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fields of cloud-scale water vapor, temperature, and airflow in the BL and to learn how these factors control the initiation or suppression of deep, moist convection. Determining the susceptibility of the local boundary layer airflow to cumulus formation and convection initiation (CI) requires 3D, time-dependent fields of lifting condensation level (LCL) and the level of free convection (LFC). The need for spatially variable, evolving parcel stability parameters in turn requires fields of water vapor
fields of cloud-scale water vapor, temperature, and airflow in the BL and to learn how these factors control the initiation or suppression of deep, moist convection. Determining the susceptibility of the local boundary layer airflow to cumulus formation and convection initiation (CI) requires 3D, time-dependent fields of lifting condensation level (LCL) and the level of free convection (LFC). The need for spatially variable, evolving parcel stability parameters in turn requires fields of water vapor
cyclone airflows into regions of convergence and ascent and thus to illustrate the relationship between warm conveyor belts and atmospheric rivers. There is some debate in the literature regarding the relationship between warm conveyor belts and atmospheric rivers. To avoid confusion in this paper, we first clarify what we understand by these terms. An atmospheric river is a long, narrow, and transient corridor of strong horizontal water vapor transport ( Ralph et al. 2017 ). They are identified using
cyclone airflows into regions of convergence and ascent and thus to illustrate the relationship between warm conveyor belts and atmospheric rivers. There is some debate in the literature regarding the relationship between warm conveyor belts and atmospheric rivers. To avoid confusion in this paper, we first clarify what we understand by these terms. An atmospheric river is a long, narrow, and transient corridor of strong horizontal water vapor transport ( Ralph et al. 2017 ). They are identified using
1. Introduction Most of the previous studies of island-induced airflow and weather over the Hawaiian Islands have focused on the largest island of the Hawaiian Island chain, the island of Hawaii (also known as the “Big Island”), with several international field programs. The latest field campaign, the Hawaiian Rain Band Project (HaRP), was conducted in July–August 1990 ( Chen and Nash 1994 ). Even though considerable advances in the understanding of island effects under trade wind weather have
1. Introduction Most of the previous studies of island-induced airflow and weather over the Hawaiian Islands have focused on the largest island of the Hawaiian Island chain, the island of Hawaii (also known as the “Big Island”), with several international field programs. The latest field campaign, the Hawaiian Rain Band Project (HaRP), was conducted in July–August 1990 ( Chen and Nash 1994 ). Even though considerable advances in the understanding of island effects under trade wind weather have
increase from the high interior of both East and West Antarctica to the coast. Mather and Miller (1966) first estimated the mean airflow at the surface over Antarctica, depicting a radially outward drainage off the high plateau of East Antarctica. At the time of the Mather and Miller streamline map, the large-scale terrain over the East Antarctic ice sheet had not been mapped. The Mather and Miller streamlines, however, successfully represented the broad-scale surface airflow patterns. By the 1970s
increase from the high interior of both East and West Antarctica to the coast. Mather and Miller (1966) first estimated the mean airflow at the surface over Antarctica, depicting a radially outward drainage off the high plateau of East Antarctica. At the time of the Mather and Miller streamline map, the large-scale terrain over the East Antarctic ice sheet had not been mapped. The Mather and Miller streamlines, however, successfully represented the broad-scale surface airflow patterns. By the 1970s
in sections 3 and 4 , respectively; and a discussion is given in section 5 . 2. Review of stratified flow regimes over mountains The nature of stratified airflow over mountains is defined by the nondimensional mountain height ĥ = Nh/U , where U is the speed of the oncoming flow, h is the mountain height, and N is the Brunt–Väisälä frequency ( Baines 1995 ). Airflow over sufficiently high orography is characterized by large ĥ (i.e., ĥ > 1) and by highly nonlinear phenomena such
in sections 3 and 4 , respectively; and a discussion is given in section 5 . 2. Review of stratified flow regimes over mountains The nature of stratified airflow over mountains is defined by the nondimensional mountain height ĥ = Nh/U , where U is the speed of the oncoming flow, h is the mountain height, and N is the Brunt–Väisälä frequency ( Baines 1995 ). Airflow over sufficiently high orography is characterized by large ĥ (i.e., ĥ > 1) and by highly nonlinear phenomena such
1. Introduction On a global scale, rainfall in the trade wind belt is minimal. However, rainfall over the Hawaiian Islands is frequent and abundant ( Schroeder et al. 1977 ) because of terrain and local trade wind interactions. Hawaii’s array of microclimates, ranging from its humid and tropical windward flanks to dry and sometimes desertlike leeward areas ( Giambelluca et al. 1986 ), make it an ideal laboratory for the study of the atmospheric response of airflow to island size
1. Introduction On a global scale, rainfall in the trade wind belt is minimal. However, rainfall over the Hawaiian Islands is frequent and abundant ( Schroeder et al. 1977 ) because of terrain and local trade wind interactions. Hawaii’s array of microclimates, ranging from its humid and tropical windward flanks to dry and sometimes desertlike leeward areas ( Giambelluca et al. 1986 ), make it an ideal laboratory for the study of the atmospheric response of airflow to island size
1. Introduction Topography is one of the critical factors that determine the intensity and distribution of precipitation ( Medina and Houze 2003 ; Rotunno and Ferretti 2003 ; Houze and Medina 2005 ; Rotunno and Houze 2007 ). Houze (2012) concluded that the nature of orographic precipitation is determined largely by topographic features, the direction and strength of airflow, environmental conditions, stability, and microphysical processes. The main topographic features in South Korea are
1. Introduction Topography is one of the critical factors that determine the intensity and distribution of precipitation ( Medina and Houze 2003 ; Rotunno and Ferretti 2003 ; Houze and Medina 2005 ; Rotunno and Houze 2007 ). Houze (2012) concluded that the nature of orographic precipitation is determined largely by topographic features, the direction and strength of airflow, environmental conditions, stability, and microphysical processes. The main topographic features in South Korea are
which exceeds 4100 m in elevation. The trade wind flow past both islands is characterized by an upstream blocking regime ( Smolarkiewicz et al. 1988 ; Smith 1989 ; Yang et al. 2005 ; Carlis et al. 2010 ) with the low-level flow on the windward side of both islands being deflected laterally. With the presence of these volcanic mountains extending well above the typical height of the trade wind inversion (~2 km), the inversion serves as a lid forcing the low-level incoming trade wind airflow to
which exceeds 4100 m in elevation. The trade wind flow past both islands is characterized by an upstream blocking regime ( Smolarkiewicz et al. 1988 ; Smith 1989 ; Yang et al. 2005 ; Carlis et al. 2010 ) with the low-level flow on the windward side of both islands being deflected laterally. With the presence of these volcanic mountains extending well above the typical height of the trade wind inversion (~2 km), the inversion serves as a lid forcing the low-level incoming trade wind airflow to
was a useful supplement to the composite Q-VAD divergence to examine small-scale variations in airflow around the melting layer. In Fig. 6a , the convergence–divergence couplet in the Q-VAD div profile is notable between the 0°C level (4.8 km AGL) and 3.5 km AGL. Mapes and Houze (1995) attributed the couplet to a warm–cold–warm temperature anomaly due to melting, suggesting that the convergence–divergence couplet is a result of a response to cold temperature perturbations within and warm
was a useful supplement to the composite Q-VAD divergence to examine small-scale variations in airflow around the melting layer. In Fig. 6a , the convergence–divergence couplet in the Q-VAD div profile is notable between the 0°C level (4.8 km AGL) and 3.5 km AGL. Mapes and Houze (1995) attributed the couplet to a warm–cold–warm temperature anomaly due to melting, suggesting that the convergence–divergence couplet is a result of a response to cold temperature perturbations within and warm
theories have been developed and compared to experimental studies, there is still uncertainty about the parameterization of the wave growth and the modulation of the wind because of the presence of waves, the so-called wave-induced fluctuations. The spatial and temporal structure of the wave-induced airflow holds the key to the physics of wind–wave coupling, because it determines the pressure and shear stress distributions on the interface, and therefore the wave growth rate. One of the leading
theories have been developed and compared to experimental studies, there is still uncertainty about the parameterization of the wave growth and the modulation of the wind because of the presence of waves, the so-called wave-induced fluctuations. The spatial and temporal structure of the wave-induced airflow holds the key to the physics of wind–wave coupling, because it determines the pressure and shear stress distributions on the interface, and therefore the wave growth rate. One of the leading
