Spatial solar irradiance emulation of Monte Carlo ray tracing with multi-view all-sky imagery

Explicit 3D radiative transfer captures the complex spatial variability of global horizontal irradiance (GHI) under shallow cumulus clouds, but is computationally prohibitive for real-time operational use. We address this by introducing a simulation-to-reality framework that emulates 3D radiative transfer via a neural network trained on synthetic data to translate multi-view all-sky imagery into 2D GHI maps. To produce the training data, we ran large eddy simulations at 50 m horizontal resolution for 10 selected cloud dynamic days (April to July 2022) over a 14 km x 14 km domain. The resulting cloud fields were coupled with Monte Carlo ray tracing to render synthetic all-sky images from virtual camera locations matching the Eye2Sky camera network and to compute the corresponding GHI maps. Two datasets are generated, (1) raw synthetic renderings and (2) enhanced renderings with camera-specific characteristics from the real-world. Identical image-to-image neural networks are trained on these datasets and applied to real Eye2Sky imagery, with predicted GHI maps validated against co-located pyranometers. By incorporating sensor-specific characteristics, we quantify the benefit of reducing the simulation-to-reality gap and assess whether synthetic pre-training using neural network emulations can support operational solar irradiance mapping as an alternative to computationally expensive physical simulations.