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Jonny William Malloy and Randall S. Cerveny

sounding signatures for the planetary boundary layer (PBL), as well as upper levels, for ozone exceedance days (i.e., DMO8 value ≥ 70 ppb). To do so, data collected includes the morning vertical environment (1200 UTC or 0500 LST) before peak ozone production in the PNA, which typically occurs in the afternoon hours (e.g., Atkinson-Palombo et al. 2006 ). The technique of composite analysis is both viable and accepted in the scientific community for identification of vertical atmospheric variables

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Ricardo Fonseca, Marouane Temimi, Mohan Satyanarayana Thota, Narendra Reddy Nelli, Michael John Weston, Kentaroh Suzuki, Junya Uchida, Kondapalli Niranjan Kumar, Oliver Branch, Youssef Wehbe, Taha Al Hosari, Noor Al Shamsi, and Abdeltawab Shalaby

et al. 2016 ; Duan et al. 2017 ). Another option is to compare the model’s performance to that of other numerical models that have different physics and dynamics but are forced by the same dataset. The intercomparison of model predictions has been extensively performed for more than three decades now, with emphasis on the different components of the climate system such as the atmosphere only (e.g., Xiang et al. 2017 ), coupled atmosphere–ocean (e.g., Meehl et al. 2005 ), carbon cycle (e

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Yanfeng Zhao, Donghai Wang, and Jianjun Xu

employed, the physical configuration in WRF was kept the same for all experiments. Fig . 1. The nested model domains, with terrain shaded. The interior SN was applied to the horizontal wind components, potential temperature, and geopotential height at all the levels above the planetary boundary layer, with an interval of 6 h, and the nudging fields were from the GFS predictions. The nudging coefficients for all variables were set to be 0.0003 s −1 . SN is applied in WRF to wavelengths longer than a

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John V. Cortinas Jr. and David J. Stensrud

determine the severe weather threat. Section5 discusses why it is important to include convectiveparameterization schemes. Section 6 discusses techniques to monitor the model performance, while a discussion and conclusions are presented in section 7.2. Model description and initialization For simulations of deep convection, a mesoscalemodel typically has parameterization schemes for theconvection itself, the surface energy budget, the planetary boundary layer, and cloud microphysical processes. There

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Wim C. de Rooy and Kees Kok

variations. Wieringa also defines a macrowind at the top of the planetary boundary layer. In most downscaling studies, geostrophic drag laws are used to transform this macrowind down to the blending height ( Verkaik and Smits 2001 ; Landberg and Watson 1994 ). For this transformation, a large-scale roughness length must be applied. Wieringa (1986) , for example, uses a roughness independent of wind direction and representative for an area of about 8-km radius. The horizontal resolution of NWP models

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Dileep M. Puranik and R. N. Karekar

, there is an incursion of upper-atmospheric dry air over the peninsula. The atmosphere becomes very unstable along the boundary of the incoming dry air. In both of these cases sudden thunderstorms occur at any time of the day. The third type of wind discontinuity occurs where two low-level airstreams from the Arabian Sea and the Bay of Bengal meet east of the Western Ghats, causing evening thunderstorms. These occurrences are not well captured by the surface and the upper-air observation network. The

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Jia Sun, Hailun He, Xiaomin Hu, Dingqi Wang, Cen Gao, and Jinbao Song

parameterization scheme (CPS), microphysics parameterization scheme (MPS), and the aforementioned surface layer and planetary boundary layer schemes ( Skamarock et al. 2008 ; Bao 2016 ; Li et al. 2016 , 2018 ). The CPS is used to parameterize the subgrid-scale effects of convective and/or shallow clouds, which represent vertical fluxes due to unresolved updrafts and downdrafts and the compensating motions outside clouds. In contrast, the MPS includes explicitly resolved water vapor, cloud, and precipitation

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Steven J. Caruso and Steven Businger

-level advection of moisture and the best lifted index (BLI) are analyzed. Fujita et al. (1970) used the BLI as a measure of how conducive the atmosphere is for deep convection. Similarly, low-level moisture advection has been linked to convective rainfall (e.g., Junker et al. 1999 ). Areas of enhanced low-level moisture advection and negative values of BLI have been shown to coincide with areas of deep convection in kona lows ( Morrison and Businger 2001 ). The absence of a correlation between low

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Mary M. Cairns and Jonathan Corey

and Shapiro (1996a , b) and Doyle et al. (1995) have simulated several cases of topographically induced severe winds. Using the Naval Research Lab's Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS; Hodur 1993 ), they found in these cases that a horizontal grid spacing of 3–5 km was required to obtain a fairly accurate simulation of the high winds along the coast of California and the coastal and inland areas of Norway. As a result of recent advances in computing capabilities, the

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Mace L. Bentley and Stonie R. Cooper

across eastern Nebraska. During 8 July 1993, increased destabilization of the atmosphere took place in northeastern Colorado (the derecho MCS genesis region) as a surface low to the south strengthened due to a weak midlevel shortwave trough seen in a satellite water vapor detection channel. As the day progressed, strengthening of the surface low increased winds and backed the flow from southerly to southeasterly. Southeasterly winds of 8–10 m s −1 were advecting moist air from central and eastern

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