Diffuse fraction as a tool for exploring the sensitivity of parametric clear-sky models to changing aerosol conditions

TL;DR

This paper introduces a novel aerosol influence quantifier to evaluate how sensitive parametric clear-sky solar irradiance models are to aerosol variations, aiding in selecting appropriate models under uncertain aerosol data conditions.

Contribution

The study presents a new measure for assessing the sensitivity of clear-sky models to aerosols and demonstrates its utility in model selection based on aerosol data quality.

Findings

The aerosol influence quantifier effectively measures model sensitivity to aerosol changes.

Models with lower sensitivity may perform better when aerosol data quality is poor.

Using the quantifier can help identify suitable models for uncertain aerosol inputs.

Abstract

Aerosols' impact on the performance of a clear-sky solar irradiance model is often evaluated from the perspective of the overall accuracy of estimates. This study assesses the aerosol role in clear-sky solar irradiance modelling from a totally different perspective, namely the ability of a model to accurately separate global solar irradiance into its fundamental direct and diffuse components. In an innovative approach, the analysis is focused on the sensitivity of parametric solar irradiance models to changes in aerosol conditions. A new measure, the aerosol influence quantifier, is introduced for assessing the correlation strength between the relative variation in clear-sky diffuse fraction and the relative variation in atmospheric aerosol load. The effectiveness of the aerosol influence quantifier is explored as a tool for assessing the sensitivity of three parametric clear-sky models to variations in input aerosol data. A rather surprising result is reported, e.g. if high quality aerosol data are not available, one could potentially maximize the estimate's accuracy by using a parametric clear-sky solar irradiance model with low sensitivity to aerosol variations. In such cases, it is shown that the aerosol influence quantifier can be used as a tool for identifying an adequate model for input data with a given uncertainty.