Search Results
–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite, launched in 2006, carries a two-wavelength polarization lidar, the first space-based lidar optimized for cloud and aerosol layer detection. This instrument is capable of detecting subvisible cirrus layers with optical depths of 0.01 or less ( Winker et al. 2007 ). Two contrasting formation mechanisms for TTL cirrus have been advanced in the literature: Detrainment: Outflow from the anvil region of deep convective clouds has
–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite, launched in 2006, carries a two-wavelength polarization lidar, the first space-based lidar optimized for cloud and aerosol layer detection. This instrument is capable of detecting subvisible cirrus layers with optical depths of 0.01 or less ( Winker et al. 2007 ). Two contrasting formation mechanisms for TTL cirrus have been advanced in the literature: Detrainment: Outflow from the anvil region of deep convective clouds has
, and vertical structure. Temperature profiles and wind data were obtained for the lidar operational period from the Aircraft Communication Addressing and Reporting System (ACARS) of commercial aircraft data. Profiles obtained for aircraft departing and arriving at Los Angeles International Airport (LAX) were selected for temporal and spatial coincidence with the lidar observations. In addition, satellite images of the smoke distribution over the basin were obtained from Moderate Resolution Imaging
, and vertical structure. Temperature profiles and wind data were obtained for the lidar operational period from the Aircraft Communication Addressing and Reporting System (ACARS) of commercial aircraft data. Profiles obtained for aircraft departing and arriving at Los Angeles International Airport (LAX) were selected for temporal and spatial coincidence with the lidar observations. In addition, satellite images of the smoke distribution over the basin were obtained from Moderate Resolution Imaging
. 2010 ; Ern et al. 2018 ). On the other hand, different instruments covering different spectral windows of the GW spectrum yield complementary observations. The combination of different instruments, e.g., lidar and satellite, usually provide a more detailed picture of GW parameters ( Llamedo et al. 2019 ). In practice, it is often convenient to have an average reference GW field being representative for a certain latitude and season. Such averaged GW fields, also called climatologies, were
. 2010 ; Ern et al. 2018 ). On the other hand, different instruments covering different spectral windows of the GW spectrum yield complementary observations. The combination of different instruments, e.g., lidar and satellite, usually provide a more detailed picture of GW parameters ( Llamedo et al. 2019 ). In practice, it is often convenient to have an average reference GW field being representative for a certain latitude and season. Such averaged GW fields, also called climatologies, were
crystals in ice clouds, as their infinite variety makes this an unrealistic goal. Instead, the depolarization ratio is used to classify particles in a given cloud area into three distinct groups: platelike, columnlike, and irregular shapes. It is important to note that platelike and columnlike refer to particles that scatter light respectively like plates (including aggregates of plates) or columns (including aggregate of columns and bullet rosettes). As lidar observations are vertically resolved, the
crystals in ice clouds, as their infinite variety makes this an unrealistic goal. Instead, the depolarization ratio is used to classify particles in a given cloud area into three distinct groups: platelike, columnlike, and irregular shapes. It is important to note that platelike and columnlike refer to particles that scatter light respectively like plates (including aggregates of plates) or columns (including aggregate of columns and bullet rosettes). As lidar observations are vertically resolved, the
larger-scale flows in the lee of mountains ( Doyle and Durran 2007 ). These simulations are helping to illuminate the importance of surface friction in rotor development by showing, for example, that rotors can fail to develop, even in instances of high shear, if the atmospheric state is unfavorable for lee wave formation. In this paper, we show that two coherent Doppler lidar scanning the same vertical–horizontal plane can provide direct observational evidence showing the spatial extent, strength
larger-scale flows in the lee of mountains ( Doyle and Durran 2007 ). These simulations are helping to illuminate the importance of surface friction in rotor development by showing, for example, that rotors can fail to develop, even in instances of high shear, if the atmospheric state is unfavorable for lee wave formation. In this paper, we show that two coherent Doppler lidar scanning the same vertical–horizontal plane can provide direct observational evidence showing the spatial extent, strength
1. Introduction In the companion paper ( Virts et al. 2010 ; hereafter, VWFA ), we introduce an analysis protocol for relating features in the frequency of occurrence of cirrus clouds in the tropical tropopause transition layer (TTL), as observed by the polar-orbiting Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), to fields of atmospheric variables throughout the tropics. The protocol involves the generation of a TTL cirrus index (cloud fraction; i.e., the
1. Introduction In the companion paper ( Virts et al. 2010 ; hereafter, VWFA ), we introduce an analysis protocol for relating features in the frequency of occurrence of cirrus clouds in the tropical tropopause transition layer (TTL), as observed by the polar-orbiting Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), to fields of atmospheric variables throughout the tropics. The protocol involves the generation of a TTL cirrus index (cloud fraction; i.e., the
–Aerosol Lidar with Orthogonal Polarization (CALIOP) can detect cirrus clouds with τ as low as 0.01 ( Dupont et al. 2010 ). By combining data received from the CALIOP lidar with near-instantaneous observations from the Cloud Profiling Radar (CPR) aboard CloudSat , information on optically thick clouds and multiple cloud decks throughout the full vertical extent of the atmosphere becomes available ( Mace et al. 2009 ; Delanoë and Hogan 2010 ; Verlinden et al. 2011 ; Huang et al. 2015 ). The addition of
–Aerosol Lidar with Orthogonal Polarization (CALIOP) can detect cirrus clouds with τ as low as 0.01 ( Dupont et al. 2010 ). By combining data received from the CALIOP lidar with near-instantaneous observations from the Cloud Profiling Radar (CPR) aboard CloudSat , information on optically thick clouds and multiple cloud decks throughout the full vertical extent of the atmosphere becomes available ( Mace et al. 2009 ; Delanoë and Hogan 2010 ; Verlinden et al. 2011 ; Huang et al. 2015 ). The addition of
tens of meters thick and the thickest around 1000 m thick on average. The thickest clouds exist during fall and the thinnest during spring. Barrow observations show substantially thicker clouds, on average, than those observed in Eureka. Thirty-minute average lidar cloud optical depths are reviewed in Fig. 5d . These statistics are skewed by the AHSRL’s inability to penetrate deeper than an optical depth of around 5 before suffering from attenuation. As shown in Fig. 4 , a large fraction of these
tens of meters thick and the thickest around 1000 m thick on average. The thickest clouds exist during fall and the thinnest during spring. Barrow observations show substantially thicker clouds, on average, than those observed in Eureka. Thirty-minute average lidar cloud optical depths are reviewed in Fig. 5d . These statistics are skewed by the AHSRL’s inability to penetrate deeper than an optical depth of around 5 before suffering from attenuation. As shown in Fig. 4 , a large fraction of these
that this result may not be exactly applicable to other datasets since the definition of lidar–radar regions depends on the sensitivities of instruments used in different projects. For the three-species ice-phase scheme in models, the cloud ice mass is generally contributed by the small particles, given the small size assumption of cloud ice. However, snow and graupel are not equivalent to the median and large modes in observations, respectively. Therefore, they need to be repartitioned with a
that this result may not be exactly applicable to other datasets since the definition of lidar–radar regions depends on the sensitivities of instruments used in different projects. For the three-species ice-phase scheme in models, the cloud ice mass is generally contributed by the small particles, given the small size assumption of cloud ice. However, snow and graupel are not equivalent to the median and large modes in observations, respectively. Therefore, they need to be repartitioned with a
-scale conditions are prescribed, it is shown that the mass flux varies mostly with the surface flux and the depth of the subcloud layer as described by the convective velocity scale, so that regarding to Grant (2001) the cloud-base mass flux m b is proportional to the subcloud-layer convective velocity scale w * . To test that relationship with observations, we need to be able to measure w * and m b . Therefore, we show in sections 3 and 4 the parameters which influence m b and how a mean mass
-scale conditions are prescribed, it is shown that the mass flux varies mostly with the surface flux and the depth of the subcloud layer as described by the convective velocity scale, so that regarding to Grant (2001) the cloud-base mass flux m b is proportional to the subcloud-layer convective velocity scale w * . To test that relationship with observations, we need to be able to measure w * and m b . Therefore, we show in sections 3 and 4 the parameters which influence m b and how a mean mass
