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Found 6 papers, 3 papers with code
Toward Routing River Water in Land Surface Models with Recurrent Neural Networks
Training of such a model over the continental United States has demonstrated that a single set of model parameters can be used across independent catchments, and that RNNs can outperform physics-based models.
Learning About Structural Errors in Models of Complex Dynamical Systems
Complex dynamical systems are notoriously difficult to model because some degrees of freedom (e. g., small scales) may be computationally unresolvable or are incompletely understood, yet they are dynamically important.
Tipping Point Forecasting in Non-Stationary Dynamics on Function Spaces
Tipping points are abrupt, drastic, and often irreversible changes in the evolution of non-stationary and chaotic dynamical systems.
Iterated Kalman Methodology For Inverse Problems
In this paper, we work with the ExKI, EKI, and a variant on EKI which we term unscented Kalman inversion (UKI).
Numerical Analysis Numerical Analysis Dynamical Systems
Calibration and Uncertainty Quantification of Convective Parameters in an Idealized GCM
Here we demonstrate an approach to model calibration and uncertainty quantification that requires only $O(10^2)$ model runs and can accommodate internal climate variability.
Gaussian Processes
Statistics Theory
Statistics Theory
Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations
Climate projections continue to be marred by large uncertainties, which originate in processes that need to be parameterized, such as clouds, convection, and ecosystems.
