An object-based analysis of cloud motion from sequences of METEOSAT satellite data
The need for wind and atmospheric dynamics data for weather modelling and forecasting is well founded. Current texture-based techniques for tracking clouds in sequences of satellite imagery are robust at generating global cloud motion winds, but their use as wind data makes many simplifying assumptions on the causal relationships between cloud dynamics and the underlying windfield. These can be summarised under the single assumption that clouds must act as passive tracers for the wind. The errors thus introduced are now significant in light of the improvements made to weather models and forecasting techniques since the first introduction of satellite-derived wind information in the late 1970s. In that time, the algorithms used to track cloud in satellite imagery have not changed fundamentally. There is therefore a need to address the simplifying assumptions and to adapt the nature of the analyses applied accordingly. A new approach to cloud motion analysis from satellite data is introduced in this thesis which tracks the motion of clouds at different scales, making it possible to identify and understand some of the different transport mechanisms present in clouds and remove or reduce the dependence on the simplifying assumptions. Initial work in this thesis examines the suitability of different motion analysis tools for determining the motion of the cloud content in the imagery using a fuzzy system. It then proposes tracking clouds as flexible structures to analyse the motion of the clouds themselves, and using the nature of cloud edges to identify the atmospheric flow around the structures. To produce stable structural analyses, the cloud data are initially smoothed. A novel approach using morphological operators is presented that maintains cloud edge gradients whilst maximising coherence in the smoothed data. Clouds are analysed as whole structures, providing a new measure of synoptic-scale motion. Internal dynamics of the cloud structures are analysed using medial axis transforms of the smoothed data. Tracks of medial axes provide a new measure of cloud motion at a mesoscale. The sharpness in edge gradient is used as a new measure to identify regions of atmospheric flow parallel to a cloud edge (jet flows, which cause significant underestimation in atmospheric motion under the present approach) and regions where the flow crosses the cloud boundary. The different motion characteristics displayed by the medial axis tracks and edge information provide an indication of the atmospheric flow at different scales. In addition to generating new parameters for measuring cloud and atmospheric dynamics, the approach enables weather modellers and forecasters to identify the scale of flow captured by the currently used cloud tracers (both satellite-derived and from other sources). This would allow them to select the most suitable tracers for describing the atmospheric dynamics at the scale of their model or forecast. This technique would also be suitable for any other fluid flow analyses where coherent and stable gradients persist in the flow, and where it is useful to analyse the flow dynamics at more than one scale.