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Paul M. Tag

Abstract

It has been suggested that the use of charged particles or electric fields be considered as a technique for dissipating warm fogs. The study presented here attempts to determine the degree of improvement one could expect as the result of one aspect of electrically enhanced coalescence—enhanced coalescence due to an externally applied electric field on neutral drops. For this purpose, a numerical simulation with a one-dimensional microphysical fog model which incorporates the process of collision-coalescence was conducted. Collision efficiencies appropriate to two extreme electric fields were utilized for the numerical experiments. It was determined that a noticeable improvement in visibility can be achieved only under extremely large field strengths, and then only for certain fog spectra.

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Paul M. Tag

Abstract

A numerical study of the use of highly charged water drops to clear warm fog has been conducted. The mechanism studied is the polarization of neutral fog droplets and their capture by the charged drops. A multi-level microphysical model is used to investigate the degree of visibility improvement resulting from variations in seeding drop size and charge, the concentration of seeding material and the fog being seeded. It is determined that visibility improvement decreases with decreasing fog droplet size and increases with increasing seeding rate and seeding drop charge. For the same amount of seeding water, a treatment spectrum with an average radius between 10 and 15 μm is ideal. In contrast to the findings of Part I (an applied electric field), visibility improvement here results both from a removal of fog water (to the ground) and from a transfer of water from the fog spectrum to the larger treatment drops.

Field tests of this technique have proven inconclusive. A further evaluation is made by comparing model results to comparable numerical experiments of hygroscopic seeding, a technique that has been field tested on several occasions. It is concluded that the charges and treatment concentrations simulated in this study would not be adequate for clearing fog; unless charges and seeding concentrations can be greatly increased, charged drop seeding is probably not a viable fog dissipation technique.

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Paul M. Tag

Abstract

The diagnosis and conservation of energy during condensation is examined. It is shown that the latent enthalpy, when defined in conjunction with the individual enthalpies of water vapor and liquid water, cannot be a function of the latent heat of condensation L but a modified value (L′) which is ∼30% larger than L. The additional energy represented in L′ can be thought of as a necessary absorption by the liquid water to bring the post-condensation air-vapor-liquid system into thermal equilibrium. The difference between L′ and L is a function of the difference in specific heats of water vaper and liquid water. If we assume that (C pd C pr is constant, as is required in our energy conservation derivation, L′ is shown to vary by only 0.59% when computed over the range −50 to +60°C; a representative value for L' is 3.142×106 J Kg−1

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Paul M. Tag

Abstract

Abstract not available.

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Paul M. Tag

Abstract

A two-dimensional model is developed to simulate dissipation of fog using passive burner lines under either cross-wind or no-wind conditions. The vorticity model developed by Murray (1970) forms the basis for the development. Among the additions to the model are a stretched vertical grid, provision for an ambient wind field and variable eddy exchange coefficients.

The model is tested by comparing results to empirical temperature distribution data resulting from burner lines, located both outdoors and in a wind tunnel, positioned in a cross wind. Equally good comparisons are achieved by running the model at these two different physical scales. It is determined that the parameterization of the eddy coefficients most influences the resulting temperature profiles, and that a form in which deformation and buoyancy are summed gives the best results. A coefficient based solely on the deformation or vorticity gradients is found to be inadequate. Several additional experiments which utilize a soil heat flux parameterization support empirical estimates of a 5% heat loss to the soil.

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Paul M. Tag

Abstract

A two-dimensional model (Tag, 1979) is used to perform sensitivity experiments simulating fog dissipation using passive burner lines under both cross-wind and no-wind conditions. For a cross-wind experiment, heat output and cross-wind speed are found to be the two overriding factors which control the height of a clearing. It is determined that the extent of the fog clearing is inversely linked to the fog liquid water content, and that the fog thermal structure as well as the moisture emitted by the hydrocarbon combustion in the line have minimal effect on the size of the resulting clearing. Under no-wind conditions, where two lines of equal heat output are positioned on either side of a runway, heat output and line separation are the key controls to a surface clearing. Depending on the strength of the line heat release, either a downdraft or updraft can form between the lines, with the latter producing the quicker surface clearing. The no-wind experiments suggest that the use of a blower system in conjunction with two burner lines would be more efficient than relying on heat-induced circulations alone.

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James E. Peak
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Paul M. Tag

Abstract

An Expert system for Shipboard Obscuration Prediction (AESOP), an artificial intelligence approach to forecasting maritime visibility obscurations, has been designed, developed, and tested. The problem-solving model for AESOP, running within an IBM-PC environment, is rule-based, uses backward chaining, and has meta-rules; a user, in a consultation session, answers questions about certain atmospheric parameters. The current version, AESOP 2.0, has 232 rules and has been designed in terms of nowcasts (0–1 h) and forecasts (1–6 h). An extensive explanation feature allows the user to understand the reasoning process behind a particular forecast. AESOP has been evaluated against 83 test cases, in which clear, hazy, or foggy conditions are predicted. The overall performance of AESOP is 75% correct. This value indicates considerable forecast skill when compared to 47% for persistence and 41% for random chance. When the distinction between clear and haze is ignored, the expert system correctly forecasts 84% of the “Fog/No fog” situations.

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Robert W. Fett
and
Paul M. Tag

Abstract

Meteorological satellite scanning radiometer data from a visual sensor during daylight hours are characteristically influenced by sunglint from the ocean surface as the sensor scans in the direction toward the sun (between the satellite subpoint and solar subpoint). When seas are calm in the region near the primary specular point (PSP), the sun's rays are either reflected directly into the spacecraft sensor yielding a high energy (bright) response, or away from the sensor yielding a low energy (dark) response. The particular effect depends on the proximity of the calm area to the PSP. This paper shows examples of bright and dark linear patterns adjacent to and tending to parallel coastlines. The patterns are interpreted to be sea-breeze-induced calm zones originating during periods of offshore flow when the pressure gradient causing the sea breeze is exactly counterbalanced by the larger-wale synoptic gradient. A two-dimensional planetary boundary layer (PBL) numerical model successfully simulates this condition and additionally shows that the calm region first appears near the coastline as daytime heating commence and then moves seaward with time as afternoon heating over land is maximized. We show that, at least initially, the rapidity of movement and the distance covered in this movement are directly related to the land-sea temperature contrast and indirectly related to the speed of the offshore flow, and are highly sensitive to small changes in these parameters.

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Paul M. Tag
and
James E. Peak

Abstract

In recent years, the field of artificial intelligence has contributed significantly to the science of meteorology, most notably in the now familiar form of expert systems. Expert systems have focused on rules or heuristics by establishing, in computer code, the reasoning process of a weather forecaster predicting, for example, thunderstorms or fog. In addition to the years of effort that goes into developing such a knowledge base is the time-consuming task of extracting such knowledge and experience from experts. In this paper, the induction of rules directly from meteorological data is explored-a process called machine learning. A commercial machine learning program called C4.5, is applied to a meteorological problem, forecasting maritime fog, for which a reliable expert system has been previously developed. Two detasets are used: 1) weather ship observations originally used for testing and evaluating the expert system, and 2) buoy measurements taken off the coast of California. For both datasets, the rules produced by C4.5 are reasonable and make physical sense, thus demonstrating that an objective induction approach can reveal physical processes directly from data. For the ship database, the machine-generated rules are not as accurate as those from the expert system but are still significantly better than persistence forecasts. For the buoy data, the forecast accuracies are very high, but only slightly superior to persistence. The results indicate that the machine learning approach is a viable tool for developing meteorological expertise, but only when applied to reliable data with sufficient cases of known outcome. In those instances when such databases are available, the use of machine learning can provide useful insight that otherwise might take considerable human analysis to produce.

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James E. Peak
and
Paul M. Tag

Abstract

A significant task in the automated interpretation of cloud features on satellite imagery is the segmentation of the image into separate cloud features to be identified. A new technique, hierarchical threshold segmentation (HTS), is presented. In HTS, region boundaries are defined over a range of gray-shade thresholds. The hierarchy of the spatial relationships between collocated regions from different thresholds is represented in tree form. This tree is pruned, using a neural network, such that the regions of appropriate sizes and shapes are isolated. These various regions from the pruned tree are then collected to form the final segmentation of the entire image.

In segmentation testing using Geostationary Operational Environmental Satellite data, HTS selected 94% of 101 dependent sample pruning points correctly, and 93% of 105 independent sample pruning points. Using Advanced Very High Resolution Radiometer data, HTS correctly selected 90% of both the 235-case dependent sample and the 253-case independent sample pruning points.

The strength of this approach is that artificial intelligence, that is, reasoning about the sizes and shapes of the emergent regions, is applied during the segmentation process. The neural network component can be trained to respond more favorably to shapes of interest to a particular analysis problem.

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