Comprehensive physics testing and adaptive weather research and forecasting physics for day-ahead solar forecasting

Numerical weather prediction (NWP) models, which attempt to simulate the full state of the atmosphere, come with many options for dealing with processes that are unable to be explicitly resolved by the model. These model parameterizations are an ongoing area of research in atmospheric science. However, with continuous contributions from the research community and subsequent upgrade of the NWP codes, there are often many options for each unresolved process-leaving the user confronted with potentially thousands of ways to configure the model. We use the weather research and forecasting (WRF) model to forecast global horizontal irradiance (GHI) and undertake the task of narrowing down these options for a location in Qinghai, China. We show that optimizing the configuration of the WRF model based on the type of day (sunny, partly cloudy, or cloudy) is 13.6% better than using a single best configuration for all types of days. We also show that this performance improvement holds true for a longer 3-month test period (17.8% improvement).

Automated summary

Numerical weather prediction models have various options for handling processes that cannot be explicitly resolved, which is a continuing focus in atmospheric science research. Optimizing the configuration of the WRF model based on weather type shows a 13.6% improvement over using a single best configuration, and this performance gain holds true for a longer 3-month test period with a 17.8% improvement.