RainPro-8: An Efficient Deep Learning Model to Estimate Rainfall Probabilities Over 8 Hours

• URL: http://arxiv.org/abs/2505.10271v1
• Date: Thu, 15 May 2025 13:22:20 GMT
• Title: RainPro-8: An Efficient Deep Learning Model to Estimate Rainfall Probabilities Over 8 Hours
• Authors: Rafael Pablos Sarabia, Joachim Nyborg, Morten Birk, Jeppe Liborius Sjørup, Anders Lillevang Vesterholt, Ira Assent,
• Abstract summary: We present a deep learning model for high-resolution probabilistic precipitation forecasting over an 8-hour horizon in Europe.<n>Our model efficiently integrates multiple data sources - including radar, satellite, and physics-based numerical weather prediction (NWP) - while capturing long-range interactions.
• Score: 4.726419619132143
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a deep learning model for high-resolution probabilistic precipitation forecasting over an 8-hour horizon in Europe, overcoming the limitations of radar-only deep learning models with short forecast lead times. Our model efficiently integrates multiple data sources - including radar, satellite, and physics-based numerical weather prediction (NWP) - while capturing long-range interactions, resulting in accurate forecasts with robust uncertainty quantification through consistent probabilistic maps. Featuring a compact architecture, it enables more efficient training and faster inference than existing models. Extensive experiments demonstrate that our model surpasses current operational NWP systems, extrapolation-based methods, and deep-learning nowcasting models, setting a new standard for high-resolution precipitation forecasting in Europe, ensuring a balance between accuracy, interpretability, and computational efficiency.

Related papers

• FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale [91.84761739154366]
  FourCastNet 3 advances global weather modeling by implementing a scalable, geometric machine learning (ML) approach to probabilistic ensemble forecasting.<n>FourCastNet 3 delivers forecasting accuracy that surpasses leading conventional ensemble models and rivals the best diffusion-based methods.<n>Its computational efficiency, medium-range probabilistic skill, spectral fidelity, and rollout stability at subseasonal timescales make it a strong candidate for improving meteorological forecasting and early warning systems through large ensemble predictions.
  arXiv  Detail & Related papers  (2025-07-16T11:22:18Z)
• Skillful joint probabilistic weather forecasting from marginals [11.348323146521931]
  This paper presents FGN, a simple, scalable and flexible modeling approach which significantly outperforms the current state-of-the-art models.<n>It is trained directly to minimize the continuous rank probability score (CRPS) of per-location forecasts.<n>It produces state-of-the-art ensemble forecasts as measured by a range of deterministic and probabilistic metrics.
  arXiv  Detail & Related papers  (2025-06-12T14:50:47Z)
• Multi-Source Temporal Attention Network for Precipitation Nowcasting [4.726419619132143]
  Precipitation nowcasting is crucial across various industries and plays a significant role in mitigating and adapting to climate change.<n>We introduce an efficient deep learning model for precipitation nowcasting, capable of predicting rainfall up to 8 hours in advance with greater accuracy than existing operational models.
  arXiv  Detail & Related papers  (2024-10-11T09:09:07Z)
• Weather Prediction with Diffusion Guided by Realistic Forecast Processes [49.07556359513563]
  We introduce a novel method that applies diffusion models (DM) for weather forecasting. Our method can achieve both direct and iterative forecasting with the same modeling framework. The flexibility and controllability of our model empowers a more trustworthy DL system for the general weather community.
  arXiv  Detail & Related papers  (2024-02-06T21:28:42Z)
• ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [57.6987191099507]
  We introduce Exloss, a novel loss function that performs asymmetric optimization and highlights extreme values to obtain accurate extreme weather forecast. We also introduce ExBooster, which captures the uncertainty in prediction outcomes by employing multiple random samples. Our solution can achieve state-of-the-art performance in extreme weather prediction, while maintaining the overall forecast accuracy comparable to the top medium-range forecast models.
  arXiv  Detail & Related papers  (2024-02-02T10:34:13Z)
• Learning Robust Precipitation Forecaster by Temporal Frame Interpolation [65.5045412005064]
  We develop a robust precipitation forecasting model that demonstrates resilience against spatial-temporal discrepancies. Our approach has led to significant improvements in forecasting precision, culminating in our model securing textit1st place in the transfer learning leaderboard of the textitWeather4cast'23 competition.
  arXiv  Detail & Related papers  (2023-11-30T08:22:08Z)
• Deep Learning for Day Forecasts from Sparse Observations [60.041805328514876]
  Deep neural networks offer an alternative paradigm for modeling weather conditions. MetNet-3 learns from both dense and sparse data sensors and makes predictions up to 24 hours ahead for precipitation, wind, temperature and dew point. MetNet-3 has a high temporal and spatial resolution, respectively, up to 2 minutes and 1 km as well as a low operational latency.
  arXiv  Detail & Related papers  (2023-06-06T07:07:54Z)
• Data-Based Models for Hurricane Evolution Prediction: A Deep Learning Approach [0.0]
  Many-to-Many RNN storm trajectory prediction models presented here are significantly faster than ensemble models used by the NHC. A detailed analysis of model forecasting error shows that Many-To-One prediction models are less accurate than Many-To-Many models owing to compounded error accumulation.
  arXiv  Detail & Related papers  (2021-10-30T00:31:48Z)
• Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting [98.57851612518758]
  Probabilistic time series forecasting involves estimating the distribution of future based on its history. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks. We show in experiments that our model produces accurate and sharp probabilistic forecasts.
  arXiv  Detail & Related papers  (2021-01-31T06:49:33Z)
• SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecture [5.28539620288341]
  We show that it is possible to produce an accurate precipitation nowcast using a data-driven neural network approach. We evaluate our approaches on a real-life datasets using precipitation maps from the region of the Netherlands and binary images of cloud coverage of France.
  arXiv  Detail & Related papers  (2020-07-08T20:33:10Z)
• A framework for probabilistic weather forecast post-processing across models and lead times using machine learning [3.1542695050861544]
  We show how to bridge the gap between sets of separate forecasts from NWP models and the 'ideal' forecast for decision support. We use Quantile Regression Forests to learn the error profile of each numerical model, and use these to apply empirically-derived probability distributions to forecasts. Second, we combine these probabilistic forecasts using quantile averaging. Third, we interpolate between the aggregate quantiles in order to generate a full predictive distribution.
  arXiv  Detail & Related papers  (2020-05-06T16:46:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.