7 citations · 16 across the 7 of their papers we have counts for
8 papers
DAWIS: Data Assimilation with Windowed Inverse Sampling via Multitask Interpolants
Erik Wikingsson, Martin Andrae, Tomas Landelius +1
Flow- and diffusion-based generative models have recently emerged as flexible and highly efficient forecasting models for dynamical systems. When combined with inference-time guida…
DAISI: Data Assimilation with Inverse Sampling using Stochastic Interpolants
Martin Andrae, Erik Wikingsson, So Takao +2
Data assimilation (DA) is a cornerstone of scientific and engineering applications, combining model forecasts with sparse and noisy observations to estimate latent system states. C…
CRPS-LAM: Probabilistic Regional Weather Forecasting with Continuous Ranked Probability Score
Erik Larsson, Joel Oskarsson, Tomas Landelius +1
Limited-Area Models (LAMs) enable weather forecasting over regional domains at higher resolutions than what is computationally feasible for global models. At such high resolutions,…
Building Machine Learning Limited Area Models: Kilometer-Scale Weather Forecasting in Realistic Settings
Simon Adamov, Joel Oskarsson, Leif Denby +8
Machine learning is revolutionizing global weather forecasting, with models that efficiently produce highly accurate forecasts. Apart from global forecasting there is also a large…
Diffusion-LAM: Probabilistic Limited Area Weather Forecasting with Diffusion
Erik Larsson, Joel Oskarsson, Tomas Landelius +1
Machine learning methods have been shown to be effective for weather forecasting, based on the speed and accuracy compared to traditional numerical models. While early efforts prim…
Continuous Ensemble Weather Forecasting with Diffusion models
Martin Andrae, Tomas Landelius, Joel Oskarsson +1
Weather forecasting has seen a shift in methods from numerical simulations to data-driven systems. While initial research in the area focused on deterministic forecasting, recent w…
Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks
Joel Oskarsson, Tomas Landelius, Marc Peter Deisenroth +1
In recent years, machine learning has established itself as a powerful tool for high-resolution weather forecasting. While most current machine learning models focus on determinist…
Graph-based Neural Weather Prediction for Limited Area Modeling
Joel Oskarsson, Tomas Landelius, Fredrik Lindsten
The rise of accurate machine learning methods for weather forecasting is creating radical new possibilities for modeling the atmosphere. In the time of climate change, having acces…