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 for details).
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 ftp.cdc.noaa.gov/Datasets/ncep.reanalysis.derived/surface); (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 (now version 3.4) using ERSST version 3 (downloadable from http://www.ncdc.noaa.gov/oa/climate/research/sst/ersstv3.php; Smith et al. 2008).