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Alexandra M. Keclik, Clark Evans, Paul J. Roebber, and Glen S. Romine

events preceding mesoscale convective system (MCS) formation. Key findings include a mean absolute displacement error of 105 km, no systematic timing bias, and no significant relationship between CI forecast skill and large-scale forcing magnitude, the latter of which was attributed primarily to the importance of smaller-scale features to the CI process. Similar results were obtained by Burghardt et al. (2014) for 27 warm-season CI episodes in the central High Plains in subkilometer horizontal grid

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Lei Zhang and Zhaoxia Pu

. , and J. Sun , 2002 : Assimilating radar, surface and profiler data for the Sydney 2000 forecast demonstration project . J. Atmos. Oceanic Technol. , 19 , 888 – 898 . Dudhia , J. , 1989 : Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model . J. Atmos. Sci. , 46 , 3077 – 3107 . Fritsch , J. M. , and R. E. Carbone , 2004 : Improving quantitative precipitation forecasts in the warm season: A USWRP research and

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Luca Mathias, Volker Ermert, Fanni D. Kelemen, Patrick Ludwig, and Joaquim G. Pinto

: Discrimination of mesoscale convective system environment using sounding observations . Wea. Forecasting , 22 , 1045 – 1062 , doi: 10.1175/WAF1040.1 . 10.1175/WAF1040.1 Coniglio , M. C. , and D. J. Stensrud , 2001 : Simulation of a progressive derecho using composite initial conditions . Mon. Wea. Rev. , 129 , 1593 – 1616 , doi: 10.1175/1520-0493(2001)129<1593:SOAPDU>2.0.CO;2 . 10.1175/1520-0493(2001)129<1593:SOAPDU>2.0.CO;2 Coniglio , M. C. , J. Y. Hwang , and D. J. Stensrud , 2010

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Ezio L. Mauri and William A. Gallus Jr.

-producing convective systems . Wea. Forecasting , 19 , 320 – 337 , https://doi.org/10.1175/1520-0434(2004)019<0320:AOSODC>2.0.CO;2 . 10.1175/1520-0434(2004)019<0320:AOSODC>2.0.CO;2 Coniglio , M. C. , D. J. Stensrud , and L. J. Wicker , 2006 : Effects of upper-level shear on the structure and maintenance of strong quasi-linear mesoscale convective systems . J. Atmos. Sci. , 63 , 1231 – 1252 , https://doi.org/10.1175/JAS3681.1 . 10.1175/JAS3681.1 Coniglio , M. C. , S. F. Corfidi , and J. S

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Dorita Rostkier-Edelstein and Joshua P. Hacker

forecast uncertainty via a probabilistic procedure is important even for very short-term forecasts. Real-time mesoscale ensemble systems capable of providing skillful high-resolution [ O (1) km] probabilistic nowcasts and short-range forecasts of the PBL are beyond the capacity of present computational resources. Augmenting current high-resolution deterministic forecasts with probabilistic information is an alternative. Rostkier-Edelstein and Hacker (2010 , hereafter RH10 ) proposed one approach for

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Xiaoshi Qiao, Shizhang Wang, and Jinzhong Min

focus on the forecast skill of strong UH. Meanwhile, given that UH is a localized quantity, only grid points with UH values of greater than 1 m 2 s −2 are used in the FSS calculations. Otherwise, the FSS will primarily reflect the forecast skill of zero UH. In addition, the FSS is a scale-selective verification; thus, a spatial scale must be defined. Mittermaier and Roberts (2010) proposed that this scale should be large enough to encompass the length of a mesoscale system. Given the scale of UH

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Steven E. Koch, Ahmet Aksakal, and Jeffery T. McQueen

mesoscale prediction models. Cloud radiative effects are usually parameterized in mesoscale models as a simple function of the relative humidity. Mesoscale model forecasts of precipitation and (less frequently) mean sea level pressure appear to benefit from the addition of mesoscale detail in the initial state of humidity ( Perkey 1976 ; Mills 1983 ; Mills and Davidson 1987 ; Mailhot et al. 1989 ; Bell and Hammon 1989 ). These benefits typically are realized only during the early stages of the

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Xingqin Fang, Ying-Hwa Kuo, and Anyu Wang

impacts of Taiwan’s topography on the typhoon mesoscale structures and precipitation forecasts from a stochastic perspective. In this paper, we present results from high-resolution ensemble forecast experiments on Typhoon Morakot with and without Taiwan’s topography. Section 2 presents the experiment design. Section 3 discusses the role of Taiwan’s topography in Typhoon Morakot’s extreme rainfall. Section 4 examines the rainfall variability impacted by Taiwan topography. Section 5 investigates

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Benjamin W. Green, Fuqing Zhang, and Paul Markowski

Research core of the Weather Research and Forecasting Model (WRF-ARW; Skamarock et al. 2005 ) was used for this study. There were four two-way nested model domains (D1–D4) with the grid spacing decreased by factors of 3 from 40.5 to 1.5 km. The coarsest three model domains—with 160 × 120, 253 × 253, and 325 × 325 horizontal grid points in D1, D2, and D3, respectively—were initialized at 2000 UTC 27 August 2005 with a WRF-based ensemble Kalman filter [EnKF; Meng and Zhang (2008a , b) ] that

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Dustan M. Wheatley, Nusrat Yussouf, and David J. Stensrud

, which can be responsible for generating large swaths of damaging surface winds, and producing widespread heavy rainfall and flash flooding. For example, Aksoy et al. (2009 , 2010) used the Weather Research and Forecasting Model (WRF) configured as a simplified cloud model to perform EnKF analyses and forecasts of supercellular, linear, and multicellular organization. They found that the EnKF performs effectively across all convective regimes, and that the representation of mesoscale uncertainty

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