Intraday Solar Irradiance Forecast, Sattelite Derived Data, Post Processing WRF, Kalman Filter
Summary / Abstract:
The objective of this study is to improve day ahead Weather Research and Forecasting Model (WRF) forecasts in any geographical location in the Inter Tropical Zone (ITZ). The WRF model is a physical one with high biased outputs. A post processing step is required to reduce the WRF model output’s bias. This step is obtained by post processing WRF output with a Kalman filter bias correction method (hybrid physical statistical model). We proposed to use Satellite images as observation instead of ground measurements for the post processing of WRF model. Since Satellite images have a broader coverage than ground measurements. We validated our methodology over three months of Global Horizontal Irradiance (GHI) data from six stations located in French Guiana, a South America territory with a tropical climate regulated by the Intertropical-Convergence Zone. To compute the model accuracy, we used three metrics; the mean bias error, mean absolute error and root mean square error. For all sky conditions and all stations merged, the post-processing method using satellite-derived data has a similar performance to the post-processing method using ground-based irradiation measurements. Satellite-derived data perform better than ground one for clear sky conditions with low variability. Moreover, we note a better performance with satellite-derived data for high variability and independent of sky condition.