Intra-hour Cloud Index Forecasting with Data Assimilation
Travis M. Harty1, William F. Holmgren2, Antonio T. Lorenzo2, Matthias Morzfeld3
1University of Airzona, Program in Applied Mathematics, Tuson, AZ, United States
/2University of Airzona, Department of Hydrology & Atmospheric Sciences, Tucson, AZ, United States
/3University of Arizona, Department of Mathematics, Tucson, AZ, United States

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.