Neural network forecasts of the tropical Pacific sea surface temperatures
Aiming Wu, William W. Hsieh and Benyang Tang
A neural network (NN) model had been used by our group to predict the
sea surface temperature anomalies (SSTA) in the Nino3.4 region in the equatorial Pacific (Tang et
al, 2000). The
NN model has been extended to forecast the SSTA
over the entire tropical Pacific, and in the latest version 3.5
(introduced in Mar. 2010), the model used is a Bayesian NN (Wu et
Click here to see details about this model.
Using sea level pressure and sea surface temperature data up to the
end of July 2016,
forecasts were made with the NN model.
Ensemble-averaged forecasts for
the SSTA in the Nino3.4 region at various lead times are shown in Fig.1,
and the forecasted SSTA fields over the tropical Pacific are displayed
in Fig.2, showing a
slightly cool pattern in the central-eastern equatorial Pacific
in autumn 2016, and near normal conditions in the equatorial Pacific
during winter 2016-17.
Figure 1. The SSTA (in degree Celsius) in the Nino3.4
area (170ºW-120ºW, 5ºS-5ºN) predicted by the ensemble-averaged nonlinear
model at 3, 6, 9 and 12 months of lead time (circles), with
observations denoted by the solid line. Tick marks along the abscissa
indicate the January of the given years. (The observed Nino3.4 SSTA was
computed from the ERSST data, with the mean defined over 1950-2009.)
(The pdf file of Fig.1 is also available).
Figure 2. SSTA (in ºC) predicted by the ensemble-averaged
nonlinear model for
the four consecutive seasons starting with
SON (September-November 2016).
A thick black curve is used for the zero contour.
(The pdf file of Fig.2 is also available).
Data in tabular format for the Nino3.4 SSTA predicted
for the next four seasons:
|SON || 2016 ||-0.41 ºC|
|DJF || 2016-17 ||0.06 ºC|
|MAM || 2017 ||0.24 ºC|
|JJA || 2017 ||-0.01 ºC|
Tang, B., W.W. Hsieh, A.H. Monahan and F.T. Tangang, 2000. Skill
comparisons between neural networks and canonical correlation analysis
in predicting the equatorial Pacific sea surface temperatures.
J.Climate, 13: 287-293.
Wu, A., W.W. Hsieh and B. Tang, 2006. Neural network forecasts of the
tropical Pacific sea surface temperatures. Neural Networks. 19: 145-154.
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