Efficient Helmholtz Preconditioning for Compressible Euler Equations
Effiziente Helmholtz-Vorbedingung für die Kompressible Euler-Gleichungen.
Helmholtz Preconditioning for Compressible Euler Equations with Mixed Finite Elements
Implicit solvers for atmospheric models are accelerated via Helmholtz preconditioning, ensuring stability and efficiency in solving compressible Euler equations.
Unified Framework for Precipitation Nowcasting: DiffCast
Modeling precipitation evolution with global deterministic motion and local stochastic variations improves prediction accuracy.
Explainable Graph Neural Networks Impact on Atmospheric State Estimation
Using Graph Neural Networks to analyze the impact of observations on atmospheric state estimation.
Generative Ensemble Deep Learning for Severe Weather Prediction from a Deterministic Convection-Allowing Model
The author employs conditional generative adversarial networks (CGANs) and convolutional neural networks (CNN) to post-process convection-allowing model forecasts, resulting in skillful severe weather predictions with improved Brier Skill Scores. The approach combines deep generative models with neural networks to enhance severe weather prediction accuracy.
Analyzing Helmholtz Preconditioning for Compressible Euler Equations with Mixed Finite Elements
The author presents a novel preconditioner for the compressible Euler equations, showcasing improved stability and efficiency compared to existing methods.
Transformer-based Nowcasting of Radar Composites from Satellite Images for Severe Weather Prediction
The author presents a Transformer-based model for nowcasting radar image sequences using satellite data, aiming to bridge the gap between ground- and space-based observations for more accurate weather prediction.
Developing an End-to-End AI-Driven Global Weather Forecasting System
The authors present Adas, a novel data assimilation model for global weather variables, combined with FengWu to create the first end-to-end AI-based global weather forecasting system. This system demonstrates stable long-term operation and superior performance in real-world scenarios.
SFTformer: Radar Echo Extrapolation Transformer
The author proposes the SFTformer, a Spatial-Frequency-Temporal correlation-decoupling Transformer, to effectively model radar echo dynamics by decoupling spatial morphology and temporal evolution. The model outperforms existing methods in short-term precipitation forecasting.
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