CSSI-GW Project

A blog post by Dr. Sun about the new high-resolution WRF library "A Library of High-Resolution Gravity Wave Simulations." link

• How we plan to use it to develop data-driven parametierizations for climate models.
• The Need for High-Resolution Data for Data-Driven Parameterization.
• Building a Library of High-Resolution Data.
• Challenges in Utilizing High-Resolution Data for Machine Learning.

Chattopadhyay et al. published "Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence" at Environmental Data Science.

• We propose a convolutional variational autoencoder-based stochastic data-driven model that is pre-trained on an imperfect climate model simulation from a 2-layer quasi-geostrophic flow and retrained, using transfer learning, on a small number of noisy observations from a perfect simulation.
• This re-trained model performs stochastic forecasting with a noisy initial condition sampled from the perfect simulation.
• Our ensemble-based stochastic data-driven model outperforms a baseline deterministic encoder-decoder-based convolutional model in terms of short-term skills while remaining stable for long-term climate simulations yielding accurate climatology.

Kruse et al. published "Do Nudging Tendencies Depend on the Nudging Timescale Chosen in Atmospheric Models?" at Journal of Advances in Modeling Earth Systems.

• Nudging tendencies, or nudging forces per unit mass, depend strongly on the nudging time scale chosen (i.e. are ∝ τ−1).
• Nudging tendencies cannot be quantitatively interpreted as errors in total (dynamics + physics) model forces on the atmospheric fluid.
• Still, nudging tendencies can be useful for identifying model errors, even if only qualitatively.

Mojgani et al. published "Discovery of interpretable structural model errors by combining Bayesian sparse regression and data assimilation: A chaotic Kuramoto–Sivashinsky test case" at Chaos: An Interdisciplinary Journal of Nonlinear Science.

• We introduce a framework named MEDIDA: Model Error Discovery with Interpretability and Data Assimilation.
• MEDIDA only requires a working numerical solver of the model and a small number of noise-free or noisy sporadic observations of the system.
• The framework proposed here integrates machine learning (ML) and data assimilation (DA) techniques to discover close-form, interpretable representations of these model errors.
• This framework can leverage the ever-growing abundance of observational data to reduce the errors in models of nonlinear dynamical systems, such as the climate system.
• Using the chaotic Kuramoto–Sivashinsky system as the test case, we demonstrate the excellent performance of MEDIDA in discovering different types of structural/parametric model errors.

Guan et al. published "Stable a posteriori LES of 2D turbulence using convolutional neural networks: Backscattering analysis and generalization to higher Re via transfer learning" at Journal of Computational Physics.

• A deep learning-based, non-local data-driven SGS model is introduced.
• This SGS model accurately captures the effect of small-scale structures.
• Transfer learning enables the SGS model to extrapolate to more turbulent flows.
• Resulting LES model is stable and generalizable with accuracy superior to baseline LES.

Espinosa et al. published "Machine learning gravity wave parameterization generalizes to capture the QBO and response to increased CO2." at Geophysical Research Letters

• • We present single-column gravity wave parameterizations (GWPs) that use machine learning to emulate non-orographic gravity wave (GW) drag and demonstrate their ability to generalize out-of-sample.
• • A set of artificial neural networks (ANNs) are trained to emulate the momentum forcing from a conventional GWP in an idealized climate model, given only one view of the annual cycle and one phase of the Quasi-Biennial Oscillation (QBO).
• • We investigate the sensitivity of offline and online performance to the choice of input variables and complexity of the ANN.
• • When coupled with the model, moderately complex ANNs accurately generate full cycles of the QBO.
• • When the model is forced with enhanced CO2, its climate response with the ANN matches that generated with the physics-based GWP.
• • GThat ANNs can accurately emulate an existing scheme and generalize to new regimes given limited data suggests the potential for developing GWPs from observational estimates of GW momentum transport.

Kruse et al. published "Observed and Modeled Mountain Waves from the Surface to the Mesosphere near the Drake Passage." at Journal of the Atmospheric Sciences

• • Four state-of-the-science numerical weather prediction (NWP) models were used to perform mountain wave (MW)-resolving hindcasts over the Drake Passage of a 10-day period in 2010 with numerous observed MW cases. All models reproduced observed middle-atmosphere MWs with remarkable skill.
• • This study had three purposes: to quantitatively evaluate how well four state-of-the-science weather models could reproduce observed mountain waves (MWs) in the middle atmosphere, to compare the simulated MWs within the models, and to quantitatively evaluate two MW parameterizations in a widely used climate model.
• • Meridionally propagating MWs can significantly influence zonal winds over the Drake Passage. Parameterizations that handle horizontal propagation may need to consider horizontal fluxes of horizontal momentum in order to get the direction of their forcing correct.

Subel et al. published "Data-driven subgrid-scale modeling of forced Burgers turbulence using deep learning with generalization to higher Reynolds numbers via transfer learning" at Physics of Fluids.

• Developing data-driven subgrid-scale (SGS) models for large eddy simulations (LESs) has received substantial attention recently.
• Despite some success, particularly in a priori (offline) tests, challenges have been identified that include numerical instabilities in a posteriori (online) tests and generalization (i.e., extrapolation) of trained data-driven SGS models.
• Using the stochastically forced Burgers turbulence as the test-bed, we show that deep neural networks trained using properly pre-conditioned (augmented) data yield stable and accurate a posteriori LES models.
• We show that transfer learning enables accurate/stable generalization to a flow with 10× higher Reynolds number.

Lindgren et al. published "Study Seasonal and Latitudinal Variability of the Gravity Wave Spectrum in the Lower Stratosphere" at J. Geophys. Res. Atmos.

• Loon LLC superpressure balloons can be used to infer lower stratospheric gravity wave (GW) motions over most latitudes and all seasons.
• Seasonal and latitudinal variability is found in GW spectral slopes and amplitudes over the full intrinsic frequency spectrum.
• GW amplitudes are higher during positive QBO phases compared to negative phases for intrinsic frequencies up to the buoyancy frequency.