution wind fields are potentially important in small-scale ocean processes, and a companion paper ( Dong and Oey 2005 ) describes ocean dynamics driven by the COAMPS winds derived herein. Section 2 describes the COAMPS experiment and section 3 describes the results. Section 4 compares COAMPS with wind station observations, Special Sensor Microwave Imager (SSM/I) data, and also the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis. Section 5

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Tadashi Tsuyuki

and Negri (1988) . Theindirect nature of the relationship between IR observations and precipitation has limited the success of the IR techniques. Satellite microwave data, especially from the Special Sensor Microwave/Imager (SSM/I) on polar-orbiting satellites in the U.S. Defense Meteorological Satellite Program (DMSP), provide more direct information on precipitation. Although microwave estimates of precipitation are more accurate than IR estimates, they are available only a few times a day from

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