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T. N. Krishnamurti, Anu Simon, Aype Thomas, Akhilesh Mishra, Dev Sikka, Dev Niyogi, Arindam Chakraborty, and Li Li

schematically illustrated in Fig. 4 . In this paper the observational aspects using conventional datasets and vertical cross sections from CloudSat , sensitivity using a mesoscale high-resolution Advanced Research Weather Research and Forecasting Model (ARW-WRF), and validation of this scenario are addressed. The goal of this study is to illustrate the major role of soil moisture, stratiform cloud, and divergent circulations for the motion of the onset isochrones from Kerala at 10°N to New Delhi near 25°N

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Yu Du, Richard Rotunno, and Qinghong Zhang

D model is applied to explain the differences in these diurnal winds and the underlying physical reasons for them. The final section summarizes our results. 2. Data and model In this section, the hourly Weather Research and Forecasting (WRF) Model dataset established by Du et al. (2014) and a simple analytical 1D model developed by Du and Rotunno (2014) are briefly reviewed. a. Review of the Du model data Du et al. (2014) used the high-resolution nonhydrostatic mesoscale Advanced Research

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Scott W. Powell, Robert A. Houze Jr., Anil Kumar, and Sally A. McFarlane

cooling at rates less than 2 K day −1 . Only the Milbrandt and Morrison schemes—which both produce thick anvils containing small amounts of water—show a distinctive peak in warming at a magnitude of 1–2 K day −1 , but it occurs at an NH ~ 0.4. 8. Conclusions Using a high-resolution weather forecasting model (WRF version 3.3), we have simulated three mesoscale convective systems observed by instrumentation at Niamey, Niger, during the summer of 2006. We have simulated each case using six microphysical

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Jason A. Sippel and Fuqing Zhang

intensification (e.g., Krishnamurti et al. 2005 ; Hendricks et al. 2004 ; Montgomery et al. 2006 ), generally remains the least accurate forecast element at all scales ( Olson et al. 1995 ). Islam et al. (1993) and Snyder and Zhang (2003) demonstrated that errors grow rapidly at convective scales in weakly forced warm-season events, and such error growth in the presence of moist convection can significantly impact mesoscale predictability ( Zhang et al. 2002 , 2003 , 2006a , 2007 ). Focusing on an

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Fei Chen, Thomas T. Warner, and Kevin Manning

precipitation. Even though the skill of the operational model predictions of large-scale circulations has improved markedly during the period, progress has been especially slow in improving summer-season quantitative precipitation forecasts ( Fritsch et al. 1998 ; Georgakakos and Hudlow 1984 ). One of the impediments has been the lack of conventional in situ data on the thunderstorm scale. Thus, the development of techniques for the variational initialization of mesoscale models with WSR-88D radar

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Justin W. Whitaker and Eric D. Maloney

1. Introduction Panama Bight mesoscale convective systems (MCSs) are important to the climate and weather of the east Pacific basin. The convective complexes contribute to yearly average rainfall totals ranging from approximately 2600 to 5250 mm in the region ( Mapes et al. 2003a ). Velasco and Fritsch (1987) and Mapes et al. (2003a) found that MCSs and precipitation in the Panama Bight generally occur year-round, but most often during boreal summer. Mapes et al. (2003b) describes a

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Cathy Hohenegger and Christoph Schär

; Hohenegger et al. 2006 ) have underlined the importance of convective instability in disrupting mesoscale predictability. The existence of such alternate amplification mechanisms, in place of the baroclinic instability active for synoptic-scale medium-range forecasting (see, e.g., Molteni and Palmer 1993 ), may require the development of new EPS strategies. It is the goal of this study to identify the mechanisms responsible for rapid perturbation growth in a mesoscale model case study involving moist

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Reuben Demirdjian, James D. Doyle, Carolyn A. Reynolds, Joel R. Norris, Allison C. Michaelis, and F. Martin Ralph

the initial-condition errors during impactful weather phenomena by collecting in situ observations of critical regions where there may otherwise be gaps in the observing systems ( Shoemaker et al. 1990 ; Gray et al. 1991 ; Martin and Gray 1993 ; Majumdar 2016 ). However, the optimal sampling locations providing the greatest improvement to the forecast are often not obvious. The Naval Research Laboratory (NRL) Coupled Ocean–Atmosphere Mesoscale Prediction system (COAMPS) including its moist

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Stefan F. Cecelski, Da-Lin Zhang, and Takemasa Miyoshi

Research and Forecasting Model (WRF) ( Skamarock et al. 2005 ) and the ensemble simulations using the coupled WRF and local ensemble transform Kalman filter (LETKF) system ( Hunt et al. 2007 ; Miyoshi and Kunii 2012 , hereafter MK12 ); and (ii) identify the fundamental synoptic-scale and mesoscale differences between developing and nondeveloping ensemble members, or developers and nondevelopers for short, with an emphasis on upper-level warming ( Zhang and Zhu 2012 ; Cecelski and Zhang 2013 ), the

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Yamei Xu, Tim Li, and Melinda Peng

) demonstrated that the ISO can influence TC formation through both a dynamic effect (primarily via low-level convergence and cyclonic vorticity) and a thermodynamic (moisture) effect. Besides the strong intraseasonal and synoptic variability, the WNP is also the active region of high-frequency (at a period of shorter than 3 days) eddies (HFEs). These eddies have a typical spatial scale of 200 km or less, often including mesoscale convective systems and small-scale convective vortices. Small-scale convective

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