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Peter J. Marinescu, Susan C. van den Heever, Max Heikenfeld, Andrew I. Barrett, Christian Barthlott, Corinna Hoose, Jiwen Fan, Ann M. Fridlind, Toshi Matsui, Annette K. Miltenberger, Philip Stier, Benoit Vie, Bethan A. White, and Yuwei Zhang

atmospheric ice nuclei distributions and their impacts on climate . Proc. Natl. Acad. Sci. USA , 107 , 11 217 – 11 222 , . 10.1073/pnas.0910818107 Du , J. , 2011 : GCIP/EOP surface: Precipitation NCEP/EMC 4KM gridded data (GRIB) stage IV data, version 1.0. UCAR/NCAR Earth Observing Laboratory, accessed 1 January 2017 , . 10.5065/D6PG1QDD ECMWF , 2016 : IFS documentation CY41R2: Parts III–VI. ECMWF Rep., 556 pp. ,

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Stacey Kawecki, Geoffrey M. Henebry, and Allison L. Steiner

domain To produce a realistic evolution of the temporal formation and chemical composition of aerosols, we use the fully interactive Weather Research Forecast (WRF) Model coupled with chemistry (WRF-Chem v. 3.6; Grell et al. 2005 ) to simulate aerosol–cloud interactions during a severe weather event over the CGP on 27 May 2013. During this event, several reports of hail (>1.0 in. in diameter), strong winds (>60 kt), and tornadoes were recorded ( NCDC 2013 ). A model domain with 4-km grid spacing is

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Baolin Jiang, Bo Huang, Wenshi Lin, and Suishan Xu

aerosols and the atmosphere requires fully coupled models, among which the Weather Research and Forecasting Model with Chemistry (WRF-Chem) is a representative online model ( Grell et al. 2005 ) that can provide prognostic CCN size distributions, compositions, and number concentrations. Using WRF-Chem, we conducted three simulations to investigate the effects of anthropogenic aerosols on the precipitation, thermodynamic structure, and microphysical processes of Typhoon Usagi (2013). This typhoon

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Yun Lin, Yuan Wang, Bowen Pan, Jiaxi Hu, Yangang Liu, and Renyi Zhang

. During and after the cold front passage at the SGP site, weakly precipitating stratus developed at 2100 LST 26 May and persisted throughout 27 May, with relatively thick cloud physical and optical depths. b. Model configuration A Weather Research and Forecasting Model, version 3.1.1, with an aerosol-aware two-moment bulk microphysics scheme developed by Li et al. (2008b) at Texas A&M University (TAMU-WRF) is employed in this study. TAMU-WRF has been successfully used for investigations of AME on

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Yan Yang, Jiwen Fan, L. Ruby Leung, Chun Zhao, Zhanqing Li, and Daniel Rosenfeld

mechanisms contributing to the suppression of precipitation that was concluded unanimously by those observational studies. The modeling study is carried out on the convection-permitting scale over a large regional domain in central China by real-case simulations with coupled chemistry and aerosols to explore the major mechanisms responsible for the reduced precipitation over the Mt. Hua area. 2. Methods Model simulations are conducted using the improved Weather Research and Forecasting (WRF) Model with

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Nicholas R. Nalli, William L. Smith, and Quanhua Liu

, respectively. The calc for the individual NAST-I FOVs were conducted based upon a combination of the in situ dropsonde temperature and water vapor profile data along with model outputs from the European Centre for Medium-Range Weather Forecasts (ECMWF) model (1800 UTC analysis, along with the 1200 UTC forecast for 1500 and 2100 UTC). Figure 5 shows skew- T diagrams of the dropsonde temperatures and dewpoints up to 700 hPa, along with the skin SST (MW–IR blended analysis; see below for more on the SST

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Jianjun Liu, Zhanqing Li, and Maureen Cribb

-Range Weather Forecasts (ECMWF) model runs for ARM analysis provided by the ECMWF ( ECMWF 1994 ). Output from model runs are generated hourly for a 0.56° × 0.56° box centered on the site and include zonal and meridional wind components, temperature, relative humidity, and vertical velocity at 91 pressure levels from the surface to 10 hPa. In this study, clouds with LWP < 20 g m −2 (when retrieval errors are large) or LWP > 700 g m −2 (when precipitation contamination likely occurs) ( Dong et al. 2008

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Luke B. Hande, C. Hoose, and C. Barthlott

concentrations are mostly lower than those suggested by the Meyers et al. (1992) parameterization, except when the aerosol size becomes large. 3. Application in a simulation of a deep convective cloud a. Model description The nonhydrostatic regional-weather-forecasting Consortium for Small-Scale Modeling (COSMO) Model, version 5.01 ( Schättler et al. 2008 ), was run at high resolutions capable of resolving energy-containing turbulence ( Barthlott and Hoose 2015 ). To evaluate the above parameterization and

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Wojciech W. Grabowski and Hugh Morrison

summertime southern Great Plains (SGP)—using the Weather Research and Forecasting Model with bin microphysics and contrasted simulations specifying pristine and polluted conditions. The impact on the surface rain accumulation was relatively small: the accumulation was a few percent larger in the polluted case for the TWP and SEC simulations and a few percent smaller (larger) for the polluted case for the initial 20 days (final 10 days) for the SGP site. However, the polluted minus pristine difference was

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Wojciech W. Grabowski

increasing CCN) simply because different convective systems develop over the subdomain (cf. Fig. 7 therein). Applying the piggybacking technique to simulations as in Gayatri et al. (2017) would allow accurate estimation of aerosol effects over a subdomain as well. Work is in progress to incorporate the piggybacking technique into the NCAR’s Weather Research and Forecasting (WRF) Model used in the Gayatri et al. (2017) study. Finally, the novel use of the piggybacking methodology presented here

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