Papers for

meteorological service 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.

Model improves weather forecasts by combining station errors and locations

WxFM-XL: Adapting Univariate Foundation Models to Multi-Station Weather Forecasting

Abstract: With the rise of univariate time series foundation models (e.g., Sundial, Timer), initial efforts have been made to extend them to multivariate settings. However, these models mainly focus on modeling correlations among variables. When they are applied to multi-station weather forecasting, two important factors are often overlooked: (1) the spatial information of stations, and (2) different error priors of different stations relative to the foundation model. In this paper, we propose WxFM-XL, a model for adapting univariate time series foundation models to multi-station weather forecasting. WxFM-XL introduces a cross-station error correlation prior graph to capture stationwise error priors with respect to the foundation model. Building on this, we further propose a dynamic fusion mechanism that adaptively integrates a spatial correlation graph with the error correlation prior graph. Experiments on multiple datasets demonstrate that our model outperforms state of the art baselines.

Wed 7 OctMachine Learning

The gist

Weather forecasts often use models that predict the weather based on data from one spot at a time. The authors found that these models miss two important things when predicting across many weather stations: the distances between stations and how prediction mistakes vary by location. They made a new model called WxFM-XL that connects these ideas by using graphs that show relationships between stations and their typical forecast errors. This new model helps make better weather predictions over multiple locations than previous methods.

Open → 2610.10057v1