On the Relationship Between Shallow Cumulus Cloud Field Properties and Surface Solar Irradiance

Abstract

Shallow cumulus clouds exhibit highly three-dimensional (3-D) spatial structure leading to complex variability in the surface solar irradiance (SSI) beneath. This variability is captured by the typically bimodal shape of the SSI probability density function (PDF). Using large eddy simulation to generate well-resolved cloud fields and Monte Carlo 3-D radiative transfer to reproduce realistic associated SSI PDFs, we seek direct relationships between the cloud field properties and the SSI PDF shape. Applying both random forest and artificial neural network algorithms, we find variations in the two modes of the SSI PDF are well predicted by just a handful of cloud field properties. The two algorithms utilize cloud properties similarly, with indistinguishable performance despite their different architectures. These results offer a marked improvement in realism relative to one-dimensional radiative transfer while bypassing computationally expensive 3-D radiative transfer, with immediate application to renewable energy assessments, and potential for several other geophysical applications. Plain Language Summary When sunlight is intercepted by broken clouds, it is not straightforward to calculate how much of the sunlight reaches the ground in the cloud shadows or the gaps between them. To account for this properly in atmospheric models typically requires tracking the exact path of sunlight through the Earth-Atmosphere system, which can be a cumbersome task. We present an alternative approach to calculate how frequently different amounts of sunlight reach the surface under broken cloud scenes. This new approach uses just a few key pieces of information about the clouds to make predictions. Compared with existing approaches, the predicted surface sunlight is substantially more realistic. Results are consistent across two different algorithms tested and arrive at predictions in similar ways. The combined accuracy and efficiency of the new approach has several applications.

Evidence for the 3D Radiative Effects of Boundary‐Layer Clouds From Observations of Direct and Diffuse Surface Solar Fluxes

“…These results agree with previous studies, for instance similar parameters were found to explain the PDF of surface solar irradiance under 3D cumulus clouds using machine learning in Gristey et al. (2020a). Note however that since the extent to which the different parameters were perturbed is not uniform, their relative importance is not thoroughly quantified here.…”

“…Surface albedo and overlap parameters have small impact: doubling the surface albedo only slightly increases the total downward flux at the surface, while changing the overlap parameter both changes radiation and cloud cover and hence preserves the average DTR computed over a given cloud cover sub-interval. These results agree with previous studies, for instance similar parameters were found to explain the PDF of surface solar irradiance under 3D cumulus clouds using machine learning in Gristey et al (2020a). Note however that since the extent to which the different parameters were perturbed is not uniform, their relative importance is not thoroughly quantified here.…”

Evidence for the 3D radiative effects of boundary-layer clouds from observations of direct and diffuse surface solar fluxes

“…Surface albedo and overlap parameters have small impact: doubling the surface albedo only slightly increases the total downward flux at the surface, while changing the overlap parameter both changes radiation and cloud cover and hence preserves the average DTR computed over a given cloud cover sub-interval. These results agree with previous studies, for instance similar parameters were found to explain the PDF of surface solar irradiance under 3D cumulus clouds using machine learning in Gristey et al (2020a). Note however that since the extent to which the different parameters were perturbed is not uniform, their relative importance was not thoroughly quantified here.…”

Influence of Aerosol Embedded in Shallow Cumulus Cloud Fields on the Surface Solar Irradiance

“…The RF is used to predict the perturbed SSI PDF shape in the presence of aerosol. This PDF shape is quantified in a similar manner as described in Gristey et al (2020a) so the reader is referred there for full details. A brief summary is provided again here for completeness.…”

“…where 𝐴𝐴 𝜽𝜽 is the location parameter (a horizontal shift of the distribution), 𝐴𝐴 𝒔𝒔 is the shape parameter (the standard deviation of the log of the distribution), and 𝐴𝐴 𝒎𝒎 is the scale parameter (the median of the distribution). The only difference compared with Gristey et al (2020a) is that here we fit the mirror image of the large SSI mode in the presence of aerosol. This is because the presence of aerosol causes the tail of the large SSI mode to extend in the opposite direction (see Section 3.1 and Figure 4b).…”