  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-cost radiometers 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 that these clouds generate large spatiotemporal
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
cloud edges. 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.
