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Haixia Liu and Ming Xue

-scale features or flow structures that are resolved by the high-resolution model grid in the background forecast because of the insufficient spatial resolutions of such observations. In this case, the 3-km grid is able to resolve a significant portion of the convective-scale ascent forced by the horizontal convergence of the developing dryline and the outflow boundary, and by boundary layer convective eddies and rolls (cf. XM06b ). The analysis of mesoscale data, being of much coarser spatial resolutions

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Ting-Chi Wu, Hui Liu, Sharanya J. Majumdar, Christopher S. Velden, and Jeffrey L. Anderson

Meteorological Agency (JMA) found that the assimilation of MTSAT Rapid-Scan AMVs in their operational mesoscale and four-dimensional variational data assimilation (4D-Var) system provided slight improvements to forecasts of typhoon intensity but not the track, for three typhoon cases in 2010 ( Yamashita 2012 ). However, the qualitative and quantitative influence of assimilating hourly and rapid-scan AMV data on regional model predictions of TC structure and track still need to be investigated thoroughly. The

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Caren Marzban, Xiaochuan Du, Scott Sandgathe, James D. Doyle, Yi Jin, and Nicholas C. Lederer

(also called fine-tuning or calibration). This article proposes a sensitivity analysis method aimed at better understanding the effect of model parameters on the spatial structure of forecasts. The model used to illustrate the method is Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS), 1 and the forecast quantities are precipitation (convective and resolved grid scale), surface air temperature, and water vapor. It is important to distinguish between the two approaches because in spite

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David Siuta, Gregory West, and Roland Stull

large errors in the estimated hub-height wind. NWP models, such as the Weather Research and Forecasting (WRF) Model ( Skamarock et al. 2008 ), have taken the forefront in wind speed forecast research and can be used to directly forecast winds at hub height to avoid vertical interpolation. WRF has two dynamical cores: the Nonhydrostatic Mesoscale Model (NMM) and the Advanced Research version of WRF (ARW). The NMM core is used by the National Centers for Environmental Prediction (NCEP) North American

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Russ S. Schumacher and John M. Peters

1. Introduction Mesoscale convective systems (MCSs; Houze 2004 )—organized lines or clusters of convection—are the primary producers of heavy and extreme warm-season rainfall in many midlatitude locations, including the central United States (e.g., Fritsch et al. 1986 ; Ashley et al. 2003 ; Stevenson and Schumacher 2014 ). Yet the timing, location, and rainfall amounts in warm-season MCSs remain difficult to predict: forecast skill for both precipitation generally and for heavy

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Jian-Jian Wang, Hann-Ming Henry Juang, Kevin Kodama, Steve Businger, Yi-Leng Chen, and James Partain

forecast guidance than the AVN for this heavy rainfall event on Kauai. The RSM performed reasonably well in predicting the mesoscale environmental conditions and occurrence of heavy rainfall in the vicinity of Kauai. b. Cyclogenesis event of 2–4 March 1996 During the period of 2–4 March 1996, a wave developed on a stationary front northwest of Hawaii and moved northeast at 7 m s −1 , producing heavy rains and high winds across most of the island chain. Although heavy rains affected the entire state

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Mathias D. Müller and Dieter Scherer

that many other models for the treatment of radiation in complex terrain have been developed over the last several years (e.g., Dozier 1980 , 1989 ; Duguay 1993 ; Dubayah and Rich 1995 ; Kumar et al. 1997 ). Our parameterization is specially tailored for mesoscale models in the way that it deals with coarse-grid resolutions used in mesoscale weather forecast models, severe computational cost restrictions, as well as ease of implementation and portability. To the knowledge of the authors, the

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Lisa Bengtsson, Sander Tijm, Filip Váňa, and Gunilla Svensson

-permitting Applications of Research to Operations at Mesoscale model (AROME; Seity et al. 2011 ; Bénard et al. 2010 ), deep convection is computed explicitly on the model grid, using 1–2.5-km grid spacing. In this model, horizontal diffusion is applied using the LinSpec scheme developed for the ALADIN model, however, with 4-times-reduced diffusivity relative to the ALADIN implementation. This reduction in horizontal diffusion was introduced so as to have a diffusive damping that is as close as possible to the

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Marcin J. Kurowski, Damian K. Wojcik, Michal Z. Ziemianski, Bogdan Rosa, and Zbigniew P. Piotrowski

understanding of the aneleastic approximation, and to test its accuracy and representativeness for moist mesoscale modeling. We use the recently developed CE modeling framework to address the following topics. First, we compare anelastic and compressible mesoscale convection-permitting integrations for the same realistic initial and boundary conditions, and validate them against observations. Second, various simplifications to the representation of reference pressure in the model physics are discussed and

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Yunji Zhang, Fuqing Zhang, David J. Stensrud, and Zhiyong Meng

” method in which two (or more) simulations employed an identical numerical model but slightly, observationally indistinguishable, different ICs. Based on the examination of upscale error growth within idealized moist baroclinic waves and complimentary to earlier studies on the forecast error growth of a winter snowstorm in Zhang et al. (2002 , 2003) , a generalized conceptual model of how mesoscale intrinsic predictability becomes limited was presented in Zhang et al. (2007) . In this model

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