U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecaster

Salva Rühling Cachay; Duncan Watson-Parris; Rose Yu

cs.LGApr 10, 2026

U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecaster

Authors: Salva Rühling Cachay, Duncan Watson-Parris, Rose Yu

Organizations: UC San Diego

Abstract

AI-based weather forecasting now rivals traditional physics-based ensembles, but state-of-the-art (SOTA) models rely on specialized architectures and massive computational budgets, creating a high barrier to entry. We demonstrate that such complexity is unnecessary for frontier performance. We introduce \ours, a probabilistic forecaster built on a standard U-Net backbone trained with a simple recipe: deterministic pre-training on Mean Absolute Error followed by short probabilistic fine-tuning on the Continuous Ranked Probability Score (CRPS) using Monte Carlo Dropout for stochasticity. As a result, our model matches or exceeds the probabilistic skill of GenCast and IFS ENS at $1.5^\circ$ resolution while reducing training compute by over $10\times$ compared to leading CRPS-based models and inference latency by over $10\times$ compared to diffusion-based models. U-Cast trains in under 12 H200 GPU-days and generates a 15-day ensemble forecast in 3 seconds. These results suggest that scalable, general-purpose architectures paired with efficient training curricula can match complex domain-specific designs at a fraction of the cost, opening the training of frontier probabilistic weather models to the broader community.

Figures & tables

Figure 1: The Efficiency-Accuracy Pareto Frontier. We visualize forecast skill (y-axis, % improvement over IFS ENS), inference latency (x-axis), and training cost (bubble size). Our model (top-left) achieves state-of-the-art performance while requiring an order of magnitude less compute for training and/or inference compared to leading baselines. See Appendix C.1 for detailed methodology.

Panel of Figure 2 — Figure 2: $1.5^{\circ}$ CRPS comparison of U-Cast DeepEns against IFS ENS (left) and GenCast (right). Blue indicates lower (better) CRPS for U-Cast; red indicates baseline superiority. U-Cast broadly outperforms IFS ENS and is competitive with GenCast despite the latter’s finer native resolution ( $0.25^{\circ}$ ). See text for details.

Figure 3: WeatherBench 2 Comparison (

1.5^{\circ}

resolution). We report the CRPS skill relative to the operational IFS ENS (%, lower is better) as a function of forecast horizon. Baseline scores are sourced from the official leaderboard ( Rasp et al., 2024 ) . Numbers after variable abbreviations denote pressure levels in hPa. Note that the GenCast baseline is the native

0.25^{\circ}

model regridded to

1.5^{\circ}

(see Section 4.2 for discussion of this resolution asymmetry). U-Cast consistently outperforms baselines on geopotential variables while remaining highly competitive on other fields.

Model	z500			10u
Model	1d	3d	10d	1d	3d	10d
U-Cast	20.3	55.2	256	0.349	0.61	1.55
U-Cast (DE)	19.6	53.5	253	0.345	0.60	1.54
IFS ENS	22.4	58.3	262	0.406	0.69	1.61
GenCast	20.2	54.3	254	0.332	0.60	1.55
ArchesGen	21.2	55.8	254	0.370	0.62	1.55

Table 1: Absolute CRPS results (lower is better) for geopotential at 500hPa and 10m u-component of wind for 1, 3, and 10-day ensemble forecasts. See Figure 3 for more comprehensive results.

Model	Training Cost (in days)	Inference Speed
Coarser Resolution ( $\sim 1.5^{\circ}$ )
ArchesWeatherGen	45 (V100)	3.5 min (A100)
NeuralGCM ENS	1280 (TPUv5e)	18.6 sec (TPUv4)
ERDM	20 (H200)	7 min (A100)
Swift	360 (Intel Max1550)	–
MOSAIC	16 (H100)	3 sec (H100)

Table 2: Training compute costs and inference speeds for probabilistic weather forecasting models. Models are grouped by resolution. Inference speed is for one single-member 60-step rollout (15-days for NeuralGCM and IFS ENS, or if a 6h-resolution model). † TPU benchmarks for

1^{\circ}

GenCast unavailable, but official documentation indicates a

\approx 3\times

speed-up over a H100 GPU at

0.25^{\circ}

. ∗ For each of the 4 individual models.

1^{\circ}

cost is estimated.

Figure 4: U-Cast ablations. We report CRPS relative to U-Cast (top row) and spread-skill ratio (bottom row; closer to 1 is better).

Figure 5: Curriculum ablation. Validation CRPS ( t850 , 12h lead time) during probabilistic training. The curriculum (orange) fine-tunes from a deterministic checkpoint (dashed blue line) and converges rapidly to a CRPS of 0.218. Training from scratch on CRPS alone (gray) requires

>3\times

more steps to reach comparable performance and plateaus at a worse score (0.225).

Appendix figures & tables11 assets

Supplementary material from the paper’s appendix.

Appendix

Type	Variable name	Short name	Role
Atmospheric	Geopotential	z	Input/Predicted
Atmospheric	Specific humidity	q	Input/Predicted
Atmospheric	Temperature	t	Input/Predicted
Atmospheric	U component of wind	u	Input/Predicted
Atmospheric	V component of wind	v	Input/Predicted
Atmospheric	Vertical velocity	w	Input/Predicted

Appendix

Table 3: Description of data variables used.

Figure 6: The Efficiency-Accuracy Pareto Frontier. We visualize forecast skill (y-axis, % improvement over IFS ENS in terms of CRPS on the left and RMSE on the right), training cost (x-axis), and inference latency (bubble size). Our model (top-left) achieves state-of-the-art performance while requiring an order of magnitude less compute for training or inference compared to leading baselines.

Figure 7: WeatherBench 2 Comparison (

1.5^{\circ}

resolution): Absolute CRPS. We report the CRPS skill as a function of forecast horizon (lower is better). This figure visualizes the same as Figure 3 but in absolute terms, without normalizing by the performance of IFS ENS.

Figure 8: WeatherBench 2 Comparison (

1.5^{\circ}

resolution): RMSE. We report the 50-member ensemble-mean RMSE skill relative to the operational IFS ENS (%, lower is better) as a function of forecast horizon. Baseline scores are sourced directly from the official leaderboard ( Rasp et al., 2024 ) . Numbers after the variable abbreviations refer to the pressure level in hPa. Note that the GenCast baseline uses native

0.25^{\circ}

forecasts regridded to

1.5^{\circ}

, which may confer an advantage over models strictly operating at

1.5^{\circ}

(like U-Cast, ArchesWeatherGen, and NeuralGCM). Relative to the CRPS results, GenCast performs stronger in terms of RMSE than U-Cast.

Figure 9: WeatherBench 2 Comparison (

1.5^{\circ}

resolution): SSR. We report the Spread-Skill ratio skill as a function of forecast horizon (closer to 1 is better). Baseline scores are sourced directly from the official leaderboard ( Rasp et al., 2024 ) . Numbers after the variable abbreviations refer to the pressure level in hPa. U-Cast generates more overconfident forecasts than the baselines, especially in the 1-to-7-day regime. U-Cast (DE) improves calibration, with SSR rarely falling below 0.85 and achieving better short-range calibration than NeuralGCM ENS for several variables.

Panel of Figure 10 — Figure 10: Comparison of U-Cast (DE) evaluated on 2022 against IFS ENS (left) and against U-Cast (DE) evaluated on 2020 (right). Blue indicates that U-Cast achieves a lower (better) CRPS, while red favors the baseline. U-Cast consistently outperforms IFS ENS on 91.5% of metrics, with notable exceptions in long-range 2-meter temperature and a few variables at the 12-hour lead time (e.g., a 10.8% deficit in u500 ). On average, U-Cast improves upon IFS ENS by 4.45%, with the strongest gains ( $\approx 18\text{--}22\%$ ) observed at 12-to-36-hour lead times for mslp . These results are comparable to those obtained for the test year 2020 ( Figure 2 ), demonstrating that U-Cast, despite being trained only on 1979–2019 data, generalizes well to more distant years. This robustness is further supported by the right panel, where the maximum degradation of U-Cast (DE; 2022) relative to its 2020 performance remains below 4.5%, concentrated in geopotential variables (especially z500 ), while specific humidity variables even show improved CRPS scores (up to 2%).

Figure 11: Score card comparison of U-Cast (DeepEns) vs. U-Cast. Deep ensembling U-Cast via fine-tuning four different versions of it consistently improves CRPS scores, especially for short-to-mid-range geopotential and stratospheric (by up to 4%; except q50 ) variables.

Figure 12: Curriculum and ensemble-size ablations (full evaluation). Relative CRPS vs. U-Cast across variables and lead times (higher means worse than U-Cast). End-to-end CRPS (orange) trains from scratch without deterministic pre-training; it consistently degrades short-range CRPS by 3–5% and stratospheric variables by 5–15% across all lead times, while recovering or slightly improving long-range scores for select variables (e.g., 2t ). U-Cast (

M\!=\!4

) (blue) doubles the training ensemble size; it yields only marginal improvements despite doubling per-step cost, with modest gains concentrated at long-range stratospheric variables (up to 3% for t50 ).

Figure 13: Spectral density of 10-day forecasts, averaged over mid latitudes ( $[25^{\circ},55^{\circ}]$ ). While U-Cast generates realistic spectra for the surface and specific humidity variables, it tends to generate excess power at high frequencies for the other variables.

Figure 14: Example visualizations of U-Cast (second row), the corresponding ground truth (first row), and the bias (last row) for specific humidity at 700 hPa ( q700 ) and forecast lead times $3,7,10,$ and $14$ days.

Panel of Figure 15 — Figure 15: Example visualizations of U-Cast (second row), the corresponding ground truth (first row), and the bias (last row) for two example variables and forecast lead times $3,7,10,$ and $14$ days.

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Recent probabilistic weather forecasters train stochastic predictors with the continuous ranked probability score (CRPS) to generate each ensemble member in a single forward pass. These models learn the predictive distribution from the forecast context alone, which becomes difficult at longer forecast horizons where uncertainty is high. To learn the predictive distribution more effectively, we introduce auxiliary conditional denoising tasks that predict the same future state from the context and its corrupted version, which provides partial future information that can reduce prediction ambiguity. Building on distributional diffusion models, we learn the conditional distributions of these tasks with a single stochastic predictor by minimizing a proper scoring rule across noise levels. At inference, the predictor can still generate each ensemble member in a single forward pass at the fully corrupted endpoint. Standard CRPS training is recovered as the endpoint-only special case of our formulation, so our framework extends existing CRPS-based forecasters with only additional conditioning inputs. Controlled experiments show that the auxiliary tasks improve one-step forecasting across architectures, with larger gains at longer forecast horizons. The gains extend to high-dimensional global weather forecasting under both training from scratch and fine-tuning, along with improved calibration and potential benefits for generalization under distribution shift.

Abstract

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