Observed Patterns of Surface Solar Irradiance under Cloudy and Clear-sky Conditions

Surface solar irradiance varies on scales as small as seconds or meters due to scattering and absorption by the atmosphere. Clouds are the main driver of this variability, but moisture structures in the atmospheric boundary layer and aerosols have an influence too, and depend on wavelength. The highly variable nature of solar irradiance is not resolved by most atmospheric models, yet it affects most notably the land-atmosphere coupling, which in turn can change the cloud field, and the quality of solar energy forecasting.Spatially and spectrally resolved observational datasets of solar irradiance at such high resolution are rare,but they are required for characterising observed variability, understanding the mechanisms, and developing fast models capable of accurately resolving this variability. In 2021, we deployed a spatial network of low-costradiometers at the FESSTVaL (Germany) and LIAISE (Spain) field campaigns, specifically to gather data on cloud-driven surface patterns of irradiance, including spectral effects, with the aim to address this gap in observations and understanding. We find in case studies of cumulus, altocumulus, and cirrus clouds thatthese clouds generate large spatio temporal variability in irradiance, but through different mechanisms and at difference spatial scales, ranging from 50 m to 30 km. Spectral irradiance in the visible range varies at similar spatial scales, with significant blue enrichment in cloud shadows, most strongly for cumulus, and red enrichment in irradiance peaks, particularly in the case of semi-transparent clouds or near cumulus cloudedges. Under clear-sky conditions, solar irradiance varies significantly in water vapour absorption bands at the minute scale, due to local and regional variability in atmospheric moisture. In conclusion, observing detailed spatio temporal irradiance patterns is possible using a relatively small, low-cost sensor network, and these network observations provide insights and validation for the development of models capable of resolving irradiance variability.