Improving weather radar by fusion and classification
This work addresses the need for accurate weather data in air traffic management to enhance safety, though it is incremental as it builds on existing image processing and fusion techniques.
The paper tackled the problem of disturbances in weather radar images by using texture analysis and geometric operators to detect artefacts and correct them with multi-spectral satellite data, resulting in significantly improved data quality for more reliable weather forecasts.
In air traffic management (ATM) all necessary operations (tactical planing, sector configuration, required staffing, runway configuration, routing of approaching aircrafts) rely on accurate measurements and predictions of the current weather situation. An essential basis of information is delivered by weather radar images (WXR), which, unfortunately, exhibit a vast amount of disturbances. Thus, the improvement of these datasets is the key factor for more accurate predictions of weather phenomena and weather conditions. Image processing methods based on texture analysis and geometric operators allow to identify regions including artefacts as well as zones of missing information. Correction of these zones is implemented by exploiting multi-spectral satellite data (Meteosat Second Generation). Results prove that the proposed system for artefact detection and data correction significantly improves the quality of WXR data and, thus, enables more reliable weather now- and forecast leading to increased ATM safety.