We present an artificial neural network model to predict the sea surface temperature (SST) and delineate SST fronts in the northeastern Arabian Sea. The predictions are made one day in advance, using current day’s SST for predicting the SST of the next day. The model is used to predict the SST map for every single day during 2013–2015. The results show that more than 75% of the time the model error is ≤ ±0.5ºC. For the years 2014 and 2015, 80% of the predictions had an error ≤±0.5ºC. The model performance is dependent on the availability of data during the previous days. Thus during the summer monsoon months, when the data availability is comparatively less, the errors in the prediction are slightly higher. The model is also able to capture SST fronts.
|Number of pages||18|
|Journal||International Journal of Remote Sensing|
|Publication status||Published - 18-06-2018|
All Science Journal Classification (ASJC) codes
- Earth and Planetary Sciences(all)