Weather systems such as tropical cyclones, fronts, troughs and ridges affect our daily lives. Yet, they are often manually located and drawn on weather charts based on forecasters' experience. To identify them, multiple atmospheric elements need to be considered, and the results may vary among forecasters. In this paper, we propose an automatic weather system identification method. A generic model of weather systems is designed, along with a genetic algorithm-based framework for finding them automatically from multidimensional numerical weather prediction data. The framework allows multiple weather elements to be analyzed. It is found that our method not only can locate weather systems with 80–100% precision, but can also discover features that could indicate the genesis or dissipation of such systems that forecasters may overlook. The method provides an independent and objective source of information to assist forecasters in identifying and positioning weather systems.

Remote sensing technology is widely used in meteorology for weather system positioning. Yet, these remote sensing data are often analyzed manually based on forecasters' experience, and results may vary among forecasters. In this chapter, we briefly introduce the problem of weather system positioning, and discuss how evolutionary algorithms can be used to solve the problem. A genetic algorithm-based framework for automatic weather system positioning is introduced. Examples on positioning tropical cyclones and line-shaped weather systems on radar data are used to demonstrate its practical use.

Identification of centers of circulating and spiraling vector fields, sources and sinks are important in many applications. Tropical cyclone tracking, rotating object identification, analysis of motion video and movement of fluids are but some examples. In this paper, we introduce a method for finding the centers of circulating and spiraling vector field patterns. It can handle vector fields with multiple centers and is robust against noise. We provide a theoretical analysis on the validity of our method, and application examples in the fields of multimedia processing and meteorological computing to demonstrate its practical use.

Tropical cyclones (TCs) are weather systems with vast destructive power. To forecast TC tracks, forecasters need to locate their circulation centers, or eyes. This eye fix process is often done manually in practice. Since subjective elements are involved in the process, forecasters could disagree on the results even when multiple factors are considered. This paper presents an objective TC eye fix method that utilizes genetic algorithm to search for the optimal values of a six-parameter TC model. The results of this study indicate that the proposed method gives the best average error of 0.127° in latitude/longitude on Mercator projected map with respect to the best track data. This is well within the relative errors of about 0.3° from the results of different TC warning centers. The method can process 6min of radar data in about 10s when implemented on a notebook computer, meeting practical real time constraints. It provides a practical, independent and objective source of information to assist forecasters to fix TC eye centers.

Identification of centers of circulating and spiraling vector fields are important in many applications. Tropical cyclone tracking, rotating object identification, analysis of motion video and movement of fluids are but some examples. In this paper, we introduce a fast and noise tolerant method for finding centers of circulating and spiraling vector field pattern. The method can be implemented using integer operations only. It is 1.4 to 4.5 times faster than traditional methods, and the speedup can be further boosted up to 96.6 by the incorporation of search algorithms. We show the soundness of the algorithm using experiments on synthetic vector fields and demonstrate its practicality using application examples in the field of multimedia and weather forecasting.

Tropical cyclone (TC) is among the most destructive of weather phenomena. A key element in issuing early TIC warnings is an accurate and timely knowledge of the location of the circulation centre, or 'eye', of the TC. However, TIC radar eye fix is often carried out manually in practice, and results may vary among forecasters. In this paper, a novel motion field structure analysis method is described as an aid to locate TIC eyes more accurately. The method is comprehensive, and can handle incomplete data and landfalling cases. Implemented on a standard desktop computer, an average error of 0.15 degrees in latitude/longitude on a Mercator Projection map for TICS that are completely inside the radar image is obtained. This is well within the relative errors given by different TC warning centres. The method provides an independent and objective source of information to assist forecasters to fix the TC centre. Copyright (c) 2007 Royal Meteorological Society.

Squall lines are strong indicators of potential severe weather. Yet, automated positioning and tracking algorithms are not common. We propose three different ways to model and identify squall lines using radar images. The three methods are ellipse fitting, Hough transform, and the use of a genetic algorithm-based framework. They model a squall line as an ellipse, a straight line, and adjoining segments of arc respectively. We compare the advantages and limitations of each method in terms of speed, flexibility, stability and sensitivity to parameter settings. It is found that ellipse fitting is the most efficient, followed by Hough transform. Both methods lack flexibility and stability. The genetic algorithm-based framework is stable, has flexibility in modelling and analysis, but comes with a cost of efficiency. The proposed methods provide independent and objective information sources to assist weather forecast.

Weather systems such as tropical cyclones, fronts, troughs and ridges affect our daily lives. Yet, they are often manually located and drawn on weather charts based on forecasters’ experience. To identify them, multiple atmospheric elements need to be considered, and the results may vary among forecasters. In this paper, we contribute to the fields of pattern recognition and meteorological computing by designing a generic model of weather systems, along with a genetic algorithm-based framework for finding them from multidimensional numerical weather prediction data. It was found that our method not only can locate weather systems with 80% to 100% precision, but also discover features that could indicate the genesis or dissipation of such systems that could be ignored by forecasters.

Identification of centers of circulating and spiraling vector fields, sources and sinks are important in many applications. Tropical cyclone tracking, rotating object identification, analysis of motion video and movement of fluids are but some examples. In this paper, we introduce a framework for finding the centers of circulating and spiraling fields. It can handle vector fields constructed by motion estimation of objects observed at an oblique angle. We provide theoretical analysis on the validity of our method, and application examples in the fields of multimedia processing and meteorological computing to demonstrate its practical use

Rotational motion can often be seen in video. However, comparatively little research has been done to investigate rotational motions in video, whose analysis could be useful. For example, if we can efficiently identify the rotation center of a spinning object, extraction and tracking of it can be made easier by grouping points moving at the same radial speed. It could also improve compression by synthesizing analyzed spin transitions, and help tracking of rotating objects. In this paper, we introduce a set of rotation center location methods using only the motion field constructed during video encoding, along with a few methods for improving their performances. These methods can be implemented using integer operations only. They are up to 1.81 times faster than the traditional circulation analysis method with little sacrifice in accuracy, and are not affected by asymmetric fields caused by translational motions.

Tropical cyclones (TCs) are weather systems with vast destructive power Accurate location of their circulation centers, or "eyes", is thus important to forecasters. However, the eye fix process is often done manually in practice. While multiple factors are considered in the process, with subjective elements in these methods, forecasters could disagree. This paper describes a TC eye fix system that uses a novel motion field structure analysis method. It can handle TCs without well-defined structure that are partially out of the image. The system also adapts user inputs and past results to improve its accuracy. Implemented on a commodity desktop computer, the system can process about 5 images per minute, giving an average error of about 0.16 degrees in latitude/longitude on Mercator projected map for TCs that are completely inside the radar image. This is well within the relative error of about 0.3-0.4 degrees given by different TC warning centers. This TC eye fix system is useful in giving an objective TC center location in contrast to traditional manual analysis.

Tropical cyclones (TCs) are weather systems with vast destructive power. To give early TC warnings, accurate location of their circulation centers, or “eyes”, is required. The pattern matching solution to this TC eye fix problem works by analyzing individual remote sensing images as if they are independent. Temporal information is ignored in such approach and results from individual image are only smoothed afterwards. In this paper, a TC eye fix method using genetic algorithm (GA) with temporal information is proposed. This method gives an improvement of 35% in speed and 20% in accuracy compared to the one without the use of temporal information. Our proposed method can process 6 minutes of radar data in about 10 seconds on a notebook computer, meeting practical real time constraints. It gives an average accuracy within 0.107 to 0.156 degrees in latitude/longitude on the Mercator projected map, well within the relative error of about 0.3 degrees given by different TC warning centers.

Estimating the selectivity of a simple path expression (SPE ) is essential for selecting the most ecient evaluation plans for XML queries. To estimate selectivity, we need an ecient and exible structure to store a summary of the path expressions that are present in an XML document collection. In this paper we propose a new structure called SF-Tree to address the selectivity estimation problem. SF-Tree provides a exible way for the users to choose among accuracy, space requirement and selectivity retrieval speed. It makes use of signature les to store the SPEs in a tree form to increase the selectivity retrieval speed and the accuracy of the retrieved selectivity. Our analysis shows that the probability that a selectivity estimation error occurs decreases exponentially with respect to the error size.

Weather forecasting often requires extensive computation- ally expensive numerical analysis on remote sensing data. For example, to determine the position of a tropical cyclone (the TC eye x problem), computationally intensive techniques, such as the analysis of wind elds or processing of elds of motion vectors, are needed. Given the volume and rate of data to be processed, these problems are often solved using mainframe computers or clusters of computers for timely results to be given. In this paper, a template matching method is proposed to solve a subclass of TC eye x problems. Together with the use of genetic algo- rithm, an accuracy within 0.139 to 0.257 degrees in latitude/longitude on the Mercator projected map is possible on a desktop computer at a rate of about 12 seconds per 6 minutes of radar data. The accuracy is comparable to the relative error of about 0.3 degrees given by dieren t TC warning centers.

A time-honored way of finding, or fixing, the center of a tropical cyclone (TC) is to overlay templates of spirals onto a printout of radar or satellite image. Modern methods, however, mostly focus on wind field analysis, or other motion vector techniques. These techniques cannot be applied effectively if the image is sampled infrequently, or when the TC moves fast. In this paper, we present a TC eye fix method that uses automatically generated templates to match spiral rainbands of TCs. Temporal information can be utilized optionally in the method to improve results. The method gives an average error of about 0.16 degrees in latitude/longitude on the Mercator projected map in our test radar image sequences. This is comparable to the relative error of about 0.3 degrees given by different TC warning centers.

Estimating the selectivity of a simple path expression (SPE) is essential for selecting the most efficient evaluation plans for XML queries. To estimate selectivity, we need an efficient and flexible structure to store a summary of the path expressions that are present in an XML document collection. In this paper we propose a new structure called SF-Treeto address the selectivity estimation problem. SF-Tree provides a flexible way for the users to choose among accuracy, space requirement and selectivity retrieval speed. It makes use of signature files to store the SPEs in a tree form to increase the selectivity retrieval speed and the accuracy of the retrieved selectivity. Our analysis shows that the probability that a selectivity estimation error occurs decreases exponentially with respect to the error size.

Weather forecasting often requires extensive computation- ally expensive numerical analysis on remote sensing data. For example, to determine the position of a tropical cyclone (the TC eye x problem), computationally intensive techniques, such as the analysis of wind elds or processing of elds of motion vectors, are needed. Given the volume and rate of data to be processed, these problems are often solved using mainframe computers or clusters of computers for timely results to be given. In this paper, a template matching method is proposed to solve a subclass of TC eye x problems. Together with the use of genetic algo- rithm, an accuracy within 0.139 to 0.257 degrees in latitude/longitude on the Mercator projected map is possible on a desktop computer at a rate of about 12 seconds per 6 minutes of radar data. The accuracy is comparable to the relative error of about 0.3 degrees given by dieren t TC warning centers.

Weather forecasting often requires extensive computationally expensive numerical analysis on remote sensing data. For example, to determine the position of a tropical cyclone (the TC eye fix problem), computationally intensive techniques, such as the analysis of wind fields or processing of fields of motion vectors, are needed. Given the volume and rate of data to be processed, these problems are often solved using mainframe computers or clusters of computers for timely results to be given. In this paper, a template matching method is proposed to solve a subclass of TC eye fix problems. Together with the use of genetic algorithm, an accuracy within 0.139 to 0.257 degrees in latitude/longitude on the Mercator projected map is possible on a desktop computer at a rate of about 12 seconds per 6 minutes of radar data. The accuracy is comparable to the relative error of about 0.3 degrees given by different TC warning centers.