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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 an

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 an