Improving the Reconstruction of Vertical Temperature Profiles on Account of Oceanic Front Impacts

The application of remote sensing observations in estimating ocean sub-surface temperatures has been widely adopted. Machine learning-based methods in particular are gaining more and more interest. While there is promising relevant progress, most temperature profile reconstruction models are still built upon the gridded Argo data regardless of the impacts of mesoscale oceanic processes. As a follow-on to the previous study that demonstrates the influence of ocean fronts is negligible, we focus on the improvement of temperature profile reconstruction by introducing the sea surface temperature (SST) gradient into the neural network model. The model sensitivity assessments reveal that the normalization of the input variables achieves a higher estimation accuracy than the original scale. Five experiments are then designed to examine the model performances with or without the SST gradient input. Our results confirm that, for a given model configuration, the one with the input of the SST gradient has the lowest reconstruction bias in comparison to the in situ Argo measurements. Such improvement is particularly pronounced below 200 m depth. We also found that the non-linear activation functions and deeper network structures facilitate the performance of reconstruction models. Results of this work open new insights and challenges to refine the mapping of upper ocean temperature structures. While more relevant machine learning methods are worth further exploitation, how to better characterize the mesoscale oceanic processes from surface observations and bring them into the reconstruction models is the key and needs much attention