A method for the reconstruction of missing data based on an EOF decomposition has been applied to a large data set, a test case of Sea Surface Temperature satellite images of the Adriatic Sea. The EOF decomposition is realised with a Lanczos method, which allows optimising computational time for large matrices. The results show that the reconstruction method leads to accurate reconstructions as well as a low cpu time when dealing with realistic cases. The method has been tested with different amounts of missing data, artificially adding clouds ranging from 40% to 80% of data loss, and then compared to the same data set with no missing data. A comparison with in situ data has also been made. These validation studies show that results are robust, even when the amount of missing data is very high. The reconstruction of the data from the Adriatic Sea shows realistic features and a reliable temperature distribution. In addition, the method is compared to an Optimal Interpolation reconstruction. The results obtained with both methods are very similar. The main difference is the computational time, which is reduced nearly 30 times with the method presented here. Once the reconstruction has been performed, the EOF decomposition is analysed to show the method's reliability, and a cold event on the Albanian coast is studied. The reconstructed data reflect the effect of wind on the Albanian coast, that led to a cold-water episode in this zone for a 6-day period.
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