Papers for

meteorological forecast teams

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.

Regional weather forecasts improved by aligning global and local models

Learning Kilometer-Scale Weather Prediction with Global-Regional Alignment

Abstract: Kilometer-scale regional weather forecasting is essential for local weather warnings and weather-sensitive decisions. Existing data-driven approaches often rely on numerical forecasts for large-scale guidance or require additional training of global forecasting components. Pretrained global weather models offer an efficient source of large-scale forecasts, motivating their reuse to guide high-resolution regional prediction. However, this coupling requires aligning global and regional representations across different grids and integrating global guidance with local interactions to advance regional states. We propose ScaleCast, a regional forecasting framework that addresses these challenges through Global-Regional Alignment. Its Global-Regional Conversion module aligns joint global and regional representations with regional locations, while the Global-Regional Alignment and Dynamics block combines aligned guidance with regional neighborhood interactions. Experiments using ERA5 global analyses on a 0.25-degree grid and CERRA regional reanalysis at 5.5 km spacing demonstrate improved regional forecasts across surface and upper-air variables, with a single trained model supporting multiple global forecast drivers (i.e., Pangu-Weather, GraphCast, and HRES) without specific retraining. Fine-tuning on HRRR at 3 km spacing further demonstrates the framework's adaptability to a different regional domain and spatial resolution. Windstorm case studies show improved cyclone positioning and core-pressure estimates, while comparisons with HadISD station observations show closer agreement with local temperature and humidity changes.

Thu 8 OctMachine Learning
The gist
Making weather forecasts accurate at a small, local scale is important for things like warnings about storms. The authors developed a new method called ScaleCast that combines big-picture (global) weather information with detailed local data. They created ways to match and merge these two types of weather information so the local forecasts become better. Tests showed ScaleCast can work with different global weather models and can adapt to various local regions and resolutions. This leads to more accurate predictions of things like windstorms and local temperature changes.
Open → 2610.12401v1

Non-Gaussian smoothing improves data assimilation in complex simulators

PR-Smoother: Simulator-Preserving Non-Gaussian Smoothing for Data Assimilation

Abstract: Many physical data assimilation (DA) workflows require smoothing methods that represent non-Gaussian posteriors over physical state variables, scale to high-dimensional simulators, train from observation windows alone, and remain compatible with calibration of the prescribed simulator. We introduce PR-Smoother, a simulator-preserving amortized smoother designed for this prescribed-simulator DA regime. Its key design principle is to keep the prescribed simulator explicit in both the evidence lower bound and the variational family: rather than learning replacement dynamics or a learned trajectory prior, PR-Smoother learns only future-conditioned corrections around the prescribed rollout. This yields an explicit non-Gaussian smoothing distribution over physical trajectories and supports joint state, parameter, and sensor-bias learning from observations alone. The variational family contains the exact smoother in deterministic and linear-Gaussian limits. Empirically, PR-Smoother captures multimodal posteriors in 4-dimensional Lorenz-96, remains accurate under ambiguous nonlinear observations and process noise in 40-dimensional Lorenz-96, and scales to joint state-parameter-bias inference in 16,384-dimensional Kolmogorov flow.

Tue 22 SeptMachine Learning
The gist
Data assimilation is a way to combine physical models with real-world observations to better understand systems like weather or fluid flow. Traditional methods often assume simple (Gaussian) errors and struggle with complex, high-dimensional systems. The authors propose PR-Smoother, a method that keeps the original simulator intact but learns corrections to better capture uncertain, complicated outcomes. This method can handle tricky cases where observations are ambiguous and works well even for very large systems.
Open → 2609.26890v1