Peer-Reviewed Journal Details
Mandatory Fields
Ward, B., Redfern,S.
1999
December
International Journal Of Remote Sensing
A neural network model for predicting the bulk-skin temperature di_erence at the sea surface
Published
()
Optional Fields
SENSOR MICROWAVE IMAGER BOUNDARY-LAYER OCEAN SURFACE COOL SKIN FLUX
20
3533
3548
Night-time radiometric sea surface temperature (SST) observations were carried out on a research platform in the North Sea. during the second campaign of the ASGAMAGE experiment. An extensive series of atmospheric measurements was also made, allowing a comparison between measurements of the bulk-skin temperature difference, Delta T, and several current theoretical models. An artificial neural network (ANN) was empirically designed and trained on a subset of the net heat flux and wind speed parameters. The remaining dataset was then applied to the output of the ANN. The neural network-based model reproduced the observed Delta T values with a higher level of accuracy than any of the other current models.
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