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Robert Redl, Andreas H. Fink, and Peter Knippertz

) identified cold pools over the northwest African Atlas Mountains based on a subjective and labor-intensive manual inspection of station data and infrared satellite images. The purpose of this paper is the presentation of a new objective method suitable for creating multiyear climatologies of cold pool events based on station observations of standard meteorological variables combined with microwave satellite data. The algorithm is objective in the sense that individual case decisions are based on fixed

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Roger Daley and Edward Barker

reports, Special Sensor Microwave Imager (SSM/I) total precipitable water and surface windspeeds, cloud drift and water vapor winds, and scatterometer surface winds. The handling of these observations is described in more detail in NSB2K and will be only briefly described here. a. Radiosondes and pibals NAVDAS uses temperature rather than geopotential as its primary variable. Since the geopotential observations from radiosondes are derived hydrostatically from the observed temperatures, it makes sense

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A. T. C. Chang, L. S. Chiu, and G. Yang

monthly oceanic rain rates derived from the DMSP (Defense MeteorologicalSatellite Program) F-8 SSM/I (Special Sensor Microwave/Imager) data are used to study the diurnal cycles.Annual mean rainfall maps based on the SSM/I morning and evening observations are presented, and theirdifferences are examined using a paired t test. The morning estimates are larger than the afternoon estimates byabout 20% over the oceanic region between 50-S and 50-N, with significant differences located mainly alongthe

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Virginie Marécal and Jean-François Mahfouf

Administration They are calculated from the profiling algorithm developed by Kummerow et al. (1996) using a cloud database coupled to a microwave radiative transfer model. To be consistent with the analysis dates/times chosen, the datasets selected correspond to TMI observations from 10 February 1998 at 2100 UTC to 11 February 1998 at 0300 UTC and from 26 August 1998 at 0900 UTC to 26 August 1998 at 1500 UTC. As in the operational 4DVAR assimilation system, the 6-h observations are divided into seven time

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Richard J. Reed, Georg A. Grell, and Ying-Hwa Kuo

)-~ and 33 mb (24 h)-l. At the mature stage a thermal gradient of 7-C (35 km)-~ wasobserved near the surface by a low-flying research aircraft that traversed the occluded fron~al zone. A full-physics simulation, carried out on a movable 30-km grid embedded within a 90-km fixed grid, closelyreproduced the storm development, as verified by surface ship and buoy observations, flight level and dropsondedata from research aircraft, and satellite infrared and microwave imagery. Sensitivity tests reported

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Keiji Imaoka and Kenji Nakamura

instruments on board TRMM. Kondo et al. (2006) investigated the relationship between the evolution stage of cloud systems and collocated TRMM precipitation parameters. They found that rain rates from the TRMM Microwave Imager (TMI) and PR show maximum values at the time of minimum Tb or earlier. Futyan and Del Genio (2007) investigated the life cycle of MCSs over Africa using a cloud tracking methodology and compared this with observations from PR and the Lightning Imaging Sensor (LIS) on TRMM. Their

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Eric W. Uhlhorn and David S. Nolan

observations are drawn from the numerical model described in section 2 . Of particular interest are aircraft-based observations from a stepped-frequency microwave radiometer (SFMR), which is now installed on all National Oceanic and Atmospheric Administration (NOAA) WP-3D ( Aberson et al. 2006 ) and Air Force Reserve Command WC-130J hurricane-penetrating aircraft (details available online at ). Along with GPS dropwindsondes, the SFMR has become a primary

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Zaizhong Ma, Ying-Hwa Kuo, F. Martin Ralph, Paul J. Neiman, Gary A. Wick, Ellen Sukovich, and Bin Wang

data is crucial for improving the skills of NWP models. The integrated water vapor (IWV) observations gathered from Special Sensor Microwave Imager (SSM/I) ( Hollinger et al. 1990 ) payloads aboard polar-orbiting satellites have proven crucial for monitoring these transient features over oceanic regions ( Ralph et al. 2004 , 2006 ; Neiman et al. 2008a , b ). However, SSM/I observations contain no information on the vertical structure of moisture or winds associated with AR events. Passive

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Xiang Wang and Haiyan Jiang

; Kidder et al. 2000 ), the Advanced Technology Microwave Sounder (ATMS; Zhu and Weng 2013 ), and dropsonde observations ( Durden 2013 ) have found that the strength of the warm core generally increases with the TC intensity. However, these studies mainly focused on a limited number of cases. The extent of the correlation between the warm-core strength and TC intensity and how it varies in different geographical locations and under different environmental conditions are still open questions

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Simon W. Chang, Randall J. Alliss, Sethu Raman, and Jainn-Jong Shi

, 0930, and 2200 UTC 4 January. To facilitatethe comparison with synoptic analysis, the frontal positions of Neiman et al. (1993) were linearly interpolated in time to produce the front structure at 0930and 2200 UTC (Fig. 4). We should note that thesefrontal positions are based on eyeball interpolation ofNeiman et al. and are not supported by any observations.3. SSM/I: Sensor and algorithm The microwave sensor SSM/I, from which observations were made for this study, was launched in June1987. This

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