A new arXiv paper explores the effectiveness of various statistical and machine learning methods for interpolating weather forecast data at unobserved locations. The study, which focused on 2-m temperature and 10-m wind speed forecasts from the European Centre for Medium-Range Weather Forecasts in Germany, compared traditional statistical approaches with advanced techniques like distributional regression networks, transformers, and graph neural networks. While post-processing generally improved forecast accuracy, no single method consistently outperformed others across all scenarios, though a proposed altitude-aware linear pool showed a slight improvement for temperature at unobserved sites. AI
IMPACT This research contributes to improving the accuracy of weather forecasts in data-sparse regions by evaluating advanced machine learning techniques.
RANK_REASON The cluster contains a research paper published on arXiv detailing a comparison of statistical and machine learning methods for weather forecasting. [lever_c_demoted from research: ic=1 ai=0.7]
- arXiv
- distributional regression networks
- EMOS
- European Centre for Medium-Range Weather Forecasts
- Germany
- graph neural networks
- transformers
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