OmniCast: A Masked Latent Diffusion Model for Weather Forecasting Across Time Scales
Authors: Tung Nguyen
Tuan Pham
Troy Arcomano
Rao Kotamarthi
Ian Foster
Sandeep Madireddy
Aditya Grover
Venue: NeurIPS 2025 poster ()
Keywords: weather forecasting
S2S prediction
latent diffusion models
masked autoregressive models
Primary area: machine_learning_for_sciences
TL;DR: We propose OmniCast, a scalable and skillful probabilistic model that unifies weather forecasting across timescales.

Abstract: Accurate weather forecasting across time scales is critical for anticipating and mitigating the impacts of climate change. Recent data-driven methods based on deep learning have achieved significant success in the medium range, but struggle at longer subseasonal-to-seasonal (S2S) horizons due to error accumulation in their autoregressive approach. In this work, we propose OmniCast, a scalable and skillful probabilistic model that unifies weather forecasting across timescales. OmniCast consists of two components: a VAE model that encodes raw weather data into a continuous, lower-dimensional latent space, and a diffusion-based transformer model that generates a sequence of future latent tokens given the initial conditioning tokens. During training, we mask random future tokens and train the transformer to estimate their distribution given conditioning and visible tokens using a per-token diffusion head. During inference, the transformer generates the full sequence of future tokens by iteratively unmasking random subsets of tokens. This joint sampling across space and time mitigates compounding errors from autoregressive approaches. The low-dimensional latent space enables modeling long sequences of future latent states, allowing the transformer to learn weather dynamics beyond initial conditions. OmniCast performs competitively with leading probabilistic methods at the medium-range timescale while being 10× to 20× faster, and achieves state-of-the-art performance at the subseasonal-to-seasonal scale across accuracy, physics-based, and probabilistic metrics. Furthermore, we demonstrate that OmniCast can generate stable rollouts up to 100 years ahead. Code and model checkpoints are available at https://github.com/tung-nd/omnicast.

OmniCast: A Masked Latent Diffusion Model for Weather Forecasting Across Time Scales
Tung Nguyen' Tuan Pham² Troy Arcomano
Veerabhadra Kotamarthi Aditya Grover!
Ian Foster* Sandeep Madireddy
UCLA UCI AArgonne National Laboratory
4 *Allen Institute for Al
Abstract
Accurate weather forecasting across time scales is critical for anticipating and mitigating the impacts of climate change. Recent data-driven methods based on deep learning have achieved significant success in the medium range, but struggle (S2S) horizons due to error accumulation their autoregressive approach. In this work, we propose OmniCast, scalable and skillful probabilistic model that unifies weather forecasting across timescales. OmniCast consists of two components, VAE model that encodes raw weather data into continuous, lover-dimensional latent space, and diffusion- -based transformer model that generates a sequence of future latent tokens given the initial conditioning tokens. During training, we mask random future tokens and train the transformer to estimate their distribution given conditioning and visible tokens using per-token diffusion head. During inference, the transformer generates the full sequence of future tokens by iteratively unmasking random subsets of tokens. This joint sampling across space and time mitigates compounding errors from autoregressive approaches. The low-dimensional latent space enables modeling long sequences of future latent states, allowing the transformer to learn weather dynamics beyond initial conditions. OmniCast performs competitively with leading probabilistic methods at the medium-rets maang timescale while being 10x to 20 faster, and achieves state-of oftte-of-th-arr performance at the taaseasonallto-seasoono scale across accuracy, physics -bsedd and probabilistic metrics. Furthermore, demonstrate that OmniCast can generate stable rollouts up to 100 years ahead. Code and model checkpoints available at httts://gitubbcoommmnnaaaooooiio
at longer jubseasonal-to-seaanne
1 Introduction
Accurate weather forecasting across time scales is essential for anticipating extreme events, managing resources, and mitigating the impacts climate change. While medium-range forecasting, which encompasses predictions up to approximately two weeks, has seen remarkable progress with both numerical and data-driven approaches, extending prediction skill beyond this horizon remains significant challenge. soteeassoalltosscssoooo (S2S) forecasting, which aims to predict atmospheric conditions from two to six weeks ahead, represents this next frontier. This timescale bridges the gap between short-term weather forecasts and longer-term climate projections, enabling more informed mociing for extreme weather events such as droughts, floods, and heatwaves [31] 36 52 g). S2S prediction is particularly challenging due to the interplay between atmospheric initial conditions, essential for short-term and medium- rrange forecasting, and boundary conditions dominating seasonal and climate predictions [27 28 Traditional numerical weather prediction (NWP) models, built upon solving differential equations of fluid dynamics and thermodynamics. have been instrumental in advancing S2S weather prediction 36 47 48 However, numerical methods ineur substantial computational costs due to the complexity of integrating large systems of differential equations,
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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particularly at fine spatial and temporal resolutions. This computational bottleneck also constrains the ensemble size of ensemble systems, which is crucial for achieving accurate S2S predictions.
To overcome the challenges of NWP systems, there has been a growing interest in data-driven
approaches based on deep learning for weather forecasting [10] 43 50 These approaches involve
training deep neural networks on historical datasets, such as ERA5 14.
15
39 40), to learn the
underlying weather patterns. Once trained, they can produce forecasts in seconds compared to the hours required by NWP models. Recent deep learning methods such as Pangu
[2]. Graphcast [221, and Stormer [33] have also shown superior accuracy in medium- -aanggg weather forecasting, surpassing operational IFS [49], the state-of-the-an NWP system. However, their application the S2S timescale has been limited [3]]. One possible explanation for this limitation is the rapid error compounding in their autoregressive designs, in which model learns to forecast the future weather state a small interval and iteratively feeds its prediction back as input to achieve longer-horizon forecasts. Even though previous works have proposed multi-step finetuning to mitigate this issue, back propaagatn through large number of forward passes required for S2S timescales is computationally prohibitive. Moreover, training a neural network to forecast at a small interval only allows the model to learn the initial conditions problem, ignoring boundary conditions that are critical for prediction at S2S timescales.
In this work, we propose OmniCast, novel latent diffusion model for skillful probabilistic weather forecasting across time scales. OmniCast follows a two-stage training process. In the first stage, we train VAE model [99] that compresses raw weather data into a continuous,
dineessnnnsi latent space. In the second stage, we train transformer to model the distribution of a sequence of future latent tokens given the initial conditioning tokens using a masked generative framework 55 Specifically, during training, we randomly mask subset of future tokens, and task the transformer to unmask these tokens based on the conditioning tokens and the visible tokens. Since the latent tokens lie in continuous space, we use small diffusion network on top of the transformer model to estimate the per-token distribution of unmasked tokens. In addition to the diffusion loss, we apply a mean-squared error (MSE) objective to enforce the model to accurately predict the first few latent frames deterministically. After training. OmniCast generates forecasts for the full sequence of future tokens through iterative process. In each iteration, the model selects subset of future tokens to unmask given the conditioning tokens and previously unmasked tokens, continuing this process until all future tokens are generated. The unmasking operation involves sampling from the diffusion model, with the number and positions of tokens selected to unmask in each iteration determined by a predefined schedule and unmasking order. This joint generation of future tokens across time and space significantly mitigates the compounding errors issue of an autoregressive approach. Furthermore, training on the full sequence of future frames enables OmniCast to address both initial condition problems and boundary condition challenges, which are critical for S2S prediction.
We evaluate OmniCast on ChaosBench [1I, recent benchmark for subscasonal -o-sssss--- predic- tion. OmniCast achieves state-of-the-arr performance on key atmospheric variables across various accuracy, physics-based based, and probabilistic metries. Additionally, we carefully study the impact of different design choices, including the auxiliary MSE loss, training sequence lengths, unmasking order, and diffusion sampling temperature, on the forecasting performance of OmniCast.
2 Related Work
Data-driven weather forecasting Deep learning has become a promising approach in the field of weather forecasting. Recent advancements with powerful architectures have achieved significant successes, providing faster inference and superior forecasting accuracy compared to IFS, the gold- standard numerical weather prediction system. Notable methods include FourCastNet (35). which utilizes an adaptive neural operator architecture; Keisler [7]ss ss GraphCast 22]. and AIFS 24). which leverage graph neural networks; and a series of transformer-
models such as Pan- gu Weather [2]. Stormer [33], and others [32 6 77. Beyond deterministic predictions, the field has increasingly focused on probabilistic forecasting to better account for forecast uncertainty. Common approaches involve integrating existing architectures with generative frameworks, including diffusion models [37 30, normalizing flows 77. and latent variable models (4. Others explore ensemble
predictions through initial condition perturbations, exemplified by methods like AIFS-CRPS 24 and
NeuralGCM (20].

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Data-driven S2S prediction Recent benchmarks have emerged to evaluate data-driven methods at S2S timescales. While many focus on regional forecasts such as the US T6 29 ChaosBench [3] offers a comprehensive framework for global S2S prediction, providing extensive numerical baselines and physics-based metrics. key finding from ChaosBench shows that state-of-the-ar deep learning methods struggle to extend to S2S timescales. These methods predominantly rely on autoregressive approaches that generate predictions iteratively at short time intervals, leading to error accumulation with increasing lead times. While multi-step finetuning helps mitigate this issue for medium-range forecasts, it becomes computationally prohibitive for S2S predictions due to the extensive number of required forward passes. Moreover, training models with short time intervals fails to capture boundary conditions essential for long-term weather patterns. While Fuxi-S2S [5] was proposed for S2S prediction, it focuses on forecasting daily averaged statistics, which fundamentally alters the underlying weather dynamics and makes it inapplicable to forecasting at instantaneous time steps.
3 Background and Preliminaries
3.1 Weather forecasting
The goal of weather forecasting is to forecast future weather conditions Xy V based on initial conditions X₂ E VRWWHWW where represents the target lead time, V denotes the number of input and output physical variables (e.g., temperature and geopotential), and H x W corresponds to the spatial resolution of the data, determined by the density of the global grid. In subseasonal- to-seasonal (S2S) forecasting, we focus on lead times ranging from 2 to 6 weeks. Autoregressive modeling is a dominant paradigm in data-driven weather forecasting, where a model iteratively produces forecasts X&t at short interval øt to reach the target lead time T. In this work, we propose an alternative approach: training a generative model to estimate the distribution of the entire sequence of future weather states X1.T given initial conditions Xp. This approach mitigates error accumulation and enables the model to learn both initial and boundary condition dynamics by considering the complete sequence of weather states.
3.2 Masked generative modeling
Masked generative modeling is efficient and powerful approach for image and video generation in computer vision 3 555 (25). In this framework, visual data X1.T E T X XW (T = for images) is first embedded by VAE encoder into sequence of tokens E RND, where N represents the length of the flattened token sequence. During training, we apply a binary mask to randomly select subset of tokens be predicted, creating a corrupted sequence. We then train a transformer model to recover the original tokens at masked positions based on both the visible tokens and any additional conditioning information such as initial frames. For generation, the framework employs an iterative decoding process that starts with fully masked sequence of future tokens. In each iteration, the model predicts random subset of masked tokens in parallel, where the number and positions of the unmasked tokens follow a predefined schedule and order. This process continues until all tokens are unmasked, which point the generated tokens are decoded back to the original domain through a VAE decoder. This framework offers key advantages for weather forecasting: it allows the model capture long-range dependencies across the entire sequence while avoiding the error accumulation typical in autoregressive approaches, and the iterative refinement process enables the model maintain consistency across both spatial and temporal dimensions.
3.3 Modeling continuous tokens with diffusion models
In the masked generative modeling framework, common practice is embed the raw visual data into a discrete latent space and train the transformer model using a cross-entropy objective. However, this approach relies on quantiiqe VAE models [45), which are sensitive to gradient approximation strategies [4] 38 21 and typically achieve lower reconstruction quality than continuous-valle VAES. Recent works 44 26 have demonstrated that discretization be eliminated by directly modeling the per-token probability distribution in a continuous latent space. In this work, we adopt diffusion models for continuous distribution modeling.
Given data x E R and its conditioning information E RDD, we model the conditional distribution p(z| 2) using diffusion process that gradually transforms Gaussian prior into the target distribution.
3

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2
Transformer backbone
Figure The denoising network predicts the noise from and x
CAxw
322
[M]
xN-3
Figure 1: OmniCast processes the latent tokens through trans- former backbone that outputs a vector 2; for each position in the sequence.
Figure 3: The deterministic net- work predicts directly v; from z;.
The forward diffusion process progressively adds Gaussian noise to the data x following:
Va,z + v1 a.c.
(1)
where s indicates the diffusion step, as determines the noise schedule, and ~ NO I) represents Gaussian noise. The reverse process employs denoising network cØ (I parameterized by 0 to predict the noise component from the noisy input 2, and condition 2:
(0) Ex, [Ileo(12,, 8,2) =l
(2)
At inference time, conditional sampling begins with random Gaussian noise rs ~O(0,) I) and iteratively applies the reverse diffusion process:
1 Cxe co( Vl-a, as
x- Vas
Taz
where a, IT. Ore. 8 NO. and a, controls the magnitude of noise added each step. This
iterative process generates samples from the learned conditional distribution pe(z 2). Following (26],
we additionally scale the noise σ, by the temperature that controls the sample diversity from the diffusion model.
Methodology
We present OmniCast, novel method for subseasonal-to-sse -o-ssssoallll prediction. Similar to previous works in video generation, OmniCast consists of two components: a VAE model that compresses the raw weather data into lower-dimensional space, and masked generative transformer model in this latent space. We present the two components and their key design choices this section.
4.1 VAE for weather data embedding
A VAE encoder embeds weather state X E RVxHxW into map of h x w latent tokens, where H and < W. In vector-quantizer VAEs, each entry in the latent map an integer index from fixed-size vocabulary, representing discrete latent space. While this discretization is widely adopted computer vision due compatibility with cross-entropy training and straightforward sampling from softmax distributions, it presents significant challenges for weather data. Unlike RGB images with three channels, weather states can contain hundreds physical variables, resulting an extreme compression requirement. For instance, consider compressing weather data with 100 variables (32 bits per value) by a factor of 4 in each spatial dimension, using a vocabulary size of 213 8192 (13 bits latent token). This results in compression ratio of (32 x 100 x H W13 (H/4) (W/4)) se 3938. Such aggressive compression leads to substantial reconstruction errors, ultimately degrading the performance of the second-stage stage generative modeling.
Therefore, we adopt a continuous VAE model for OmniCast, where each token in the h latent map is a continuous vector of D dimensions. With D = 16, for example, the compression ratio becomes (32 100 H (3/
16 x (H/4) (W/4)) = 100, substantially lower than the discrete approach. While it is also possible to compress sequence of weather states X1.r E x in both temporal and spatial dimensions, our preliminary experiments showed no clear benefits from temporal compression, leading to adopt per-frame embedding.

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4.2 Masked generative modeling for S2S prediction
After training the VAE, we embed the initial condition into a sequence of tokens c Similarly, each future weather state is embedded into sequence of tokens, which are concatenated to form the complete sequence of future tokens x = ( : where N = T x h x w represents the total number of future tokens. Fach latent token is continuous vector of dimension D. Our generative modeling objective to estimate the conditional distribution p(x c) from the training data.
(ci,
We achieve this using masked generative framework, as illustrated in Figure During training, we sample a binary mask m [m,]] Pu and replace tokens I; with learnable, continuous [MASK] token where m, = creating a corrupted sequence m(x.. The generative objective is to estimate the distribution of masked tokens conditioned on the visible and conditioning tokens:
Lgen
E m--ppt
Σ ses. m,=1
- log -oggpo (o
c, c,,x
The model processes the imput by concatenating the conditioning tokens e with the corrupted future tokens X, adding positional encodings to the embedded sequence, and passing it through bi- directional transformer backbone to obtain vectors 2; for each masked position. Given these vectors, the per-token objective log (r;; c, X) in Equation simplifies to log po 2;). To model this continuous distribution, we employ a diffusion model where 2; serves as conditional information for a denoising network implemented as small MLP on top of the transformer (Figure (2). We train the denoising network and the transformer backbone jointly using the diffusion loss specified in Equation? 2 Conceptually, this diffusion objective encourages the model to produce representations 2 that facilitate effective denoising.
Auxiliary deterministic objective To encourage accurate predictions of near-term future tokens, we incorporate an auxiliary mean-squared error loss the latent space. We implement this through a separate MLP head that produces deterministic predictions 2; from 24, training it jointly with the transformer backbone. Since weather dynamics become increasingly chaotic beyond day 10, making deterministic predictions progressively less meaningful, we apply this loss only to the first 10 future frames. Furthermore, we employ an exponentially decreasing weighting scheme to emphasize the importance of accurate predictions for earlier frames. The deterministic objective is thus:
Laeter (
E IIU-P
Σ w(i]l> 2.112
(5)
Appendix A.2 presents the details of this objective. The complete training objective combines both losses: C(0) Lgen( (O).
Sampling from OmniCast At inference time, we generate samples from p(x c) through an iterative decoding process, starting from sequence of fully masked future tokens. Each iteration consists of three steps: first, the transformer backbone processes the conditioning tokens and corrupted future tokens produce vectors 2; for each masked position; second, a subset of masked positions randomly selected according to a predefined schedule for unmasking: third, for each selected position, the diffusion model generates token 2; by conditioning on 2; and performing fixed number of diffusion steps. This process iterates until all future tokens are revealed, at which point the VAE decoder maps the generated tokens back the weather domain. To generate ensemble of forecasts, we simply replicate the initial tokens and perform independent sampling for each copy. Four hyperparameters affect the sampling procedure: the number of unmasking iterations, the unmasking order, the number of diffusion steps, and the diffusion temperature.
4.3 Implementation details
Architectural details For the transformer backbone, we adopt the encoder-decoder architecture from Masked Autoencoder (MAE) [13]. The model processes an input sequence in two stages: first, the encoder processes the conditioning and visible tokens; second, the encoded sequence augmented with learnable [MASK] tokens at appropriate positions and passed through the decoder to produce 2, for each position i. Both the encoder and decoder are bidirectional, employing full attention. Before feeding to either the encoder or decoder, we add the input sequences with positional
5

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embeddings that combine two components: temporal embeddings to distinguish different frames, and spatial embeddings to differentiate tokens within each frame. The encoder and decoder follow the Transformer [46] implementation in ViT [9], each having 16 layers with 16 attention heads, hidden dimension of 1024, and dropout rate of 0.1.
Mask sampling During training, we sample a masking ratio ~ (0055, 1.0] and generate a corresponding binary mask m, where 7 = 0.75 indicates that 75% of entries inference, we start with full masking (7 = 1.0) and gradually reduce it to 0.0 following a cosine schedule (3]. We set the number of unmasking iterations match the number of future weather states T by default. We employ random masking orders across both spatial and temporal dimensions for training and inference.
are 1. For
Diffusion loss details We use a linear noise schedule with 1000 steps at training time that are resampled to 100 steps inference. The denoising network eg is implemented following Li et al. (26). Specifically, the network consists of six residual blocks, each comprising a LayerNorm (LN), linear layer, a SiLU activation, and another linear layer, with residual connection around the block. Each block maintains a width of 2048 channels. The network takes the vector ž; from the transformer as conditioning information, which is combined with the time embedding of the diffusion step through adaptive layer normalization (AdaLN) in each block's LN layers.
small MIP
5 Experiments
We compare OmniCast with state-of-the-art deep learning and numerical methods on both medium- range and S2S time scales, using WeatherBench2 [40] (WB2) and ChaosBench [31] as benchmarks, respectively, and conduct extensive ablation studies to assess the contribution of each component in OmniCast. We further test the stability of OmniCast up to 100 years ahead in Appendix
Across both tasks, we train and evaluate OmniCast on 69 variables from the ERA5 reanalysis dataset [1), including four surface-level variables 2-meter temperature (T2m), 10-meter U and V wind components (U10, V10), and mean sea-level pressure (MSLP), as well as five atmospheric variables - geopotential (Z), temperature (T), U and V wind components, and specific humidity (Q), each at 13 pressure levels (50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000) hPa. For medium-range forecasting, we use native 0.25 resolution (721 1440 grids) and follow WB2 to train on years 2018- validate on 2019, and test on 2020 using initial conditions at OOUTC and 12UTC. For S2S prediction, we downsample the data to 1.40625" (128 x 256 grids) and follow ChaosBench to train on 1979-2020, validate on 2021, and test on 2022 using OOUTC initializations.
5.1 OmniCast for S2S prediction
Training and inference details We train a VAE that embeds each weather state of shape 69 x 128 x 256 into a latent map of shape 1024 8 x 16. reducing spatial dimensions by factor of 16. The architectural details and training process of the VAE are described in Appendix A. We train OmniCast to forecast a sequence of T 44 future weather states at 24hr intervals, covering lead times from to 44 days. Each training example consists of 45 x 8 x 16 = 5760 latent tokens, including the initial condition. During inference, we generate the complete future sequence in 44 iterations (1 iteration per frame) using a diffusion temperature of r = 1.3. We produce an ensemble of 50 forecast sequences for each initial condition.
Baselines We compare OmniCast with Pangu Weather (PW) (2 and GraphCast (GC) 22 two leading open-sourced deep learning methods, and ensemble systems of four numerical models from different national agencies: UKMO-ENS (UK) [53]. NCEP-ENS (US) 442. CMA-ENS (China) [54], and ECMWF-ENS (Europe) M.. We refer to ChaosBench for details about these baselines. Following ChaosBench, we report results on T850, Z500, and Q700 at lead times from to 44 days. We additionally compare OmniCast with ClimaX 32 and Stormer [33 in Appendix B.2 We do not compare against Fuxi-S2S [3] as Fuxi-S2S forecasts daily average values from past daily averages, making it incomparable with OmniCast and the rest of the methods, which perform point-in-time weather forecasting based on an initial condition. We are also not able to run Gencast 37 and NeuralGCM 20 for S2S due to their significant computational demands.
Results Figure 4 compares different methods on three deterministic metrics: Root Mean-Squared Error (RMSE), Absolute Bias (ABS BIAS), and Multi-scale Structural Similarity (SSIM). At shorter

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BCWWW ENS
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Figure 4: Deterministic performance of different methods at lead times from to 44 days across three key variables. Solid curves are deep learning methods and dashed curves are numerical methods.
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Figure 5: Physics-based metries of different methods lead times from 44 days across three key variables. Solid curves are deep learning methods and dashed curves are numerical methods.
lead times, OmniCast shows slightly worse performance on RMSE and SSIM than other baselines, which is expected since we train OmniCast to model a full sequence of future weather states rather than optimizing for short- and medium-range predictions. However, OmniCast's s relative performance improves with increasing lead time, ultimately matching ECMWF-ENS performing methods beyond day 10. Notably, OmniCast demonstrates the lowest bias among all baselines, maintaining near-zero bias across all three target variables.
of the top two
Physical consistency also plays a crucial role in S2S prediction, particularly for ensemble systems. We evaluate this aspect using two physics-based metrics: Spectral Divergence (SDIV) and Spectral Residual (SRES), which measure how closely the power spectra of predictions match those of ground-truths. As shown in Figure OmniCast achieves substantially better physical consistency than other deep learning methods, often outperforms all baselines on these metrics. These results demonstrate how OmniCast effectively preserves signals across the frequency spectrum.

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2300 19766)
T800
07700 (phey)
302
1.2
CRPS
002
202
162
0.1
Lead Time (Dayst
OmniCast
EOWWF
ENS
ENS
Figure 6: Probabilistic performance of different methods at lead times from 1 to 44 days across three key variables. Solid curves are deep learning methods and dashed curves are numerical methods.
Finally, we compare OmniCast with the four numerical ensemble systems on two probabilistic metries: Continuous Ranked Probability Score (CRPS) and Spread/Skill Ratio (SSR) (closer to better). Figure 6 shows that OmniCast and ECMWF-ENS are the two leading methods across variables and lead times. Similar to deterministic results, OmniCast performs worse than ECMWF-ENS shorter lead times but outperforms this baseline beyond day 15.
5.2
OmniCast for medium-range forecasting
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Figure 7: Probabilistic performance different methods in medium- rangge forecasting, Solid curves are deep learning methods and dashed curves are numerical methods.
addition its strong performance on S2S task, we demonstrate that OmniCast also performs competitively at the medium-rande range timescale. We train VAE model with spatial downsampling ratio 16, compressing each weather state of shape x 721 x 1440 latent representation of size 256 45 x 90. We then train OmniCast to predict two steps ahead at 12-hour intervals, following

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the setup of Gencast (371. During inference, we use autoregressive sampling, recursively feeding the most recent predicted frame as the new initial condition until the target lead time reached. We generate forecasts using a single sampling iteration per frame with diffusion temperature = 1.0. and produce an ensemble of 50 members.
We compare OmniCast against Gencast [37]. a leading deep learning method for probabilistic fore- casting, and IFS-ENS [23], the gold-standard numerical ensemble system. Following WeatherBench2, we use ensemble RMSE, CRPS, and spread-skill ratio (SSR) as evaluation metrics, As shown in OmniCast performs comparably with IFS-ENS across all variables and metries, and is only slightly behind Gencast. These results indicate that OmniCast achieves strong performance across both medium meedium-range range and S2S timescales.
Figure 7
5.3 Efficiency of OmniCast
Beyond its strong empirical perfor- mance, OmniCast offers substantial efficiency gains over existing meth- ods. We trained OmniCast for 4 days using 32 NVIDIA A100 GPUs. In comparison, Gencast requires 5 days of training on 32 TPUv5e devices
Runtime Resolution
Omni Cast (A100)
Gencasi (A100)
Gencast: ITPU
107
ENS (96 Rome CPUs!
NeuralGGM (4100)
Runlime (veconds)
hardware significantly more powerful than A100s, and NeuralGCM 20 re-
quires 10 days on 128 TPUv5e de- vices. Additionally, Geneast employs a two-stage training pipeline, first pre- training on 1.0% resolution and then finetuning on 0.25%, while we trained OmniCast single stage.
Resolution (cegrees)
Figure 8: Runtime vs resolution to produce 15 15-day forecast.
At inference time, OmniCast is orders of magnitude faster than Gencast, NeuralGCM, and IFS IFS-ENS. Figure compprees the runtime (in seconds) required to generate 1 15-day forecast across different resolutions. At 0.25% resolution, Gencast requires 480 seconds on TPUv5, whereas OmniCast achieves the same forecast in just 29 seconds on an A100. At 1.0", OmniCast completes inference in only 11 seconds, compared to 224 seconds for Gencast on the same hardware. These results highlight the scalability and practicality of OmniCast for operational forecasting.
The efficiency of OmniCast stems from two key architectural innovations. First, OmniCast operates in much lower-dimensional latent space (45 x 90 latent grid vs 721 x 1440 original grid), significantly reducing the computational cost of training and inference. Second, OmniCast employs a highly efficient sampling mechanism. Unlike Gencast, which performs 50 full forward passes through the entire network for 50 diffusion steps, OmniCast requires only single forward pass through the transformer backbone. The subsequent diffusion steps involve only lightweight forward passes through a compact MLP diffusion head, resulting in inders-of-magniuus lower inference time. Together, these design choices enable OmniCast deliver fast and scalable forecasts.
5.4 Ablation studies
We analyze four key factors that influence OmniCast's performance: the auxiliary deterministic objective, training sequence length T, unmasking order during sampling, and diffusion sampling temperature r. We present results for T850 on RMSE, CRPS, and SSR. We additionally study the impact of IC perturbations Appendix B.3
Impact of the deterministic objective Figure demonstrates the important role of the deterministic loss in OmniCast's performance. Removing the MSE objective (No-MSE) degrades both RMSE and CRPS scores, with particularly noticeable impact at short lead times. However, naively applying MSE to all future frames (lSF--All-rames) also proves counterproductive, as forces deterministic predictions even for S2S timescales where weather systems become inherently chaotic. Our approach of applying MSE only to the first 10 frames achieves the best RMSE and CRPS scores across modiuu-raage and S2S timescales.

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132015
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(a) Impact of the deter- ministic objective.
(b) Impact of the train- ing sequence length.
(c) Comparison of differ-
(d) Impact of diffusion sampling temperature 7.
ent unmasking strategies.
Figure 9: Ablation studies showing the impact of different components in OmniCast.
Impact of training sequence length In the main S2S experiment, we train OmniCast to generate 44 future weather states 24 hour intervals. One could alternatively train the model shorter sequences and/or smaller intervals, then apply multiple roll-outs during inference to reach longer horizons. Figure Derre99bsshh shows that models trained on shorter sequences smaller intervals excel short- and medium- -range forecasting but underperform at S2S timescales. This trade-off emerges because shorter sequences allow models to specialize near-term predictions, leading to better performance at shorter lead times. However, these models suffer from error accumulation at longer horizons, ultimately performing worse than the model trained on full sequences.
Impact of unmasking orders While our approach randomly masks tokens across both space and structured masking strategies inference. We evaluate two such alternatives: an autoregressive strategy that unmasks entire frames sequentially, and a random framewise approach that unmasks complete frames in random order. Figure 9c shows that fully randomized strategy achieves the best SSR scores, while both alternatives produce under-dispersive ensembles. The superior performance of the fully randomized approach stems from its introduction of additional randomness through the unmasking order, generating more diverse ensemble forecasts. This greater diversity consequently leads to better performance across other metrics.
time during training, one may try
Impact of diffusion sampling temperature Higher values of the temperature 7 produce more diverse forecasts. Figure demonstrates this empirically. Setting
produces under- under-dispersive ensembles, degrading performance across other metrics. Increasing boosts sample diversity, improving SSR scores and overall better performance. However, pushing high (e.g., 7 1.5) causes samples to deviate from the mean prediction, compromising RMSE and CRPS performance. We identify 1.3 as the optimal value, providing the best balance between ensemble diversity and forecast quality, which we adopt for our main experiments.
6 Conclusion
We present OmniCast, novel latent diffusion model for S2S prediction. By combining the masked generative framework with diffusion objective, our approach enables direct modeling of long sequences future weather states while avoiding error accumulation inherent in autoregressive meth- ods. OmniCast achieves state-of-the art performance in deterministic and probabilistic metrics while maintaining exceptional physical consistency. medium-range forecasting, OmniCast performs competitively with existing methods while being significantly more efficient. Future work could study the fundamental trade-off between VAE reconstruction quality and transformer modeling capacity, and explore more sophisticated generative frameworks to enhance the diffusion objective.
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A Implementation details
A.1 VAE details
Our VAE model follows the UNet implementation from PDEArena
12
We use the following
hyperparameters for UNet in our experiments.
Table 1: Default hyperparameters of UNet
Hyperparameter
Meaning
Value
Padding size
Padding size of each convolution layer
Kernel size
Kernel size of each convolution layer Stride of each convolution layer
Stride
Input channels
The number of channels of the input The number of channels of the output The base hidden dimension of the UNet Determine the number of output channels for Down and Up blocks
69
Input channels
69
Base channels
256
Channel multiplications
[1,2,4,4,8]
Dimension of 2 Blocks
The dimension of the latent space Number of blocks If use attention in Down and Up blocks Dropout rate
1024
Use attention Dropout
False 0.0
The VAE encoder embeds each weather state of shape 69 x 128 x 256 to latent map of shape 1024 16, reducing the spatial dimensions by 16. We use KL weight of 5e 5 and optimize the VAE model with Adam [18] for 200 epochs with a batch size of 32, base learning rate of 2e 4, parameters (31 0.9, 32 0.95), and weight decay of le The learning rate follows linear warmup for the first 20 epochs, followed by cosine decay schedule for the remaining 180 epochs.
A.2 Weighted deterministic objective
In OmniCast, we employ a weighted MSE objective to encourage accurate deterministic predictions for near-term frames. The objective is formulated as:
Caeter (0)
B mPMPP
Σ w(i)l2 L
2.112
(6)
where w(t) is an exponentially decreasing weighting function. We compute this weight in three steps. First, for each token i, we determine its corresponding frame index h = - where h x w represents the spatial dimensions of each frame's latent map. Second, we assign weights to tokens based on their frame index: w(i) = - e - ensuring all tokens from the same frame receive equal weight. Third, we set wi) = for tokens beyond frame 10 and normalize the remaining weights to sum to one.
A.3 Optimization details
We optimize OmniCast with AdamW 18 for 100 epochs with a batch size of 32, a base learning rate of 2e 4, parameters (91 0.9, 32 0.95), and weight decay of 1e 5. The learning rate follows a linear warmup for the first 10 epochs, followed by a cosine decay schedule for the remaining 90 epochs.
B Additional experiments
B.1
VAE reconstruction quality
The VAE model critical component in OmniCast, since imposes an upper bound on the forecasting performance. We dedicated substantial efforts to designing the VAE model that balances
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reconstruction quality and compression. Our primary goal was to achieve a high compression ratio along the spatial dimensions, as this directly reduces the number of training tokens required for the subsequent transformer model. We employed 16x spatial reduction for both the medium-range setting with 0.25 data and the $2S setting with the 1.40625 data. We then incrementally increased the latent dimension until we obtained an acceptable reconstruction error. Table shows that increasing the latent dimension consistently improves the reconstruction errors across different variables. We did not increase the latent dimension beyond 1024 since it would create difficulties for training with the diffusion objective. is also noticeable that the VAE model trained on 0.25 data performs much better than the trained on 1.40625 with the same spatial compression ratio and latent dimension. This is expected since higher-resolution data has more spatial redundancy, leading to easier compression for the VAE model. Figures [00 and IT visualize the VAE reconstructions for 6 surface and pressure-level variables. The VAE was able to retain important details and structures of the input, albeit slightly smoothing the data.
Table 2: Reconstruction error of VAE models for different physical variables and latent dimen-
sions (D). Results shown for datasets two spatial resolutions: 1.40625 (left) and 0.25 (right). Lower values indicate better reconstruction.
1.40225
resolution
0.25
resolution
T2m
U10
V10
Z500
T850
T2m
U10
V10
Z500
T850
256
0,96
0.65
0.62
48.72
0.77
0.55
0.25
0.23
18.77
0.37
1) 512
0.80
0.51
0.48
35.42
0.64
D 1024
0.71
0.43
0.40
27.34
0.57
2r temperature
10m, v. v_comporent st.wind
Figure 10: Reconstructions of the VAE model for T2m, U10, and V10.
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mean Je1_ presuue
foverradeo
tempenatrre.o 810
Figure 11: Reconstructions of the VAE model for MSLP, 7500, and T850.
Before selecting the specific VAE model presented in our paper, also tried two alternative VAE architectures: VQ-VAE [45], which compresses data into a discrete latent space, and Video VAE (T. which compresses data across both spatial and temporal dimensions. Our early experiments with VQ-VEE did not achieve satisfactory reconstruction qualities, as the errors were consistently 2 to 3 times higher than those obtained with continuous VAE using the same spatial downsampling factor. We also found that for an equivalent effective compression ratio, a per-frame VAE consistently outperformed video VAE. These results led opt for the per-frame continuous VAE model.
B.2 Comparison with more deep learning baselines
addition to PanguWeather Weather and GraphCast, we compare OmniCast with two advanced transformer- based methods: ClimaX (32] and Stormer 33 Figure 12 shows that Stormer achieves superior accuracy in short-to-medium timescales, consistent with its reported results. However, as an autore- gressive method, its performance degrades more rapidly than OmniCast, eventually falling below Climatology, albeit at slower rate than Pangu Weather and GraphCast. ClimaX takes a different approach as direct forecasting method, where model trained large-scale climate data is finetuned specifically for individual lead times. This approach avoids error accumulation and achieves com- parable performance with OmniCast S2S scales. However, ClimaX requires fine-tuning separate models for each target lead time, while a single OmniCast model can simultaneously generate the complete sequence of future weather states.
B.3 Impact of IC perturbations
Initial condition (IC) perturbations-addii random noise to initial conditions X,- are a standard technique in numerical methods for generating ensemble forecasts. This approach complements
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2500 (m//s?)
7830 (X)
0700 (g/9g)
1932
1.50
19655
9.2
1.25
430
0.50
420
355
302
0.25
1.0
220
0.8
0.20
0.15
0.12
6.7
0.05
6.0
0.00
1.00
1.0
0.45
0.50
380
0.3
0.70
10
30
13
20
20
40
(Days)
Climatllgyy
Figure 12: Comparison of deterministic performance of OmniCast with more deep learning methods.
2500 mm/ts))
3.5
1.C.
1.0
-900
220
420
2.0
3.5
330
0.80
1.6
3.4
250
1.2
1.50
2.0
0.55
0.30
59
0.05
0.50
0.07
0.56
0.00
0.54
035
0.52
20
Lead Time [Days]]
nobee
Figure 13: Performance of OmniCast with different levels of IC noise.
generative framework. Figure 13 evaluates OmniCast's performance across different noise levels, varying the standard deviation Gaussian distribution used for generating perturbations. The results demonstrate OmniCast's robustness to input noise, maintaining consistent RMSE and CRPS
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scores across noise levels from 0.0 to 0.2, with only minor variations in SSR scores at short lead times.
B.4 Scaling inference compute
Finally, we examine how increasing inference compute affects OmniCast's performance through two hyperparameters: the number of ensemble forecasts and the average number of unmasking iterations per frame, i.e., 1-iter means a total of 44 iterations for 44 frames. Figure 14 shows that generating more ensemble forecasts improves both system diversity (higher SSR) and mean prediction accuracy (lower RMSE). Interestingly. while increasing the number of unmasking iterations shows minimal impact on RMSE, it yields slight improvements SSR. This improvement likely stems from the increased randomness in unmasking order with more iterations, leading to greater ensemble diversity.
T850 [K]
3.5
3.0-
3.0
2.5
2255
2.0
2.0
1.5
0.74
0.725
0.700
0.650
0.625
0.575
Lead Time (Days)
Lead Time (Days)
10.samples
20-samples
1-ter
Biiter
Figure 14: Performance OmniCast as we vary the number of ensemble forecasts (left) and the number of unmasking iterations.
B.5 Testing OmniCast stability
We tested the stability of OmniCast by rolling out the model to 100 years into the future. We found that OmniCast consistently produces stable and physically feasible forecasts, even at 100 years ahead. Please see below for visualizations of OmniCast's 's rollouts for various weather variables with 4 samples each.
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T2M
Day 100
363 11
3000
Dav 10230 150 W01
Day 3599 I8
Figure 15: Rollouts for 2-meter temperature up to 100 years ahead.
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---

Sample
3590 IN0 Ore 182300 1S0 3050
Figure 16: Rollouts for 10-meter u component wind up to 100 years ahead.
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---

MSLP
10
bay
Day 100
363 11
Day 3030
Day 35992 IN 2rs1 Dow 10230 150 urrg
Figure Rollouts for mean sea level pressure up to 100 years ahead.
22

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Z500
De 583 11
Day 35992 IE 2ES Doe 189 20 150 WET
Figure 18: Rollouts for 500hPa geopotential up to 100 years ahead.
23

---

Q780
Sample
3592 08 Dov TRE30 150
Figure 19: Rollouts for 700hPa specific humidity up to 100 years ahead.
24

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NeurIPS Paper Checklist
The checklist is designed to encourage best practices for responsible machine learning research, addressing issues of reproducibility, transparency, research ethics, and societal impact. Do not remove the checklist: The papers not including the checklist will be desk rejected. The checklist should follow the references and follow the (optional) supplemental material. The checklist does NOT count towards the page limit.
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Please provide short (1-2 sentence) justifiicton right after your answer (even for NA).
The checklist answers are an integral part of your paper submission. They are visible to the reviewers, area chairs, senior area chairs, and ethics reviewers. You will be asked to also include (after eventual revisions) with the final version of your paper, and its final version will be published with the paper.
The reviewers of your paper will be asked to use the checklist as one of the factors in their evaluation.
While "[Yes] " is generally preferable to "[No] ", it is perfectly acceptable to answer "[No] provided proper justification is given (e.g., "error bars are not reported because it would be too computationally expensive" or "we were unable to find the license for the dataset we used"). In general, answering "[No] or "[NA] is not grounds for rejection. While the questions are phrased in binary way, we acknowledge that the true answer often more nuanced, so please just use your best judgment and write justification to elaborate. All supporting evidence can appear either in the main paper or the supplemental material, provided in appendix. If you answer [Yes] to a question, in the justification please point to the section(s) where related material for the question can be found.
IMPORTANT, please:
Delete this instruction block, but keep the section heading "NeurIPS Paper Checklist",
Keep the checklist subsection headings, questions/answers and guidelines below.
Do not modify the questions and only use the provided macros for your answers.
1. Claims
Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Question:
Answer: [Yes]
Justification: The abstract and introduction reflect the paper's contributions the paper proposes a novel method for probabilistic S2S prediction.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made the paper and important assumptions and limitations. A No or
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
It is fine to include aspirational goals as motivation as long as is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors? Answer: [Yes]
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Justification:
We discussed limitations in the Conclusion section.
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
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Reviews:
[{"review_id": "NzSt5ZPGKJ", "reviewer": "Reviewer_mH3R", "summary": "Autoregressive architectures like GraphCast and PanguWeather struggle with subseasonal-to-seasonal (S2S) forecasting due to error accumulation across long horizons. This paper circumvents that bottleneck by replacing iterative rollouts with masked latent diffusion. A continuous-latent VAE compresses 69-channel ERA5 inputs into dense tokens. A bidirectional masked transformer, paired with a lightweight diffusion head, samples all masked space-time tokens in parallel, generating 44-day forecasts in a single pass. On ChaosBench, SeasonCast matches or exceeds ECMWF ensemble skill beyond day 15, while reducing inference cost by 10–20×. Ablations on masking strategy, latent dimension, temperature, and auxiliary loss suggest the method is robust under architectural perturbations.", "questions": "- Regarding the loss function:\n    \n    (a) How do you ensure the auxiliary MSE term does not bias the diffusion posterior, causing the ensemble mean to drift from the true predictive mean?\n    \n    (b) Do you need to adjust the relative weights between the two loss terms, if so, how? by calibration, ablation, or trial-and-error?\n    \n- FuXi-S2S generates daily means. Could you average SeasonCast outputs and evaluate against FuXi-S2S to give a rough comparison?\n- For GenCast and NeuralGCM, could you run comparisons on scaled-down domains or resolutions to offer partial but informative benchmarks?", "limitations": "The limitations of the methods are barely discussed.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "he paper introduces a sound, practical approach, but the overall presentation falls short, and the technical novelty is modest. The main components (continuous VAE + masked-token transformer + diffusions) are standard in the literature. Their application to S2S is thoughtful, yet not fundamentally new. More importantly, the experimental analysis lacks depth. Metrics are plotted, but insight is scarce: the narrative rarely moves past curve comparisons, and the impact of architectural choices is not dissected. Without stronger analysis, the empirical story feels shallow. The paper shows promise, but in its current form, it is not ready for publication.\n\nStrengths\n\n- Sensible architectural design that addresses a real limitation in autoregressive systems.\n- Demonstrated significant performance gains, particularly in the long-range regime.\n- Efficient inference makes the method attractive for operational use.\n\n Weaknesses\n\n**Clarity & Presentation**\n\n- The manuscript is verbose in parts and under-explained in others.\n- Background and methodology blur together; the structure lacks discipline.\n- Notations appear before being defined (e.g., pUpU above Eq. (4)).\n- Figure 1 omits key components and does not reflect the sampling pipeline; details are scattered rather than integrated.\n\n**Technical Contribution**\n\n- The core method, VAE + masked generative transformer, is standard in adjacent fields. The novelty lies in adaptation, not invention.\n- The loss function blends diffusion loss with exponentially-weighted MSE for the mean prediction. This coupling feels ad hoc and risks undermining the benefits of probabilistic output that diffusion model provides.\n\n**Empirical Analysis**\n\n- The evaluation section reads more like a figure dump than an analysis.\n- No statistical testing, no confidence intervals, no analysis of forecast reliability.\n- The source of SeasonCast’s advantage over GraphCast or PanguWeather is left unexplored. If autoregressive error is to blame, it should be demonstrated explicitly in a controlled setting.", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the constructive feedback, and for recognizing the significant performance and efficiency gains of SeasonCast. We hope to address the reviewer’s remaining concerns and questions below.\n\n> The manuscript is verbose in parts and under-explained in others. Background and methodology blur together; the structure lacks discipline.\n\nSince our methodology is built on top of existing techniques, i.e., masked generative modeling and diffusion models, we presented the background of these techniques in the Background section, and how we adapted them to SeasonCast in the Methodology section. **Can the reviewer point out which specific part of the manuscript is verbose and which is under-explained?** We’re happy to adjust the writing to make the paper clearer.\n\n> Notations appear before being defined (e.g., pUpU above Eq. (4)).\n\n$p_{\\mathcal{U}}$ is the distribution of the binary mask, which we explain in more detail in Section 4.3 (lines 221-222). We will edit the paper to explain this clearly.\n\n> Figure 1 omits key components and does not reflect the sampling pipeline; details are scattered rather than integrated.\n\nWe believe Figure 1 includes all key components of SeasonCast: the transformer backbone, the masking mechanism, the diffusion head, and the auxiliary deterministic head. We will further include an inference diagram in the next version of the manuscript.\n\n> The core method, VAE + masked generative transformer, is standard in adjacent fields.\n\nWhile our work adapts known techniques from other fields, we firmly believe that a well-executed adaptation can yield significant impact when applied to a new domain such as weather and climate. **We dedicated substantial effort to the design of SeasonCast to achieve state-of-the-art performance**, specifically through the full-sequence training and the integration of the proposed auxiliary loss in the masked generative model training. Furthermore, **the most impactful works in this field are themselves adaptation-focused**, including MetNet (which adapted a Video GAN model), GraphCast (which adapted GNN), and GenCast (which adapted GNN + a diffusion model).\n\n> The loss function blends diffusion loss with exponentially-weighted MSE for the mean prediction. This coupling feels ad hoc and risks undermining the benefits of probabilistic output that diffusion model provides.\n\nWe respectfully disagree with the reviewer's assessment. **The deterministic and diffusion components operate as separate prediction heads atop the transformer backbone, thus preventing direct interference. Instead, the deterministic loss actively aids the training of the diffusion head**. This is because the deterministic loss provides an easy-to-optimize signal during training, enabling the model to learn meaningful $z_i$ that the diffusion head can effectively utilize for the probabilistic loss. Our ablation experiment in Figure 11a empirically demonstrates the positive impact of incorporating the deterministic loss across various metrics. Furthermore, our main experiments consistently show that SeasonCast remains well-calibrated across diverse scenarios, which further supports our argument.\n\n> The evaluation section reads more like a figure dump than an analysis. No statistical testing, no confidence intervals, no analysis of forecast reliability.\n\nDue to strict page limits, we focused our main comparisons on the most crucial baselines in the experiment section, which effectively highlighted SeasonCast's advantages. **For a more comprehensive understanding of SeasonCast's design, we included detailed ablation studies in Appendix B.3**. These studies thoroughly investigate the impact of various design choices, such as the deterministic loss, training sequence length, unmasking strategies, and diffusion sampling temperature.\n\n**Regarding statistical testing and confidence intervals, these are not standard practices in the field.** Training weather models on ERA5 is a computationally intensive process, especially given academic compute constraints. Leading papers like GraphCast, PanguWeather, Stormer, and GenCast have also omitted statistical testing and confidence intervals, making it unfair to penalize our paper on this basis.\n\n> The source of SeasonCast’s advantage over GraphCast or PanguWeather is left unexplored. If autoregressive error is to blame, it should be demonstrated explicitly in a controlled setting.\n\nFigure 4 clearly illustrates SeasonCast's superior performance beyond 10-day lead times, where GC and PW degrade due to autoregressive error accumulation. SeasonCast, however, maintains stability up to day 44. Further evidence from Figure 11b demonstrates that training SeasonCast with shorter future sequence lengths leads to increased error accumulation and poorer long-term performance. These experiments definitively show that SeasonCast's advantage in the S2S setting stems from its non-autoregressive nature and its ability to learn from extended sequences of future weather states, unlike other methods' one-step prediction approaches.\n\n> How do you ensure the auxiliary MSE term does not bias the diffusion posterior, causing the ensemble mean to drift from the true predictive mean?\n\nAs we discussed above, the experiments in Figure 11a showed that the deterministic loss helps improve the performance of SeasonCast significantly across different metrics and lead times.\n\n> Do you need to adjust the relative weights between the two loss terms, if so, how? by calibration, ablation, or trial-and-error?\n\nWe simply used equal weighting between the two terms without any tuning because we found that to work sufficiently well.\n\n>  Could you average SeasonCast outputs and evaluate against FuXi-S2S to give a rough comparison?\n\nWe compared the daily forecasts from SeasonCast with Fuxi-S2S. As shown in the table below, SeasonCast demonstrates strong performance for 2-meter temperature and geopotential at 500hPa across four different lead times. Notably, Figure 8 in the Fuxi-S2S paper (https://arxiv.org/pdf/2312.09926) illustrates that the RMSE of Fuxi-S2S exceeds 2.45 for T2M and 800 for Z500 at lead times beyond 15 days. While their SSR scores are comparable, these results clearly indicate that SeasonCast either matches or surpasses Fuxi-S2S in both RMSE and SSR metrics. This further underscores the significant capabilities of SeasonCast, as it **outperforms Fuxi-S2S even without being specifically trained for daily average prediction, unlike Fuxi-S2S.**\n\n| Lead time (days) | RMSE (T2M) | RMSE (Z500) | SSR (T2M) | SSR (Z500) |\n|------------------|------------|-------------|-----------|------------|\n| 15               | 2.40       | 787.35      | 0.94      | 0.92       |\n| 25               | 2.40       | 794.64      | 0.93      | 0.91       |\n| 35               | 2.38       | 791.86      | 0.93      | 0.91       |\n| 40               | 2.37       | 794.81      | 0.94      | 0.91       |\n\n> For GenCast and NeuralGCM, could you run comparisons on scaled-down domains or resolutions to offer partial but informative benchmarks?\n\nAs Figure 8 illustrates, both GenCast and NeuralGCM demand substantial computational resources. Even at a reduced resolution, generating a single 15-day forecast consumes several minutes. To evaluate these models on ChaosBench, we would need to run them for over a thousand initial conditions in the test set, producing 50 ensemble predictions for each, with a 44-day lead time. This extensive experiment is simply not feasible within the limited rebuttal period and the immense computational power it would necessitate."}, {"review_id": "8kCKVtFnGT", "reviewer": "Reviewer_SUwF", "summary": "This paper proposes SeasonCast for S2S prediction, which consists of two components, a VAE model that encodes raw weather data into a continuous, lower-dimensional latent space, and a diffusion-based transformer model that generates a sequence of future latent tokens given the initial conditioning tokens. It achieves SOTA accuracy and efficiency. While experimentally thorough, it lacks physics constraints and extreme-event validation.", "questions": "1. How does SeasonCast enforce physical laws (e.g., mass conservation) in generated forecasts? Are there constraints in the loss function or latent space?\n2. What is the per-variable reconstruction error (e.g., RMSE) for the VAE? Does compression degrade key variables like precipitation?\n3. Can SeasonCast predict rare events (e.g., heatwaves)? Please provide case studies on ChaosBench extreme indices.\n4. How does performance degrade beyond 44 days? Is there a plan to support flexible forecasting windows?", "limitations": "no", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "strength\n1. Masked diffusion transformer applies diffusion loss for probabilistic forecasting + auxiliary MSE loss (weighted for near-term frames) for deterministic accuracy.\n2. Masked diffusion generates full future sequences (44 days) in a single forward pass, eliminating autoregressive error accumulation and capturing boundary conditions.\n3. Outperforms deep learning (DL) baselines (PanguWeather, GraphCast) and matches ECMWF-ENS (top numerical model) on ChaosBench for deterministic, probabilistic, and physics-based metrics beyond Day 10.\nWeaknesses\n1. Aggressive compression (100×) may lose fine-scale features critical for regional forecasts (e.g., extreme events). No analysis of reconstruction error per variable.\n2. Performance and efficiency actually depend a lot on the VAE’s reconstruction performance, compression ratio, and network structure. VAE has no statement in this regard, especially in experiments.\n3. Trained on fixed 44-day sequences; performance degrades beyond this window (Appendix B.3). Unable to dynamically extend forecasts without retraining.\n4. The scope of the comparative papers is too small. There are many excellent works now, but the authors did not compare.", "was_revised": true, "final_justification": "Thank you for the author's rebuttal, which solved my questions. I will increase my review score.", "rebuttal": "We thank the reviewer for the constructive feedback and for appreciating the design of SeasonCast and its strong performance in S2S prediction. We hope to address the reviewer’s questions and concerns below.\n\n> Aggressive compression (100×) may lose fine-scale features critical for regional forecasts (e.g., extreme events). No analysis of reconstruction error per variable.\n\nWhile we significantly compressed the spatial dimensions of the data to reduce the training sequence length for the transformer model, we actually increased the latent channel dimension. Specifically, each raw frame has a shape of 69x128x256, where 69 represents the number of weather variables. **Each latent frame has a shape of 1024x8x16, resulting in an effective compression ratio of (69x128x256) / (1024x8x16) = 17.25. Please refer to the tables below for the RMSE of the reconstruction across some key variables.**\n\n| T2m  | U10  | V10  | Z500  | T850 |\n|------|------|------|-------|------|\n| 0.71 | 0.43 | 0.40 | 27.34 | 0.57 |\n\n> Performance and efficiency actually depend a lot on the VAE’s reconstruction performance, compression ratio, and network structure. VAE has no statement in this regard, especially in experiments.\n\nWe fully agree with the reviewer's observation that the VAE's performance is important to the overall efficacy of SeasonCast. Indeed, we dedicated substantial effort to developing a robust VAE model. Our primary goal was to achieve a high compression ratio along the spatial dimensions, as this directly reduces the number of training tokens required for the subsequent transformer model. Therefore, **we employed 16x spatial reduction across all experiments. We then incrementally increased the latent dimension until we obtained an acceptable reconstruction error.** Please refer to the table below for a detailed breakdown of performance across various latent dimensions.\n\n| D       | T2m  | U10  | V10  | Z500  | T850 |\n|---------|------|------|------|-------|------|\n| *D = 256*  | 0.96 | 0.65 | 0.62 | 48.72 | 0.77 |\n| *D = 512*  | 0.80 | 0.51 | 0.48 | 35.42 | 0.64 |\n| *D = 1024* | **0.71** | **0.43** | **0.40** | **27.34** | **0.57** |\n\n**Before selecting the specific VAE model presented in our paper, we also tried two alternative VAE architectures:**\n- VQ-VAE: This approach compresses data into a discrete latent space. However, we were unable to achieve satisfactory reconstruction errors; they were consistently 2 to 3 times higher than those obtained with a continuous VAE using the same spatial downsampling factor.\n- Video VAE: Similar to Cosmos [1], this variant compresses data across both spatial and temporal dimensions. Our early experiments showed that for an equivalent effective compression ratio, a per-frame VAE consistently outperformed a video VAE. This led us to opt for the per-frame VAE model.\n\nWe thank the reviewer again for bringing this up and we will update the paper to discuss the VAE component in detail.\n\n[1] Agarwal, Niket, et al. \"Cosmos world foundation model platform for physical ai.\" arXiv preprint arXiv:2501.03575 (2025).\n\n> Trained on fixed 44-day sequences; performance degrades beyond this window (Appendix B.3). Unable to dynamically extend forecasts without retraining.\n\nThere appears to be a misunderstanding here. Figure 11b demonstrates that SeasonCast models trained on short windows exhibit performance degradation beyond their training window, whereas the SeasonCast model trained on the full 44-day window maintains consistent stability across all lead times. This finding is consistent with previous research indicating that autoregressive models typically struggle beyond the medium range. This also supports our assertion that training on the complete sequence of future frames is crucial for stabilizing the model in S2S prediction.\n\n**To further substantiate SeasonCast's ability to produce stable and well-calibrated predictions beyond its training window, we extended the model's rollout to 100 days ahead.** To achieve predictions beyond the initial training window, we iteratively employed the latest predicted frame (day 44) as the initial condition for predicting the subsequent window (day 45 to day 88), continuing this process until the desired lead time was reached. The table below presents SeasonCast's performance across key variables and various metrics at lead times exceeding 44 days. **SeasonCast remains exceptionally stable and well-calibrated, showing no signs of degradation even at day 100.**\n\n| Lead time (days) | RMSE (Z500) | RMSE (T850) | RMSE (Q700) | CRPS (Z500) | CRPS (T850) | CRPS (Q700) |\n|------------------|-------------|-------------|-------------|-------------|-------------|-------------|\n| 50               | 826.66      | 3.50        | 1.60        | 361.94      | 1.72        | 0.81        |\n| 70               | 821.64      | 3.46        | 1.60        | 358.96      | 1.70        | 0.81        |\n| 90               | 818.89      | 3.44        | 1.59        | 357.38      | 1.69        | 0.81        |\n| 100              | 812.41      | 3.44        | 1.59        | 355.44      | 1.69        | 0.81        |\n\n> The scope of the comparative papers is too small. There are many excellent works now, but the authors did not compare.\n\nCould the reviewer please elaborate on the specific baselines they have in mind? **We have made every effort to compare SeasonCast with state-of-the-art methods, including GC, PW, leading numerical models for the S2S setting, and GenCast and IFS-ENS for the medium-range settings**, and SeasonCast consistently demonstrates strong performance across benchmarks. We further compared it with Fuxi-S2S, another prominent method in S2S prediction. Please refer to our response to Reviewer mH3R for details on this experiment.\n\n> How does SeasonCast enforce physical laws (e.g., mass conservation) in generated forecasts? Are there constraints in the loss function or latent space?\n\nSeasonCast employs a purely learning-based approach, without imposing any physical constraints in the loss function for either the VAE or the transformer model. Incorporating physical laws is an orthogonal and promising direction that we aim to explore in future work.\n\n> Can SeasonCast predict rare events (e.g., heatwaves)? Please provide case studies on ChaosBench extreme indices.\n\nChaosBench does not provide extreme case studies or indices, but instead provides a comprehensive evaluation framework across a wide range of metrics. SeasonCast consistently demonstrated strong performance within this benchmark, excelling across key variables, diverse metrics, and various lead times. Furthermore, its robust performance in the medium-range setting further highlights SeasonCast's versatile capabilities."}, {"review_id": "YFuFtZCEwQ", "reviewer": "Reviewer_pne4", "summary": "This paper proposes a novel model named SeasonCast for subseasonal-to-seasonal (S2S) scale weather forecasting. By combining a masked Transformer model with diffusion models to directly model complete sequences of future weather states in a low-dimensional latent space, SeasonCast effectively avoids the error accumulation problems inherent in numerical weather prediction (NWP) and traditional autoregressive approaches. Experimental results demonstrate that SeasonCast achieves state-of-the-art performance at S2S scales while remaining competitive for medium-range forecasting, with higher computational efficiency than existing methods.", "questions": "1. Could you provide visual examples of forecasts compared with observations, especially for extreme events? This would help readers better understand the spatial and temporal characteristics your model captures.\n\n2. Have you evaluated SeasonCast’s skill using standard meteorological correlation metrics like ACC and ACT? If not, would you consider adding these to better align with operational forecast verification practice?", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "#### Strengths:\n\n1. **Clear motivation and solid problem formulation.** The paper provides a thorough discussion of the challenges in S2S forecasting, particularly the limitations of autoregressive architectures in modeling both initial and boundary conditions at long lead times. The proposed masked generative framework is well-motivated as a natural solution to address these issues.\n2. **Innovative architecture design.** The combination of a continuous VAE to compress high-dimensional weather states, with a Transformer trained under a masked latent diffusion objective, is novel for weather forecasting and technically sound. \n3. **Substantial research effort.** The experimental design is comprehensive, with thorough comparisons to multiple baselines, including leading numerical weather prediction systems (e.g., ECMWF, UKMO, and CMA) and recent AI models like PanguWeather and GraphCast. This is particularly valuable given that some NWP outputs (such as CMA) are not easily accessible, showing the authors’ strong commitment to robust benchmarking.\n3. **Well-structured presentation.** The methodology is described in detail and is well-structured, making the proposed approach easy to follow and the experiments reproducible in principle.\n4. **Strong results.** The model achieves state-of-the-art performance at subseasonal-to-seasonal scales and remains competitive at medium-range timescales, with notable computational efficiency improvements.\n\n#### Weaknesses:\n\n1. **Limited interpretability and case studies.** While the paper includes extensive quantitative benchmarks, it would benefit from more qualitative analyses or illustrative case studies (e.g., maps of extreme events, typical forecast snapshots) to show when and why SeasonCast outperforms or underperforms compared to other systems.\n2. **Missing some common meteorological verification metrics.** Although the paper uses several meaningful metrics, it does not report widely adopted indicators such as the Anomaly Correlation Coefficient (ACC) or Forecast Activity (ACT), which are standard in meteorological skill assessments. Including these would strengthen the claim of operational relevance.", "was_revised": true, "final_justification": "Authors explained the absence of qualitative figures as an oversight and committed to adding them. The effective architecture, comprehensive experiments, and strong long-lead performance together justify acceptance. While the novelty is somewhat incremental, I believe its solid practical effectiveness warrants acceptance.", "rebuttal": "We thank the reviewer for the constructive feedback and for recognizing the contributions, novel architecture design, presentation, and strong performance of SeasonCast. We hope to address the reviewer’s main questions and concerns below.\n\n> While the paper includes extensive quantitative benchmarks, it would benefit from more qualitative analyses or illustrative case studies.\n\nWe agree with the reviewer that additional illustrative studies would enhance our understanding of SeasonCast's performance. As the rebuttal phase prohibits uploading PDF files or linking to external pages, we will incorporate VAE reconstructions, sample forecasts, and extreme weather events into a future version of the paper.\n\n> Although the paper uses several meaningful metrics, it does not report widely adopted indicators such as the Anomaly Correlation Coefficient (ACC) or Forecast Activity (ACT), which are standard in meteorological skill assessments.\n\nWe further evaluated SeasonCast’s performance on the Anomaly Correlation Coefficient (ACC) metric, which we summarized in the table below. We left out Z500 because there was a bug in the climatology data provided by ChaosBench. We have contacted the authors to have this fixed and will report the results on more variables in the updated version of the paper.\n\n| Lead time (days) | SeasonCast (T850) | SeasonCast (Q700) | ECMWF-ENS (T850) | ECMWF-ENS (Q700) |\n|------------------|-------------------|-------------------|-------------------|-------------------|\n| 25               | 0.08              | 0.09              | 0.08              | 0.13              |\n| 35               | 0.08              | 0.08              | 0.06              | 0.10              |\n| 40               | 0.08              | 0.08              | 0.06              | 0.09              |\n\nThis result demonstrates that SeasonCast performs competitively with ECMWF-ENS at S2S timescales in terms of the ACC metric and also maintains stable performance even up to day 40, reinforcing the conclusion observed across other metrics presented in our paper."}, {"review_id": "c1gPHUEVUN", "reviewer": "Reviewer_Er6Q", "summary": "This paper introduces SeasonCast, a novel latent diffusion model tailored for subseasonal-to-seasonal (S2S) weather prediction, targeting lead times of 2 to 6 weeks. Unlike traditional autoregressive methods that often struggle with error accumulation over longer time horizons, SeasonCast takes a two-stage approach to address this challenge:\n\t1\tA Variational Autoencoder (VAE) compresses high-dimensional weather data into a smooth, lower-dimensional latent space.\n\t2\tA masked transformer with a diffusion head then generates future latent tokens iteratively, conditioned on both the initial state and partially revealed future tokens.\nThis design allows SeasonCast to jointly model spatial and temporal dependencies, effectively reducing compounding errors. It also enables the model to learn both short-term dynamics (initial conditions) and long-term patterns (boundary conditions).\nExperimental results show that SeasonCast not only outperforms deep learning baselines like Pangu-Weather and GraphCast, but also rivals numerical ensemble models such as ECMWF. It delivers strong results across deterministic (RMSE, SSIM), physics-based (SDIV, SRES), and probabilistic metrics at S2S scales—while also demonstrating competitive performance in medium-range forecasting (1–15 days), all with minimal computational overhead.", "questions": "1. Clarify Initial and Boundary Conditions I recommend that the authors provide a brief explanation or context when referring to initial conditions and boundary conditions. This would be especially helpful for readers who may not have a background in climate science or differential equations, allowing them to better understand the modeling framework and its assumptions.\n\n2. Include Surface-Level Variable Performance It would also strengthen the paper to report performance on key surface-level variables, such as 2-meter temperature (T2m) and 10-meter wind components (U10, V10). These variables are critical for many practical applications of S2S forecasting and would offer a more complete picture of the model’s real-world utility.", "limitations": "Yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "The paper presents a thoughtfully designed two-stage framework, SeasonCast, which combines a VAE with a masked transformer and diffusion head to tackle the persistent challenge of error accumulation in autoregressive S2S forecasting. The use of latent diffusion for probabilistic modeling is a particularly clever and well-justified choice.\n\nThe model is rigorously evaluated on both ChaosBench and WeatherBench2, with comparisons against leading deep learning models (Pangu-Weather, GraphCast) and numerical baselines (ECMWF-ENS). The evaluation spans a wide range of metrics, including deterministic (RMSE, SSIM), physics-based (SDIV, SRES), and probabilistic (CRPS, SSR) measures.\n\nHowever, I do have some concerns about SeasonCast’s performance at S2S scales. Looking at Figure 4, the model often performs on par with—or even worse than—climatology across most variables and metrics, with the exception of absolute bias for Z500 and T850. This raises concerns about its readiness for operational use. Additionally, the paper only presents SeasonCast’s performance on a subset of atmospheric variables, leaving out key surface-level indicators like 2-meter temperature and 10-meter wind components (U and V). Including these would provide a more complete picture of the model’s practical utility.", "was_revised": true, "final_justification": "Thank you to the authors for their time and effect during the rebuttal. I still think Climatology is a very crucial benchmark in S2S forecasting, and there are prior works showing ML models can outperform Climatology at S2S scale. For this reason,  I would like to  keep my original score.", "rebuttal": "We thank the reviewer for the constructive feedback and for recognizing the design, rigorous evaluation, and strong performance of SeasonCast. We hope to address the reviewer’s main questions and concerns below.\n\n> Looking at Figure 4, the model often performs on par with—or even worse than—climatology across most variables and metrics, with the exception of absolute bias for Z500 and T850.\n\nTheoretical limits in deterministic forecasting state that no model can surpass climatology beyond 14 days. **However, S2S prediction is not purely about deterministic skill; robust probabilistic uncertainty estimation is crucial.**\n- Climatology inherently lacks uncertainty estimates, rendering it impractical for real-world forecasting applications where uncertainty is important for informed decision-making.\n- SeasonCast demonstrably improves upon climatology by providing reliable probabilistic forecasts, which are far more valuable for decision-making in risk-sensitive applications.\n\n**Across all benchmarks and metrics, SeasonCast consistently produces stable, physically consistent, and well-calibrated forecasts for S2S.** While achieving comparable RMSE due to theoretical limits, SeasonCast offers significant benefits over climatology through its superior probabilistic forecasting capabilities.\n\n> Additionally, the paper only presents SeasonCast’s performance on a subset of atmospheric variables, leaving out key surface-level indicators like 2-meter temperature and 10-meter wind components (U and V).\n\nWe evaluated SeasonCast on a total of 69 different weather variables, but were not able to show all of them due to the page limits. **Please see below the performance of SeasonCast across different key surface-level variables and various metrics.** We will also update the Appendix to include this result.\n\n| Lead time (days) | RMSE (T2M) | RMSE (U10) | RMSE (V10) | CRPS (T2M) | CRPS (U10) | CRPS (V10) | SSR (T2M) | SSR (U10) | SSR (V10) |\n|------------------|------------|------------|------------|------------|------------|------------|-----------|-----------|-----------|\n| 25               | 2.59       | 3.87       | 4.00       | 1.11       | 1.88       | 1.94       | 0.69      | 0.83      | 0.85      |\n| 35               | 2.56       | 3.85       | 3.98       | 1.10       | 1.87       | 1.93       | 0.69      | 0.83      | 0.85      |\n| 40               | 2.56       | 3.84       | 3.98       | 1.10       | 1.87       | 1.92       | 0.70      | 0.83      | 0.85      |\n\n> I recommend that the authors provide a brief explanation or context when referring to initial conditions and boundary conditions\n\nWe fully agree with the reviewer and will update the paper to discuss the initial conditions and boundary conditions problems in the future version of the paper."}]

Author rebuttal:

Meta-review: This paper explores the use of a masked transformer in the latent space of a variational autoencoder for weather prediction. A combination of a diffusion loss and an MSE loss is used to learn a distribution over masked tokens representing future states. The initial reviews of this paper were mixed, with an average rating of 3.75. On the positive side, the reviewers acknowledged the architectural design and promising performance in long-range forecasts. The diffusion loss and inherent circumvention of error accumulation through sequence prediction, in particular, were perceived as strengths of the proposed approach. Although the architecture's novelty is modest, it is appealing and well-executed from a design point of view. Supporting the diffusion loss with a short-range MSE loss was demonstrated to be beneficial. The feedback on the quality of the manuscript was mixed, with some reviewers expressing concerns about its structure and the lack of more qualitative insights beyond the quantitive evaluation. Performance-wise SeasonCast compares favourably to two deep learning approaches and competitive relative to more computationally expensive numerical models, especially in the long-term regime. In their rebuttal the authors addressed many of the raised concerns, proposing improvements to the manuscript and highlighting the benefits and contributions of a non-autoregressive approach. Additional experiments, such as a comparison to FuXi-S2S, fine-grained reconstruction errors, longer lead times up to 100 days, and a (preliminary) evaluation of the Anomaly Correlation Coefficient (ACC) provide additional context around SeasonCast’s performance.

Overall, SeasonsCast is a well-designed architecture with demonstrated strengths at its targeted forecasting horizon. The reported experiments show advantages over deep learning-based alternatives and validate the design choices in a series of insightful ablation studies. Additional comparisons to established numerical models show competitive performance at faster inference runtimes. The paper is therefore recommended for acceptance as a poster, with the expectation that the feedback received during the review process will be incorporated into the camera-ready manuscript. Including comparisons to Gencast/NeuralGCM and improving the coverage of the runtime analysis, in particular, would strengthen the paper.
Decision: Accept (poster)