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
Venue: ICLR 2026 Poster (2026)
Keywords: Precipitation Forecasting
Probabilistic Forecasting
High-Resolution Forecasting
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: We introduce a deep learning model for high-resolution probabilistic precipitation forecasting in Europe, integrating radar, satellite, and NWP data to improve accuracy, uncertainty quantification, and computational efficiency over 8-hour forecasts.

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.

Published as a conference paper at ICLR 2026
RAINPRO-8: AN EFFICIENT DEEP LEARNING MODEL TO ESTIMATE RAINFALL PROBABILITIES OVER 8 HOURS
Rafael Pablos Sarabia Aarhus University & Cordulus rpablos@cs. au. dk
Joachim Nyborg
Morten Birk Cordulus mbC mbfcordulus.cm com
Cordulus
jn@cordulus.com
Jeppe Liborius Sjørup Cordulus jls@cordulus.com
Anders Lillevang Vesterholt Cordulus
Ira Assent
Aarhus University i irařcs au dk
al alv@cordulus.com
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 nu- merical weather prediction (NWP) while capturing long-range interactions, re- sulting in accurate forecasts with robust uncertainty quantification through consis- tent probabilistic maps. Featuring compact architecture, it enables more efficient training and faster inference than existing models. Extensive experiments demon- strate 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 balance be- tween accuracy, interpretability, and computational efficiency. Code is available at hhttss://g com wwwm:/aaaaabll//aaai..o. R
INTRODUCTION
Recent advances in artificial intelligence have generated significant interest in deep learning for weather forecasting (Rasp et al. 2024 An et al. 2024). Deep learning excels at handling complex, large-scale, high-dimensional data, making it well-suited for capturing intricate, nonlinear patterns in spatio-temporal systems (Manzhu Yu & Li 2024). Although deep learning has achieved remark- able success in both nowcasting (Gao et al. 2024bb G024 et al. (024) and medium-range forecasting (Lam et al.] 2023; Price et al., (024)) significant challenges remain. Deep learning models for now- casting often limited very short lead times (up
hours). In contrast, medium-range models, which predict broader atmospheric dynamics for up to 10 days. typically operate at coarser resolutions and are influenced by precipitation-specine biases in the training datasets (Lavers al., 2022). As a result, they struggle to capture small-scale precipitation features, like local showers, often leading to the exclusion precipitation forecasts in medium-range models (Lam al. 2023).
This work addresses the challenge of forecasting precipitation for up to 8 hours at high spatio- temporal resolutions, bridging the gap between nowcasting and medium-rann forecasting. Fore- casting over an 8-hour horizon is critical for timely predictions that help mitigate risks like flooding and optimize resource management in agriculture, energy, or transportation. This task is particularly difficult due to the stochastic and sparse nature of precipitation, especially over extended lead times. Designing a deep learning model for this task requires addressing skewed and intermittent precipi- tation distributions, incorporating multi-sensor data, and ensuring robust uncertainty quantification.
Most existing deep learning- basedd nowcasting methods focus on generating radar-like precipitation forecasts (Ravuri et al. 202 Gao et al. 2024b), but these become increasingly challenging and less accurate with longer lead times. Instead, probabilistic models emphasize uncertainty estimation and provide more accurate and practical representation of rainfall varying intensities. The MetNet

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family of models (Senderby al. 2020 Espeholt al. 2022 Andrychowicz et al. 2023) repre-
sents state- of-te-of-the-art deep learning- baaed systems for probabilistic precipitation forecasting. These models produce high-resolution forecasts for 8-24 hours in the United States, outperforming oper- ational NWP systems. They achieve efficient probabilistic forecasting using cross-entropy loss on precipitation bins, requiring only one forward pass rather than an ensemble. However, this approach neglects the ordinality between bins. In addition, they rely on lead time conditioning, where the model generates forecasts for only one lead time at time. While this approach is fully paralleliz- able across multiple GPUs, it significantly increases inference computational demands. Moreover, training MetNet requires substantial computational resources and is limited the United States.
We propose RainPro-8, an efficient deep learning model to estimate rainfall probabilities over 8 hours by determining the probability of different levels of precipitation at a given location and time. RainPro-8 is an efficient model based on MetNet-3 (Andrychowicz et al. 2023) that uses less than 20% of MetNet-3's training parameters to achieve 8-hour high-resolution precipitation forecasting across Europe precipitation dynamics (Ehmele et al. 2020). Its training objective explicitly accounts for the ordi- nality between precipitation bins. Lastly, demonstrates that it is possible to generate all lead times simultaneously by downweighing later lead times during training, greatly enhancing inference ef- ficiency and improving temporal consistency. RainPro-8 model shows substantial improvements over traditional precipitation forecasting methods, including NWP models and extrapolation-be techniques, as well as deep-lcarning nowcasting models, across various rain intensities for the next 8 hours. Evaluation on the widely used SEVIR (Veillette et al. 2020) benchmark also demonstrates strong and robust performance beyond 8-hour multi-source tasks. Our main contributions include:
region characterized by diverse climates, complex terrain, and highly variable
We propose an efficient neural architecture and training strategy designed to integrate multi- source data with varying temporal and spatial resolutions. The model produces consistent probabilistic predictions across all forecast lead times in a single forward pass, enabling efficient and improved forecasts.
We conduct an extensive empirical evaluation demonstrating that RainPro-8 outperforms existing operational forecasting methods by 65% and achieves significant improvements over state-of-the-an deep learning nowcasting models. We further provide comprehensive ablation studies and model attribution analysis quantify the impact of key design choices and input modalities.
We demonstrate the versatility of RainPro by adapting it to radar-only 2-hour precipita- tion prediction on the SEVIR benchmark, where it achieves sfate-of-the-ar performance compared to both deterministic and generative noweasting approaches.
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RELATED WORK
Traditional methods for weather forecasting are mostly numerical weather prediction (NWP) mod- els, which simulate atmospheric dynamics using mathematical equations, also as ensembles over multiple simulations (Toth & Kalnay] 1997). NWP models demand significant compute, especially for ensembles, which restricts their spatial and temporal resolution. Their spin-up time results in poor performance for short lead times (Ma et al. [021). Extrapolation-basel methods, e.g. PyS- TEPS (Pulkkinen al., 2019), RainyMotion (Ayzel al., 2019) are limited reduced forecast lengths due to their assumptions on constant motion and intensity (van Nooten et al. 2023).
Deep learning formulates precipitation nowcasting as spatio-temporal prediction (An al. 2024
Approaches in this area include the use of convolutions in recurrent networks (Shi et al.
2015
2017b| Wang et al. 2023b Ma et al. 2024), , the U-Net architecture (Ayzel et al. 2020 Fernández & Mehrkanoon] 2021: ZhangZhang et al.
Trebing et al. 2021). transformers (Gao et al.
2024b] Wu
ct al. 2024), diffusion models (Leinonen et al. [023 Gao et al. 22244a] Yu et al. 20244] Gong et al. 2024.. and adversarial training (Ravuri et al. 2021). Some work improves loss functions to tackle data imbalance, blurriness, and sparsity in precipitation nowcasting models (Xu et al. 2024a Ko
et al. 2022 Yan et al.) 2024). Still, most efforts focus short-term forecasts (1 to hours) using only radar, where reduced lead time eliminates the need for multiple data sources, large contexts, or accounting for higher uncertainty associated with h-hour predictions.
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Limited work exists for lead times beyond 3 hours. The Weather4Cast competition (Gruca al. 2022) tackles 8-hour precipitation forecasting from satellite alone, but it has shown limited success compared to traditional methods (Li 2023) due to challenges in estimating current precipitation from satellite imagery and resolution issues compared to radar. For 6-hour precipitation nowcasting, NPM (Park et al. 2024) relies solely on satellite data without data fusion, while Nowcast-to- to--eccatt An (023) combines satellite and radar but is limited by a biased rainy-day dataset. Kim et al. (2024) only compare hourly forecasts extrapolation baselines, which are ineffective for longer-term pre- dictions. TAFFNet (Wang et al., 2023a) provides 12-hour predictions but neglects uncertainty and relies on radar and NWP data equal resolutions not always available in practice.
M (Andrychowicz et al. 2023) ) is a leading deep learning model for precipitation forecasting, delivering 24-hour forecasts with high spatio-temporal resolution in the U.S. by integrating radar, weather station, satellite imagery, assimilated weather states, and other data in transformer-based architecture. However, its training requires significant time and resources, involving hundreds of Tensor Processing Units (TPUs) for multiple days. Its reliance high-quality data available only for the contiguous U.S. and the lack of public access to its code and data restrict its broader use. In this work, we show how adapt its approach to problem formulation, data preprocessing, and training optimization for smaller and more efficient models and specific data requirements.
Medium-range forecasting models like GraphCast (Lam al. 2023), Pangu-Weather Weather (Bi et al. 2023), or GenCast (Price et al. 2024) rely on reanalysis datasets like FRAS (Hershach et al. 2020).
which suffer from low resolution, delays, and biases, especially in surface variables and precipitation
(Lavers et al. 2022). Precipitation, highly variable and challenging to predict, is underrepresented in such models (Rasp et al., 2024), with most failing to integrate critical observational data like radar. While some models, like NeuralGCM (Yuval et al. 2024), incorporate global precipitation predic- tions, they lack the high resolution and operational frequency needed for our setting. Other work (Xu et al. 2024b) has tried to address the gap between nowcasting and medium- -aange forecasting, but model performance drops at high temporal resolutions and focuses only on low precipitation.
RAINPRO-8 METHOD
3.1 PROBABILISTIC PRECIPITATION FORECASTING
Precipitation forecasting is spatiotemporal prediction problem, where the goal is to predict Tout future radar frames Y based on a sequence of Tin past radar frames X:
X [ 00
T- T.- T.-IERTTXHHW
(1)
y = R THe E RTx
where R₂ represents the precipitation intensity at timestep based on radar maps of size H W.
Radar data alone is insufficient for accurate precipitation forecasting over 8 hours because its ground-based systems limit its coverage and cannot capture atmospheric conditions beyond wa- ter vapor. Additional data sources like satellite or NWP offer broader coverage and more com- prehensive representation of the atmosphere. Such additional sources introduce challenges due to differences in temporal frequency and spatial resolution, requiring careful preprocessing and align- The generalized input X, with heterogenous sources Sources,
ment as detailed in Appendix A given by:
x= X=U [S
SESources
where S, E RC.xH,xW, represents frame from the data source S, characterized by specific number of channels, spatial dimensions, and resolution.
In addition to spatiotemporal accuracy, a critical priority are probabilistic forecasts: What is the probability of a specific amount of rainfall at given location and time? Commonly, quantifying uncertainty involves generating multiple forecasts for ensembles to calculate probabilities, suffering from substantial computational demands. Instead of ensemble methods, our approach directly pre- dicts the probability distribution of precipitation intensities, similar to MetNet 1-3 (Sonderby al.) 2020 Espphollt et al. 2022 Andrychowicz et al. 2023). Our goal is to generate accurate probability maps capture uncertainty and variability in precipitation patterns, instead of radar-like outputs,
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which become less reliable over longer lead times. To model probabilities for different precipitation intensity classes I, we redefine the target as probability maps for each precipitation intensity class:
The In
Y P ERT 1=1c=1
where / is the set of intensity classes that divides the possible precipitation intensities into ranges or bins, and Pt.c is the probability map for R, with respect class Ie.
In MetNet, Pt.c = P(Re E Le) predicts the probability of precipitation intensity within predefined ranges, trained under cross entropy loss. However, this approach ignores the intrinsic order of the intensity classes, which we address with our proposed Ordinal Consistent loss.
3.2 ORDINAL CONSISTENT LOSS
To generate probabilistic forecasts that preserve ordinality among precipitation classes, we model Pt. as P(R, 2 min(..). To ensure monotonicity (Pt.e < Pt,e-1). we reformulate Pt.e using Bayes' theorem, given that P(R, 2 min(Te-1) 2 min(Te) = 1, and redefine the model outputs:
Pt.e P(R 2 min(le))
P(R, 2 min(I.) R4 min(T P(R, 2 min(Ie-1)) P(R 2 min(4e-1) 2 min(l.)
(4)
P(Re 2 min(L.) R2 2 min(Ic-))) Pt,c1
Model output P(R, 2 min R, 2 min(I,-1)) for each intensity class guarantees monotonic
probabilities Pt.e that respect the inherent order of precipitation classes. For the lowest intensity class, the model outputs P(R, 2 min(Ti)). This approach, as in Fernandes & Cardoso (2018) for semantic segmentation, improves both interpretability and consistency in the generated forecasts. For any class, probability Pt.c is the cumulative product of model outputs for all preceding classes:
Pl.c = P(R, 2 min(I.) R 2 min(I x Pto--
II P(R. 2 (1)( Re 2 min(1z-1)) x P(Re 2 min(41)) j=2
(5)
The loss uses target binary masks indicating whether R2 > min((.) for each class and timestep. The loss between the target masks and predicted probabilities is Binary Cross Entropy (BCE) without re- duction to yield a per-pixel loss for each spatial location, class, and timestep. The ordinal consistent mask for every sample (R, 2 min(Tc-1)) ensures that for each intensity class c, the loss is averaged only over pixels where the previous class is activated, encouraging the model take advantage of class ordinality. Note that the loss is not averaged over prediction pixels that lack ground truth values (Re (h, w) -1), typically missing due to the limited coverage of radar data.
C
BCE(t, c,
where {(t, c, w) Re(h, w) 2 min(Ia-1)) (6)
S
(t,c,h,,)ES
3.3 SINGLE-PARE PREDICTIONS
Precipitation nowcasting becomes significantly harder lead times increase, which can negatively impact the model training. Auto-regressive models focus on one step at a time for stability but struggle when input sources differ from targets. MetNet tackles this with lead time conditioning, which supports multiple sources, but this approach significantly reduces inference efficiency and may cause temporal inconsistencies between lead times.
Instead, we make single-pas. predictions to generate all forecast timesteps in one forward pass, reducing resource requirements, accelerating training convergence and inference, and improving temporal consistency in the outputs. The model encodes timestamps into the channel dimension, with the output B(CC)WW HW reshaped BT H W. where T represents all timesteps.
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MetNet-3 uses lead time sampling for more frequent of samples with shorter lead times during training. In contrast, we propose lead time weights, where lead times are included in every training sample, but weighted in the loss function to reduce the impact of longer lead times. This ensures that the model still prioritizes earlier lead times during training, and improves performance across the entire range of lead times, including the more challenging longer ones. Thus, we multiply the pixel-wise loss function by lead time weight Wr, normalized from an exponential distribution with decay rate a, determining the relative weight of the first timestep over the last one.
weights exp(-a X
weights
weights
[
(8)
weights
W, weights norm [t]
(9)
weights
where weightsexp is the weights based exponential decay, weights is the normalized weights, and W, is the rescaled weights so the total loss scale remains consistent with the unweighted case.
3.4 ARCHITECTURE
We design our architecture to forecast precipitation for a 512 512km" patch. Assuming an average precipitation displacement rate of 1km per minute (Sønderby et al., 2020), use 512km of spatial context on every side of the target patch (1536*km* km²) to provide our model with enough input context for all 8-hours lead time. We use U-Net (Ronneberger et al. 2013) with Max ViT (Tu et al. 2022) blocks to efficiently process multi-resolution data (Figure D. similar to MetNet-3 (Andrychowiez ct al. 2023). Key differences include single-pass predictions without lead time conditioning (Section early downsampling in the encoder, halving internal channels, and removing topographical em embeddings, all contributing to a reduced parameter count of 36.7M from the original 227M.
resolution
sources 44m (102244ml) Pm "
Slack & Embed
un PJ
Center 766' km/
9
Concal
crop 512" um?
2
Downsample 2x
Upeample x2
Upsample x2 2xReeNet Layar
2x ResNet
resolution
2
2x ResNet
T-ah
eourcess Bkm
(15336 Pmm?
Stack Emibed
Center 768Pum?
Concal
9
6
5
Merge
resolution
Downssmple 2x
Upsample
sources 16km (1636%km²)
Eaaol T
Center 764
Output: with Outputii P(R; min(l]] R2 min()
Mergo
blocks
2km resolution
Figure 1: RainPro-8 architecture, optimized for reduced parameter count and efficiency, integrating multiple data sources of varying resolution for simultaneous prediction of all lead times.
The architecture begins with data fusion on the encoder, using Space-to-Depth convolutions (Sunkara Luo 2023) and ResNet blocks (He et al.) (0166). Input data is transformed from BTCHW to B and merged with sources at matching resolutions. To balance efficiency and performance, we only use the full input context of 512km at 8km and 16km inputs, and lower input context of 256km at 4km resolution. The encoder aggregates features for each resolution into low-resolution representation while preserving skip connections for the U-Net decoder. To improve memory efficiency, apply downsampling before ResNet blocks instead of after.
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The low-resolution representation is processed by 12 Max VIT blocks. Based on Vision Transformer Doosovitskiy et al.] 202I), they combine local neighborhood and global gridded attention aggre- gate information across the full 15362km² input patch. The decoder, using Transposed Convolu- tions (Long et al.) 2015) and ResNet blocks, reconstructs high-resolution probability maps from the MaxViT output, while leveraging encoder skip connections. All forecast timesteps and intensity classes generated by outputting P(R, min(Te) R, 2 min (I-)) (Section 3.2) in single forward pass with timestamps encoded into the channel dimension (Section (.3). For geographical alignment, representations
padded and cropped at each resolution. For example, 4km represen- tations are padded equally on all sides before concatenating with 8km inputs, and Max ViT output is cropped to the target region with extra context for upsampling, later cropped to 5m22km? for the final output.
4 EXPERIMENTS
RainPro-8 is implemented with PyTorch (Ansel et al. 2024) and PyTorch Lightnnig (Falcon & team 2025), trained for 100k steps using a batch size of 16, validating every 2,000 steps. Training uses static learning rate of 3g-4, AdamW optimizer with a weight decay of 0.1 and betas (0.9, 0.999), Exponential Moving Average (EMA) decay of 0.99975, dropout of 0.1, maximum stochastic depth (Huang et al. 2016) of 0.2, and lead time decay rate of 10. We use 256 channels throughout the entire network, totaling 36.7 million parameters. Any other network hyperparameters follow those of MetNet-3 (Andrychowicz et al. 2023). The model with the lowest validation loss is selected. Training is performed on NVIDIA HI00 SXM5 GPU and requires approximately 13 hours (cf. App. F
We use multiple data sources input to our precipitation forecasting model, each matched to its supported spatial resolutions. Rain Viewer radar composites serve as high-resolution ground truth at 4km and 8km resolutions to capture local detail and broader context. Satellite imagery from EUMETSAT provides Proovdde cloud-related features at 8km resolution, and NOAA'S provides atmo- spheric variables and precipitation forecasts at 16km resolution. Topographical information from Copernicus DEM'I is incorporated at 4km resolution. To ensure consistency across these heteroge- neous sources, all datasets are resampled achieve spatial and temporal alignment. They further undergo normalization, clipping, and binning to handle varying scales and precipitation skewness. The data cover one year and over one million samples, with defined training, validation, and testing splits to ensure reliable model evaluation. Details on data, preprocessing, and samples in App. A
raadar
We use Critical Success Index (CSI) (Schaefer 1990) at different thresholds, a standard accuracy metric in precipitation nowcasting (Andrychowicz.ed et al., 2023 Gao et al.) 2024b Gong et al.. 2022). and Continuous Ranked Probability Score (CRPS) (Hersbach (2000), which assesses alignment of predicted probability distribution with observed values, rewarding sharp and reliable predictions. Fractions Skill Score (FSS) (Roberts & Lean 2008) accounts for intensity shifts and offers tolerance to translation and deformation. Frequency Bias Index (FBI) (Termonia al.] (018)) quantifies over- and underforecasting, but not forecast quality. We also report Mean Absolute Error (MAE) and Mean Squared Error (MSE), but note that they sensitive high frequency of no-rain cases and thus less suitable for skewed distributions (Andrychowicz et al. 2023) (cf. App. [.T). Taking inspiration from the thresholding approach in MetNet (Espeholt al. 2022). intensity-based metries use the mean of the highest activated bucket of the cumulative output distribution. A bucket is activated if its predicted probability mass exceeds the corresponding threshold, where thresholds computed for each bucket and lead time using the validation set. This approach effectively captures rare high- precipptciittnnonnn events, which tend to have lower predicted probability masses.
http://////eeeeetttt.aaaaaaa///a generaiinooo http:///rggisryyooppattt..o

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Table 1: Performance (CSI, FSS), bias (FBI), emror (MAE, MSE) across lead times and intensities.
Model
Radar-only?
CSI (1)
FSS (t)
FBI
MAE (4)
MSE (L)
RainPro-8 (ours)
X
0.279
0.537
1.262
0.126
1.503
MetNet-3*
0.270
0.517
1.318
0.132
1.620
GFS
0.110
0.253
0.780
0.164
1.453
PySTEPS
0.149
0.364
0.983
0.162
2.324
RainPro-8K
0.229
0.449
1.346
0.144
1.735
Earthformer
0.111
0.267
0.163
0.110
1.358
VP
0.122
0.287
0.189
0.118
1.340
4.1 PERFORMANCE EVALUATION
RainPro-8 is benchmarked against global NWP GFS and PySTEPS (Pulkkinen et al. 2019 which extrapolates radar echoes with a 512 km context. GFS forecasts are bilinearly interpolated to km/px and temporally aligned 10 min for radar comparison. No other deep-learning nowcasting model can learn from all our input sources: Earthformer (Gao et al. (2024)) and SimVP (Gao et al. 2022) operate only on radar with fixed resolution and coverage, and other data sources cannot be handled by their architectures. For fairness, all models use the same input region (with added con- text) but are evaluated only on the central target region, including a radar-only RainPro-8 variant (RainPro-8R) to demonstrate that our approach provides further performance benefits beyond its multiple data source capabilities. SimVP, which produces outputs matching its input length, is run autoregressively with inputs interpolated from 4 km to 2 km to achieve the target resolution. Earth- former trained at km due to attention limits, with outputs upsampled to km for evaluation. MetNet-3 (Andrychowicz et al. (023) is challenging to evaluate due to its private code, reliance US US-scciffic data, and substantial computational requirements (227M parameters trained on 512 TPU v3 cores over 7 days). MetNet-3* is our faithful reimplementation of the architecture and training described in the original paper, adapted to our data and compute constraints, presenting a repro- ducible competitor under fair conditions. It incorporates lead time conditioning and cross-entropy loss, and it only computes the loss on the high-resolution precipitation maps, not on accumulated rain NWP initial state since we do not perform densification from weather stations.
As shown in Table I. RainPro-8 outperforms all competitors on both precipitation metrics, CSI and FSS. RainPro-8 performs slightly better than MetNet-3* and offers additional advantages: a 48x in- ference speedup through single-step prediction and more coherent probability maps, enabled by the ordinal-consistem loss that accounts for class ordinality probabilistic forecasts. RainPro-8P sur- passes existing radar-only baselines, demonstrating that our approach offers superior performance, but also lagging behind RainPro-8 at longer lead times, underscoring the benefit of our multi-source integration in our full model. FBI indicates that RainPro-8 slightly overpredicts precipitation, Earth- former and VP significantly underpredict, and PySTEPS the least biased. Although SimVP and Earthformer achieve lowest MAE and MSE due to their MSE loss, these do not translate into higher CSI or FSS, likely suffering from impact of the dominating no-rain class.
Figure 2 iillustrates model performance (CSI) at different rain intensities and lead times up to eight hours (further details in App. B..2). Our model demonstrates superior performance across lead times and rainfall thresholds, from light heavy rain. The skill gap with extrapolation-base based PySTEPS grows as lead times increase due to its assumption of constant motion and intensity. The skill gap also widens with lead time, where Earthformer and Sim lack sufficient input and probabilistic output capabilities. RainPro-8P 8R confirms the impact of lack of additional data sources over time. In contrast, GFS shows skill gap shorter lead times because of its longer convergence time. Perfor- mance is slightly lower for all models at higher precipitation intensities, likely due to complexity in forecasting extreme weather, which often involves more chaotic and unpredictable patterns.
Figure visualizes a km"">km² cropped region of at least 4 target patches of 512*km² at 3 lead times (Europe-wide in App. D). RainPro-8 effectively captures areas with rainfall, both in intensity and location, with some blurriness as lead time increases uncertainty. GFS shows major deviations

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1.0 mm/h
2.0 mm/h
0.75
0.75
0.50
0.50
8
0.25
0.25
0.00
5h
5.0 mm/h
0.75
0.50
MetNett33
Earthformer
GFS
SimvP
0.25
PySTEPS
0.00
1h
2h
5h
8h
Lead time (h)
Figure 2: Critical Success Index (CSI) across different thresholds and lead times.
from ground truth, with large differences in intensity and rain-covered areas. PySTEPS struggles to capture changes in intensity or account for non-linear motion patterns at later lead times as it only extrapolates the latest radar image. RainPro-S8 8R, Earthformer, and Sim VP cannot exploit additional data, and the latter two suffer from MSE loss favoring no-rain events.
Ground Truth
RainPro-8
MetNet-3*
GFS
PYSTEPS
RainPro-BR
Earthformer
SimVP
+10m
20.0
5.0
+2h
2.0
0.2
+-hh
0.0
Figure 3: Sample ground truth and forecasts for different models selected lead times with origin 2024-01-23 11:20 UTC in cropped region. Dark grey areas indicate regions heyond radar coverage.
4.2 ABLATION STUDIES
Table 2 shows ablation studies of our ordinal-consistent loss, single-pass predictions, lead time weighting and multi-source input, compared to MetNet-3* using non-ordinal cross-entropy loss and lead time conditioning (Andrychowicz et al. (223). Results confirm the effectiveness of our approach, and the contribution of each of our design choiges and learning strategies. The slight consistent gains of our model over the cross-entropy-basee model (cf. App G) are due to the latter enforcing ordinality only at inference time via cumulative probability computation, rather than learning it during training. Compared to models using lead time conditioning, our single-pass prediction not only improves inference speed (cf. App F) but also delivers superior performance across metrics. Excluding lead time weights leads noticeable drop in performance. Finally, an architecture capable of integrating all available data is key to top accuracy compared to radar-only PR.
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Table 2: Ablation loss function, timesteps per forward pass, lead time weights, data sources.
Model
C
Tize
OCtw
Sres.
CRPS (L)
CSI (1)
FSS (t)
RainPro-8 (ours)
OC
48
0.06096
0.2791
0.5367
CE loss (no OC)
CE
48
0.06098
0.2787
0.5357
Lead time conditioning
OC
1
0.06203
0.2695
0.5191
No lead time weights Radar-only (RainPro-8R)
OC
48
0.06156
0.2733
0.5258
OC
48
V
X
0.06574
0.2289
0,4491
MetNet-3"
CE
V
0.06199
0.2697
0.5173
4.3 PROBABILITY MAPS
RainPro-8 not only outperforms baselines in precipitation intensity forecasts but also quantifies un- certainty for each intensity. Figure 4 displays probability maps and ground truth for two rain in- tensities and lead times (same sample and crop as Figure B). with intensities derived from these probabilities. Uncertainty is higher for longer lead times and light rain, reflecting the challenge of predicting scattered light rain far into the future at high resolution. Higher intensities are predicted with lower probabilities because their low likelihood, particularly extended lead times. The model ensures consistency by never assigning higher probabilities higher precipitation levels.
Ground Truth +1h 0m
Probability Map +1h 0m
Ground Truth +8h 0m
Probability Map +8h 0m
1.0
20.2 mm/h
0.8
0.6
0.4
22.0 mm/h
0.2
Figure 4: Ground truth and RainPro-8 probability map for different rain intensities and lead times, origin at 2024-01-23 11:20 UTC. Dark grey areas indicate regions beyond radar coverage.
4.4 ATTRIBUTION
We analyze RainPro-8 with Integrated Gradients (IG) (Sundararajan et al., 2017), as in MetNet-2 (Espeholt et al. 2022), attributing predictions to inputs to reveal how data sources are used. Rather than isolating each source with separate experiments, across lead times and variables (cf. App- C). Results show recent high-res radar drives short lead times, while low-res radar gains value around 4 hours for broader coverage. Beyond 4 hours, satel- lite data becomes more useful, though visible channels contribute little due to daytime limits. GFS variables become more influential for longer lead times, with key contributors including wind com- ponents, vertical velocity, storm motion parameters, specific humidity, and surface metrics such as pressure and the Best Lifted Index (a measure of atmospheric instability). which are particularly relevant for forecasting cloud movement, precipitation, and thunderstorms. GFS forecasts become increasingly impactful at longer lead times, while less relevant for the first hours.
use IG to assess their relative importance
4.5 RADAR-ONLY SHORT-TERM NOWCASTING (SEVIR BENCHMARK)
While RainPro-8 targets 8-hour high-resolution precipitation forecasts using multi-source data, we also evaluate a radar-only short-term version (RainPro-2R) for 2-hour predictions on the SE- VIR benchmark for direct comparison with existing nowcasting models (App. H). Table shows

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Published as a conference paper at ICLR 2026
RainPro-2R outperforms state-of-the-ar deterministic models. Compared to generative models, it achieves higher CSI and HSS spixel-wise metrics) but lower pooled CSI and perceptual metrics (LPIPS, SSIM), reported in Yu et al. (022) (App. [H). Pooled CSI only checks whether any pixel within a neighhorhood is hit, ignoring how many pixels match, whereas FSS rewards both correct pixels and the fraction of pixels correctly predicted, giving more nuanced measure of spatial curacy at full resolution (Figure 5). Perceptual metrics reflect a trade-off: sharper outputs sacrifice per-pixel performance (Yan et al. 2024). RainPro-2R's probabilistic predictions appear blurrier but capture uncertainty and higher accuracy. RainPro-2R also outperforms DiffCast on CRPS and FSS while being 13x faster at inference. These results highlight RainPro's performance both for the targeted 8-hour multi-source forecasting and SEVIR's short-term radar-only nowcasting setting.
Table 3: SEVIR benchmark; best results in bold per category; from DiffCast Yu et al.
2024
Method
CSI (t)
CSI-p4 (t)
CSI-p16 (t)
HSS (1)
LPIPS (U)
SSIM(1)
RainPro-2R
0.3524
0.3834
0.4171
0.4501
0.2353
0.5966
Generative Deterministic
PhyDnet*
0.2560
0,2685
0.3005
0.3124
0.3785
0.6764
MAU
0,2463
0,2566
0.2861
0,3004
0,3933
0.6361
ConvGRU
0.2416
0.2554
0.3050
0.2834
0.3766
0.6532
Sim
0,2662
0.2844
0.3452
0.3369
0.3914
0.6304
Earthformer*
0,2513
0.2617
0.2910
0.3073
0.4140
0.6773
STRPM
0,2512
0.3243
0.4959
0,3277
0,2577
0.6513
MCVD
0.2148
0.3020
0,4706
0.2743
0.2170
0.5265
PreDiff*
0.2304
0.3041
0.4028
0,2986
0,2851
0.5185
DiffCast*
0.3077
0.4122
0.5683
0.4033
0.1812
0.6354
CRPS Lead Time
FSS Threshold
FSS Lead Time
Model RainPro 28 DiffCast
0.5
0.8
0999
0.03
0.02
0.6
Neighborhood
0.4
1x]
0.4
5xd
200
20
Lead
Threshold
Figure CRPS and FSS across different neighborhoods, thresholds, and lead times.
CONCLUSION & FUTURE WORK
We introduce RainPro-8, deep learning model for precipitation nowcasting that outperforms exist- ing operational systems and deep learning nowcasting models. RainPro-8 addresses the challenges of forecasting probabilistic predictions different precipitation levels. It leverages multiple data sources with an efficient model that forecasts all lead times simultaneously, exploiting ordinality of precipitation levels. Going forward, RainPro-8 could extended to explicitly address robustness to missing data which is of particular importance in operational settings where some input source could become absent, and to automatically learn thresholds.
ACKNOWLEDGMENTS
This work is partly funded by the Innovation Fund Denmark (IFD) under File No. 2052-00064B. Computational resources were provided by Lambda and the GenomeDK cluster at Aarhus Univer-
sity.
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REPRODUCIBILITY STATEMENT
The code is publicly available athttps::/githiiii http://cc//aaaaalaa......a Dat.eaapallo//inPPooo along with instructions for accessing data via the official sources. The paper provides comprehensive details on the RainPro-8 framework, architecture, and evaluation, while the appendices serve as support- ing material. Specifically, Section describes the task definition, loss function, output generation, and model architecture. Section 4 summarizes the training setup and hyperparameters (with fur- ther details in Appendices E and F), data sources (including preprocessing and sample details in Appendices and I), and baselines, metric definitions, and performance comparisons (expanded in Appendices and G). Section 4 also presents visualizations (also in Appendix D), ablation studies, attribution analyses (also in Appendix C), and an evaluation of RainPro-8 variant adapted for the benchmark task on standard precipitation nowcasting benchmark (also in Appendix H).
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A DATASET
A.1 DATA SOURCES
We use Rain radar composite data over Europe, which consists of high-resolution rainfall intensity measurements at 10-minute intervals and resolution of kilometers per pixel. Geostation- ary satellite data is obtained from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT(] capturing cloud-related characteristics that significantly influence precip- itation, despite lower resolution (3-11km) and the challenges of correlating satellite and radar data (Stock et al. 2024). Topographical data is retrieved from the Copernicus Digital Elevation Model (DEM Atmospheric observations and derived physical parameters are sourced from the Global Forecast System (GFS managed by the National Oceanic and Atmospheric Administra- tion (NOAA) in the United States, with a spatial resolution 28km and update frequency of hours. In contrast, MetNet (Andrychowiez et al. 2023) uses HRRR, with 3km resolution, hourly updates, and US-only coverage.
A.2 DATA PREPROCESSING
Each data source is modified to match the desired resolution for model input, either through upsam- pling, such GFS data from 28km/px 16km/px, downsampling, like radar data from 2km/px to both 4km/px and 8km/px. The time dimension also presents challenges to consider. Satellite data, for instance, has 1-hour operational delay, making the most recent timestep available at -60 minutes. For the source gfs 16km, we select 122 channels representing multiple weather variables different pressure levels (Appendix [T. As GFS is only initialized 4 times day, we use the latest forecast lead time as our initial state. The subsequent lead times form the goreeccet /6km, from which we use only the precipitation rate variable. Table summarizes data sources with the corresponding spatial and temporal details.
Table 4: Outputs and inputs for the precipitation forecasting model.
Size
Res.
Size
Timesteps
Variable
Source
Channels
(px)
(km/px)
(km)
(min)
target 2km
Viewer
256
2
512
[10,20....,480]
radar 4km
Rain Viewer
256
1024
[-60,-50,....0]
1
radar 8km
Rain Viewer
192
8
1536
[0]
1
satellite_ 8km
EUMETSAT
192
8
1536
11
16km
GFS
96
16
1536
[0]
122
gfs 16km
GFS
96
16
1536
[60, 120,..,,4801
1
xyz_4km
DEM
256
1024
N/A
3
minute 4km
256
4
1024
N/A
1
We tackle challenges such as varying data ranges and skewed distribution of precipitation intensitics using normalization, clipping, and binning. Min-max normalization is applied to all data sources, as many variables are non-normally distributed. Radar reflectivity values (dBZ) are clipped to a range of -1 to 64 remove outliers. To address rainfall skewness, target radar maps are categorized into predefined rain intensity classes for multi-label classification. Using the Marshall-Paimer equation Marshall & Palmer 1948), dBZ values are converted mm/h, with finer granularity for light rain (2 0.1, 0.2, > 0.4) and broader ranges for heavier rainfall (up to > 25.0 mm/h) (Appendix This approach captures subtle light rain variations while accounting for rare intense precipitation events, aligning with the original dBZ distribution.
https::/www.raiiviisett
http://///ororcctttaaaa////aa/aa http:://tttttttt-teecoonnggeeees secord-generation
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A.3 SPLIT GENERATION
Data spans from December 2023 to November 2024 and is partitioned into training, validation, and testing using multi-day cycles (12, 2, and 2 days, respectively). A 12-hour blackout period between cycles prevents data leakage.
Samples are generated using sliding window along the temporal dimension for non-overlapping 512x512km patches, ensuring alignment with the timesteps and spatial dimensions in Table The training set includes 1,063,658 samples, each with at least 50% radar coverage. During training, we apply uniform random offsets of #256km horizontally and vertically to patches to make the training dataset as large as possible. Validation and test sets comprise 202 and 15,360 randomly selected samples, respectively, including challenging cases with radar coverage below 50%. Although evalu- ation is limited to pixels with radar coverage, boundary regions remain more challenging to predict due to the absence of nearby radar coverage.
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B METRICS
B.1 METRIC DEFINITIONS
Critical Success Index (CSI) CSI, also known as the Threat Score, evaluates the accuracy of event detection by comparing the correctly predicted precipitation events to all events that were either predicted or actually occurred. It balances misses and false alarms, making particularly useful in rare-event forecasting like heavy rainfall. A value of 1 indicates perfect accuracy, while indicates no skill.
TP
CSI TP + FP FN
(10)
where TP, FP, and FN are true positives, false positives, and false negatives, based on the threshold (rate threshold). The thresholds which the metric is evaluated 0.5, 2, 5, and 10 mm/h.
Continuous Ranked Probability Score (CRPS) CRPS measures the accuracy of the entire pre- dicted cumulative distribution function (CDF) relative to the observed outcome. It generalizes MAE for probabilistic forecasts, with lower values indicating better forecast reliability, sharpness, and calibration. It rewards predictions that assign high probability to the correct intensity range.
11 CRPS (P(RR min( 1(R, min((.)))² [Tels
(11)
where iterates all intensity classes, min(Te) is the lower end of class c. R₂ is the target radar, and P(R,- < min( = P(R, 2 min(Te), based on the model probability Pt.c-
Fraction Skill Score (FSS) FSS provides an estimate of spatial accuracy by comparing the fore- cast and observed precipitation fields over local neighborhood, rather than point-wise accuracy. FSS is particularly useful for evaluating high-resolution forecasts, as it accounts for small spatial displacements in predicted precipitation. A perfect forecast yields FSS of 1, while lower values indicate poorer spatial agreement.
(Fe - -O,)) 11 Σ G Σ i= 1 0
ΣH E
FSS
(12)
where Fi, and Oi, refer to the fraction of predicted positives and fraction of observed positives, respectively, in the neighborhood of the (i,j) pixel. This metric is computed at different thresholds (0.5, 1, 5, and 10 mm/h) and neighborhood sizes (2km, 10km, and 20km). This metric also requires binarization based on the computed thresholds.
Frequency Bias Index (FBI) FBI quantifies whether a model tends to overforecast or underfore- cast precipitation events. A value greater than I indicates overprediction, while value less than indicates underprediction. While a bias of is ideal, it doesn't 't imply the forecast is accurate-just that the frequency of forecasted events matches the observed frequency.
TP FP FBI
(13)
TP + FN
where TP, FP, and FN are true positives, false positives, and false negatives, based on the threshold (rate > threshold). The thresholds which the metric is evaluated are 0.5, 1, 2, 5, and 10 mm/h.
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B.2 ADDITIONAL EVALUATION
Table 5: Critical Success Index (CSI) across different thresholds for precipitation forecasting mod- els. Our model RainPro-8 outperforms all other models across different rain intensities.
CSI (t)
Model
0.2
1.0
2.0
5.0
10.0
RainPro-8 (ours)
0.447
0.344
0.296
0.204
0.146
MetNet-3*
0.437
0.335
0.287
0.194
0.136
GFS
0.253
0.161
0.100
0.027
0.009
PySTEPS
0.271
0.180
0.141
0.090
0.062
RainPro-8R
0.398
0.292
0.245
0.164
0.112
Earthformer
0.265
0.125
0.088
0.039
0.015
VP
0.256
0.154
0.116
0.047
0.023
Table 6: Fraction Skill Score (FSS) across different thresholds for precipitation forecasting models
with a neighborhood size of 10km. Our model RainPro-8 outperforms all other models across different rain intensities.
FSSS-1kmm (t)
Model
0.2
1.0
2.0
5.0
10.0
RainPro-8 (ours)
0.728
0.639
0.590
0.469
0.363
MctNet-3*
0.716
0.625
0.572
0.445
0.336
GFS
0.489
0.370
0.268
0.101
0.041
PySTEPS
0.552
0.430
0.369
0.283
0.225
RainPro-8R
0.665
0.554
0.496
0.378
0.275
Earthformer
0.509
0.279
0.211
0.116
0.056
SimVP
0.485
0.338
0.277
0.134
0.075
Table Frequency Bias Index (FBI) across different thresholds for precipitation forecasting models. Our model, RainPro-8, has tendency to over-predict high-precipitation events, in contrast to GFS, Earthformer, and SImVP, which consistently under-predict rainfall. PySTEPS appears the most balanced in terms of bias, with over- and under preeictoesstsoon occurring at similar rates rates-thouglt though this does not translate to higher forecast accuracy.
FBI (As 1)
Model
0.2
1.0
2.0
5.0
10.0
RainPro-8 (ours)
1.140
1.137
1.142
1.268
1.636
MetNet-3*
1.124
1.136
1.160
1.364
1.821
GFS
1,602
1.177
0.700
0.292
0.127
PySTEPS
0.899
0.995
0.914
1.029
1.078
RainPro-8R
1.131
1.141
1.180
1.356
1.932
Earthformer
0.397
0.182
0.135
0.063
0.027
Si
0.384
0.244
0.197
0.074
0.034
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The following plots expand the previous tables in terms of lead times.
0.2 mmnh
1.0 mm/h
2.0 mm/h
0.75
0.75
8
0.50
0.25
0:25
0.25
0.00
0.00
0.00
5,0 mm/h
10.0 mn/h
RainPro- 0
MetNet
GFS
8 0.50
0.50
PYSTEPS
0.25
0:25
farthformer
SimVi
0.00
1h
20
3h
46
7h
8h
1h
2h
5h
6h
7h
8h
Figure 6: Critical Success Index (CSI) for Eurepe-wide precipitation forecasting models across different thresholds and lead times.
0.2 mm/h
1.0 mm/h
2.0 mm/h
1.0
1.0
1.0
1h
20
3h
4n
5h
6h
7h
1h
2h
3h
4h
5h
6n
7h
1h
2h
3h
40
5h
60
7h
mm/h
mm/h
MetNet-3"
PySTEPS
RainPro-BR
SimvP
0.0
0.0
1h
20
3h
4h
5h
1h
2h
3h
4h
5h
60
Load time (h)
Figure 7: Fraction Skill Score (FSS) wude precipitation forecasting models across differ- ent thresholds and lead times with neighborhood size of 10km.
2
5.0 mm/h
10.0 mm/h
Rain/rro8
MetNet-3"
PySTEPS
Earthformer
SimivP
1h
2h
30
5h
50
7h
8h
1h
2h
3h
4h
5h
6h
7h
8n
Lead time
Figure 8: Frequency Bias Index (FBI) for Europe-wide precipitation forecasting models across dif- ferent thresholds and lead times.
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C INPUT ATTRIBUTION WITH INTEGRATED GRADIENTS
The following plots show the results obtained the input attribution by using Integrated Gradients from which the key findings were obtained in Section 4.4 Integrated Gradients estimates feature importance by interpolating between a baseline and the actual input, averaging model gradients along this path. The final attribution is obtained by scaling these averaged gradients by the input difference, quantifying each feature's contribution to predictions. To ensure meaningful attribution, we use the minimum value of each feature as the baseline, as this results in near-zero precipitation probabilities. We then aggregate feature attributions across space, time, and samples, yielding single importance score per feature.
Radar 4km
radar_4km (t--60m)
3
radar 4km (0mm5
Attribution
radar_4km (L=+40m)
radar 4km (t--30m)
radar_ 4km (t= (0mm
radar 4km (t=-10m)
radar_4e 4km (t=0m)
10m
1h
2h
3h
4h
Forecast lead
Figure 9: Attribution of radar radar_4km input timesteps forecast lead time.
1e-12
Radar
1.4
radar 8km (t=0m)
Attribution
1.0
0.8
10m
1h
2h
Forecast lead time
Figure 10: Attribution of radar 88km over forecast lead time.
1e-13 13
Satellite 8km
satellite_00 8kkm (t= 120ml
6
satellite_Bkee (0-m105m)
satellite_ Bkmm (t=-90m)
satellite 8km (t=-75m)
satellite 8km (t=-60m)
2
10m
2h
5h
7h
8h
Forecast lead time
Figure 11: Attribution satellite 8km input timesteps over forecast lead time.
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1e-13
Satellite 8km
10m
Attribution
1h
2h
4
3h
4h
2
5h
6h
7h
VI50.6 VIS0.8 NIR16
IR3.9
WV6.2
WV7.3
IR8.7
IR9.7
IR10.8
IR12.0
IR13.4
Band
8h
Figure 12: Attribution of satellite 8km input variables forecast lead time.
10 11
GFS 16km
UGAD:500
1.5
VGRD:850 mb
D2DT:500 mb
UGRD:850 mb
0.5
VSTM:6000-0 ground
ALFTX:surface
PRES:surface
10m
1h
2h
3h
4h
5h
7h
8h
Forecast lead time
SPFH:100
USTM:6000-0 above ground
Figure 13: Attribution of 16s_ input variables over forecast lead time.
1e-12 12
GFS Forecast 16km
gfs. fereerrtt_6600 16km (t=1h)
6
gfs. forecast (t=2h)
Altribution
fol.foorccs__tue 16km (t=3h)
forecast 16km (t-4h)
gfs [1s_forcast__1 16km (t=Sh)
2
gfs. forecast 16km (t=6h)
gts forecast 16km forecast__6km (t=7h)
10m
1h
3h
4h
5h
6h
8h
forecast
Forecast lead time
gfs fo__ecrcttt 16km (t=9h)
Figure 14: Attribution of gfs www.foott...6kkrr input timesteps over forecast lead time.
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D VISUALIZATIONS
This section displays a Europe-wide comparison of forecast models and probability maps for RainPro-8 at various precipitation levels, based on three different origins.
Origin:
2024-01-33111:2::0
+ +10m
+2h
+8h
Ground Truth
RainPro-8
Methee-3-
10.0
5.0
GPS
2.0
PySTEpS
1.0
RainPre-BR
0.2
0.0
Earthfommer
Simvp
Figure 15: Sample ground truth, RainPro-8, MetNet-3*, GFS, and PySTEPS, RainPro-8R, Earth- former, and SimVis VP forecasts at selected lead times with origin at 2024-01-23 11:20 UTC.
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20.2mm/h
21.0mm/h
Ground Truth
Probability Map
0.9
0.8
0.7
0.6
22.0mm/h
25.0mm/h
+1h 0m
+8h 0m
+1h 0m
+8h 0m
0.5
0.4
Ground Truth
0.3
0.2
0.1
Probability Map
Figure 16: Probability map with corresponding ground truth for four different rain intensities and two different lead times with origin at 2024-01-23 11:20 UTC.
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Origin: 2024-04-19006:00:00
+10m
+2h
+8h
Ground Truth
RainPro-S
MetNet-3
5.0
GFS
2.0
PYSTEPS
1.0
RainPro-BR
0.2
0.0
Earthfommer
SinWPP
Figure 17: Sample ground truth, RainPro-8, MetNer-3*, GFS, and PySTEPS, RainPro-8R, Earth- former, and Sim VP forecasts selected lead times with origin at 2024-04-19 06:00 UTC.
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20.2mm/h
21.0mm/h
Ground Truth
Probability Map
0.8
0.7
0.6
22.0mm/h
25.0mm/h
+1h 0m
+8h 0m
+1h 0m
+8h 0m
0.5
0.4
Ground Truth
0.3
0.2
0.1
Probability Map
Figure 18: Probability map with corresponding ground truth for four different rain intensities and two different lead times with origin at 2024-04-19 06:00 UTC.
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Origin: 2024-06-28111:20.0
+10m
+2h
+8h
Ground Truth
RainPro-S
MetNet-3
5.0
GFS
2.0
PYSTEPS
1.0
RainPho-BR
0.2
0.0
Earthfommer
SinWPP
Figure 19: Sample ground truth, RainPro-8, MetNer-3*, GFS, and PySTEPS, RainPro-8R, Earth- former, and Sim VP forecasts selected lead times with origin at 2024-06-28 11:20 UTC.
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20.2mm/h
21.0mm/h
Ground Truth
Probability Map
0.9
0.8
0.7
0.6
22.0mm/h
25.0mm/h
+1h 0m
+8h 0m
+1h 0m
+8h 0m
0.5
0.4
Ground Truth
0.3
0.2
0.1
Probability Map
Figure 20: Probability map with corresponding ground truth for four different rain intensities and two different lead times with origin at 2024-06-28 11:20 UTC.
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E INTENSITY CLASSES
Table 8 presents the intensity classes used by our model along with their distribution across different splits. Note that both the validation and test contain significantly more missing values due to patches with radar coverage below 50% to obtain Europe-wide forecasts.
Table 8: Intensity classes and distribution for each split.
Bucket (mm/h)
Train (%)
Validation (%)
Test (%)
0000, 0.1)
79.64
57.44
57.05
001,0.2)
1.44
1.00
1.10
00.2, 0.4)
2.25
1.62
1.87
0.64
0.84
0.62
0.74
0.6, 0.8)
0.51
0.37
0.44
00.8, 1.0)
0.50
0.36
0.44
2.00
0.84
0.63
0.73
2.0,3.0)
0.38
0.30
0.35
3.0,4.0)
0.19
0.15
0.17
4.00,.00
0.17
0.13
0.14
3.0,
0.06
0.05
0.05
6.0,
0.05
0.04
0.04
7.0,8.0)
0.04
0.04
0.03
8.0,9.0)
0.03
0.02
0.02
9..0)
0.02
0.02
0.02
10.0, 15.0)
0.03
0.03
0.02
15.0, 20.0)
0.02
0.02
0.01
20.0, 25.0)
0.01
0.01
0.01
25.0
0.02
0.02
0.01
missing
12.97
37.15
36.78
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F COMPUTE RESOURCES
Training is performed on an NVIDIA H100 80GB SXM5 GPU with 26 vCPUs and 225 GiB RAM. Table[Shhws the training and inference times for each experiment, including baselines and ablation studies.
Table Training and inference times for all models trained.
Experiment
Training Time (h)
Inference Time (s/sample)
RainPro-8
13:36
0.03
MetNet-3*
18:16
2.12
RainPro-8R
13:22
0.03
Earthformer
24:03
0.03
Si
6:36
0.12
RainPro-8 w/ CE loss (no OC)
15:52
0.03
RainPro-8 w/ lead time conditioning
18:06
2.10
RainPro-8 w/o lead time weights
13:21
0.03
G ROBUSTNESS ANALYSIS
The primary experiments in this work were conducted with single training run due to the sub- stantial computational resources required. While this setup provides a fair basis for comparison, the closeness of certain results motivates analysis of the variability that may arise from different random seeds. Therefore, we conducted three independent runs for the two most comparable exper- iments: RainPro-8 and its cross-entropy variant, RainPro-8 w/ CE loss (no OC). Table (0)lle(0rree reports the mean and standard deviation across these runs for multiple evaluation metrics. The results show that both models converge consistently, with RainPro-8 demonstrating slight but consistent advantage across all metrics.
Table 10: Aggregated results from three independent runs (mean + std). Best results are highlighted in bold.
Metric
RainPro-8
RainPro-8 w/ CE loss (no OC)
CRPS
0.06096 # 0.00004
0.06099 + 0.00002
CSI
0.27923 = 0.00054
0.27832 0.00032
FSS
0.53663 = 0.00119
0.53532 = 0,00041
FBI
1.26048 + 0.01224
1.26999 00.00222
MAE
0.12593 # 0.00102
0.12610 # 0.00043
MSE
1.50463 # 0.01819
1.52142 = 0.01898
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H SEVIR NOWCASTING EXPERIMENTS
H.1 DATASET AND PREDICTION TASK
SEVIR (Veillette et al. 2020) is widely used benchmark for precipitation nowcasting, containing 20,393 weather events radar frame sequences of 4-hour duration covering 384 x 384 km2 (1 km/pixel) at 5-minute intervals. Following Diffcast YYu et al. (0024) we formulate the prediction task as forecasting 20 frames given 5 initial frames (5 + 20), and the spatial resolution is downsam- pled to 128 x 128 pixels reduce computational cost.
H.2 RAINPRO-2R
We modify RainPro-8 to create RainPro-2R, radar-only variant suitable for short-term nowcasting. RainPro-2R uses only radar frames as input, without additional data sources or spatial context with respect to the targets. The model predicts 20 frames at a downsampled resolution of 128 x 128 pixels, while retaining the same three downsampling layers used in RainPro-8. Training follows the Diffcast (Yu et al. 2024) pipeline, employing our ordinal consistent loss and single-step prediction approach. RainPro- 2R is trained using the Adam optimizer with learning rate of 0.0001 for total of 200K iterations, and the model with the lowest validation loss is selected for evaluation.
The intensity classes that define the probability distribution based the SEVIR benchmark thresholds: [0.0, 16.0, 31.0, 59.0, 74.0, 100.0, 133.0, 160.0, 181.0, 219.0, 255.0]. Given the shorter lead times, the decay rate for the lead time weights is
H.3 EVALUATION
To assess the accuracy of precipitation nowcasting, we evaluate RainPro-28 against both determin- istic and stochastic generative models. Deterministic baselines include SP (Gao et al. 2022) and Earthformer (Gao et al. 2024b), which employ a recurrent- free strategy to generate frames simul- tancously, well PhyDNet (Guen & Thome, (020), MAU (Chang et al. 2021 and ConvGRU (Shi et al., 2017a), which generate frames sequentially using recurrent strategies. For state-of-the- generative baselines, include STRPM (Chang et al. 2022). MCVD(Voleti al. 2022). and PreDiff (Gao et al. 2024a).
Evaluation metrics follow prior work YYu al.) 2024) and include Critical Success Index (CSI),
Heidke Skill Score (HSS), pooled CSI, and visual perception metrics LPIPS Zhang et al. (0018) and SSIM Wang al. (2004). In addition, introduce the previously discussed Fraction Skill Score (FSS) and Continuous Ranked Probability Score (CRPS).
Details on CSI, CRPS, and FSS are in Appendix B.1 Pooled CSI consists of computing CSI after applying max-pooling to the predinctions and targets. Therefore, pooled CSI only checks whether any pixel within a neighborhood is hit, ignoring how many pixels match.
The models are evaluated at thresholds of [16, 74, 133, 160, 181, 219] (Yu et al. 2024). Pooled CSI computed over 4x4 and 16x16 neighborhoods, and CRPS over 5x5 and 17x17 neighborhoods, to have 2 or 8 pixels in each direction.
Heidke Skill Score (HSS) HSS measures forecast accuracy while accounting for correct hits ex- pected by chance. The values range between -0.5 and 1.0, with scores greater than indicating skill by improving over random forecast.
2TT TN -N-NN.1 -F) HSS = (TP FN) + TN) (TP FP) TN)'
(14)
where TP, FP, and FN are true positives, false positives, and false negatives, based on the threshold (rate: 2 threshold).
Learned Perceptual Image Patch Similarity (LPIPS) LPIPS (Zhang et al. 2018 measures per- ceptual similarity between predicted and observed images using deep network features. It can cap-
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ture differences in texture, structure, and high-level perceptual qualities. Lower LPIPS values indi- cate predictions that are perceptually closer to the observations.
Structural Similarity Index Measure (SSIM) SSIM Waag al. 2004) evaluates image simi- larity by comparing luminance, contrast, and structural information between predicted and observed images. It reflects how well the predicted patterns match the true spatial structures. Higher SSIM values indicate better preservation of structural and visual information in the forecast.
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I
GFS INPUT VARIABLES
Table 11: Selected GFS variables input to the model with their corresponding levels.
Var.
Description
Levels
4LFTX
Best (4 layer) Lifted Index
surface
ABSV
Absolute Vorticity
100mb, 250mb, 500mb, 850mb, 1000mb surface, 180-0mb, 90-Omb, 255-0mb
CAPE
Convective Available Poten-
tial Energy
CFRZR
Categorical Freezing Rain
surface
CICEP
Categorical Ice Pellets
surface
CIN
Convective Inhibition
surface, 180-0mb. 90-Omb, 255-0mb 250mb, 500mb, 850mb, 1000mb
CLMR
Cloud Mixing Ratio
CPOFP
Percent frozen precipitation
surface
CRAIN
Categorical Rain
surface
CSNOW
Categorical Snow
surface
CWAT
Cloud Water
entire atmosphere
DPT
Dew Point Temperature
2m
DZDT
Vertical Velocity (Geometric)
100mb, 250mb, 500mb, 850mb, 1000mb 100mb, 250mb, 500mb, 850mb, 1000mb surface
GRLE
Graupel
GUST
Wind Speed (Gust)
HGT
Geopotential Height
100mb, 250mb, 500mb, 850mb, 1000mb, surface, trop.
HPBL
Planetary Boundary
Layer surface
Height
ICEG
Ice Growth Rate
10m surface
ICETK
Ice Thickness
ICMR
Ice Water Mixing Ratio
250mb, 500mb, 850mb, 1000mb low cloud layer surface middle cloud layer
LCDC
Low Cloud Cover
LFTX
Surface Lifted Index
MCDC
Medium Cloud Cover
MSLET
(Eta model reduction)
mean sea level 255-0mb
PLPL
Pressure of level from which parcel was lifted
PRATE
Precipitation Rate
surface surface, trop.
PRES
Pressure
PRMSL
Pressure Reduced to MSL
mean sea level
PWAT
Precipitable Water
entire atmosphere
REFC
Composite reflectivity
entire atmosphere
RH
Relative Humidity
100mb, 250mb, 500mb, 850mb, 1000mb, 2m, entire atmosphere
RWMR
Rain Mixing Ratio
100mb, 250mb, 500mb, 850mb, 1000mb 100mb, 250mb, 500mb, 850mb, 1000mb surface
SNMR
Snow Mixing Ratio
SNOD
Snow Depth
SPFH
Specific Humidity
100mb, 250mb, 500mb, 850mb, 1000mb, 2m
TCDC
Total Cloud Cover
250mb, 500mb, 850mb, 1000mb, entire atmosphere 100mb, 250mb, 500mb, 850mb, 1000mb, surface, 2m, trop.
TMP
Temperature
UGRD
U-Component of Wind
100mb, 250mb, 500mb, 850mb, 1000mb, 10m, trop. 6000-0m
USTM
U-Component Storm Motion
VGRD
V-Component of Wind Visibility
100mb, 250mb, 500mb, 850mb, 1000mb, 10m, trop. surface
VIS
VSTM
V-Component Storm Motion
6000-0m
VVEL
Vertical Velocity (Pressure)
100mb, 250mb, 500mb, 850mb, 1000mb trop.
VWSH
Vertical Speed Shear
WEASD
Water Equivalent of Accumu-
surface
lated Snow Depth
34

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Published as a conference paper at ICLR 2026
J LLM USAGE
Large Language Models have been employed solely for proofreading and to polish writing.
35


Reviews:
[{"review_id": "mzrSMRSbLh", "reviewer": "Reviewer_AAd8", "summary": "The authors propose RainPro-8, a deep learning model for high-resolution, 8-hour probabilistic precipitation forecasting over Europe. The model is based on the MetNet-3 architecture but is modified to have significantly fewer parameters. It introduces a *single-pass prediction* strategy to improve inference efficiency and an Ordinal Consistent Loss to handle probabilistic bins. The authors claim the model surpasses existing deep learning and numerical weather prediction methods on several metrics.", "questions": "See weaknesses.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 1, "strengths": "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.\n2. 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 that treats classes as independent.", "weaknesses": "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.\n- Aside from the new loss function, the work's primary contribution is an application of existing techniques to create an efficient system.\n- The model architecture is just based on MetNet-3. The main architectural changes include *early downsampling* and *halving internal channels*. These are standard engineering practices for model compression and efficiency, not **novel architectural designs or new methods for learning representations**.\n2. **Lack of comparison on GAN-based models**: The paper's experimental validation lacks a crucial component in its discussion of *GAN-based generative models for precipitation*. In the related work section (Section 2), the authors correctly identify the importance of deep generative models, citing high-impact work such as DGMR (Ravuri et al., 2021) and NowcastNet (Zhang et al., 2023). These models, both published in *Nature* and recognized for their strong performance, are a key pillar of the state of the art in this field.\nDespite acknowledging this work, the main experimental comparison in Table 1 completely **omits comparisons against these (or any other) GAN-based generative methods**. This is a significant gap in the evaluation. Without benchmarking against this entire class of SOTA models, it is impossible for the reader to assess the paper's true performance, and the claim to \"surpass... deep-learning nowcasting models\"  remains unsubstantiated.\n3. **Contradiction Between Quantitative Metrics and Qualitative Results**: A significant weakness undermines the paper's experimental conclusions: the quantitative metrics and the qualitative case studies appear to contradict each other directly.\n- Quantitative Metrics: The paper's aggregated metrics, specifically the Frequency Bias Index (FBI) in Table 7 and Figure 8, suggest a key advantage for RainPro-8 in forecasting heavy precipitation. At the 10.0 mm/h threshold, RainPro-8 reports an FBI of 1.636, which is notably lower (i.e., less over-forecasting) than the 1.821 reported for MetNet-3*. **This metric suggests the author's model is more balanced**.\n- Case Study Results: The paper's own qualitative visualizations repeatedly show the opposite. In the case study for the +8h forecast in Figure 19, the Ground Truth shows only a small area of precipitation at the >10.0 mm/h (yellow) level. However, **RainPro-8 predicts a large, distinct area of heavy rain, while the MetNet-3 forecast for the same event shows a substantially smaller overforecasted area**. This same pattern, where RainPro-8 visually over-forecasts heavy rain more severely than MetNet-3, is apparent in the other examples provided (Figure 17).\nThis fundamental discrepancy, in which all qualitative examples in the paper contradict the aggregate metrics for heavy rain, is neither acknowledged nor explained. This casts serious doubt on the reliability of the reported results and the validity of the paper's evaluation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "DielpHGwKZ", "reviewer": "Reviewer_iBcq", "summary": "The paper describes a  deep-learning model for high-resolution probabilistic precipitation\nforecasting over an 8-hour horizon. The main advantage of the proposed model is in its ability to predict precipitation for a longer time frame than the standard nowcasting, up to 2 hours. To achieve this goal, the authors fuse radar, satellite, and physics-based nu-\nmerical weather prediction (NWP) data.", "questions": "1. \"Extensive empirical evaluation demonstrating that RainPro-8 outperforms existing operational methods by 65% \" I am not sure I can follow where this is described, it only appears in the introduction\n2. I am not sure I can follow Figure 2, it is very small. From what I can follow, does the proposed method essentially follow very closely MetNet-3*, is that correct? In that case, the main claim could be that you achieve slightly higher performance, but reduce the computational costs  48 times.\n3. For Table 3, the ablation study results show only small differences in performance. Could the authors give confidence intervals, if possible?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "The paper covers an important topic, precipitation forecasting. \n\nOriginality: the paper leverages multi-source prediction and claims prediction of up to 8 hours (however, see the weaknesses below)\n\nQuality: 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", "weaknesses": "Clarity and quality: see questions below\n\nSignificance: I think the authors need to clarify on this point.  The contributions cite the following:\n-  *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, all contributing to a reduced parameter count of 36.7M from the original 227M.\" Would this be the contribution? I think it could be better to have some sort of takeaway message justifying these architectural solutions, and why it could help develop better new precipitation forecasting architectures\n- \"significant gains over deep-learning nowcasting models\" See Q1\n- \"Demonstration of RainPro’s versatility for radar-only 2-hour predictions on the SEVIR benchmark, achieving state-of-the-art performance compared to both deterministic and generative nowcasting models\" I am not entirely sure I get it. If the claim is to get 8 hour prediction, is achieving state-of-the-art results on 2-hour radar predictions of the contribution?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "aWUaneALbq", "reviewer": "Reviewer_mScv", "summary": "This paper presents RainPro-8, an efficient and compact deep learning model for 8-hour high-resolution probabilistic precipitation forecasting in Europe. RainPro-8 integrates heterogeneous multi-source data (radar, satellite, and numerical weather prediction, NWP), and adopts an optimized U-Net + MaxViT architecture. The model generates probabilistic maps for all time steps and precipitation classes in a single forward pass. The authors introduce an \"ordinal consistent loss\" to explicitly encode the ordinal nature of precipitation bins, improving interpretability and consistency of the probabilities. RainPro-8 uses less than 20% of MetNet-3's parameters but achieves superior performance across lead times and precipitation intensities, outperforming NWP baselines, extrapolation methods, and the latest deep learning models (e.g., Earthformer, SimVP, MetNet-3*). Extensive experiments, including ablations, attribution, and efficiency analysis, are provided, as well as competitive results on the short-term SEVIR benchmark. Code will be public, and datasets and experimental details are thorough.", "questions": "does the ordinal consistent loss have any theoretical advantage (e.g., calibration, capturing uncertainty, or predicting extremes)? Could you provide more empirical or theoretical analysis on loss stability and generalization?\n\nIn known European extreme rainfall cases or strong out-of-distribution samples, how does RainPro-8's predicted probability align with actual precipitation? Are confidence intervals and rare event probabilities calibrated and unbiased?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "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.", "weaknesses": "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.\n\nWhile RainPro-8 claims to handle varying precipitation intensities, most experiments focus on average metrics. The model’s ability to capture extreme/rare strong precipitation events (e.g., ≥25 mm/h) is not thoroughly assessed. Only the class coverage is reported, but there are no case studies or targeted metrics for extreme events, missing a rigorous quantification of robustness or uncertainty calibration for outlier scenarios.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "SsBdrwZcIX", "reviewer": "Reviewer_MSzr", "summary": "The paper introduces RainPro-8, a deep-learning model for probabilistic precipitation forecasting. It proposes an Ordinal-Consistent Loss that maintains the natural ordering of rainfall intensity classes and a Single-Pass Prediction method that predicts all future timesteps at once for greater efficiency and temporal consistency. Built on a U-Net with MaxViT blocks, the architecture efficiently processes multi-resolution atmospheric inputs with significantly fewer parameters than prior MetNet models.", "questions": "- Many models that perform 6-hour nowcasting are discussed in the Related Works section. What is the logical reasoning and supporting justification for forecasting 8 hours instead of 6, as done in those prior works?\n- Besides SEVIR, is it possible to evaluate performance on other relevant benchmark datasets such as the Shanghai dataset or CIKM?\n- In Line 109, the paper states that “its training requires significant time and resources, involving hundreds of Tensor Processing Units (TPUs) for multiple days.” However, if this method uses an “NVIDIA H100 SXM5 GPU,” isn’t that also an extremely powerful computational setup? Please summarize the computational power and training duration for the compared baseline methods.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper identifies the limitations of radar-only forecasting methods and proposes a multi-source approach to improve prediction accuracy.\n- It provides a detailed analysis of recent Climate AI research, highlighting their characteristics and limitations, and conducts experiments following established protocols and evaluation metrics.\n- To address the ordinal nature of precipitation classes and the difficulty of long lead-time forecasts, it introduces a specialized loss function and a single-pass prediction method.\n- The appendix provides comprehensive details on the variables, data assimilation, and preprocessing steps for the diverse data sources used.", "weaknesses": "- Including the baseline model MetNet, RainPro-8 relies on a variety of data sources, which limits its applicability to regions—such as developed countries—where high-resolution data from multiple sensors, satellites, and NWP outputs are readily available.\n\n- The model lacks significant technical novelty. Moreover, its performance improvement over the existing baseline, MetNet-3, is marginal.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)