Vector Field Analysis for Oriented Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of Singular Points from Dense Motion Fields: An Analytic Approach
Journal of Mathematical Imaging and Vision
Automatic Template Matching Method for Tropical Cyclone Eye Fix
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
An Intelligent Tropical Cyclone Eye Fix System Using Motion Field Analysis
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
IdentifyingWeather Systems from NumericalWeather Prediction Data
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Automatic tropical cyclone eye fix using genetic algorithm
Expert Systems with Applications: An International Journal
Relation-based aggregation: finding objects in large spatial datasets
Intelligent Data Analysis
Economic design of variable sampling intervals T2 control charts using genetic algorithms
Expert Systems with Applications: An International Journal
Tropical cyclone eye fix using genetic algorithm with temporal information
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
A machine vision based method for atmospheric circulation classification
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Mathematical methods to quantify and characterise the primary elements of trophic systems
International Journal of Computer Applications in Technology
| Hi-index | 12.05 |
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.