Comparison of global horizontal irradiance forecasts based on numerical weather prediction models with different spatio‐temporal resolutions. (20th July 2016) - Lorenz, Elke; Kühnert, Jan; Heinemann, Detlev; Nielsen, Kristian Pagh; Remund, Jan; Müller, Stefan C.
Abstract: In the framework of the IEA SHC Task 46 "Solar Resource Assessment and Forecasting" we compare solar global horizontal irradiance forecasts based on numerical weather predictions for a variety of different models. These include direct model output of several numerical weather prediction models, a rapid update cycle model assimilating satellite derived cloud products as well as radar data, the multi model ensemble prediction system GLAMEPS, and two MOS systems. In order to allow for a transparent and comparable analysis of the different methods we have set up a joint, consistent framework of evaluation. As a basis for the comparisons we have compiled a common data set of hourly measured solar irradiance values for Denmark, Germany, and Switzerland. Local and regional forecasts are analyzed with respect to different properties. In particular we show that spatial and temporal averaging effects have a strong impact on the root mean square error when comparing solar irradiance forecasts of numerical weather prediction models with different output resolutions. Furthermore, we investigate a new approach to evaluate the models' ability to represent and forecast solar irradiance and cloud variability. The benefit of high resolution mesoscale models in this respect is demonstrated. Copyright © 2016 John Wiley & Sons, Ltd. Abstract : In the framework of the IEA SHC Task 46 "Solar Resource Assessment and Forecasting" we compare solar global horizontal irradiance forecasts based on different numerical weather predictions models. These include direct model output of global and limited area models, a rapid update cycle model, a multi model ensemble prediction system, and two MOS systems. In particular we analyze the impact of spatial and temporal averaging and evaluate the model's ability to represent and forecast solar irradiance and cloud variability.