19 citations · 19 across the 9 of their papers we have counts for
6 papers · 1 filter
WaveSim: A Wavelet-based Multi-scale Similarity Metric for Weather and Climate Fields
Gabriele Accarino, Viviana Acquaviva, Sara Shamekh +2
We introduce WaveSim, a multi-scale similarity metric for the evaluation of spatial fields in weather and climate applications. WaveSim exploits wavelet transforms to decompose inp…
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…
Representing Subgrid-Scale Cloud Effects in a Radiation Parameterization using Machine Learning: MLe-radiation v1.0
Katharina Hafner, Sara Shamekh, Guillaume Bertoli +4
Improvements of Machine Learning (ML)-based radiation emulators remain constrained by the underlying assumptions to represent horizontal and vertical subgrid-scale cloud distributi…
CuMoLoS-MAE: A Masked Autoencoder for Remote Sensing Data Reconstruction
Anurup Naskar, Nathanael Zhixin Wong, Sara Shamekh
Accurate atmospheric profiles from remote sensing instruments such as Doppler Lidar, Radar, and radiometers are frequently corrupted by low-SNR (Signal to Noise Ratio) gates, range…
PnP-DA: Towards Principled Plug-and-Play Integration of Variational Data Assimilation and Generative Models
Yongquan Qu, Matthieu Blanke, Sara Shamekh +1
Earth system modeling presents a fundamental challenge in scientific computing: capturing complex, multiscale nonlinear dynamics in computationally efficient models while minimizin…
Strictly Constrained Generative Modeling via Split Augmented Langevin Sampling
Matthieu Blanke, Yongquan Qu, Sara Shamekh +1
Deep generative models hold great promise for representing complex physical systems, but their deployment is currently limited by the lack of guarantees on the physical plausibilit…