Sea surface temperature reconstruction in the Mediterranean Sea using deep learning

Sea surface temperature (SST) is a fundamental variable influencing  the variability of the ocean and atmosphere on synoptic, decadal and climate timescales. Satellites play a major role in its estimation and particularly measurements from infrared (IR) radiometers, which provide high-resolution observations of SST. However, IR retrievals are contaminated by  the presence of clouds that are therefore removed resulting in gaps in the retrieved fields. Because many applications rely on a gap-free SST field, including  marine heatwaves studies and ocean reanalysis, a high-quality reconstruction of missing SST is required.

Traditional techniques to address this issue include Empirical orthogonal functions (EOFs) and Optimal interpolation (OI). However, those techniques often result in over-smoothing, even where observations are present. Recently, deep learning (DL) techniques have been employed, leveraging their capacity of capturing non-linearities to better reconstruct data with gaps. 

Recently Asperti et al. (2025) developed DL models based on U-Net and transformer architectures with several configurations implemented in the Italian Seas to reconstruct SST using Level 3 products. The results show that DL based models are promising to reconstruct SST fields even close to complex coastlines. In this work, we extend the methodology introduced in their study to the entire Mediterranean Sea, starting from the best performing configuration, based on U-Net architecture. Here the method used to train the neural network is to add an additional cloud mask from a randomly picked day, to the input SST, in order to have a ground truth to use for the loss computation. The extended Mediterranean Sea model skill is comparable to the model in Asperti et al. (2025) on the overlapping regions. Since the modulation of observed fields is negligible by U-Net, our model shows better skill compared to the Level 4 products based on OI. Finally, we will also present results from an independent validation against in-situ drifter SST observations that are mainly located in the western Mediterranean basin. Level 3 SST products show discrepancies relative to drifters in terms of both overall error and mean bias, which are preserved by the U-Net in cloud-free regions. In reconstructed regions, only a modest degradation in skill relative to drifter observations is observed, indicating that the reconstruction introduces limited additional error.

 

Deep Learning for Sea Surface Temperature Reconstruction under Cloud Occlusion by Asperti et al. 2025. Applied Ocean Research. In review. https://arxiv.org/abs/2412.03413