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- Author or Editor: Morton Glass x
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Abstract
A time-dependent, one-dimensional model of the life cycle of an isolated warm cumulus cloud is presented that combines the vertical equation of motion, the equation of mass continuity, the first law of thermodynamics, and the equations of continuity of water vapor and liquid hydrometeors. The dynamic interaction between the cloud and its environment is modeled by two entrainment terms: turbulent entrainment representing lateral mixing at the side boundaries of the cloud, and dynamic entrainment representing the systematic inflow or outflow of air required to satisfy mass continuity. The formation and growth of drops by condensation, stochastic coalescence, and droplet breakup are modeled in detail for 67 logarithmically-spaced Eulerian size classes covering a range of particle sizes from 2 to 4040 μm in radius. The numerical method of simulating microphysical processes was investigated 1) by systematically reducing the number of mass classes used to represent the hydrometeor size spectrum and 2) by replacing the rigorous formulation of the microphysical processes by a set of parameterized expressions.
Calculations with the model gave results which were in good agreement with observations of the dynamical and microphysical properties of warm maritime cumuli. The formation and development of the tropical rain shower was particularly well simulated. Reasonable model predictions were obtained when as few as 45 logarithmically-spaced mass classes were used to characterize the hydrometeor size spectrum. When fewer mass classes or parameterized microphysical techniques were used, the model results were significantly different.
Abstract
A time-dependent, one-dimensional model of the life cycle of an isolated warm cumulus cloud is presented that combines the vertical equation of motion, the equation of mass continuity, the first law of thermodynamics, and the equations of continuity of water vapor and liquid hydrometeors. The dynamic interaction between the cloud and its environment is modeled by two entrainment terms: turbulent entrainment representing lateral mixing at the side boundaries of the cloud, and dynamic entrainment representing the systematic inflow or outflow of air required to satisfy mass continuity. The formation and growth of drops by condensation, stochastic coalescence, and droplet breakup are modeled in detail for 67 logarithmically-spaced Eulerian size classes covering a range of particle sizes from 2 to 4040 μm in radius. The numerical method of simulating microphysical processes was investigated 1) by systematically reducing the number of mass classes used to represent the hydrometeor size spectrum and 2) by replacing the rigorous formulation of the microphysical processes by a set of parameterized expressions.
Calculations with the model gave results which were in good agreement with observations of the dynamical and microphysical properties of warm maritime cumuli. The formation and development of the tropical rain shower was particularly well simulated. Reasonable model predictions were obtained when as few as 45 logarithmically-spaced mass classes were used to characterize the hydrometeor size spectrum. When fewer mass classes or parameterized microphysical techniques were used, the model results were significantly different.
Abstract
The vertical growth, diameters and trajectories of cumulus clouds observed over the San Francisco Peaks near Flagstaff, Ariz., were determined from photogrammetric measurements. New clouds formed in a small, well defined area. Their trajectories indicate the complex nature of orographic effects on the wind field. Active cloud elements were found to have growth characteristics similar to thermals observed in laboratory experiments. In particular, thermals broadened with height along a cone whose interior angle was about 30°, increasing their volumes by an order of magnitude prior to destructive erosion. Computed values of buoyancy decreased from a maximum initially, approaching zero or becoming negative near the erosion level, depending on the form of the momentum equation used.
Abstract
The vertical growth, diameters and trajectories of cumulus clouds observed over the San Francisco Peaks near Flagstaff, Ariz., were determined from photogrammetric measurements. New clouds formed in a small, well defined area. Their trajectories indicate the complex nature of orographic effects on the wind field. Active cloud elements were found to have growth characteristics similar to thermals observed in laboratory experiments. In particular, thermals broadened with height along a cone whose interior angle was about 30°, increasing their volumes by an order of magnitude prior to destructive erosion. Computed values of buoyancy decreased from a maximum initially, approaching zero or becoming negative near the erosion level, depending on the form of the momentum equation used.
Abstract
Classification algorithms have been developed to distinguish six categories of cloud ice particles. These algorithms have been incorporated in schema which, when applied to shadowgraph images produced by the Precision Measurement System laser scanning device, have demonstrated the capability of classifying with more consistency than human classifiers, and with almost no sensitivity to particle orientation.
The data used to derive the algorithms consisted of observations obtained on four separate aircraft flights. Two human classifiers, interacting with a preliminary machine classification, defined the correct answers for this training data set. The algorithms were then tested against arbitrarily selected segments from two additional flights. The ADAPT Service Corporations eigenvector, or empirical orthogonal function (EOF) technique, defined the features objectively, and the ADAPT independent eigenscreening algorithm development program related these features to the particle type.
Analysis of the performance suggests that considerable variation is to be expected, based on the set-to-set variation of the distribution of particle types between real data sets. The classification schema have been developed to allow the user to change key parameters in order to compensate for this variation.
It was concluded that the machine classification was superior to manual classification for the identification of large numbers of particles in terms of speed and consistency.
Abstract
Classification algorithms have been developed to distinguish six categories of cloud ice particles. These algorithms have been incorporated in schema which, when applied to shadowgraph images produced by the Precision Measurement System laser scanning device, have demonstrated the capability of classifying with more consistency than human classifiers, and with almost no sensitivity to particle orientation.
The data used to derive the algorithms consisted of observations obtained on four separate aircraft flights. Two human classifiers, interacting with a preliminary machine classification, defined the correct answers for this training data set. The algorithms were then tested against arbitrarily selected segments from two additional flights. The ADAPT Service Corporations eigenvector, or empirical orthogonal function (EOF) technique, defined the features objectively, and the ADAPT independent eigenscreening algorithm development program related these features to the particle type.
Analysis of the performance suggests that considerable variation is to be expected, based on the set-to-set variation of the distribution of particle types between real data sets. The classification schema have been developed to allow the user to change key parameters in order to compensate for this variation.
It was concluded that the machine classification was superior to manual classification for the identification of large numbers of particles in terms of speed and consistency.
Abstract
A major impediment to the development of computer algorithms for the automatic classification of ice particle types found in the atmosphere as measured by a Particle Measuring System two-dimensional probe is the difficulty of obtaining training data. This is especially true when, as is usually the case, the particle shapes do not correspond to any of the pure crystal types found in textbooks.
This paper presents the results of testing such a training set. Sources of bias among human observers include the effect of training and previous familiarity with the data, fatigue, and particle orientation, as well as subjective differences among observers. The deviation of individual human observers from the classifications arrived at by consensus indicates an upper bound to the accuracy possible in automated classification schemes.
Abstract
A major impediment to the development of computer algorithms for the automatic classification of ice particle types found in the atmosphere as measured by a Particle Measuring System two-dimensional probe is the difficulty of obtaining training data. This is especially true when, as is usually the case, the particle shapes do not correspond to any of the pure crystal types found in textbooks.
This paper presents the results of testing such a training set. Sources of bias among human observers include the effect of training and previous familiarity with the data, fatigue, and particle orientation, as well as subjective differences among observers. The deviation of individual human observers from the classifications arrived at by consensus indicates an upper bound to the accuracy possible in automated classification schemes.
