Reconstruction of Daily Sea Surface Temperature Based on Radial Basis Function Networks

A radial basis function network (RBFN) method is proposed to reconstruct daily Sea surface temperatures (SSTs) with limited SST samples. For the purpose of evaluating the SSTs using this method, non-biased SST samples in the Pacific Ocean (10 degrees N-30 degrees N, 115 degrees E-135 degrees E) are selected when the tropical storm Hagibis arrived in June 2014, and these SST samples are obtained from the Reynolds optimum interpolation (OI) v2 daily 0.25 degrees SST (OISST) products according to the distribution of AVHRR L2p SST and in-situ SST data. Furthermore, an improved nearest neighbor cluster (INNC) algorithm is designed to search for the optimal hidden knots for RBFNs from both the SST samples and the background fields. Then, the reconstructed SSTs from the RBFN method are compared with the results from the OI method. The statistical results show that the RBFN method has a better performance of reconstructing SST than the OI method in the study, and that the average RMSE is 0.48 degrees C for the RBFN method, which is quite smaller than the value of 0.69 degrees C for the OI method. Additionally, the RBFN methods with different basis functions and clustering algorithms are tested, and we discover that the INNC algorithm with multi-quadric function is quite suitable for the RBFN method to reconstruct SSTs when the SST samples are sparsely distributed.