5 papers · 1 filter
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…
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…
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…
Uncertainty-permitting machine learning reveals sources of dynamic sea level predictability across daily-to-seasonal timescales
Andrew Brettin, Laure Zanna, Elizabeth A. Barnes
Reliable dynamic sea level forecasts are hindered by numerous sources of uncertainty on daily-to-seasonal timescales (1-180 days) due to atmospheric boundary conditions and interna…