1. 1 Cloud Screening and Classification in Satellite Imagery Shona Mackie PhD student, University of Edinburgh CASE studentship: Met Office, U.K. Supervisor: Chris Merchant

2. 2 Clouds are important! Weather and climate –Energy budget –Temperature –Precipitation www.larc.nasa.gov/SCOOL/cloudeffects.jpg –Height –Optical thickness –Diurnal variation

3. 3 Numerical Forecast –Cloud treatment not perfect »Spatial and temporal »Sub-grid parameterisation Satellite Imagery –Visual inspection »amendments –Automated assimilation Weather Forecasting

4. 4 Overall Aim Bayesian Cloud Classification (Chris Old) –Physically based –Flexible –Expand »To land »To cloud types

5. 5 Current state of project Problems –Ocean fronts –Coastline –Land Clustering

6. 6 General Method Small area of one image scene –Cloud detection on clusters »Classification?

7. 7 Clustering to aid detection Expand technique to land Expand to cloud types (low, medium, high) Expand to refined cloud types Implement method at the MET Office PLAN

8. 8 The End