Observing Cloud-Driven Surface Irradiance Patterns

Clouds cast shadows and locally enhance solar irradiance through absorbing and scattering sunlight, resulting in fast and large solar irradiance fluctuations on the surface.  
The resulting spatiotemporal variability poses a challenge for solar energy production amidst increasing need for reliable renewable energy. It furthermore influences biological processes and the exchange of water and energy. Yet, no numerical weather prediction model is able to reproduce the observed local properties of irradiance, due to the complexity of radiative transfer and its dependence on accurately resolved cloud fields. Improving the radiative transfer models, whether it involves running full Monte Carlo raytracing in academic setups or simplified paramerizations,  ultimately requires observations for validation. However, dense spatial observation of irradiance on the scale of cloud shadows are rare. Even single 1D time series are rarely available at high enough temporal resolution to capture irradiance variability.

In ongoing work, we provide those missing observations using a dense network of our custom, low-cost radiometers that we deployed at two field campaigns in summer 2021, FESSTVaL (Germany) and LIAISE (Spain). I will present our gathering and analyses of these new and detailed observations of surface irradiance to address knowledge gaps in our physical understanding and provide validation datasets for models. The instruments, which sample at 10 Hz, are able to closely match expensive conventional instruments, and combined with skyview imagery, the spatial observations are directly linked to observed clouds. Information about atmospheric water content can be retrieved using the information from water vapour absorption bands. 

To complement these short term spatial data, long-term statistics of irradiance variability are derived from a 10-year 1 Hz resolution dataset from the Baseline Surface Radiation Network station in Cabauw, the Netherlands. Distributions and typical spatio-temporal scales of cloud shadows and irradiance peaks can be related to cloud type and meteorological conditions. The gathering and study of these datasets will lead to a better understanding of the physics. E.g., whether the dominant mechanism driving irradiance peaks is either forward scattering in transparent parts of clouds or 'reflections' from cloud sides. Furthermore, these datasets will help validate models, and ultimately improve our ability to accurately forecast irradiance variability at the small scales.