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Sangwon Joo, John Eyre, and Richard Marriott

ways in which AMVs are generated. This indicates that the number of observations is the main factor in determining the AMV impact of each platform in the Met Office global NWP system. Fig . 3. The AMV impact on the forecast error reduction between platforms. The (a) observation impact and (b) mean impact per observation. c. Observation impact by technique The observation impact of satellite by technique is shown in Fig. 4 . The microwave and infrared sounders together are measured as having an

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Clark Amerault and Xiaolei Zou

transfer model (RTM), which includes the interaction of radiation with both frozen and liquid hydrometeors, is needed to calculate T b ’s from model fields. Microwave radiance observations are two-dimensional data being used to improve the analysis of three-dimensional atmospheric fields, making the assimilation procedure an underdetermined problem. A background field is required to make it a well-posed problem. This requires knowledge of the background error covariance matrix. This matrix must be

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Douglas K. Miller and Grant W. Petty

studies have not taken advantage of high quality remotely sensed microwave data for confirmation of essential model output features, such as synoptic and mesoscale frontal rainfall patterns. Studies of cyclogenesis over oceans (the preferredregion of rapid cyclogenesis) are complicated by the lack of synoptic surface and upper-air observations, particularly over the vast Pacific Ocean. This lack of data has sometimes proven to be a problem for generating dependable initial analyses required for

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Dong-Eon Chang, James A. Weinman, Carlos A. Morales, and William S. Olson

were not available. These include additional spaceborne microwave radiometers to measure distributions of precipitation, integrated water vapor (IWV) over oceans, and sea surface temperatures (SSTs), and a spaceborne weather radar that was also recently placed into orbit. A long-range lightning observing network that furnished continuous observations of convective activity had also been established. Electrification of precipitating clouds is coupled to vertical air motions and microphysical

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Byoung-Joo Jung, Hyun Mee Kim, Thomas Auligné, Xin Zhang, Xiaoyan Zhang, and Xiang-Yu Huang

1. Introduction In numerical weather prediction (NWP) systems, analysis is conducted using a data assimilation (DA) system that combines observations and the background by considering their respective error statistics. Although the number of observations has rapidly increased, it is not clear that these observations are always beneficial to forecast performance. Thus, it is necessary to monitor and evaluate how observations are used in DA and forecast systems. The impact of observations on

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Rolf H. Langland

1. Introduction From mid-October to mid-December 2003, a major targeted observing field campaign was conducted over the North Atlantic region, including portions of eastern North America, Greenland, and Europe. The set of special observations resulting from this North Atlantic Observing-System Research and Predictability Experiment (THORPEX 1 ; http://www.wmo.int/thorpex ) Regional Campaign (NA-TReC) includes dropsondes, radiosondes, land and ship-surface data, commercial aircraft data, and

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James S. Goerss

), Australian bogus, and synthetic TC observations. The satellite observations assimilated in these experiments consisted of feature-track winds from geostationary and polar-orbiting satellites, Special Sensor Microwave Imager (SSM/I) total column precipitable water (PW) and wind speeds, Advanced Microwave Sounding Unit-A (AMSU-A) radiances, and Quick Scatterometer (QuikSCAT) and European Remote Sensing Satellite-2 ( ERS-2 ) 1 scatterometer winds. In Fig. 1a , the TC track forecast errors for the 2005

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Jason A. Otkin, Daniel C. Hartung, David D. Turner, Ralph A. Petersen, Wayne F. Feltz, and Erik Janzon

periods with little manual intervention. Profile observations were created to emulate the Doppler wind lidar (DWL), microwave radiometer (MWR), and Atmospheric Emitted Radiance Interferometer (AERI) sensors. Observations were also created to emulate the state-of-the-art Raman lidar (RAM) system run operationally at the Department of Energy’s Atmospheric Radiation Measurement (ARM) site in north-central Oklahoma ( Goldsmith et al. 1998 ; Turner et al. 2000 , 2002 ); however, this sensor is currently

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Mingjing Tong, Yanqiu Zhu, Linjiong Zhou, Emily Liu, Ming Chen, Quanhua Liu, and Shian-Jiann Lin

parameterizations of cloud and precipitation in forecast model (e.g., Forbes et al. 2016 ). The first operational implementation of all-sky microwave radiance assimilation was achieved at the European Centre for Medium-Range Weather Forecasts (ECMWF) in 2009, with observations from the Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) assimilated in all-sky approach ( Bauer et al. 2010 ). Since then, radiances from more microwave imagers

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Gabriel Susca-Lopata, Jonathan Zawislak, Edward J. Zipser, and Robert F. Rogers

situ datasets are used here. However, the present study expands upon previous studies by also utilizing passive microwave satellite data. The inclusion of passive microwave observations allows for a documentation of the coverage of both intense convection and at-least moderate rainfall before, during, and immediately after RI at a temporal resolution of ≤6 h. With this comprehensive collection of observations and objective analyses, the present study will further examine the relationship between

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