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their small size places them at the limits of resolution of conventional observation networks and most current numerical weather prediction models. Moreover, since the processes responsible for their formation are not fully understood, conceptual models and forecasting heuristics have not been developed. This study aims to examine MAHTE characteristics using data collected within a MAHTE and to examine the possible mechanisms for MAHTE formation through mesoscale modeling. Deep convection forming on
their small size places them at the limits of resolution of conventional observation networks and most current numerical weather prediction models. Moreover, since the processes responsible for their formation are not fully understood, conceptual models and forecasting heuristics have not been developed. This study aims to examine MAHTE characteristics using data collected within a MAHTE and to examine the possible mechanisms for MAHTE formation through mesoscale modeling. Deep convection forming on
-of-the-art of mesoscale models in simulating the coupled evolution of the mountain boundary layer and the valley wind system. The mean structure of the slope and valley winds has been well investigated and is described in numerous reviews (e.g., Wagner 1938 ; Egger 1990 ; Whiteman 1990 , 2000 ). Slope winds are understood to arise as a consequence of horizontal density gradients between the surface layer over the slopes and the air over the center of the valley. The diurnal along-valley winds are
-of-the-art of mesoscale models in simulating the coupled evolution of the mountain boundary layer and the valley wind system. The mean structure of the slope and valley winds has been well investigated and is described in numerous reviews (e.g., Wagner 1938 ; Egger 1990 ; Whiteman 1990 , 2000 ). Slope winds are understood to arise as a consequence of horizontal density gradients between the surface layer over the slopes and the air over the center of the valley. The diurnal along-valley winds are
1. Introduction Improvement in mesoscale atmospheric model simulations is often thought to be primarily a matter of finer spatial resolution, at least until all the surface features having a significant impact on the meteorological scales of interest have been resolved. At this point, further improvement in model performance often must come from other aspects of the simulation. Even if a specific surface feature is well resolved, its impact on the atmosphere must be adequately described and
1. Introduction Improvement in mesoscale atmospheric model simulations is often thought to be primarily a matter of finer spatial resolution, at least until all the surface features having a significant impact on the meteorological scales of interest have been resolved. At this point, further improvement in model performance often must come from other aspects of the simulation. Even if a specific surface feature is well resolved, its impact on the atmosphere must be adequately described and
1. Introduction The Fourier method and the mesoscale model have long been used separately to study mountain waves ( Baines 1995 ). The Fourier method describes only linear processes, including refraction by a vertically varying background, three-dimensional dispersion, and diffraction near caustics. Certain nonlinear processes, such as wave breaking or saturation, can be parameterized to some extent in a linear framework (e.g., Fritts et al. 2006 ), as they are for the linear parameterizations
1. Introduction The Fourier method and the mesoscale model have long been used separately to study mountain waves ( Baines 1995 ). The Fourier method describes only linear processes, including refraction by a vertically varying background, three-dimensional dispersion, and diffraction near caustics. Certain nonlinear processes, such as wave breaking or saturation, can be parameterized to some extent in a linear framework (e.g., Fritts et al. 2006 ), as they are for the linear parameterizations
development of sting-jet events in the light of the current conceptual model ( Clark et al. 2005 ; reviewed in section 2 ). To achieve these two objectives, two different limited-area mesoscale models have been used to investigate the passage over the United Kingdom of an explosive cyclone on 26 February 2002 that produced strong winds over Wales and the north of England during the early hours of that day. The models that have been used are the MetUM version 6.1 and the Consortium for Small Scale
development of sting-jet events in the light of the current conceptual model ( Clark et al. 2005 ; reviewed in section 2 ). To achieve these two objectives, two different limited-area mesoscale models have been used to investigate the passage over the United Kingdom of an explosive cyclone on 26 February 2002 that produced strong winds over Wales and the north of England during the early hours of that day. The models that have been used are the MetUM version 6.1 and the Consortium for Small Scale
within a computational fluid dynamics or large-eddy simulation (LES) domain, 2) explicit treatment of elevated drag and turbulent mixing to represent aggregation of several turbines within regional or mesoscale models, and 3) implicit parameterization through enlarged surface aerodynamic roughness length to represent the general impacts of wind farms on synoptic or global scales. The first, or bottom-up approach, focuses on the interaction of individual turbines with the atmosphere and the impact
within a computational fluid dynamics or large-eddy simulation (LES) domain, 2) explicit treatment of elevated drag and turbulent mixing to represent aggregation of several turbines within regional or mesoscale models, and 3) implicit parameterization through enlarged surface aerodynamic roughness length to represent the general impacts of wind farms on synoptic or global scales. The first, or bottom-up approach, focuses on the interaction of individual turbines with the atmosphere and the impact
; Dedner and Klöfkorn 2011 ; Yu and Wang 2014 ; Park et al. 2014 ). In the numerical simulation of nonhydrostatic mesoscale atmospheric modeling, very high-order polynomials can be used to approximate the solution, as shown by Giraldo and Restelli (2008) . Under this scenario, the implementation of hierarchical limiters will be very complicated. Furthermore, after limiting, the solution might be represented by a lower-order or even piecewise constant reconstruction. This polynomial order reduction
; Dedner and Klöfkorn 2011 ; Yu and Wang 2014 ; Park et al. 2014 ). In the numerical simulation of nonhydrostatic mesoscale atmospheric modeling, very high-order polynomials can be used to approximate the solution, as shown by Giraldo and Restelli (2008) . Under this scenario, the implementation of hierarchical limiters will be very complicated. Furthermore, after limiting, the solution might be represented by a lower-order or even piecewise constant reconstruction. This polynomial order reduction
these scales ( Wyngaard 2004 ; Teixeira et al. 2008 ). Thus, considering the multiscale nature of both atmospheric boundary layer (ABL) flows ( Drobinski et al. 2007 ) and wind energy facilities ( Sumner et al. 2010 ), it is of prime interest to develop a single approach allowing for the modeling of both microscale and mesoscale ABL flows. This is a first necessary step to allow, in the long term, the study of the microscale-mesoscale gap. In general, approaches to modeling ABL flows can be
these scales ( Wyngaard 2004 ; Teixeira et al. 2008 ). Thus, considering the multiscale nature of both atmospheric boundary layer (ABL) flows ( Drobinski et al. 2007 ) and wind energy facilities ( Sumner et al. 2010 ), it is of prime interest to develop a single approach allowing for the modeling of both microscale and mesoscale ABL flows. This is a first necessary step to allow, in the long term, the study of the microscale-mesoscale gap. In general, approaches to modeling ABL flows can be
temperatures by changing the atmospheric transmissivity ( Stanhill and Kalma 1995 ), it is beyond the scope of this paper. 2. Methods a. Regional model MM5 We used the limited-area numerical weather prediction fifth-generation Pennsylvania State University–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5; Grell et al. 1995 ) for our simulations. This nonhydrostatic model simulates and predicts mesoscale atmospheric circulation and is typically used on a regional scale. The distinction
temperatures by changing the atmospheric transmissivity ( Stanhill and Kalma 1995 ), it is beyond the scope of this paper. 2. Methods a. Regional model MM5 We used the limited-area numerical weather prediction fifth-generation Pennsylvania State University–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5; Grell et al. 1995 ) for our simulations. This nonhydrostatic model simulates and predicts mesoscale atmospheric circulation and is typically used on a regional scale. The distinction
-error scheme performance. Here we implement a scheme using multiple physics combinations (“multiphysics scheme”) and a stochastic kinetic-energy backscatter scheme into the same ensemble system and compare their performance to that of the system without model-error representation. We use the U.S. Air Force Weather Agency (AFWA) Joint Mesoscale Ensemble (JME; Hacker et al. 2011 ), which is a limited-area ensemble system using the Weather Research and Forecasting model (WRF) with Advanced Research WRF
-error scheme performance. Here we implement a scheme using multiple physics combinations (“multiphysics scheme”) and a stochastic kinetic-energy backscatter scheme into the same ensemble system and compare their performance to that of the system without model-error representation. We use the U.S. Air Force Weather Agency (AFWA) Joint Mesoscale Ensemble (JME; Hacker et al. 2011 ), which is a limited-area ensemble system using the Weather Research and Forecasting model (WRF) with Advanced Research WRF
