FlowCast: Advancing Precipitation Nowcasting with Conditional Flow Matching
Authors: Bernardo Perrone Ribeiro
Jana Faganeli Pucer
Venue: ICLR 2026 Poster (2026)
Keywords: conditional flow matching
precipitation nowcasting
generative models
spatiotemporal forecasting
machine learning in environmental science
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: FlowCast is the first direct noise-to-data Conditional Flow Matching (CFM) model for precipitation nowcasting. It outperforms state-of-the-art diffusion models while leveraging CFM's efficiency to require a small number of sampling steps.

Abstract: Radar-based precipitation nowcasting, the task of forecasting short-term precipitation fields from previous radar images, is a critical problem for flood risk management and decision-making. While deep learning has substantially advanced this field, two challenges remain fundamental: the uncertainty of atmospheric dynamics and the efficient modeling of high-dimensional data. Diffusion models have shown strong promise by producing sharp, reliable forecasts, but their iterative sampling process is computationally prohibitive for time-critical applications. We introduce FlowCast, the first end-to-end probabilistic model leveraging Conditional Flow Matching (CFM) as a direct noise-to-data generative framework for precipitation nowcasting. Unlike hybrid approaches, FlowCast learns a direct noise-to-data mapping in a compressed latent space, enabling rapid, high-fidelity sample generation. Our experiments demonstrate that FlowCast establishes a new state-of-the-art in probabilistic performance while also exceeding deterministic baselines in predictive accuracy. A direct comparison further reveals the CFM objective is both more accurate and significantly more efficient than a diffusion objective on the same architecture, maintaining high performance with significantly fewer sampling steps. This work positions CFM as a powerful and practical alternative for  high-dimensional spatiotemporal forecasting.

Published as a conference paper at ICLR 2026
FLOWCAST: ADVANCING PRECIPITATION NOWCASTING WITH CONDITIONAL FLOW MATCHING
Bernardo Perrone Ribeiro University of I.jubljana brbmpê gma com
Jana Faganeli Pucer
University of Ljubljanaa jana. faganel ipucerffri .nw.effrriiiii.e.. .uni-1j 1oii-1j... jss
ABSTRACT
Radar-based precipitation nowcasting, the task of forecasting short-term precipi- tation fields from previous radar images, critical problem for flood risk man- agement and decision-making. While deep learning has substantially advanced this field, two challenges remain fundamental: the uncertainty of atmospheric dynamics and the efficient modeling of nigh-dimensional data. Diffusion mod- els have shown strong promise by producing sharp. reliable forecasts, but their iterative sampling process is computationally prohibitive for time-critical applica- tions. We introduce FlowCast, the first end-to-end probabilistic model leveraging Conditional Flow Matching (CFM) as a direct noise-to-data generative framework for precipitation nowcasting. Unlike hybrid approaches, FlowCast learns a direct noise-to to-tt mapping in compressed latent space, enabling rapid, high-fideling sample generation. Our experiments demonstrate that FlowCast establishes a new of-e-offthheart in probabilistic performance while also exceeding deterministic baselines in predictive accuracy. A direct comparison further reveals the CFM objective is both more accurate and significantly more efficient than diffusion objective on the same architecture, maintaining high performance with signifi- cantly fewer sampling steps. This work positions CFM as a powerful and practical alternative for high-dimensional spatiotemporal forecasting,
INTRODUCTION
Accurate and timely short-term precipitation forecasts, or nowcasting, are of paramount importance due to their significant socio-economic impacts, such as issuing flood warnings and managing water resources. Precipitation nowcasting, as defined in this work, involves predicting sequence of future radar images from historical observations for the immediate future up to few hours (An et al.) 2025). Traditional methods, like Eulerian and Lagrangian persistence (Germann & Zawadzki 2002). rely on advecting the current precipitation field. However, their simplified physical assumptions limit their ability to capture the complex, non-linear dynamics of atmospheric processes, especially for rapidly evolving weather systems (Prudden et al. 2020).
Deep learning has introduced a paradigm shift in precipitation nowcasting. Deterministic mod- els based recurrent and transformer architectures learn complex spatiotemporal patterns directly from large volumes radar data (Prudden et al.) 2020 An et al. (025)) While these models outper- form traditional methods, optimizing for metrics like Mean Squared Error (MSE) compels them to produce a single, best-guess forecast. This often results in overly smooth predictions at longer lead times, failing to capture the inherent uncertainty precipitation evolution and underrepresenting high-impact weather events.
To address this, probabilistic generative models have become central modern nowcasting, aiming to predict a distribution over many plausible futures. Diffusion models (Ho et al.) 2020), in particu- lar, have emerged as the tftte-of-the-art, producing sharp and reliable ensemble forecasts (Gao al., 2023 Leinonen et al.) 2023 Gong et al., 2024). However, their reliance on an iterative denoising process, often requiring hundreds of function evaluations for a single forecast, makes them compu- tationally expensive. This high Number of Function Evaluations (NFE) poses significant barrier time-critical scenarios where rapid ensemble generation crucial.
Our implementation is available utttpps: //tht:///////////////// www http:/hwww....ooommmom//////o///

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This work introduces FlowCast, novel probabilistic nowcasting model built Conditional Flow Matching (CFM) (Lipman et al. 2023; Tong et al. 2024), a powerful and efficient alternative de- signed for rapid sampling. While recent work has applied rectified flows for the deterministic refine- ment of blurry forecasts (Feng et al. 2025), FlowCast is, to our knowledge, the first to successfully apply CFM
full, noise-to-data generative model for this task. We demonstrate that FlowCast alleviates the tension between accuracy and efficiency, establishing a new state-of-the-art by ex- ceeding the performance of leading diffusion models while offering superior performance-cost trade-off.
We argue that CFM offers not only a computational advantage but also a superior inductive bias for this domain, specifically regarding the simplified transport of probability mass. Radar reflectivity distributions are highly multi-modal yet exhibit strong local temporal consistency. Standard diffu- sion models map Gaussian noise to this complex manifold via stochastic denoising curved proba- bility flow ODEs, often necessitating many sampling steps to resolve fine-grained structures without blurring modes. In contrast, CFM imposes straight-line ODE prior on the generative process. This enforces the simplest possible mapping between the noise and data distributions. In the context of spatiotemporal forecasting, where temporal coherence is essential, this linear interpolation provides a much stronger and more stable prior than the winding paths of diffusion. We demonstrate that this geometric simplification allows FlowCast to maintain high fidelity with significantly fewer function evaluations.
Our contributions are summarized follows:
We introduce FlowCast, a novel full-probabilistic application of Conditional Flow Match- ing to precipitation nowcasting.
We establish a new ofate-of-the-ard in both probabilistic performance and predictive accu- racy diverse radar datasets, the benchmark SEVIR dataset (Veillette al. 2020) and the local ARSO dataset.
We provide direct ablation study showing that the CFM objective both accurate and more computationally efficient than a diffusion objective on the same architecture, maintaining high performance with substantially fewer sampling steps.
2 RELATED WORK
2.1 DETERMINISTIC NOWCASTING
Deep learning for precipitation nowcasting has evolved from RNN-based architectures to Transformer- based models. Early work includes ConvL.STM (Shi et al. 2015), extending L.TTMs with convolutions for spatiotemporal data, and the PredRNN family (Wang at. 2017 2023). 2023) which
introduced spatiotemporal memory flow for improved long-ram range dependency modeling. More cently, Transformer architectures like Earthformer (Gao et al. 2022) and Earthfarseer (Wu et al. 2024 have new benchmarks by using attention to model complex global dynamics. A common limitation of deterministic models is that they produce overly smooth forecasts when trained with pixel-wise losses (e.g., MSE), as they average over possible futures.
2.2 PROBABILISTIC NOWCASTING
To address uncertainty quantification, probabilistic models have become central to nowcasting, aim- ing sample from the full distribution of future states.
GANS and Diffuusion. GANs (avvrii et al. 2021) were an early approach for producing sharp forecasts but suffer from training instability. Diffusion models (Ho et al.] (020)) have recently emerged as the state-of-the-art, offering stable training and high-quality samples. PreDiff (Gao al. 2023) and LDCast (Leinonen et al. (023) prominent latent diffusion models for ensemble forecasting, A notable hybrid is CasCast (Gong et al. 2024), which uses deterministic model for large-scale patterns and conditional diffusion model refine stochastic details.
Flow-Based Generative Models. Generative modeling with flows offers an attractive alternative to diffusion. Traditional Continuous Normalizing Flows (CNFs) (Chen al. 2018) model data
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via ODEs but require expensive numerical integration during training to compute likelihoods, mak- ing them computationally prohibitive for high-dimensional spatiotemporal data. Conditional Flow Matching (CFM) (Lipman al. 2023) and Rectified Flows (Liu et al. 2023) overcome this by regressing a vector field against a conditional probability path, enabling simulation-free training. However, while standard Rectified Flows typically utilize singular conditional path (effectively + 0), our application of Independent CFM (I-CFM) incorporates a Gaussian probability path with a O. This "thickens" the training trajectory, providing crucial regularization that stabilizes the learning of the vector field for high-dimensional data compared to the singular paths of rectified flows.
Crucially, while diffusion models rely on stochastic denoising paths that are often curved and require many sampling steps. CFM allows for learning straight-line ODE trajectories between noise and data (Tong et al. 2024). This geometric property enforces direct mapping that preserves temporal coherence and allows for rapid sampling. While Feng et al. (2025) recently used rectified flow module to strictly refine deterministic forecasts, FlowCast applies CFM standalone probabilistic generative model. This allows to learn the full noise-to-data distribution and capture multimodal uncertainty without relying a deterministic base forecast.
METHOD
Our approach probabilistic nowcasting is based Conditional Flow Matching (CFM) within compressed latent space. This section details our methodology. covering the problem formulation, our latent CFM framework, the model architecture, and the training and sampling procedures.
3.1 TASK FORMULATION
Precipitation nowcasting is framed as video prediction task. Given sequence of Tin past radar observations, ppast = (01, (2..,2.... IT.,., J. where each x2 E HR xC is a radar map, the objective
2Tn Toat generate a probabilistic forecast for the next Tout frames, Xfuture = (ITm+11.... ,
3.2 LATENT CONDITIONAL. FLOW MATCHING
To reduce the high computational cost of generative modeling, adopt two-stage ap- proach inspired by latent diffusion models (Rombach et al. [022). A Variational Autoencoder (VAE) KKigmma & Welling 2014) compresses high-dimensional radar frames into low-dimensional latents, which are used to train a generative model in the latent space.
Our generative model built the Conditional Flow Matching (CFM) framework (Lipman al., 2023). which trains a continuous normalizing flow by learning vector field 0g that maps samples from a prior distribution (e.g., Gaussian) to the target data distribution. We use Independent CFM (I- CFM) Tong et al. 2024), which defines a probability path Pt | I1) as Gaussian distribution with mean (1 f) + tx and a small constant standard deviation a. This path interpolates between a noise sample 3o ~ A(0,, 7) and data sample 21. The corresponding target vector field is their difference, u, = 2 Tig. This formulation enables direct, simulation-fre training of the model da by regressing it against this target field.
3.2. FRAME-WISE AUTOENCODER
To learn compact latent space, we train VAE on individual radar frames. The architecture, inspired by [Esser et al.] (202T), uses a hierarchical encoder & and decoder D with residual and self-attention blocks for high-fidelity reconstructions. The VAE is trained with combination of LI reconstruction loss, a KL-divergence regularizer, and PatchGAN adversarial loss (Isola et al. 2017) to enhance perceptual quality. After training, the VAE's weights frozen and it is used to encode inputs and decode latent predictions.
3.2.2 FASTWAST ARCHITECTURE
We propose FlowCast, which consists of the adaptation Earthformer (Gao et al. 2023) for the CFM objective. FlowCast employs U-Net-like encoder-decoder structure where the
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building blocks Cuboid Attention layers from Earthformer (Gao al. 2022 This mechanism efficiently processes spatiotemporal data by applying self-attention locally within 3D "cuboids" of the data, capturing local dynamics, while global information is shared across the hierarchical U- Net structure. The model conditioned on the flow time I, which is converted into an embedding and injected each level of the network, enabling the model to accurately approximate the time- dependent vector field Vg.. The architecture is illustrated in Figure
Imput
Tpen (Homm 2.-0 Renas: A/8.0
Conv, Block
Observation Meaking
Group
ComiaD
Group
Comva
Dumki Block
Donmampll
4x
Time Donyy Glblid ATt.
Group Norm
SIUU
Cenvi Ixdioc
Group Norm
SLUU
ConviD 3x3
Ax
Time Comn: Cubold Ant.
Litear Layer
Vg
C, TTuu
Figure 1: The FlowCast architecture. A U-Net with Cuboid Attention blocks processes latent spa- tiotemporal data. Conditioning on the flow time enables the model learn the time-dependent vector field for generating forecasts.
Training. The ClowCast model learns the vector field vo (7,, 7pass.. The complete training pro- cedure is detailed in Algorithm
Algorithm FlowCast Training Process
Require: Dataset D. Pre-trained VAE Encoder E, FlowCast Model up, standard deviation a.
1: Initialize model parameters
2: while not converged do
3: Sample batch of radar sequences (X past, Xfuture).
4: Encode sequences into latent space: Zpust +- ((X past) and Zramure + {(Xuuuur)
5: Sample prior noise Zp (O, I). time t (0, 1) and path Gaussian noise NO,
6:
Compute interpolated latent state: 7, (1 l) 1 + are
7:
Compute target vector field: We +- Ziauue Zr
8:
Predict vector field: è +- vo Ze, t, 7asst
9: Compute Loss: C+ 1100- un
Gradient step: f V
11:
end while
Sampling. To generate ensemble of forecasts, solve the learned ODE starting from noise, using the Fuler method (Hairer et al. 1993). This process described in Algorithm 2
4 EXPERIMENTS
4.1 EXPERIMENTAL SETTING
More details about the experimental setting are provided in Appendix A.1
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Algorithm 2 FlowCast Ensemble Sampling
Require: Past radar sequence Xast, Trained FlowCast Model ve, VAE Encoder & Decoder D. Require: Ensemble size N, Number of ODE steps S.
Ensure: Ensemble forecasts {Xi(e)
future k=
1: Encode past observations: peat f- {(X past)
2: for == N do
3: Sample initial state: 2( ~ 10,
4: Set step size: At 1-155
5: Set current state: Zeurr -- Z(0)
6: for to
do
7: Current time: / < i A
8: Predict vector field: f- 0g (Z t, Zpsst)
9: Update state (Euler step): Zeurr f Zeurr + V At
10: end for
11: Final latent forecast: Z(1) Zeurr
12: Decode to pixel space: (() t -=)((1))
13:
end for
return Ensemble predictions (*((+) I.
14:
future k==1
4.1.1 DATASETS
We evaluate on two 5-minute, 1 km-resolution radar datasets: SEVIR, US benchmark, and ARSO, a Slovenian composite for local deployment setting. For both, we predict 12 frames (1 hour) from 13 past frames (65 minutes), per the SEVIR Nowcasting Challenge protocol (Veillette et al. 2020).
Table 1: Summary of datasets used for evaluation.
Dataset
Nirain
Naa
Niest
Resolution
Dimensionality
Interval
Lag/Lead
SEVIR
36,351
9,450
12,420
I km
384
5 min min
13/12
ARSO
38,229
12,743
12,744
1 km
301 401
13/12
SEVIR. SEVIR (Veillette et al. 2020) provides over 10,000 weather events in 384 x 3844 km US domain, each spanning hours at 5-minute resolution. We use the 1-km Vertically Integrated Liquid (VIL) field. Following the standard chronological split, extract 25-frame sequences (13 context, 12 target) with a stride of 12, yielding 36,351 training, 9,450 validation, and 12,420 test samples.
ARSO. The ARSO dataset contains 5-minute, 1-km radar reflectivity composites over a 301 x00 km Slovenian grid, capturing complex Alpine and coastal dynamics. Using the same 25-frame sequence setup but with stride a 60/20/20 chronological split yields 38,229 training, 12,743 vali- dation, and 12.744 test samples.
4.1.2 EVALUATION
Threshold-basee based categorical scores: Following prior work (Veilletee et al. 2020 Gao et al., 2023 Gong et al., 2024), we evaluate forecasts by converting radar fields to binary masks at given thresholds and computing the False Alarm Ratio (FAR), Critical Success Index (CSI), and Heidke Skill Score (HSS). For spatial validation, we compute the max-pooled CSI and Fractions Skill Score (FSS) over 16 16 km neighborhoods (CSI-M-P16 and FSS-M-P16). We report the mean of these scores across all thresholds ("-M") to evaluate general performance. Furthermore, to rigorously as- sess the detection of extreme weather events, separately report the categorical metrics specifically at the highest intensity thresholds for each dataset.
For SEVIR, follow the literature in using the thresholds [16, 74, 133, 160, 181, 219). For ARSO, we use the thresholds [15, 21, 30, 33, 36, 39) dBZ, derived through quantile mapping to ensure that each threshold corresponds approximately the same exceedance probability in both datasets.
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Continuous Ranked Probability Score (CRPS): We use the CRPS to evaluate probabilistic skill. A lower CRPS indicates a more accurate and sharp forecast. For deterministic forecasts (N = 1), CRPS reduces to the Mean Absolute Error.
Ensemble forecasting: Let Ft,., represent the ground truth pixel value at location (i,j) and lead time t. All probabilistic models are evaluated using an ensemble of N = realizations. For categor- ical scores, evaluate the ensemble mean prediction (Metric of Ensemble Mean), first computing the ensemble mean Lt,iJ = E a(k) and then the metric on this mean forecast.
4.1.3 TRAINING DETAILS
VAE. We train separate Variational Autoencoder (VAE) for each dataset to create a specialized latent space. We follow the architecture and training procedure from Rombach et al. (2022). with Kullback- Leibler divergence loss weight of lc-4, the AdamW optimizer with a learning rate of le-4, and batch size of 12. The compressed latent space dimensions are shown Table 2
Table 2: VAE latent space dimensions
Dataset
Original Dimensions (T x x W x Cin) (TinTaw x H2 x W. x C.)
Latent Dimensions
SEVIR
13/12 x 384 x 384 X 1
13/12 x 48 x 48 x 4
ARSO
13/12 x 301 401 1
13/12 38 52
FlowCast. We train our CFM model for 200 epochs using the Adam optimizer with a learning rate of 5e-4 and cosine scheduler. We set the standard deviation of the I-CFM probability path to a small constant o = 0.01 (Tong et al.] 2024). We observed that the training process was notably stable; unlike diffusion objectives which often require complex loss weighting schedules, the I-CFM objective utilizes a simple regression loss that converged robustly without extensive hyperparameter tuning. Model checkpoints are maintained using an exponential moving average of weights (Ho itt al. 2020), with a decay factor of 0.999, and we keep the model checkpoint with the highest CSI- M evaluated subset of the validation set. The model trained with 4 NVIDIA H100 for 7 days, with global batch size of 12. Further implementation details are provided in Appendix A.1
4.1.4 INFERENCE DETAILS
Generating a forecast with FlowCast involves solving the learned ODE to transform noise- initialized latent sequence into a prediction, conditioned on encoded past observations. Following the procedure outlined in 3.2.2, ODE solver. To generate a probabilistic ensemble forecast, this process is repeated eight times with different initial noise samples Z(0) ~ I).
the Euler method (Hairer et al., 1993) with 10 steps the
4.2 COMPARISON TO THE STATE OF THE ART
We evaluate FlowCas against four deterministic baselines: U-Net (Veillette al.) 2020). Earth- former (Gao et al. 2022). Earthfarseer (Wu et al. 2024), and Siv (Tan et al.) 2025). as well as two probabilistic baselines: PreDiff (Gao al. 2023) and CasCast (Gong et al. 2024). All models are trained following their publicly released code, with the training budget fixed at 200 epochs. For probabilistic models, we adopt our evaluation protocol by selecting the checkpoint with the highest CSI-M on validation subset, using exponential moving average weights.
General Performance (SEVIR ARSO). As shown in Table 3 Figure Table 4 and Figure
FlowCast establishes new state-of-the-art across both diverse datasets. On SEVIR, it achieves the highest overall CSI-M, FSS- PSS-M-P16, and HSS-M and the lowest CRPS, demonstrating superior probabilistic calibration. On ARSO, FlowCast outperforms all baselines in all metrics besides FAR- M. Notably, on SEVIR, while the probabilistic baseline CasCast achieves the highest CSI-PI6-M, suffers from significantly higher FAR-M compared to FlowCast (0.383 vs. 0.325). This indicates

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Published as a conference paper at ICLR 2026
that FlowCast strikes superior balance between detection sensitivity and precision, avoiding the tendency to vver-predict precipitation coverage. Deterministic models achieve the lowest FAR-M scores by predicting blurry fields at longer lead times, missing the most extreme events (CSI-219).
Performance on Extreme Events. To evaluate the ability of the models to detect extreme events, report performance the highest intensity thresholds for both datasets in Table 5 FlowCast demonstrates a decisive advantage here. On SEVIR (Threshold 219), FlowCast achieves a CSI of 0.202, outperforming the best deterministic baseline (Sim 0.137) by over 47% and the leading probabilistic baseline (CasCast, 0. 0.195). The trend holds for ARSO (Threshold 39 cBZ), where FlowCast achieves the highest CSI (0.183) and HSS (0.291).. Crucially, FlowCast maintains this high detection skill while achieving lower FAR than CasCast across all extreme thresholds, confirming its ability generate sharp, intense features without resorting to excessive false alarms.
Table 3: Comparison of FlowCast with baseline models on the SEVIR dataset. All metrics are computed over a 12-step forecast, except "Forecast @ +65 min" which only uses the last frame.
Forecast @ 12 steps
Forecast +65 min
Model
CRPS
CSJ-M f CSI-PI6-M1
FSS-P16-M t IISS-M
FAR-M
CSI-M
CSI-219
U-Net
0.0273
0.394
0.384
0.661
0.497
0.308
0.259
0.009
Earthformer
0.0252
0.411
0.407
0.016
Earthfarseer
0.0256
0.389
0.393
0.636
0.486
0.289
0.247
0.001
SimVP
0,0249
0.423
0.424
0.701
0.532
0.298
0.280
0.012
PreDiff
0.0189
0.413
0.423
0.699
0.523
0.313
0.281
0.018
CasCast
0.0201
0.442
0.520
0.763
0.562
0.383
0.311
0.054
Flow
0.0182
0.460
0.506
0.767
0.580
0.325
0.324
0.057
Treme-atte CSLM
Frame-wttee CS-219
U.d
Sirve
Paa
PrECET
CoaCast
CarCalt
CS:219
0.4
0.2
Inad 117e (7111)
Send time (11in)
Figure 2: CSI-M and CSI the 219 threshold per lead time on the SEVIR dataset. FlowCast shows consistent improvement over baselines for CSI-M and avoids the oversmoothing of deterministic models at longer lead times (CSI-219).
Table 4: Comparison of FlowCast with baseline models on the ARSO dataset. All metrics are computed over 12-step forecast, except "Forecast +65 min" which only considers the last frame.
Forecast 12 steps
Forecast @ +65 min
Model
CRPS 1
CSI-M
CSI-PI6-M t FSS-P16-M
HSS-M f
FAR-M |
CSI-M
CSI-39
U-Net
0.0264
0.399
0.432
0.659
0,505
0.371
0.260
0.011
Earthformer
0.0270
0.403
0.439
0.691
0.512
0.409
0.274
0.010
Earthfarseer
0.0280
0.368
0.406
0.588
0.358
0.233
0.004
Vi
0.0267
0.462
0.699
0.401
0.029
PreDiff
0.0211
0.369
0.411
0.614
0.471
0.400
0.241
0.010
CasCast
0.0253
0.373
0.511
0.712
0.483
0.488
0.277
0.057
FlowCast
0.0209
0.420
0.514
0.738
0.535
0.422
0.315
0.073

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Framewise
Fame-wise
U-Nt:
FHOOT
Costart
CanCagt
Rove aac
05
0.
04
02
63
0.3
33 15
20
20
60
0.5
10
35
20
Figure 3: CSI-M and CSI the 39 DBz threshold per lead time the ARSO dataset. FlowCast shows significant improvements over probabilistic baselines for earlier lead times, and over deter- ministic baselines for later lead times.
Table 5: Comparison of FlowCast with baseline models on SEVIR and ARSO datasets for extreme events using categorical metries with the highest thresholds per dataset. All metries are computed over 12-step forecast.
SEVIR HSS
ARSO
CSI
FAR
CSI
HSS
FAR
Model
219
181
219
219
36
39
36
39
U-Net
0.205
0.122
0.314
0.193
0.366
0.508
0.209
0.145
0.318
0.226
0.474
0.505
Earthformer
0.229
0.109
0.348
0.180
0.354
0.343
0.216
0.145
0.335
0.231
0.531
0.553
Earthfarseer
0.194
0.097
0.291
0.152
0.341
0.412
0.159
0.104
0.245
0.164
0.445
0.449
SimVP
0.244
0.137
0.365
0.220
0.370
0.404
0.238
0.162
0.362
0.254
0.507
0,540
PreDiff
0.237
0.128
0.361
0.206
0.384
0.467
0.176
0.118
0.277
0.193
0.520
0.566
CasCast
0.286
0.195
0.427
0.309
0.501
0.567
0.202
0.142
0.320
0.235
0.647
0.694
FlowCast
0.301
0.202
0.443
0.317
0.425
0.482
0.254
0.183
0.388
0.291
0.547
0.589
Figure 4 qualitatively compares forecast sequences from FlowCast with the baselines on the SEVIR dataset. FlowCast produces sharp, perceptually realistic forecasts, avoiding the smoothness of deter- ministic models. Compared to the fest-performing probabilistic baseline CasCast, observe more realistic precipitation patterns, especially at longer lead times. More examples, including on ARSO, provided in Appendix A.2
4.3 ABLATION STUDIES
Due to computational constraints, all ablation studies were run on the first 10% of the SEVIR test set using single NVIDIA A100 GPU.
4.3.1 CFM OBJECTIVE AGAINST DIFFUSION
To isolate the benefits of the CFM objective, we compare FlowCast against strong baseline using the same backbone architecture but trained with diffusion objective. We trained a DDPM (Ho cet al. 2020) for 1000 timesteps. For efficient inference, we employed a DDIM sampler (Song et al.) 2021 with varying number of steps. This provides strong and practical baseline evaluate Cast against a highly optimized diffusion process on the same powerful architecture.
The results in Table clearly demonstrate the superiority of the CFM objective. With single step, FlowCast (CFM) drastically outperforms the DDIM sampler, even a 100-step DDIM baseline, across key metrics like CRPS and CSI-M, whilst being almost 100 times faster.
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+30 min
+20
+30 min
+40
50 min
+60 min
Ground Trubh
U-Nee
181
Eartnformer
Earthfersee
Sinvp
74
PreDiff
Cascoast
FrowCest
16
Figure 4: Qualitative comparison of FlowCast with other baselines SEVIR sequence. Columns show lead times from 10 to 60 minutes. Rows show the ground-truth, followed by the models.
Table 6: Ablation study: CFM vs. diffusion objective. Results highlight the superior performance and efficiency of the CFM framework. All metrics are computed over 12-step forecast.
Model
CRPS
CSI-M t
CSI-PI6-M
FSS-M-P161
HSS-M
FAR-M
Time/Seq, (s)
CFM (1 steps)
0.0207
0.454
0.504
0.763
0.337
CFM steps)
0.0168
0.455
0.514
0.764
0.572
0.338
DDIM (10 steps)
0.0262
0.395
0.450
0.622
0.503
0.335
24
DDIM (50 steps)
0.0212
0.398
0.451
0.635
0.504
0.321
120
DDIM (100 steps)
0.0208
0.398
0.450
0.664
0.502
0.319
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4.3.2 INFERENCE EFFICIENCY: PERFORMANCE VS. NUMBER FUNCTION EVALUATIONS
We assess inference efficiency by comparing FlowCast and the diffusion backbone across range of function evaluations (NFE), where one NFE is an Euler (CFM) or DDIM step. Each NFE adds 2.4s per 8-member ensemble forecast. Figure []] shows FlowCast is highly efficient, nearing opti- mal CRPS and CSI-M scores in just 3-10 steps. In contrast, the diffusion model requires 20-50 steps to peak and degrades sharply below 10 NFE. These results highlight the superior efficiency of the CFM framework, which learns more direct mapping to the data manifold and enables high- fidelity forecasts with significantly fewer model evaluations. This efficiency is a crucial advantage for operational settings where forecasts must be both rapid and reliable.
CRPS vs. Number of Steps
CSI-M vs. Number of Steps
Hostast (L
0.445
(Diflualoo)
0.44
0.000
0.42
0.035
0.030
0.38
0.36
0.000
Cowtast (CFM
Rowcast (bitfusior)
10
20
30
10
30 Number Steps
20
40
50
Number of Steps
Figure 5: Performance vs. efficiency trade-off. Forecast quality (CRPS 4, CSI-M 1) as a function of NFE. FlowCast (CFM) achieves near-optimal performance with only 3 to 10 steps, while the DDIM-based model requires 20 steps to steps, and degrades sharply at low NFE.
5 CONCLUSION
In this paper, we introduced FlowCast, the first fully probabilistic model applying Conditional Flow Matching (CFM) as a direct noise-to-data generative framework for precipitation nowcasting. Our experiments on the SEVIR and ARSO datasets show that FlowCast achieves state-of-the perfor- mance. Through direct ablation studies, we showed that the CFM objective not only more accurate than traditional diffusion objective on the same architecture but also vastly more efficient. Flow- Cast maintains high forecast quality with as few as a single sampling step, a regime where diffusion models fail. Our results firmly establish CFM powerful, efficient, and practical alternative for high-dimensional spatiotemporal forecasting.
Limitations and Future Work:
While FlowCast shows significant promise, we identify two pri- mary areas for future development. First, its reliance solely on radar data could be limitation. Future work should explore multi-modal data fusion (e.g., satellite, NWP) to enhance robustness and accuracy. Second, our evaluation was limited to two datasets due to computational cost; broader study across more meteorological regimes is needed to confirm generalizability.
ACKNOWLEDGMENTS
This work was supported by the Slovenian Research and Innovation Agency (ARIS) research core funding P2-0209 (Jana Faganeli Pucer). Special thanks go to Janko Merše and Dr. Matic Šavli from the Slovenian Environment Agency (ARSO) for their support and for providing the ARSO dataset.
REPRODUCIBILITY STATEMENT
To ensure the reproducibility of our work, have provided comprehensive supporting materials. Code: The full source code for our FlowCast model, including scripts for training and evaluation, is available at atttpp:: pettps://githus com / rbmp/ / loCast] The repository contains detailed
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instructions for setting up the required software environment and running the experiments. Datasets: Our work utilizes two datasets. The SEVIR dataset is a public benchmark, and details for access are provided by Veillette al. (2020). The ARSO dataset was provided by the Slovenian Environment Agency (ARSO) for this research; we are actively collaborating with the agency to facilitate its public release in the near future. Experimental Details: Section [4.] of the main paper provides a detailed description of our experimental setup. Further implementation details are available in Appendix A.I including all model hyperparameters.
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A
APPENDIX
A.1
IMPLEMENTATION DETAILS
This section details the implementation of FlowCast. The code to train and evaluate FlowCast is provided as supplementary material.
A.1.1
VAE
The VAE was configured with the hyperparameters shown in Table 7 Different warmup periods were empirically selected based on the convergence speed of the VAE on the respective datasets; ARSO converged faster due to less diversity in the data, attributed to factors such as its fixed geo- graphical coverage and the significantly smaller stride used during sequence extraction.
Table 7: VAE hyperparameter summary
Category
Parameter
Value / Setting
Dataset
Source
SEVIR (vil) & ARSO (zm) 384 x 384 (SEVIR), 301 401 (ARSO)
Input Dimensionality (per frame)
Input Preprocessing
Frame values scaled to [0, 1]
Training Objective
Loss Components
Reconstruction + KI. Divergence + Adversarial
KL Divergence Weight (AKl)
1 x 10
Discriminator Weight (Aedv)
0.5
Adversarial Loss Type
Hinge Loss
Discriminator Architecture
PatchGAN Issola et al. 2017]
Discriminator Activation Warmup
35 epochs (SEVIR), 15 epochs (ARSO)
Optimization
Optimizer (Generator & Disc.)
Adam 1 10-1
Learning Rate (Initial)
Weight Decay
1 x 10-5
AdamW Betas
(0.9, 0.999)
LR Scheduler
Cosine Annealing with Linear Warmup
LR Warmup Fraction
20% of total training steps
LR Min Warmup Ratio
0.1
Min. LR Ratio
10-3
Training Configuration Batch Size
12 (Global), 3 (Local)
Max. Number of Epochs
250
Gradient Clipping Norm
1.0
Early Stopping Patience
50 epochs
Early Stopping Metric
Generator validation loss
Training Nodes
x H100 GPUs
FP16 Training
Disabled
Model Configuration
Latent Channels
GroupNorm Num
32
Layers per Block
Activation Function
SiLU
Encoder-Decoder Depth
Block Out Channels
[128, 256, 512,
Data Preprocessing and Padding. inputs must be spatially divisible by the downsampling rate. For the ARSO dataset, the native
To accommodate the VAE's downsampling factor of f 8,
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resolution of 301 X 401 is not divisible by 8. We handle this by applying replication padding to the imput frames to reach the nearest multiple of 16 prior to encoding. Specifically, the height is padded from 301 to 304, and the width from 401 416. This results in the latent dimensions of and 416/8, respectively). During inference, the generated fields cropped back the original 301 401 dimensions before evaluation metrics computed. SEVIR dimensions (384 x 384) are naturally divisible by 8, requiring no padding.
38 x 52 reported in Ta
A. 1.1.2 FLOWCAST
Architecture. The FlowCast architecture, adapted from Eerthformer-UNet (Gao et al. 2023 for latent-space Conditional Flow Matching, has the following configuration:
Core U-Net Architecture:
Hierarchical Stages: A U-Net with hierarchical stages (one level of downsam- pling/upsampling within the main U-Net body, in addition to initial/final processing). Stacked Cuboid Self-Attention Modules: Each stage in both the contracting (en- coder) and expansive (decoder) paths contains depth of 4 Stacked Cuboid Self- Attention modules.
Base Feature Dimensionality: 192 units.
Spatial Processing:
Downsampling: Achieved using Patch Merge (reducing spatial dimensions by a fac- tor of 2 and doubling channel depth).
Upsampling: Uses nearest-n interpolation followed by a convolution (halving channel depth).
Cuboid Self-Attention Details:
Pattern: Follows axial pattern, processing temporal, height, and width dimensions sequentially.
Attention Heads: 4 attention heads.
Positional Embeddings: Relative positional embeddings used.
Projection Layer: A final projection layer part of the attention block.
Dropout Rates: Dropout rates for attention, projection, and Feed-Forward Network (FFN) layers are to 0.1.
Global Vectors: The specialized global vector mechanism from the original Earthformer is disabled.
FFN and Normalization:
-FNN Activation: Feed-forward networks within the attention blocks use GELU acti- vation.
Normalization: Layer normalization is applied throughout the relevant parts of the network.
Embeddings:
Spatiotemporal Positional Embeddings: Added to the input features after an initial projection.
CFM Time Embeddings (f):
* Generation:
Generated with channel multiplier of relative to the base feature dimensionality (resulting in 192 4 768 embedding channels). * Incorporation: Injected into the network each U-Net stage using residual blocks that fuse the time embedding with the feature maps TriimenbbedResBllcct modules).
Skip Connections: Standard U-Net additive skip connections merge features from the contracting path to the expansive path.
Padding: Zero-pade proo-padding is used where necessary to maintain tensor dimensions during convolutions cuboid operations.
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Training and Inference Hyperparameters. ters are as follows:
The FlowCast training and inference hyperparame-
Table 8: FlowCast hyperparameter summary
Category
Parameter
Value / Setting
Dataset
Source
Latent-space sequences from VAE 13 x 48 x 48 x (SEVIR), 13 38 52 4 (ARSO)
Input Dimensionality
Input Preprocessing
Standardized with training statistics (mean, std)
Training
Configuration
Loss Function
MSE: L l| - (Ziuure 2p)l 12 (Global), 3 (Local)
Size
Max. Number Epochs
Gradient Clipping Norm
Early Stopping Patience
50 epochs
Early Stopping Metric
CSI-M evaluated on subset (40 batches) validation
Training Nodes
4 H100 GPUs
FP16 Training
Enabled Enabled
Exponential Moving Average Weights
Exponential Moving Average Weights Decay
0.999
Optimization
Optimizer
AdamW 5 10
Learning Rate (Initial)
Weight Decay
1 x 10"4
AdamW Betas
(0.9, 0.999)
LR Scheduler
Cosine Annealing with Linear Warmup
LR Warmup Fraction
1% of total training steps
LR Min Warmup Ratio
0.1
Min. LR Ratio
10
CFM Parameters
0.01
ODE Solver
Euler Method with 10 steps
Choice of ODE Solver. We conducted ablation study compare various ODE solvers, includ- ing adaptive methods (Adaptive Heun, Dormand Cermand-Prince 5) and fixed-step methods (Euler, Midpoint, Runge-Kutte Kutta 4) on the first 10% the SEVIR test using single NVIDIA A100 GPU. For adap- tive solvers, relative and absolute tolerance of 10-2 and 10-3 were used, respectively. Since no significant performance differences were observed, shown Table we selected the Euler method with 10 steps for its computational efficiency and simplicity.
Table Ablation study: ODE solvers. Results highlight minor differences performance between the different solvers.
Solver
CRPS
CSI-M
CSI-P16-M
FSS-M-P16
HSS-M
FAR-M
Time/Sey.
Buler (1 steps)
0.0207
0.454
0.504
0.763
0.571
0.337
2.6
Euler (10 steps)
0.0168
0.455
0,514
0.764
0.572
0.338
24
Dormand-H Prind-- 5
0.0168
0.450
0.516
0.762
0.567
0.341
46
Midpoint (10 steps)
0.0167
0.762
Runge- Kutta 4 (10 steps)
0.0167
0.451
0.516
0.762
0.567
0.341
83
Adaptive Heun
0.0167
0.451
0,516
0.762
0.568
0.341
50
A.1.3 EVALUATION METRICS
Continuous Ranked Probability Score (CRPS). The CRPS evaluated directly at the original data resolution, without applying any spatial pooling. For each ensemble of N forecast members, CRPS calculated at every pixel and then averaged across all spatial positions and forecast lead times obtain single summary metric. If the predictive distribution F at a given pixel and time step is approximated by Gaussian with mean a and standard deviation a (estimated from the ensemble), and is the observed value, the CRPS can be computed as:
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1b
(
x
1
(F))
2
- 20
where and ø denote the cumulative distribution function (CDF) and probability density function (PDF) of the standard normal distribution, respectively. Following standard practice, we normalize the CRPS for each dataset by its maximum values, which scales our reported metrics by 1/255 for SEVIR and 1/57 for ARSO.
Threshold-based categorical metrics. For each chosen intensity threshold u, we binarize the continuous ground truth field i ; obtain an observation mask [f( u/. The represen- lative forecast Te,.,y (see Section 4.1.2) is likewise thresholded to produce a binary forecast mask He [[er,,, u].
Using these binary masks and the sppeset-specific thresholds, we construct a 2 x 2 contingency table for each evaluation:
H Hits (True Positives),
Observ. yes
Observ. no
M Misses (False Negatives),
Forecast yes Forecast no
H
F
False Alarms (False Positives),
M
C
C: Correct Negatives.
The values H, M, F, C' are summed over all spatial locations, batches, and forecast lead times. The following metrics are then computed:
False Alarm Ratio (FAR): The proportion of forecasted events that did not actually occur.
FAR
(2)
H+F
Critical Success Index (CSI): rectly predicted, ignoring correct negatives. This metric is sensitive to both missed events and false alarms.
The fraction of observed and/or forecasted events that were
H
CSI
(3)
M+M+F
Heidke Skill Score (HSS):
The accuracy the forecast relative to random chance.
2 MF) HSS (H M + C) + (H FC)
(4)
Pooled CSI (CSI-P16): Forecasts and ground truth fields are first downsampled by applying max-pooling operation over non-overlapping 16 x 16 pixel blocks. CSI is then recomputed on these pooled fields. hit anywhere within 16 16 block is registered a success, thereby rewarding models that capture the local presence and intensity of precipitation, even if exact pixel- level alignment is imperfect.
Fractions Skill Score (FSS): We compute FSS to evaluate spatial alignment. Let (i, and (o denote the fraction of pixels exceeding the threshold within neighborhood of size x centered (i,j) for the forecast and observation, respectively. The FSS is calculated
- (i. (o.
FSS 1
(5)
A.2 MORE QUALITATIVE EXAMPLES
This section presents additional qualitative examples comparing FlowCast against the baseline mod- els
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A.2.1 SEVIR
+10 min
+20 min
+30 min
+40 min
+50 min
+60 min
255
Ground
219
181
Earthformer
100
FarthFarseer
PreDiff
Cascast
FlowCast
Figure 6: Qualitative comparison of Flow with other baselines on a SEVIR sequence.
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+10 min
+20 min
+30 min
+40 min
+50 min
+60 min
255
Ground
219
Earthformer
EarthFarseer
FlowCast
Figure 7: Qualitative comparison of FlowCast with other baselines SEVIR sequence.
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+10 min
+20 min
+30 min
+40 min
+50 min
+60 min
255
Ground
219
Earthformer
160
EarthFarseer
PreDiff
FlowCast
Figure 8: Qualitative comparison of FlowCast with other baselines SEVIR sequence.
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A.2.2 ARSO
-10mmh
-Winh
400 10
Grone
Cascat
Figure 9: Qualitative comparison of FlowCast with other baselines on an ARSO sequence.
-10mm
+20 -30m
Craono Tuth
Cacaa
Figure 10: Qualitative comparison of Flow with other baselines on an ARSO sequence.
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-1h
+20 mh
m20mh
+40
-5
+00
Orund
Calcat
Figure 11: Qualitative comparison of FlowCas Cast with other baselines on an ARSO sequence.
A.3 LLM USAGE STATEMENT
During the preparation of this manuscript, we utilized a large language model (LLM) as writ- ing assistant. The LLM's primary role was to help refine sentence structure, improve clarity, and ensure grammatical correctness and consistency in tone. All scientific contributions, including the research ideation, methodological design, experimental analysis, and interpretation of results, were conducted solely by the authors, who take full responsibility for the content of this work.
21


Reviews:
[{"review_id": "nt3NryuPnF", "reviewer": "Reviewer_25PR", "summary": "This paper introduces FlowCast, which for the first time applies the efficient Conditional Flow Matching (CFM) framework to radar-based precipitation nowcasting. FlowCast integrates a variational autoencoder (VAE) for latent representation with a network backbone based on the Earthformer-UNet architecture. The authors aim to address the slow sampling issue inherent in existing diffusion models and use ablation studies to demonstrate the efficiency advantage of the CFM objective over the diffusion objective when applied to the same backbone.", "questions": "**Regarding False Alarm Ratio (FAR) and Forecast Quality:** Although FlowCast achieves the highest HSS-M score, your results indicate a **higher FAR-M** (compared to Earthformer and PreDiff). This suggests that FlowCast's high-scoring forecasts may be accompanied by a high rate of 'false alarms'. Could you provide and discuss more detailed categorical skill scores, such as the Threat Score (TS) or Equitable Threat Score (ETS) across different precipitation thresholds? This would help provide a fairer assessment of the model's actual skill in predicting high-intensity precipitation events.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. **Innovation and Practicality of the Methodology:**    \n    - The paper innovatively introduces the highly efficient CFM generative framework to the high-dimensional spatiotemporal prediction problem of precipitation nowcasting. This represents a valuable exploration of cutting-edge methods in the field.\n        \n    - A direct comparison and ablation study with diffusion models clearly demonstrates the significant advantage of CFM in terms of efficiency, showing that it can maintain or closely approach optimal performance with very few function evaluations (NFE). This is of high practical value for time-sensitive nowcasting applications.\n        \n2. **Clarity and Readability:** The paper is well-structured, the methodology is detailed, and the explanation of the CFM training and sampling process is particularly concise and clear, making the working principle of FlowCast easy to understand.\n    \n3. **Recognition of Effort and Contribution:** Given that the authors performed comparisons across two generative paradigms (CFM vs. Diffusion) and conducted experiments on two diverse geographical and climatic datasets, they clearly put effort into advancing model performance and exploring efficiency.", "weaknesses": "1. **Incomplete Experimental Results and Lack of Convincing Evidence:**\n    \n    - **Inadequate Metric Presentation:** The experimental evaluation lacks quantitative verification of structured forecasts. For instance, the absence of analysis using spatial verification methods (e.g., FSS at different scales) prevents a thorough validation of the model's ability to predict the structure and location of precipitation systems. Furthermore, the authors did not provide complete performance curves for **all** baseline models across all future lead times on both datasets.\n        \n2. **Ambiguity in Innovation Positioning and Insufficient Exploration Breadth:**\n    \n    - **If positioned as Framework Innovation:** Simply applying the existing CFM training/sampling framework to a UNet backbone based on Earthformer Blocks results in a relatively low degree of methodological novelty for an ICLR-level conference.\n        \n    - **If positioned as Application Exploration:** The paper aims to explore the general applicability of CFM as an efficient generative paradigm for precipitation nowcasting, but the breadth of exploration is insufficient. The lack of comparative experiments applying CFM to at least one different type of advanced nowcasting model makes it difficult to strongly demonstrate CFM's effectiveness as a general objective function.", "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": "GQdi1gHkEU", "reviewer": "Reviewer_itD7", "summary": "The paper describes the new solution to the problem of radar-based precipitation nowcasting, which uses conditional flow matching (CFM). The authors highlight accuracy and efficiency based on the empirical evidence.", "questions": "1. I would appreciate if the authors add confidence intervals to the key results (Tables 3-5 in particular). \n\n2. The algorithms for training and sampling at Page 4 are difficult to follow, therefore I would suggest that the authors present them as algorithm blocks.\n\n3. The authors say in the abstract: \"the uncertainty of atmospheric dynamics and the efficient modeling of high-dimensional data\" I am curious whether this paper addresses this question in any way beyond the accuracy metrics?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "Significance: improving the performance and democratising access to precipitation forecasting is an important problem as addressed in this paper. \n\nClarity: the paper is in general clearly written\n\nQuality: the methodology is sound, the description seems correct and reproducible. The ablation studies show the justification for experimental decisions.", "weaknesses": "Quality: The main argument is that swapping the training procedure from the diffusion model to conditional flow matching improves the performance, both in terms of accuracy and in terms of computational efficiency. The problem I see with this argument, however, is that there is previous evidence, in other domains, that the CFM may improve upon the efficiency and performance [1].  I would expect in this case for the authors to link it to the background and then say something that goes beyond the existing literature.  . In other words, the specific link to the justification of this method is missing. \n\nOriginality: as the narrative goes now, and stemming from the previous point, there is a concern that the work is an application of CFM for the precipitation forecasting. That should, in my mind, include a justification that is unique to the precipitation nowcasting (theoretical or otherwise).  Therefore, I would invite the authors to expand upon the contribution and say how this work goes beyond the existing literature in this aspect. \n\nIn summary, my main concern is that the claims now appear to be a combination of the two: CFM and precipitation forecasting.  I would invite the authors therefore to expand upon why this reasoning could be wrong. \n\n\n[1] Lipman et al (2023) Flow Matching  for Generative Modeling, ICLR 2023", "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": "wTYARPjirQ", "reviewer": "Reviewer_EpeH", "summary": "The proposed FlowCast model is the first to apply Conditional Flow Matching to precipitation nowcasting, which utilizes VAE to compress radar data into latent space, combines Cuboid Attention and CFM to learn direct noise-data mapping for efficient sampling. On SEVIR and ARSO datasets, its various metrics outperform baselines like PreDiff and CasCast, with 10-step sampling more efficient than diffusion models.", "questions": "1. This work only validates performance on two radar datasets (SEVIR, ARSO). Are there experimental results tested on more datasets? Meanwhile, is the performance tested under shorter or longer lead times?\n2. The probabilistic baselines compared in this work only include diffusion models. Is there a comparison with GAN-based probabilistic models?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. This work is the first to apply Conditional Flow Matching to precipitation nowcasting, learning a direct noise-to-data mapping. It achieves high-fidelity forecasting with only 10 sampling steps.\n2. This work adopts VAE latent space compression and Cuboid Attention architecture, balancing the efficiency of high-dimensional radar data processing and the ability of spatiotemporal dynamic modeling.", "weaknesses": "1. Conditional Flow Matching achieves the mapping from noise to data by learning a vector field. However, the paper does not design a prior structure for the vector field in combination with the physical laws of precipitation, relying entirely on data-driven learning. This may lead the model to generate results lacking physical condition constraints.\n2. This work applies CFM to precipitation nowcasting for the first time, but the idea of CFM has already been validated in some tasks within the field of computer vision. The paper transfers it to the precipitation nowcasting scenario and fails to propose improvement strategies for CFM targeting certain characteristics of precipitation data (such as strong spatiotemporal correlation, dynamic evolution, etc.). This results in deficiencies in innovation and generative capability.", "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": "mww3W9ibh6", "reviewer": "Reviewer_zhbk", "summary": "This paper introduces FlowCast, which applies Conditional Flow Matching (CFM) to radar-based precipitation nowcasting. The authors claim this is the first attempt to use CFM for spatiotemporal probabilistic forecasting. The model operates in a latent space via a VAE encoder-decoder and adopts an Earthformer-UNet backbone to learn a noise-to-data vector field. Experiments on the SEVIR and ARSO datasets show that FlowCast achieves state-of-the-art results in CRPS, CSI, and HSS, while requiring far fewer function evaluations than diffusion-based models. The paper highlights the superior efficiency–accuracy trade-off of CFM over diffusion objectives and positions FlowCast as a practical framework for real-time nowcasting.", "questions": "1. The abstract mentions “predictive accuracy,” while the contributions section claims “probabilistic performance,” so which one defines the main contribution?\n2. The description in Section 4.1.1 on Page 5 states that the ARSO dataset uses “63,716 training samples,” while Table 1 lists only 38,229 training samples. How can this internal inconsistency in dataset splits be explained?\n3. Table 2 on Page 6 defines the ARSO latent dimensions as 13/12 × 38 × 52 × 4, which implies non-integer compression factors compared to the original 301 × 401 resolution. How is the rounding or padding handled to yield these dimensions?\n4. The results table for ARSO on Page 8 labels the highest-threshold metric as “CSI-219,” even though Section 4.1.2 defines ARSO thresholds as [15, 21, 30, 33, 36, 39] dBZ. Thus, why does the same SEVIR-specific label appear in the ARSO table, and what threshold does it actually represent?\n5. The conclusion on Page 9 asserts that FlowCast “maintains high forecast quality with as few as a single sampling step,” while all experiments use 10 steps for evaluation. How is this claim supported?\n6. Appendix Table 6 uses different VAE warmup epochs for SEVIR and ARSO despite both using the same VAE architecture. What rationale justifies this discrepancy in the training configuration?\n7.Appendix A.1.2 mentions an ODE solver ablation but gives no data. How is the claim of no difference supported?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 1, "presentation": 3, "contribution": 1, "strengths": "S1. This paper demonstrates significantly reduced inference steps compared with diffusion methods, making it relevant for operational nowcasting systems that demand fast turnaround.\n\nS2. FlowCast achieves superior or competitive scores on two diverse radar datasets, confirming generalization beyond a single domain.\n\nS3. The paper provides explicit architectural and training settings, enhancing reproducibility.\n\nS4. It addresses the real bottleneck in generative nowcasting (diffusion sampling inefficiency) with a concrete solution.", "weaknesses": "W1. The claim in the abstract and introduction that this work is the first to apply Conditional Flow Matching to precipitation nowcasting is inaccurate, since prior studies such as the ICML 2025 rectified flow model have already demonstrated flow-based precipitation refinement.\n\nW2. The literature review in Section 2 on probabilistic nowcasting omits flow-based approaches when contrasting generative paradigms, resulting in an incomplete overview of probabilistic modeling beyond GANs and diffusion.\n\nW3. The introduction of flow models in Section 2 does not provide a comparative analysis against other flow-based frameworks such as Continuous Normalizing Flows or rectified flows, leaving the advantages of CFM for spatiotemporal uncertainty unexplained.\n\nW4. The architectural description in Section 3.2.2 shows that FlowCast merely adapts Earthformer-UNet to the CFM objective without introducing new architectural mechanisms or theoretical insights that would justify CFM’s suitability for atmospheric dynamics.\n\nW5. The experimental comparison in Section 4.2 excludes recent transformer-based baselines such as Earthfarseer, which limits the credibility of the claimed state-of-the-art performance.\n\nW6. The evaluation in Section 4.2 relies heavily on aggregated scores such as CSI-M and HSS-M, which mask variations across precipitation intensities and obscure the model’s behavior on extreme rainfall events.\n\nW7. The methodological and inference descriptions in Sections 3.2.2 and 4.1.4 omit discussion of training instability and ODE-based sampling overhead, leaving open questions regarding robustness and computational scalability.", "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)