Corpus ID: 221112117

Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling

  title={Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling},
  author={Nishant Yadav and S. Chandu Ravela and Auroop Ratan Ganguly},
Systems exhibiting nonlinear dynamics, including but not limited to chaos, are ubiquitous across Earth Sciences such as Meteorology, Hydrology, Climate and Ecology, as well as Biology such as neural and cardiac processes. However, System Identification remains a challenge. In climate and earth systems models, while governing equations follow from first principles and understanding of key processes has steadily improved, the largest uncertainties are often caused by parameterizations such asโ€ฆย Expand
1 Citations
Combining data assimilation and machine learning to estimate parameters of a convective-scale model
Errors in the representation of clouds in convection permitting numerical weather prediction models can be introduced by different sources. These can be the forcing and boundary conditions, theโ€ฆ Expand


Discovering governing equations from data by sparse identification of nonlinear dynamical systems
This work develops a novel framework to discover governing equations underlying a dynamical system simply from data measurements, leveraging advances in sparsity techniques and machine learning and using sparse regression to determine the fewest terms in the dynamic governing equations required to accurately represent the data. Expand
Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted Highโ€Resolution Simulations
This work outlines how parameterization schemes can learn from global observations and targeted high- resolution simulations, for example, of clouds and convection, through matching low-order statistics between ESMs, observations, and high-resolution simulations. Expand
Using Machine Learning to Parameterize Moist Convection: Potential for Modeling of Climate, Climate Change, and Extreme Events
  • P. O'Gorman, J. Dwyer
  • Physics, Environmental Science
  • Journal of Advances in Modeling Earth Systems
  • 2018
The parameterization of moist convection contributes to uncertainty in climate modeling and numerical weather prediction. Machine learning (ML) can be used to learn new parameterizations directlyโ€ฆ Expand
Deep learning and process understanding for data-driven Earth system science
It is argued that contextual cues should be used as part of deep learning to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales. Expand
Could Machine Learning Break the Convection Parameterization Deadlock?
A novel approach to convective parameterization based on machine learning is presented, using an aquaplanet with prescribed sea surface temperatures as a proof of concept to show that neural networks trained on a high-resolution model in which moist convection is resolved can be an appealing technique to tackle and better represent moist convections in coarse resolution climate models. Expand
Fast and Scalable Gaussian Process Modeling with Applications to Astronomical Time Series
A novel method for Gaussian processes modeling in one dimension where the computational requirements scale linearly with the size of the data set, and is fast and interpretable, with a range of potential applications within astronomical data analysis and beyond. Expand
Stochastic parametrizations and model uncertainty in the Lorenz โ€™96 system
  • H. Arnold, I. Moroz, T. Palmer
  • Mathematics, Medicine
  • Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
  • 2013
The forecasting skill of the parametrizations was found to be linked to their ability to reproduce the climatology of the full model, important in a seamless prediction system, allowing the reliability of short-term forecasts to provide a quantitative constraint on the accuracy of climate predictions from the same system. Expand
Hidden physics models: Machine learning of nonlinear partial differential equations
Abstract While there is currently a lot of enthusiasm about โ€œbig dataโ€, useful data is usually โ€œsmallโ€ and expensive to acquire. In this paper, we present a new paradigm of learning partialโ€ฆ Expand
Data driven nonlinear dynamical systems identification using multi-step CLDNN
A new CLDNN model which combine convolutional layer, long short-term memory layer and fully connected layer, to address the weakness of the multi-step time-stepping schemes and provides possible corroboration for developing new deep learning based algorithms for nonlinear system identification. Expand
The Art and Science of Climate Model Tuning
Tuning is an essential aspect of climate modeling with its own scientific issues, which is probably not advertised enough outside the community of model developers. Expand