Supervised learning from noisy observations: Combining machine-learning techniques with data assimilation

07/14/2020
by   Georg A. Gottwald, et al.
0

Data-driven prediction and physics-agnostic machine-learning methods have attracted increased interest in recent years achieving forecast horizons going well beyond those to be expected for chaotic dynamical systems. In a separate strand of research data-assimilation has been successfully used to optimally combine forecast models and their inherent uncertainty with incoming noisy observations. The key idea in our work here is to achieve increased forecast capabilities by judiciously combining machine-learning algorithms and data assimilation. We combine the physics-agnostic data-driven approach of random feature maps as a forecast model within an ensemble Kalman filter data assimilation procedure. The machine-learning model is learned sequentially by incorporating incoming noisy observations. We show that the obtained forecast model has remarkably good forecast skill while being computationally cheap once trained. Going beyond the task of forecasting, we show that our method can be used to generate reliable ensembles for probabilistic forecasting as well as to learn effective model closure in multi-scale systems.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 11

page 18

08/08/2021

Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observations

We present a supervised learning method to learn the propagator map of a...
02/15/2021

Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components

We consider the problem of data-assisted forecasting of chaotic dynamica...
10/19/2020

DAN – An optimal Data Assimilation framework based on machine learning Recurrent Networks

Data assimilation algorithms aim at forecasting the state of a dynamical...
10/27/2020

Improving seasonal forecast using probabilistic deep learning

The path toward realizing the potential of seasonal forecasting and its ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.