Description of neural network model (version 3.5)
A neural network (NN) model has been used by our group to predict the
SST anomalies in the Nino3.4 region in the equatorial Pacific (Tang et
al. 2000). By version 3.1, the NN model has been extended to forecast
the SST anomalies over the whole tropical Pacific. In version 3.2, the
subsurface temperature anomalies in the tropical Pacific ocean were
included as predictors. In version 3.3, we have
removed the subsurface temperature anomalies as predictors, since their
record is relatively short (available only since 1980), and they are not
always available on time for our monthly forecasts. We have, however,
introduced a Bayesian NN in version 3.3 (see
Wu et al. 2006
The data used in version 3.3 came from two datasets: (a) the
monthly sea level pressure (SLP) on 2.5° × 2.5° grids
from the NCEP/NCAR reanalysis (Kalnay et al. 1996; downloadable from
(b) the monthly extended reconstructed sea surface temperature on 2°
× 2° grids (ERSST version 2; Smith and Reynolds 2004).
By Jan. 2010, ERSST version 2 was no longer available. We therefore had
to retrain our Bayesian NN model (version 3.4) using ERSST version 3 (downloadable from http://www.ncdc.noaa.gov/oa/climate/research/sst/ersstv3.php; Smith et al. 2008).
Relative to version 3.3 (Wu et al. 2006), the most current version
(version 3.5) has several changes:
(1) The data used to train and
validate models has been extended (Jan. 1948- Dec. 2009).
(2) A more
rigorous cross-validation method has been implemented to verify forecast
skills -- namely, data from five consecutive calendar years were held
for validation/verification, while data from the remaining years were
used as training data. Only the 3rd year in the 5-year window was
forecasted by the trained model. The 5-year window was moved forward by
one year each time until independent forecast verification was done for
(3) Among the training data, only 2/3 of the data were
randomly selected (in one-year blocks) and used to train the NN
models. The remaining 1/3 data were used as testing data for model
selection. NNs with 1, 2 and 3 hidden neurons were trained separately
and validated on the testing data, and the one model with highest correlation
skill was selected. This procedure was repeated 100 times and the
ensemble average from these 100 models was used to give the final
Cross-validation forecast correlation skills by Bayesian NN model (with
corresponding value from multiple linear regression given in parenthesis)
for the Nino4, Nino3.4, Nino3 and Nino1+2 regions and for lead times of
3, 6, 9, and 12 months (with definition of lead time same as in
Wu et al. 2006) are given in the table below:
| || Nino4 || Nino3.4 || Nino3 || Nino1+2 |
| 3 month || 0.9109 (0.9100) || 0.9051 (0.9048) || 0.8734
(0.8754) || 0.7997 (0.8022) |
| 6 month || 0.7657 (0.7615) || 0.7387 (0.7366) || 0.6810
(0.6799) || 0.5524 (0.5457) |
| 9 month || 0.6320 (0.6283) || 0.5960 (0.5921) || 0.5364
(0.5322) || 0.3500 (0.3377) |
| 12 month || 0.4899 (0.4882) || 0.4737 (0.4703) || 0.4413
(0.4367) || 0.2583 (0.2486) |
Kalnay, E., et al., 1996.
The NCEP/NCAR 40 year reanalysis project. Bull. Amer. Meteorol. Soc.,
Smith, T.M., and R.W. Reynolds, 2004. Improved extended reconstruction
of SST [1854-1997]. Journal of Climate, 17, 2466-2477.
Smith, T.M., R.W. Reynolds, Thomas C. Peterson, and Jay Lawrimore, 2008.
Improvements to NOAA's historical merged land-ocean surface temperature
analysis (1880-2006). Journal of Climate, 21, 2283-2296.
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.