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1. Introduction Techniques for the use of dropsondes for field operations over large water bodies and relatively flat terrain are well established ( Hock and Franklin 1999 ) and seldom encounter problems associated with signal loss. Similar dropsonde operations when executed in complex terrain are, however, more susceptible to negative impacts on data acquisition from signal termination. From well-known radio signal propagation literature, signal loss is generally understood as the result of
1. Introduction Techniques for the use of dropsondes for field operations over large water bodies and relatively flat terrain are well established ( Hock and Franklin 1999 ) and seldom encounter problems associated with signal loss. Similar dropsonde operations when executed in complex terrain are, however, more susceptible to negative impacts on data acquisition from signal termination. From well-known radio signal propagation literature, signal loss is generally understood as the result of
.S. Great Plains (e.g., Charba 1974 ; Craig Goff 1976 ; Engerer et al. 2008 ; Bryan and Parker 2010 ). These studies mainly analyze GFs from organized thunderstorms such as supercell thunderstorms, squall lines, or mesoscale convective systems (MCSs). In and near complex terrain, however, thunderstorms are usually less organized as single cell and multicell thunderstorms ( Bunkers et al. 2006 ; Keighton et al. 2007 ; Parker and Ahijevych 2007 ; Schneider 2009 ; Ashley et al. 2019 ). In this
.S. Great Plains (e.g., Charba 1974 ; Craig Goff 1976 ; Engerer et al. 2008 ; Bryan and Parker 2010 ). These studies mainly analyze GFs from organized thunderstorms such as supercell thunderstorms, squall lines, or mesoscale convective systems (MCSs). In and near complex terrain, however, thunderstorms are usually less organized as single cell and multicell thunderstorms ( Bunkers et al. 2006 ; Keighton et al. 2007 ; Parker and Ahijevych 2007 ; Schneider 2009 ; Ashley et al. 2019 ). In this
1. Introduction Previous numerical simulations have shown that tornadoes are sensitive to the characteristics of near-surface inflow ( Lewellen et al. 1997 ; Lewellen et al. 2000 ; Lewellen and Lewellen 2007a , b ), which in turn is influenced by characteristics of surface terrain. While understanding of tornado behavior has improved over the last few decades, the impact of complex topography on tornado dynamics remains relatively unknown. Though field projects such as Verification of the
1. Introduction Previous numerical simulations have shown that tornadoes are sensitive to the characteristics of near-surface inflow ( Lewellen et al. 1997 ; Lewellen et al. 2000 ; Lewellen and Lewellen 2007a , b ), which in turn is influenced by characteristics of surface terrain. While understanding of tornado behavior has improved over the last few decades, the impact of complex topography on tornado dynamics remains relatively unknown. Though field projects such as Verification of the
1. Introduction Convective storms frequently cross complex terrain and may encounter highly variable convective environments. These varying environments make short-term forecasting of storm intensity and storm hazards difficult, in part because many of these variations are smaller in scale than most current numerical weather prediction models can resolve. Improving the conceptual model for how convective environments vary in regions of complex terrain and understanding when terrain
1. Introduction Convective storms frequently cross complex terrain and may encounter highly variable convective environments. These varying environments make short-term forecasting of storm intensity and storm hazards difficult, in part because many of these variations are smaller in scale than most current numerical weather prediction models can resolve. Improving the conceptual model for how convective environments vary in regions of complex terrain and understanding when terrain
1. Introduction The spatial distribution of surface air temperature in complex terrain depends on the three-dimensional topography, vegetation, soil characteristics, net radiation, and speed of the large-scale flow. Predicting the variation of surface air temperature with surface elevation ( terrestrial temperature gradient ) is very complex, although a number of simplifications are possible for some circumstances. Acevedo and Fitzjarrald (2001 , their Fig. 3) find that a height
1. Introduction The spatial distribution of surface air temperature in complex terrain depends on the three-dimensional topography, vegetation, soil characteristics, net radiation, and speed of the large-scale flow. Predicting the variation of surface air temperature with surface elevation ( terrestrial temperature gradient ) is very complex, although a number of simplifications are possible for some circumstances. Acevedo and Fitzjarrald (2001 , their Fig. 3) find that a height
polar grid. These standard “terrain based” hybrid scans have been shown to effectively mask the significant blockages and have been widely used in operational radars ( Fulton et al. 1998 ) and also for improving quantitative precipitation estimation in complex orography ( Morin and Gabella 2007 ). However, the beam blockage information in these algorithms was based on standard beam propagations and only accounted for the main lobes. The actual blockages and clutter distributions can deviate
polar grid. These standard “terrain based” hybrid scans have been shown to effectively mask the significant blockages and have been widely used in operational radars ( Fulton et al. 1998 ) and also for improving quantitative precipitation estimation in complex orography ( Morin and Gabella 2007 ). However, the beam blockage information in these algorithms was based on standard beam propagations and only accounted for the main lobes. The actual blockages and clutter distributions can deviate
spatial variabilities in wind structure caused by the interactions among valley flows, sea breezes, and ambient flows is especially important for transport studies in which deviations from the mean wind structure can lead to large differences in dispersion of air pollution. Studies in complex coastal terrain have mostly focused on the influence of coastal orography on the structure of the sea breeze (e.g., Banta et al. 1993 ; Banta 1995 ; Drobinski et al. 2006 ; Bastin et al. 2005 , 2006 ). In
spatial variabilities in wind structure caused by the interactions among valley flows, sea breezes, and ambient flows is especially important for transport studies in which deviations from the mean wind structure can lead to large differences in dispersion of air pollution. Studies in complex coastal terrain have mostly focused on the influence of coastal orography on the structure of the sea breeze (e.g., Banta et al. 1993 ; Banta 1995 ; Drobinski et al. 2006 ; Bastin et al. 2005 , 2006 ). In
. 1982 ). Somewhat more elaborate diagnostic models are based on the theory of Jackson and Hunt (1975) and its extension to three dimensions by Mason and Sykes (1979) , which linearized the equations of motion to obtain an analytical solution (e.g., Walmsley et al. 1982 ; Troen and Petersen 1989 ). These models provide satisfactory results over hilly terrain ( Jenkins et al. 1981 ; Mason and King 1984 ), but their application to steep slopes typical of complex terrain regions can be problematic
. 1982 ). Somewhat more elaborate diagnostic models are based on the theory of Jackson and Hunt (1975) and its extension to three dimensions by Mason and Sykes (1979) , which linearized the equations of motion to obtain an analytical solution (e.g., Walmsley et al. 1982 ; Troen and Petersen 1989 ). These models provide satisfactory results over hilly terrain ( Jenkins et al. 1981 ; Mason and King 1984 ), but their application to steep slopes typical of complex terrain regions can be problematic
boundary is implicitly taken into account by a reconstruction step on the primitive variables at the cells cut by the solid boundaries. There is a growing interest to adopt Cartesian immersed boundary methods to simulate environmental flows under realistic conditions ( Senocak et al. 2004 ; Lundquist et al. 2010 , 2012 ; Kang et al. 2011 ; DeLeon et al. 2012 ). Simulations of wind over arbitrarily complex terrain are an active area of research because of the rapidly expanding wind energy field
boundary is implicitly taken into account by a reconstruction step on the primitive variables at the cells cut by the solid boundaries. There is a growing interest to adopt Cartesian immersed boundary methods to simulate environmental flows under realistic conditions ( Senocak et al. 2004 ; Lundquist et al. 2010 , 2012 ; Kang et al. 2011 ; DeLeon et al. 2012 ). Simulations of wind over arbitrarily complex terrain are an active area of research because of the rapidly expanding wind energy field
and stationarity of mixed-layer depth and wind speed is not unreasonable over flat terrain during portions of the day ( Stull 1988 ), but is more questionable in complex terrain where topography influences surface heating and induces wind regimes at a variety of spatial and temporal scales ( Gorski and Farnsworth 2000 ). In complex terrain, one or more of the following may influence winds: terrain-forced flows, such as flow through gaps or around obstacles; and diurnal mountain winds, including
and stationarity of mixed-layer depth and wind speed is not unreasonable over flat terrain during portions of the day ( Stull 1988 ), but is more questionable in complex terrain where topography influences surface heating and induces wind regimes at a variety of spatial and temporal scales ( Gorski and Farnsworth 2000 ). In complex terrain, one or more of the following may influence winds: terrain-forced flows, such as flow through gaps or around obstacles; and diurnal mountain winds, including
