Verifying operational intra-day solar forecasts from ECMWF and NOAA

Abstract

Global horizontal irradiance (GHI) forecasting at intra-day horizons of up to 12-h ahead is vital to grid integration of solar photovoltaics, but has been fundamentally difficult for all methods that do not involve numerical weather prediction (NWP), since non-NWP methods are unable to extrapolate the data to a horizon beyond a fews hours. The European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Oceanic and Atmospheric Administration (NOAA) are the most representative weather centers in Europe and America, respectively. To understand their operational impact and value to grid integration, the ECMWF’s High Resolution (HRES) model and two models from NOAA, namely, Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR), are validated through the Murphy–Winkler distribution-oriented verification framework, over the year 2020, at seven locations. All forecasts are retrieved at native horizontal resolutions—9 km for HRES, 13 km for RAP, and 3 km for HRRR; it depicts the “off-the-shelf” scenario if these forecasts are to be utilized by end users. Three simple linear correction methods, each being statistically optimal in its own respect, are used to post-process the raw forecasts. It was found that 1–12-h-ahead ECMWF’s HRES forecasts have a significantly lower root mean square error (14.0–33.7%) as compared to NOAA’s HRRR (19.0–53.2%) and RAP (19.2–45.9%). Even after the large biases in HRRR and RAP forecasts are removed, those post-processed versions are still inferior to the raw HRES forecasts.