RainPro-8: An Efficient Deep Learning Model to Estimate Rainfall Probabilities Over 8 Hours
Rafael Pablos Sarabia, Joachim Nyborg, Morten Birk, Jeppe Liborius Sj{\o}rup, Anders Lillevang Vesterholt, Ira Assent
TL;DR
RainPro-8 is a deep learning model that combines multiple data sources to provide accurate, high-resolution probabilistic rainfall forecasts over 8 hours in Europe, outperforming existing systems in accuracy and efficiency.
Contribution
The paper introduces a novel deep learning architecture that integrates radar, satellite, and NWP data for long-range probabilistic precipitation forecasting, surpassing current models in accuracy and speed.
Findings
Outperforms operational NWP systems and existing deep-learning models
Provides robust uncertainty quantification with probabilistic maps
Enables faster inference with a compact architecture
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,…
Peer Reviews
Decision·ICLR 2026 Poster
1. The paper addresses the challenge of 8-hour, high-resolution probabilistic precipitation forecasting. This is a critical and difficult task that bridges the gap between traditional nowcasting and medium-range forecasting. 2. The usage of Ordinal Consistent Loss models the conditional probability of exceeding intensity thresholds, is designed to explicitly account for the ordinal structure of precipitation classes , which is a more principled approach than using a standard cross-entropy loss t
1. **Limited Methodological Novelty (*Main Weakness*)**: The primary weakness of this paper lies in its limited methodological novelty relative to ICLR standards, which emphasize fundamental advances in learning representations. - Aside from the new loss function, the work's primary contribution is an application of existing techniques to create an efficient system. - The model architecture is just based on MetNet-3. The main architectural changes include *early downsampling* and *halving intern
The paper covers an important topic, precipitation forecasting. Originality: the paper leverages multi-source prediction and claims prediction of up to 8 hours (however, see the weaknesses below) Quality: the paper is well-written in general (however, there are caveats as some parts of the paper are difficult to follow). The equations, as I checked, are correct
Clarity and quality: see questions below Significance: I think the authors need to clarify on this point. The contributions cite the following: - *Efficient architecture and training strategy*: however, the architecture seems to be a well-parameterised UNet-based model (Figure 1). The authors state: "Key differences include single-pass predictions without lead time conditioning (Section 3.3), early downsampling in the encoder, halving internal channels, and removing topographical embeddings,
RainPro-8 efficiently combines heterogeneous multi-source data, achieves multi-step probabilistic forecasting in one pass with much fewer parameters than strong baselines, and its ordinal consistent loss is specifically designed for the nature of precipitation bins. The model consistently outperforms state-of-the-art competitors on accuracy, efficiency, and uncertainty quantification.
The “ordinal consistent loss” (Section 3.2) is a core novelty, but its theoretical justification is limited. The paper gives only a high-level description and refers to another field (semantic segmentation) for motivation. There is no theoretical or empirical exploration of why ordinal modeling is essential for probabilistic precipitation. Ablation is very basic, and critical aspects such as loss stability, parameter sensitivity, and generalization are not deeply examined. While RainPro-8 claim
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Taxonomy
TopicsPrecipitation Measurement and Analysis · Flood Risk Assessment and Management · Hydrological Forecasting Using AI