title: The use of neural networks in identifying error sources in satellite-derived tropical SST estimates.
abstract: An neural network model of data mining is used to identify error sources in satellite-derived tropical sea surface temperature (SST) estimates from thermal infrared sensors onboard the Geostationary Operational Environmental Satellite (GOES). By using the Back Propagation Network (BPN) algorithm, it is found that air temperature, relative humidity, and wind speed variation are the major factors causing the errors of GOES SST products in the tropical Pacific. The accuracy of SST estimates is also improved by the model. The root mean square error (RMSE) for the daily SST estimate is reduced from 0.58 K to 0.38 K and mean absolute percentage error (MAPE) is 1.03%. For the hourly mean SST estimate, its RMSE is also reduced from 0.66 K to 0.44 K and the MAPE is 1.3%.
authors.affiliation: Department of Marine Environmental Informatics, National Taiwan Ocean University, Keelung 20224, Taiwan. D93840002@ntou.edu.tw
authors.forename: Yung-Hsiang
authors.initials: YH
authors.lastname: Lee
authors.forename: Chung-Ru
authors.initials: CR
authors.lastname: Ho
authors.forename: Feng-Chun
authors.initials: FC
authors.lastname: Su
authors.forename: Nan-Jung
authors.initials: NJ
authors.lastname: Kuo
authors.forename: Yu-Hsin
authors.initials: YH
authors.lastname: Cheng
mesh_terms: D000465:Algorithms; D004784:Environmental Monitoring / Q000379:methods*; D006813:Humidity; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009792:Oceans and Seas; D015203:Reproducibility of Results; D013696:Temperature; D014329:Tropical Climate; D014887:Weather