REVIEW 6 cited by
Building Machine Learning Limited Area Models: Kilometer-Scale Weather Forecasting in Realistic Settings
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Machine learning is revolutionizing global weather forecasting, with models that efficiently produce highly accurate forecasts. Apart from global forecasting there is also a large value in high-resolution regional weather forecasts, focusing on accurate simulations of the atmosphere for a limited area. Initial attempts have been made to use machine learning for such limited area scenarios, but these experiments do not consider realistic forecasting settings and do not investigate the many design choices involved. We present a framework for building kilometer-scale machine learning limited area models with boundary conditions imposed through a flexible boundary forcing method. This enables boundary conditions defined either from reanalysis or operational forecast data. Our approach employs specialized graph constructions with rectangular and triangular meshes, along with multi-step rollout training strategies to improve temporal consistency. We perform systematic evaluation of different design choices, including the boundary width, graph construction and boundary forcing integration. Models are evaluated across both a Danish and a Swiss domain, two regions that exhibit different orographical characteristics. Verification is performed against both gridded analysis data and in-situ observations, including a case study for the storm Ciara in February 2020. Both models achieve skillful predictions across a wide range of variables, with our Swiss model outperforming the numerical weather prediction baseline for key surface variables. With their substantially lower computational cost, our findings demonstrate great potential for machine learning limited area models in the future of regional weather forecasting.
Forward citations
Cited by 6 Pith papers
-
Accurate Mediterranean Sea forecasting via graph-based deep learning
SeaCast, a graph neural network, makes 15-day Mediterranean Sea forecasts that outperform the operational MedFS system over the evaluated period, while producing a forecast in 20 seconds on one GPU.
-
A comparison of stretched-grid and limited-area modelling for data-driven regional weather forecasting
Stretched-grid and limited-area machine-learning weather models are competitive for Europe, with stretched-grid models showing better generalization to unseen forecast times and limited-area models benefiting from ext...
-
HRRRCast: a data-driven emulator for regional weather forecasting at convection allowing scales
A diffusion-based neural network trained on HRRR analysis beats HRRR forecast skill on 20 dBZ composite reflectivity across CONUS and is competitive at 30 dBZ.
-
Finetuning a Weather Foundation Model with Lightweight Decoders for Unseen Physical Processes
A frozen weather foundation model's latent space can be decoded by a small MLP to predict unseen hydrological variables, with accuracy and efficiency strongly favoring this lightweight approach over full fine-tuning.
-
Evaluating Extreme Precipitation Forecasts: A Threshold-Weighted, Spatial Verification Approach for Comparing an AI Weather Prediction Model Against a High-Resolution NWP Model
Combining HiRA neighborhood verification with threshold-weighted CRPS shows that AI-vs-NWP rankings for extreme precipitation depend strongly on neighborhood size.
-
Machine Learning (ML)-Physics Fusion Model Outperforms Both Physics-Only and ML-Only Models in Typhoon Predictions
A hybrid model that nudges machine-learning forecasts into a physics-based typhoon model reduces track and intensity errors compared to either approach alone in a full-season evaluation.
Discussion (0). Continue with ORCID to comment.