Improving weather radar by fusion and classification

TL;DR

This paper presents a system that enhances weather radar data quality by detecting artefacts and correcting missing information using texture analysis, geometric operators, and satellite data, leading to more reliable weather predictions for air traffic management.

Contribution

It introduces a novel approach combining image processing and satellite data to improve weather radar data accuracy for ATM applications.

Findings

Significant reduction in artefacts in radar images

Improved accuracy of weather predictions

Enhanced safety in air traffic management

Abstract

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.