2006 美国纽约州立大学石溪分校大气与海洋学院 博士
In my talk, I will take an example of typhoon to demonstrate potential applications of neural network in earth sciences. In this study, three algorithms are proposed that can be used to provide sea surface temperature (SST) conditions for typhoon prediction models. Different from traditional data assimilation approaches, which provide prescribed initial/boundary conditions, our proposed algorithms aim to resolve a flow-dependent SST feedback between growing typhoons and oceans in the future time. Two of these algorithms are based on linear temperature equations (TE-based) and the other is based on an innovative technique involving machine learning (ML-based). The algorithms are then implemented into a WRF model for the simulation of typhoon Soulik (2013) to assess their effectiveness, and the results show significant improvement in simulated storm intensities by including ocean cooling feedback. The ML-based algorithm is based on a neural network, consisting of multiple layers of input variables and neurons, and produces the best estimate of the cooling structure, in terms of its amplitude and position. Therefore, with an appropriate selection of input variables and neuron sizes, the ML-based algorithm appears to be more efficient in prognosing the typhoon-induced ocean cooling and in predicting typhoon intensity than those algorithms based on linear regression methods.