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IAMCS Workshop in Large-Scale Inverse Problems and Uncertainty Quantification

Faming Liang, Texas A&M University
A Dynamically Weight Particle Filter for Sea Surface Temperature Prediction


  • Faming Liang
  • Duchwan Ryu
  • Bani Mallick


In the climate system, the sea surface temperature (SST) is an important factor. An accurate understanding for the pattern of SST is essential for climate monitoring and prediction. We apply the dynamically weighted particle filter, which combines the radial basis function network and the dynamically weighted importance sampling algorithm, to analyze the SST in the Caribbean Islands area after a hurricane. The radial basis function network models the nonlinearity of SST and dynamically weighted importance sampling prevents the particles from degenerating in computation. In this study, we found that the hurricane disturbs the pattern of SST by mixing the ocean layers, while the dynamically weighted particle filter shows good prediction performance for SST with fast computational time. This is a joint work with Duchwan Ryu and Bani Mallick.