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Paul M. Tag and Steven W. Payne

Abstract

Cloud top entrainment instability, as a mechanism for the breakup of marine stratus, is examined with a three-dimensional, planetary boundary layer (PBL) model. Specifically, we examine the criterion developed by Randall and Deardorff; this criterion states that stratus will break up if the equivalent potential temperature gradient at cloud top becomes less than a critical value. To examine this hypothesis, we simulate a horizontally uniform stratus layer which is excited from above by small random temperature perturbations. The buoyancy instability ratio (BIR), defined as Δθe(Δθe)crit and computed at cloud top, is calculated locally across the domain and also averaged to define a mean value. Six cases, involving different wind speeds and above-cloud soundings, produce different initial BIRs and different breakup sequences. In general, we find that a mean BIR greater that one is a necessary condition for stratus breakup; however, we also find that the timing of breakup following achievement of the critical ratio is different from run to run. The low wind speed cases, initially most stable at cloud top, are the first to break up, while the higher wind speed (most unstable) cases require longer time to break up. We conclude that an additional mechanism is necessary to stimulate vertical motion in order to take advantage of the cloud-top entrainment instability. In our simulations, that additional stimulation comes from vertical motions generated by Rayleigh-type instability in the PBL.

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James E. Peak and Paul M. Tag

The U.S. Navy has plans to develop an automated system to analyze satellite imagery aboard its ships at sea. Lack of time for training, in combination with frequent personnel rotations, precludes the building of extensive imagery interpretation expertise by shipboard personnel. A preliminary design starts from pixel data from which clouds are classified. An image segmentation is performed to assemble and isolate cloud groups, which are then identified (e.g., as a cold front) using neural networks. A combination of neural networks and expert systems is subsequently used to transform key information about the identified cloud patterns as inputs to an expert system that provides sensible weather information, the ultimate objective of the imagery analysis.

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James E. Peak and 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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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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Paul M. Tag, Francis W. Murray, and L. Randall Koenig

Abstract

A two-dimensional model is used to test the effects of using several forms of eddy viscosity parameterization to simulate subgrid-scale turbulence. A well-documented observation of a quasi-steady cumulus cloud which formed over a refinery is used for simulation and comparison. The control parameterization of eddy viscosity is one based on both the deformation and buoyancy fields. When compared to observations, this control run overestimates somewhat the liquid water contents and slightly underpredicts the vertical velocities. Parameterizations based on deformation alone, two-dimensional turbulence theory, and several constant values of eddy viscosity result in cloud simulations that are deficient primarily in their significant overprediction of liquid water content. These experiments confirm that a buoyancy term in the prescription of eddy viscosity is necessary when thermal instability plays an active role in the subgrid forcing.

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Francis W. Murray, L. Randall Koenig, and Paul M. Tag

Abstract

A two-dimensional field-of-flow numerical model of cloud development is used to study a cloud that formed over a refinery as a result of heat dissipated to the atmosphere. The observed vertical structure of the atmosphere provided initial conditions. The wind necessarily was simplified to a unidirectional flow. The cloud-initiating perturbation consisted of sensible and latent heat equal to the waste heat rejected to the atmosphere by the refinery.

When conditions were matched to those reported, the simulated cloud agreed in most particulars with the observations. Sensitivity tests showed that the simulated cloud depends too strongly on ambient wind speed and shear. This perhaps is a generic defect of two-dimensional formulations. The response of the simulated cloud to expected changes in heat flux density appears more realistic than its response to small changes in ambient wind.

The cloud evolution consists of bubbles forming and breaking away from the main cloud mass, then moving downwind and dissipating. This behavior characterizes real clouds associated with a stationary heat source. The simulations also predict that under appropriate conditions secondary clouds form far downwind.

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Paul M. Tag, Richard L. Bankert, and L. Robin Brody

Abstract

Using imagery from NOAA’s Advanced Very High Resolution Radiometer (AVHRR) orbiting sensor, one of the authors (RLB) earlier developed a probabilistic neural network cloud classifier valid over the world’s maritime regions. Since then, the authors have created a database of nearly 8000 16 × 16 pixel cloud samples (from 13 Northern Hemispheric land regions) independently classified by three experts. From these samples, 1605 were of sufficient quality to represent 11 conventional cloud types (including clear). This database serves as the training and testing samples for developing a classifier valid over land. Approximately 200 features, calculated from a visible and an infrared channel, form the basis for the computer vision analysis. Using a 1–nearest neighbor classifier, meshed with a feature selection method using backward sequential selection, the authors select the fewest features that maximize classification accuracy. In a leave-one-out test, overall classification accuracies range from 86% to 78% for the water and land classifiers, with accuracies at 88% or greater for general height-dependent groupings. Details of the databases, feature selection method, and classifiers, as well as example simulations, are presented.

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Rolf H. Langland, Paul M. Tag, and Robert W. Fett

Abstract

Satellite imagery from 18 April 1978 suggests the presence of a semicircular zone of calm or new-calm seas in Monterey Bay, California. It is hypothesized that sea breeze circulations account for the calm zone in the bay, although a lack of in situ surface and upper air observations prevents direct verification of this theory. A three-dimensional numerical model of the marine atmospheric boundary layer is used to simulate the development of the low-level wind field on the day in question, under sea breeze conditions. The model produces a zone of winds speeds under 1.0 m s−1 over the center of the bay, near the time of the satellite image. These model results suggest that a sea breeze circulation may have accounted for the zone of very light winds and calm sea.

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Robert W. Fett, Marie E. White, James E. Peak, Sam Brand, and Paul M. Tag

The Naval Research Laboratory Marine Meteorology Division, over a period of more than 15 years, has developed a series of satellite imagery training documents called the Navy Tactical Applications Guides (NTAGs). The NTAG materials are unique because of their innovative focus on operationally relevant meteorological and oceanographic phenomena of concern to naval forces throughout the world and the exceedingly high quality of printed images. Advances in hypermedia and CD-ROM technology are enabling the enhancement and continued distribution of the NTAGs through the development of an electronic application called LaserTAG. CD-ROM technology provides large reproduction and storage capacity at a relatively low cost ($25 for LaserTAG discs versus $1000 for the 11-volume NTAG set). Hypermedia and electronic conversion supply the ability to 1) rapidly locate material through keyword searches and navigate to those locations through hypermedia links, 2) read text and view graphics simultaneously using multiple windows, and 3) create electronic annotation and bookmark files. A second technology, expert systems, is further expanding potential uses of the information documented in the NTAG series. The Satellite Image Analysis Meteorological Expert System (SIAMES) encapsulates important conclusions and rules of analysis. The SIAMES prototype described here leads the user through a hierarchy of image interpretation expertise derived from the NTAG series by querying the user about details appearing in the satellite imagery. The ultimate goal, particularly important when resident expertise is minimal or nonexistent, is to develop an automated method to deduce sensible weather parameters that affect navy operations. Applications of these technologies to environmental satellite image analysis provide new opportunities for their use, not only in the operational community, but in training and research as well.

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