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http://dx.doi.org/10.7837/kosomes.2014.20.5.526

Transfer Function Model Forecasting of Sea Surface Temperature at Yeosu in Korean Coastal Waters  

Seong, Ki-Tack (Southwest Sea Fisheries Research Institute)
Choi, Yang-Ho (Southwest Sea Fisheries Research Institute)
Koo, Jun-Ho (Southwest Sea Fisheries Research Institute)
Lee, Mi-Jin (Southwest Sea Fisheries Research Institute)
Publication Information
Journal of the Korean Society of Marine Environment & Safety / v.20, no.5, 2014 , pp. 526-534 More about this Journal
Abstract
In this study, single-input transfer function model is applied to forecast monthly mean sea surface temperature(SST) in 2010 at Yeosu in Korean coastal waters. As input series, monthly mean air temperature series for ten years(2000-2009) at Yeosu in Korea is used, and Monthly mean SST at Yeosu station in Korean coastal waters is used as output series(the same period of input). To build transfer function model, first, input time series is prewhitened, and then cross-correlation functions between prewhitened input and output series are determined. The cross-correlation functions have just two significant values at time lag at 0 and 1. The lag between input and output series, the order of denominator and the order of numerator of transfer function, (b, r, s) are identified as (0, 1, 0). The selected transfer function model shows that there does not exist the lag between monthly mean air temperature and monthly mean SST, and that transfer function has a first-order autoregressive component for monthly mean SST, and that noise model was identified as $ARIMA(1,0,1)(2,0,0)_{12}$. The forecasted values by the selected transfer function model are generally $0.3-1.3^{\circ}C$ higher than actual SST in 2010 and have 6.4 % mean absolute percentage error(MAPE). The error is 2 % lower than MAPE by ARIMA model. This implies that transfer function model could be more available than ARIMA model in terms of forecasting performance of SST.
Keywords
Air temperature; Sea surface temperature; ARIMA; Transfer function model; Forecast;
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Times Cited By KSCI : 3  (Citation Analysis)
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