Abstract
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°N–30°N, 115°E–135°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° 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 °C for the RBFN method, which is quite smaller than the value of 0.69 °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.
Highlights
High-quality sea surface temperature (SST) data play an important role in many applications, including climate predictions [1], ocean data assimilation [2], and global change research [3]
The distributions of SST samples are very sparse in this study, the reconstructed SSTs from the radial basis function network (RBFN) method may be influenced by the type of the basis function
The results show that the variations of SST biases from these two methods are quite similar, but the SST biases from the RBFN method are relatively greater than those from the optimum interpolation (OI) method when the SST samples in Figure 10 are not sparsely distributed
Summary
High-quality sea surface temperature (SST) data play an important role in many applications, including climate predictions [1], ocean data assimilation [2], and global change research [3]. Raw SST data can vary significantly between different types of measurements [4], such as in-situ measurements (e.g., ships or buoys) and satellite sensors (e.g., infrared thermal radiometers or microwave radiometers on satellite platforms). In-situ SST measurements are accurate but sparse, while satellite-retrieved SST measurements have poor accuracy but provide a dense coverage globally. Even through many different sources of SSTs are combined, due to the limitations of satellite orbits and the field of view (FOV) of sensors, global ocean coverage remains difficult to achieve [6]. High-quality SST samples usually have irregular distributions as some data are discarded, such as in-situ SST data from questionable drifters and satellite-retrieved SST data contaminated by clouds [6,7]. It is critical to reconstruct SST fields from incomplete SST samples
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