Papers for

regional weather forecasters

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Efficient high resolution heat mapping cuts climate simulation costs

Less is more: error-distance scaling relation for data-efficient kilometer-scale downscaling of extreme heat

Abstract: Extreme heat is where urban adaptation needs kilometer-scale data the most, but the simulations training a downscaler can cost more than they save, and how much is needed has not been identified. We measured it with CASPER, a U-Net with a structure-preserving loss downscaling 32 km reanalysis to 1 km temperature, humidity and wind, across 24 configurations of one to eight months. Held-out error grows linearly with climatological distance to the training data, RMSE = 0.83 + 2.95 d, explaining 90% of its variance against 7% for volume and predicting unseen months in advance. On held-out extreme summer weeks CASPER preserves the fine-scale structure and cross-variable physics that matched-budget baselines degrade, and matches station observations during documented heat waves to within 1.8 K. Transfer to a new region degrades geographically; 11 days of local simulation cuts Vancouver's held-out error from 3.8 to 1.3 K. Training periods should span the target climate: the same accuracy for four times less simulation, putting kilometer-scale downscaling of extreme heat within reach of groups without large computing facilities.

Wed 30 SeptMachine Learning
The gist
Predicting extreme heat in cities requires detailed weather data at very small scales, which usually needs expensive simulations. The authors developed a method called CASPER that uses a type of neural network to convert low-resolution climate data into high-resolution temperature and wind maps. They found that the quality of predictions depends on how close the training data climate is to the target climate, and training can be done with much less simulation than expected. This makes detailed heat mapping accessible without huge computing resources, helping cities better prepare for heatwaves.
Open → 2609.40140v1

West-WRF AI model achieves detailed 2-km weather forecasts for western US

West-WRF AI 2-km: High-Resolution Prediction of Integrated Vapor Transport and Precipitation

Abstract: We introduce a stretched-grid artificial intelligence (AI) weather forecasting model with 2-km resolution over the western United States and part of the Northeast Pacific and approximately 31-km resolution elsewhere globally. Forecasting over the western U.S. is challenging because complex topography and atmospheric rivers (ARs) strongly influence orographic precipitation. West-WRF AI 2-km builds on a global model pretrained with a 40-year European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) dataset and is fine-tuned with the Center for Western Weather and Water Extremes (CW3E) 2-km regional reanalysis to produce autoregressive 6-hourly forecasts of precipitation and integrated vapor transport (IVT). Forecasts are evaluated over winters 2020-2023 using gridded precipitation observations, rain gauges, and AR Reconnaissance dropsondes and are benchmarked against coarser-resolution AI forecasts and regional and global numerical weather prediction (NWP) systems. West-WRF AI 2-km reproduces observed precipitation-intensity distributions, retains fine-scale spectral variability, and produces sharper narrow coastal precipitation bands and localized, terrain-sensitive extremes. Its broader-scale performance remains comparable to coarser-resolution configurations while preserving large-scale skill despite higher resolution. Dropsonde verification shows lower errors and improved categorical skill at the most extreme IVT threshold. Overall, West-WRF AI 2-km provides its greatest value for localized precipitation extremes and intense AR-related moisture transport.

Tue 22 SeptArtificial Intelligence
The gist
Predicting weather accurately in the western U.S. is tough because of mountains and special weather patterns called atmospheric rivers. The authors created an AI model called West-WRF AI 2-km that makes very detailed forecasts of rain and moisture flow every six hours. They trained it on big sets of past weather data and then improved it using high-resolution regional data. Testing showed the model does a better job capturing localized, intense rain and moisture than less detailed models. This improvement can help with understanding and preparing for heavy rain and flooding events.
Open → 2609.25512v1