Travis M. Harty1, A.T.Lorenzo2, M.Morzfeld3, and W.F.Holmgren2
1Program in Applied Mathematics
2Department of Hydrology and Atmospheric Sciences
3Department of Mathematics
The University of Arizona
Satellite images provide a basis for estimating global horizontal irradiance and solar power output over areas on the scale of a city or larger. In this work, we aim to improve satellite derived irradiance forecasts by correcting cloud motion vectors which are used to advect an irradiance or clear sky index (CSI) field. In a data assimilation framework, we improve cloud motion vectors derived from the Weather Research and Forecasting (WRF) model (available every hour) by assimilating satellite images taken from the GOES-15 geostationary satellite (available every 15 minutes), and sparse optical flow vectors derived from successive satellite images. We use a data assimilation technique known as the Local Ensemble Transform Kalman Filter (LETKF). The LETKF is a square root filter in which calculations are performed in the space spanned by ensemble members, a lower dimensional subspace of the state space. This allows for a reduction in computational complexity because the number of ensemble members (around 50) is significantly lower than the dimension of the state space (hundreds of thousands or larger). We present preliminary results showing the effectiveness of this method to produce forecasts as well as to quantify the uncertainty inherent within these forecasts.