Data Assimilation with Sequential Gaussian Processes
We study a data assimilation problem using Gaussian processes (GPs) where the GPs act as latent variables for the observations. Inference is done using a convenient parameterisation and sequential learning for a faster algorithm. We are addressing the disadvantage of the GPs, namely the quadratic scaling of the parameters with data and eliminate the scaling by using a fixed number of parameters. The result is a sparse representation that allows us to treat problems with a large number of observations. We apply our method to the prediction of wind fields over the ocean surface from scatterometer data.