Wind speed super-resolution and validation: from ERA5 to CERRA via diffusion models
Abstract The Copernicus Regional Reanalysis for Europe, CERRA, is a high-resolution
regional reanalysis dataset for the European domain. In recent years, it has shown …
regional reanalysis dataset for the European domain. In recent years, it has shown …
Multiscale neural operator: Learning fast and grid-independent pde solvers
Numerical simulations in climate, chemistry, or astrophysics are computationally too
expensive for uncertainty quantification or parameter-exploration at high-resolution …
expensive for uncertainty quantification or parameter-exploration at high-resolution …
Investigating transformer‐based models for spatial downscaling and correcting biases of near‐surface temperature and wind‐speed forecasts
High‐resolution and accurate prediction of near‐surface weather parameters based on
numerical weather prediction (NWP) models is essential for many downstream and real …
numerical weather prediction (NWP) models is essential for many downstream and real …
On the effectiveness of neural operators at zero-shot weather downscaling
Machine learning (ML) methods have shown great potential for weather downscaling. These
data-driven approaches provide a more efficient alternative for producing high-resolution …
data-driven approaches provide a more efficient alternative for producing high-resolution …
Contrastive learning for climate model bias correction and super-resolution
T Ballard, G Erinjippurath - arxiv preprint arxiv:2211.07555, 2022 - arxiv.org
Climate models often require post-processing in order to make accurate estimates of local
climate risk. The most common post-processing applied is bias-correction and spatial …
climate risk. The most common post-processing applied is bias-correction and spatial …
Reinstating continuous climate patterns from small and discretized data
Wind energy is a leading renewable energy source. It does not pollute the environment and
reduces greenhouse gas emissions that contribute to global warming. However, current …
reduces greenhouse gas emissions that contribute to global warming. However, current …
A Spatial downscaling approach for windsat satellite sea surface wind based on generative adversarial networks and dual learning scheme
J Liu, Y Sun, K Ren, Y Zhao, K Deng, L Wang - Remote Sensing, 2022 - mdpi.com
Sea surface wind (SSW) is a crucial parameter for meteorological and oceanographic
research, and accurate observation of SSW is valuable for a wide range of applications …
research, and accurate observation of SSW is valuable for a wide range of applications …
[HTML][HTML] Improving the resolution of solar energy potential maps derived from global DSMs for rooftop solar panel placement using deep learning
M Hosseini, H Bagheri - Heliyon, 2025 - cell.com
This study focuses on generating high-resolution annual solar energy potential maps
(ASMs) using global Digital Elevation Models (DEMs) to aid in solar panel placement …
(ASMs) using global Digital Elevation Models (DEMs) to aid in solar panel placement …
Spectral pinns: Fast uncertainty propagation with physics-informed neural networks
B Lütjens, CH Crawford, M Veillette… - The Symbiosis of Deep …, 2021 - openreview.net
Physics-informed neural networks (PINNs) promise to significantly speed up partial
differential equation (PDE) solvers. However, most PINNs can only solve deterministic …
differential equation (PDE) solvers. However, most PINNs can only solve deterministic …
WindSR: Improving spatial resolution of satellite wind speed through super-resolution
Prediction of accurate wind speed is necessary for a variety of applications such as energy
production, agriculture, climate modeling, and weather forecasting. Various satellites …
production, agriculture, climate modeling, and weather forecasting. Various satellites …