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Rosemary M. Dyer, Morton Glass, and Herbert E. Hunter

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.

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Herbert E. Hunter, Rosemary M. Dyer, and Morton Glass

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.

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A. T. C. Chang, A. Barnes, M. Glass, R. Kakar, and T. T. Wilheit

Abstract

The retrieval of rainfall intensity over the oceans from passive microwave observations is based on a radiative transfer model. Direct rainfall observations of oceanic rainfall are virtually nonexistent making validation of the retrievals extremely difficult. Observations of the model assumptions provide an alternative approach for improving and developing confidence in the rainfall retrievals. In the winter of 1983, the NASA CV-990 aircraft was equipped with a payload suitable for examining several of the model assumptions. The payload included microwave and infrared radiometers, mirror hygrometers, temperature probes, and PMS probes. On two occasions the aircraft ascended on a spiral track through stratiform precipitation providing an opportunity to study the atmospheric parameters. The assumptions concerning liquid hydrometeors, water vapor, lapse rate, and non-precipitating clouds were studied. Model assumptions seem to be supported by these observations.

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James T. Moore, Fred H. Glass, Charles E. Graves, Scott M. Rochette, and Marc J. Singer

Abstract

Twenty-one warm-season heavy-rainfall events in the central United States produced by mesoscale convective systems (MCSs) that developed above and north of a surface boundary are examined to define the environmental conditions and physical processes associated with these phenomena. Storm-relative composites of numerous kinematic and thermodynamic fields are computed by centering on the heavy-rain-producing region of the parent elevated MCS. Results reveal that the heavy-rain region of elevated MCSs is located on average about 160 km north of a quasi-stationary frontal zone, in a region of low-level moisture convergence that is elongated westward on the cool side of the boundary. The MCS is located within the left-exit region of a south-southwesterly low-level jet (LLJ) and the right-entrance region of an upper-level jet positioned well north of the MCS site. The LLJ is directed toward a divergence maximum at 250 hPa that is coincident with the MCS site. Near-surface winds are light and from the southeast within a boundary layer that is statically stable and cool. Winds veer considerably with height (about 140°) from 850 to 250 hPa, a layer associated with warm-air advection. The MCS is located in a maximum of positive equivalent potential temperature θ e advection, moisture convergence, and positive thermal advection at 850 hPa. Composite fields at 500 hPa show that the MCS forms in a region of weak anticyclonic curvature in the height field with marginal positive vorticity advection. Even though surface-based stability fields indicate stable low-level air, there is a layer of convectively unstable air with maximum-θ e CAPE values of more than 1000 J kg−1 in the vicinity of the MCS site and higher values upstream. Maximum-θ e convective inhibition (CIN) values over the MCS centroid site are small (less than 40 J kg−1) while to the south convection is limited by large values of CIN (greater than 60 J kg−1). Surface-to-500-hPa composite average relative humidity values are about 70%, and composite precipitable water values average about 3.18 cm (1.25 in.). The representativeness of the composite analysis is also examined. Last, a schematic conceptual model based upon the composite fields is presented that depicts the typical environment favorable for the development of elevated thunderstorms that lead to heavy rainfall.

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