Virtual Event: ECMWF-ESA Workshop on Machine Learning for Earth System Observation and Prediction

At RIKEN, we have been exploring a fusion of big data and big computation, and now with AI techniques and machine learning (ML). The new Japan’s flagship supercomputer “Fugaku”, ranked #1 in the most recent TOP500 list (https://www.top500.org/) in June 2020, is designed to be efficient for both double-precision big simulations and reduced-precision machine learning applications, aiming to play...

Can Artificial Neural Network (NN) lean (and/or replace) a Data Assimilation (DA) process? What would be the effect of this approach?

DA is the Bayesian approximation of the true state of some physical systems at a given time by combining time-distributed observations with a dynamic model in an optimal way. NN models can be used to learn the assimilation process in different ways. In...

The recent introduction of machine learning techniques in the field of numerical geophysical prediction has expanded the scope so far assigned to data assimilation, in particular through efficient automatic differentiation, optimisation and nonlinear functional representations. Data assimilation together with machine learning techniques, can not only help estimate the state vector but also the...

A novel method based on the combination of data assimilation and machine learning is introduced. The combined approach is designed for emulating hidden, possibly chaotic, dynamics and/or to devise data-driven parametrisations of unresolved processes in dynamical numerical models.
The method consists in applying iteratively a data assimilation step, here ensemble Kalman filter or smoother, and...