Sea surface temperature (SST) reconstruction using S-DEIM

SST reconstruction using S-DEIM This entry corresponds to the following manuscript:Cassidy All, Kevin Ho, Maya Magnuski, Christopher Nicolaides, Louisa B. Ebby, Mohammad Farazmand, Rapid estimation of global sea surface temperatures from sparse streaming in situ observations, https://doi.org/10.48550/arXiv.2601.21913 It contains the datasets and codes required for reproducing the results of the above paper. The Sea Surface Temperature dataset (included here) is obtained from the National Oceanic and Atmospheric Administration (NOAA) website: https://www.ncei.noaa.gov/products/optimum-interpolation-sst Requirements: The code is written in Matlab and requires the installation of the following toolboxes: Deep Learning Toolbox and Mapping Toolbox (for plotting purposes only). The entry points are train.m and test.m The pre-trained recurrent neural networks are saved for convenience: trained_RC.mat and trained_LSTM.mat