Based on artificial neural network (ANN), the authors retrieved near-surface air temperature (AT) from sea surface temperature (SST), wind speed (WS) and sea level pressure (SLP) of the International Comprehensive Ocean-Atmosphere Dataset (ICOADS). Modeling sample spans from 1981 to 2000, while validating sample spans from 2001 to 2008. The adopted ANN introduces momentum factor to back propagation (BP) algorithm to escape from local extremes. In addition, batch processing gradient descent method was used to remove the effect of sequential training. Retrieving results in the South China Sea (SCS) demonstrates that ANN is better than multi-factor linear regression, especially for coastal areas during spring and winter, where strong non-linear relation exists between SST, WS, SLP and AT. In conclusion, ANN behaves similarly for each month, with root mean square error (RMSE) between 1.5 ℃ and 1.8 ℃ and mean absolute error (MAE) between 1.1 ℃ and 1.3 ℃ .
Tun-Xin-Rong
,
Han-Gui-Jun
,
Zhang-Hua-Feng
,
Wang-Chi- Dong
. Retrieving near-surface air temperature in the South China Sea using artificial neural network[J]. Journal of Tropical Oceanography, 2012
, 31(2)
: 7
-14
.
DOI: 10.11978/j.issn.1009-5470.2012.02.002
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