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STIPP: Space-time in situ postprocessing over the French Alps using proper scoring rules

arXiv 2601.02882 ↗First posted 6 Jan 2026physics.ao-ph cs.LGcited by 0

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Abstract (arXiv)

We propose Space-time in situ postprocessing (STIPP), a machine learning model that generates spatio-temporally consistent weather forecasts for a network of station locations. Gridded forecasts from classical numerical weather prediction or data-driven models often lack the necessary precision due to unresolved local effects. Typical statistical postprocessing methods correct these biases, but often degrade spatio-temporal correlation structures in doing so. Recent works based on generative modeling successfully improve spatial correlation structures but have to forecast every lead time independently. In contrast, STIPP makes joint spatio-temporal forecasts which have increased accuracy for surface temperature, wind, relative humidity and precipitation when compared to baseline methods. It makes hourly ensemble predictions given only a six-hourly deterministic forecast, blending the boundaries of postprocessing and temporal interpolation. By leveraging a multivariate proper scoring rule for training, STIPP contributes to ongoing work data-driven atmospheric models supervised only with distribution marginals.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 41 references that are in the map. First posted on arXiv 6 Jan 2026.

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18 of 41 references.

References · 18

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2017Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learningcited by 935
  4. 2022FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operatorscited by 188
  5. 2022Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecastcited by 164
  6. 2018Neural networks for post-processing ensemble weather forecastscited by 95
  7. 2023Deep Learning for Day Forecasts from Sparse Observationscited by 70
  8. 2025Skillful joint probabilistic weather forecasting from marginalscited by 53
  9. 2024ArchesWeather & ArchesWeatherGen: a deterministic and generative model for efficient ML weather forecastingcited by 29
  10. 2022Generative machine learning methods for multivariate ensemble post-processingcited by 26
  11. 2013Uncertainty Quantification in Complex Simulation Models Using Ensemble Copula Couplingcited by 21
  12. 2023Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Contextcited by 13
6 more
  1. 2025CRPS-LAM: Probabilistic Regional Weather Forecasting with Continuous Ranked Probability Scorecited by 6
  2. 2024Graph Neural Networks and Spatial Information Learning for Post-Processing Ensemble Weather Forecastscited by 4
  3. 2014Joint probabilistic forecasting of wind speed and temperature using Bayesian model averagingcited by 3
  4. 2025Generating ensembles of spatially-coherent in-situ forecasts using flow matchingcited by 3
  5. 2025A Composite-Loss Graph Neural Network for the Multivariate Post-Processing of Ensemble Weather Forecastscited by 3
  6. 2025SolarCrossFormer: Improving day-ahead Solar Irradiance Forecasting by Integrating Satellite Imagery and Ground Sensorscited by 2

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