Predicting Ocean Dynamics through Machine Learning: Application on Sea-Surface Suspended Particulate Mater

In the satellite age, geoscientist have acquired an unprecedented aboundance of data describing the earth (ocean and land) surface. This accumulation of observations with high spatio-temporal sampling has generated a demand in ways to optimally extract from these data the useful features which have the ability to forecast the evolution of some key parameter. In this work we explore the high potential of using advanced machine learning techniques  for the prediction of the temporal evolution of 2D oceanographic parameters.
We chose to present an experiment on the prediction of sea-surface fields of the total suspended particulate mater in the english chanell. This choice was motivated by the complexity of the phenomenons impacting this oceanic variable: it is driven both by the neap-tide cycle, storms and general circulation oceanic currents.
The predicting system used is constructed using three successive blocks. The first is consisting in a convolutional neural network to extract useful feature and reduce the dimension of the input. The second is a recurrent neural network which is used as a feature predictor. The last block is a convolutional neural network  used to reconstruct the image from the predicted feature of the last block.
An additional motivation was the frequent missing values caused by the cloud cover over the area. A number of neuronal methods are able to produce good predictions despite missing values.
The methodology we selected to implement is a combination of convolutionary neuronal networks and long short-term memory networks.
Preliminary results indicate a predictive power for the mean situation and also for extreme events (e.g. storms) than is comparable or better than traditional approaches.