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Neural general circulation models optimized to predict satellite-based precipitation observations
Janni Yuval, Ian Langmore, Dmitrii Kochkov +1
Climate models struggle to accurately simulate precipitation, particularly extremes and the diurnal cycle. Here, we present a hybrid model that is trained directly on satellite-bas…
4D-Var using Hessian approximation and backpropagation applied to automatically-differentiable numerical and machine learning models
Kylen Solvik, Stephen G. Penny, Stephan Hoyer
Constraining a numerical weather prediction (NWP) model with observations via 4D variational (4D-Var) data assimilation is often difficult to implement in practice due to the need…
DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems
Yair Schiff, Zhong Yi Wan, Jeffrey B. Parker +4
Learning dynamics from dissipative chaotic systems is notoriously difficult due to their inherent instability, as formalized by their positive Lyapunov exponents, which exponential…