Intra-hour Cloud Index Forecasting with Data Assimilation

We introduce a computational framework to forecast cloud index fields for up to one hour on a spatial domain that covers a city. Our method combines a 2D advection model with cloud motion vectors (CMVs) derived from a mesoscale numerical weather prediction (NWP) model and optical flow acting on successive, geostationary satellite images. We use ensemble data assimilation to combine these sources of cloud motion information based on the uncertainty of each data source. Our technique produces forecasts that have similar or lower root mean square error than reference techniques that use only optical flow, NWP CMV fields, or persistence. Further discussion and results of the forecasting system presented here can be found in [1].