Retrieval of surface solar irradiance from satellite imagery using machine learning: pitfalls and perspectives

Hadrien Verbois, Yves‐Marie Saint‐Drenan, Vadim Becquet, Benoît Gschwind, Philippe Blanc

Atmospheric measurement techniques · 19 September 2023

In one sentence. MLP retrieval of surface solar irradiance from MSG imagery can beat a physical method but depends strongly on training-set similarity, highlighting pitfalls.

Abstract

Abstract. Knowledge of the solar surface irradiance (SSI) spatial and temporal characteristics is critical in many domains, the first of which is likely solar energy. While meteorological ground stations can provide accurate measurements of SSI locally, they are sparsely distributed worldwide. SSI estimations derived from satellite imagery are thus crucial to gain a finer understanding of the solar resource. To infer SSI from satellite images is, however, not straightforward and it has been the focus of many researchers in the past thirty to forty years. For long, the emphasis has been on empirical models (simple parameterization linking the reflectance to the clear-sky index) and on physical models. Recently, new satellite SSI retrieval methods are emerging, which directly infer the SSI from the satellite images using machine learning. Although only a few such works have been published, their practical efficiency has already been questioned. The objective of this paper is to better understand the potential and the pitfalls of this new coming family of methods. To do so, simple multi-layer-perceptron (MLP) models are constructed with different training datasets of satellite-based radiance measurements from Meteosat Second Generation (MSG) with collocated SSI ground measurements from Meteo-France. The performance of the models is evaluated on a test dataset independent from the training set both in space and time. We found that the data-driven model’s performance is very dependent on the training set. On the one hand, even a simple MLP can significantly outperform a state-of-the-art physical retrieval method, provided the training set is sufficiently large and similar enough to the test set. On the other hand, in certain configurations, the data-driven model can dramatically underperform even in stations located close to the training set.

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