Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer
With the increasing level of offshore wind energy investment, it is correspondingly important
to be able to accurately characterize the wind resource in terms of energy potential as well …
to be able to accurately characterize the wind resource in terms of energy potential as well …
Machine learning for stochastic parameterization: Generative adversarial networks in the Lorenz'96 model
Stochastic parameterizations account for uncertainty in the representation of unresolved
subgrid processes by sampling from the distribution of possible subgrid forcings. Some …
subgrid processes by sampling from the distribution of possible subgrid forcings. Some …
Algorithmic hallucinations of near-surface winds: Statistical downscaling with generative adversarial networks to convection-permitting scales
This paper explores the application of emerging machine learning methods from image
super resolution (SR) to the task of statistical downscaling. We specifically focus on …
super resolution (SR) to the task of statistical downscaling. We specifically focus on …
Constraining stochastic parametrisation schemes using high‐resolution simulations
Stochastic parametrisations can be used in weather and climate models to improve the
representation of unpredictable unresolved processes. When compared with a deterministic …
representation of unpredictable unresolved processes. When compared with a deterministic …
Using stochastically perturbed parameterizations to represent model uncertainty. Part I: Implementation and parameter sensitivity
Accurately representing model-based sources of uncertainty is essential for the
development of reliable ensemble prediction systems for NWP applications. Uncertainties in …
development of reliable ensemble prediction systems for NWP applications. Uncertainties in …
History-based, bayesian, closure for stochastic parameterization: Application to lorenz'96
Physical parameterizations are used as representations of unresolved subgrid processes
within weather and global climate models or coarse-scale turbulent models, whose …
within weather and global climate models or coarse-scale turbulent models, whose …
Parametrizing the mesoscale enhancement of oceanic surface turbulent fluxes: A physical–statistical approach
The mesoscale enhancement of surface turbulent fluxes at the air–sea interface is driven by
the mesoscale surface wind‐speed variability, especially the gustiness velocity and the …
the mesoscale surface wind‐speed variability, especially the gustiness velocity and the …
Memory-based parameterization with differentiable solver: Application to Lorenz'96
Physical parameterizations (or closures) are used as representations of unresolved subgrid
processes within weather and global climate models or coarse-scale turbulent models …
processes within weather and global climate models or coarse-scale turbulent models …
Ocean surface flux algorithm effects on tropical Indo‐Pacific intraseasonal precipitation
Surface latent heat fluxes help maintain tropical intraseasonal precipitation. We develop a
latent heat flux diagnostic that depicts how latent heat fluxes vary with the near‐surface …
latent heat flux diagnostic that depicts how latent heat fluxes vary with the near‐surface …
Exploring lossy compressibility through statistical correlations of scientific datasets
Lossy compression plays a growing role in scientific simulations where the cost of storing
their output data can span terabytes. Using error bounded lossy compression reduces the …
their output data can span terabytes. Using error bounded lossy compression reduces the …