9 papers
Sampling sea state using a diffusion model
Jiarong Wu, Bertrand Chapron, Laure Zanna
Sea state prediction is essential for operational maritime applications and coupled earth system modeling, yet current spectral wave models remain computationally prohibitive for m…
Towards bridging the gap between data-driven and theoretical turbulence closures in stratified flows
Laure Zanna, Pavel Perezhogin
Turbulence closure models are essential for solving the equations of motion in realistic systems, where fully resolving all relevant scales of motion is computationally infeasible.…
Impact of Data-Driven Eddy Parameterization on Climate State in an Idealized Coupled CESM Model
Jia-Rui Shi, Pavel Perezhogin, Laure Zanna +1
Mesoscale eddies remain poorly represented in most climate models, motivating the use of parameterizations to account for their dynamical effects on the coupled system. In this stu…
Data-Driven Probabilistic Air-Sea Flux Parameterization
Jiarong Wu, Pavel Perezhogin, David John Gagne +4
Accurately quantifying air-sea fluxes is important for understanding air-sea interactions and improving coupled weather and climate systems. This study introduces a probabilistic f…
Estimation of temperature and precipitation uncertainties using quantile neural networks
Andrew Brettin, Laure Zanna
Extreme events pose significant risks and are challenging to predict. Assessing climate hazards requires placing quantitative constraints on geophysical fields under observable but…
Towards a Unified Data-Driven Boundary Layer Momentum Flux Parameterization for Ocean and Atmosphere
Renaud Falga, Sara Shamekh, Laure Zanna
Boundary layer turbulence, particularly the vertical fluxes of momentum, shapes the evolution of winds and currents and plays a critical role in weather, climate, and biogeochemica…