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John M. Peters and Paul J. Roebber

-Dynamic Meteorology and Weather Analysis and Forecasting, Meteor. Monogr., No. 33, Amer. Meteor. Soc., 5 – 34 . Bryan , G. H. , J. C. Wyngaard , and M. Fritsch , 2003 : Resolution requirements for the simulation of deep moist convection . Mon. Wea. Rev. , 131 , 2394 – 2416 , doi: 10.1175/1520-0493(2003)131<2394:RRFTSO>2.0.CO;2 . Davis , C. , B. Brown , and R. Bullock , 2006a : Object-based verification of precipitation forecasts. Part I: Methodology and application to mesoscale rain areas

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Matthew J. Carrier, Xiaolei Zou, and William M. Lapenta

. Another application of AIRS data that takes advantage of its high spectral resolution is the direct use of AIRS radiance observations at different channels for mesoscale forecast verification, which is presented in this study. Comparisons between simulated radiances and observed values are routinely done at various operational centers prior to assimilating the radiance data into their respective forecast models ( McNally et al. 2006 ; Le Marshall et al. 2006 ). This is done for several reasons: 1) to

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Eric A. Hendricks, Melinda S. Peng, Xuyang Ge, and Tim Li

3DVAR system in the Naval Research Laboratory’s mesoscale TC prediction model. A companion study (Zhang et al. 2011, manuscript submitted to Wea. Forecasting , hereafter ZLGPP) examines the dynamic initialization scheme with the Weather Research and Forecasting Model (WRF). The outline of this paper is as follows. In section 2 , the mesoscale TC prediction model is described, and both the control and dynamic initialization procedures are discussed. In section 3 , structure and intensity

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Jonathan A. Weyn and Dale R. Durran

1. Introduction The problem of mesoscale predictability in numerical weather forecasts is becoming increasingly important as computational resources allow the simulation of progressively finer-scale atmospheric features. It is also of societal importance, as the accurate prediction and localization of severe weather, including flash flooding and tornadoes, is vital to saving lives and property. Nearly 50 years ago, Edward Lorenz proposed the idea that certain deterministic fluid systems with

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Mika Peace, Trent Mattner, Graham Mills, Jeffrey Kepert, and Lachlan McCaw

realistic (as opposed to idealized) atmosphere, and they provide insights into dynamic interaction processes that may occur during a real event. This study uses the coupled Weather Research and Forecasting (WRF) Model and fire-spread model (SFIRE) module, described in detail by Mandel et al. (2011) . WRF and SFIRE couple the WRF Model with an implementation of the Rothermel (1972) fire-spread equations. Coupled fire–atmosphere models have been used in a number of studies to show that dynamical

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Raul Fernando Mendez Turrubiates, Markus Gross, and Vanesa Magar

1. Introduction Mesoscale model forecasts provide remarkable detail and realism in the resolved convective systems, as well as in the temperature and wind distributions. Less dependence on parameterizations of physical processes and more resolved physics lead to a representation of weather features that appear convincing, and their value has been reported in numerous studies ( Done et al. 2004 ; Kain et al. 2006 , 2008 ; Weisman et al. 2008 ; Schwartz et al. 2009 ). The resolution of

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Kun-Hsuan Chou and Chun-Chieh Wu

the implications from these experiments are discussed in section 3 . The conclusions are shown in section 4 . 2. Methodology and experimental design A single domain with 15-km resolution (301 × 301 grid points; 23 sigma vertical levels) of the latest version of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5, V3.7.2) is adopted to examine the role of the dropwindsonde data and the bogused vortex on the TC forecasts. The model

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Daniel P. Hawblitzel, Fuqing Zhang, Zhiyong Meng, and Christopher A. Davis

atmospheric state, but it also details uncertainties associated with the best estimate and provides valuable information for estimating the flow-dependent background error covariance that is essential for data assimilation ( Evensen 1994 ; Snyder and Zhang 2003 ; Zhang et al. 2006a ). Zhang (2005) introduced the use of ensemble forecasts to investigate the dynamics and structure of mesoscale error covariance in an extreme extratropical cyclogenesis event. It was demonstrated that underlying balanced

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David R. Novak and Brian A. Colle

1. Introduction A major challenge of cool-season quantitative precipitation forecasting (QPF) is to determine the spatial and temporal variability of precipitation within extratropical cyclones ( Ralph et al. 2005 ). Variability in the location and intensity of cool-season precipitation is often determined by the development and evolution of mesoscale precipitation bands ( Ralph et al. 2005 ). Thus, improving mesoscale band forecasts will help improve cool-season QPF. Mesoscale precipitation

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Lisa Bengtsson, Ulf Andrae, Trygve Aspelien, Yurii Batrak, Javier Calvo, Wim de Rooy, Emily Gleeson, Bent Hansen-Sass, Mariken Homleid, Mariano Hortal, Karl-Ivar Ivarsson, Geert Lenderink, Sami Niemelä, Kristian Pagh Nielsen, Jeanette Onvlee, Laura Rontu, Patrick Samuelsson, Daniel Santos Muñoz, Alvaro Subias, Sander Tijm, Velle Toll, Xiaohua Yang, and Morten Ødegaard Køltzow

Recherche Petite Échelle Grande Échelle (ARPEGE) and Integrated Forecasting System (IFS) software, developed jointly by the European Centre for Medium-Range Weather Forecasts (ECMWF) and Météo-France. A more detailed explanation of the ALADIN code architecture and its canonical model configurations, Applications of Research to Operations at Mesoscale (AROME) and Aire Limitee Adaptation/Application de la Recherche a l’Operationnel (ALARO), can be found in P. Termonia et al. (2017, unpublished manuscript

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