Version 1
: Received: 4 June 2017 / Approved: 5 June 2017 / Online: 5 June 2017 (05:01:17 CEST)
How to cite:
Liao, Z.; Dong, Q.; Xue, C.; Bi, J.; Wan, G. Reconstruction of Daily Sea Surface Temperature Based on Radial Basis Function Networks. Preprints2017, 2017060021. https://doi.org/10.20944/preprints201706.0021.v1
Liao, Z.; Dong, Q.; Xue, C.; Bi, J.; Wan, G. Reconstruction of Daily Sea Surface Temperature Based on Radial Basis Function Networks. Preprints 2017, 2017060021. https://doi.org/10.20944/preprints201706.0021.v1
Liao, Z.; Dong, Q.; Xue, C.; Bi, J.; Wan, G. Reconstruction of Daily Sea Surface Temperature Based on Radial Basis Function Networks. Preprints2017, 2017060021. https://doi.org/10.20944/preprints201706.0021.v1
APA Style
Liao, Z., Dong, Q., Xue, C., Bi, J., & Wan, G. (2017). Reconstruction of Daily Sea Surface Temperature Based on Radial Basis Function Networks. Preprints. https://doi.org/10.20944/preprints201706.0021.v1
Chicago/Turabian Style
Liao, Z., Jingwu Bi and Guangtong Wan. 2017 "Reconstruction of Daily Sea Surface Temperature Based on Radial Basis Function Networks" Preprints. https://doi.org/10.20944/preprints201706.0021.v1
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 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 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 optimum interpolation (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 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.
Keywords
sea surface temperature (SST); radial basis function network (RBFN); improved nearest neighbor cluster (INNC) algorithm
Subject
Environmental and Earth Sciences, Oceanography
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
