Aries: A Proprietary Medium-Range Weather Prediction Model for the Energy Industry

Lukas Hedegaard Morsing; Arian Bakhtiarnia; Jonas Lynge Olesen; Tómas Bragi Björnsson Leth; Christian Gøbel Bach

physics.ao-phSep 9, 2026

Aries: A Proprietary Medium-Range Weather Prediction Model for the Energy Industry

Authors: Lukas Hedegaard Morsing, Arian Bakhtiarnia, Jonas Lynge Olesen, Tómas Bragi Björnsson Leth, Christian Gøbel Bach

Organizations: InCommodities

Abstract

Medium-range weather forecasting underpins operational and planning decisions across the energy industry. Developing competitive weather models was once the domain of national meteorological centers, but recent advances in machine-learned weather prediction (MLWP) have opened the field to industry. We present Aries, a SwinTransformer-based MLWP model developed at InCommodities. Aries is trained on ERA5 reanalysis data at 0.25\textdegree{} resolution, predicting 74 prognostic and 11 diagnostic atmospheric variables. We evaluate the model on 2025 ECMWF Analysis initializations, ensuring a recent and strictly out-of-sample test period for all models compared. On 10-metre wind speed, Aries outperforms both ECMWF HRES and AIFS in terms of RMSE for lead times up to four days, while on 2-metre temperature it achieves RMSE on par with AIFS operational. These results demonstrate that proprietary development of competitive weather models is technically viable, supporting a broader set of forecasts available for operational and planning applications in the energy industry.

Figures & tables

Fig. 1: Latitude-weighted RMSE of 10 m wind speed forecasts over Europe for operational 2025 runs with — Aries, — AIFS, and — HRES against ERA5. Shaded regions denote 25th–75th percentile ranges.

Parameter	Value
Depth	14 layers
Attention heads	16
Embedding dimension	3,584
Total parameters	2.4 B

TABLE I: Aries model configuration.

Panel of Figure 3 — Fig. 2: Latitude-weighted RMSE (top) and bias (bottom). Left: 10 m wind speed, USA. Middle: 2 m temperature, Europe. Right: 2 m temperature, USA. Shaded regions denote 25th–75th percentile ranges. — Aries, — ECMWF AIFS, — ECMWF HRES.

Fig. 3: Global Aries 2 m temperature forecasts at 24, 72, 96, and 120 h lead time.

Fig. 4: Global Aries 10 m wind speed forecasts at 24, 72, 96, and 120 h lead time.

Fig. 5: 2 m temperature forecasts over Europe at 24, 72, 96, and 120 h lead time (rows) for Aries, AIFS, HRES, and ERA5 (columns). ERA5 is the ground-truth reanalysis.

Fig. 6: 10 m wind speed forecasts over Europe at 24, 72, 96, and 120 h lead time (rows) for Aries, AIFS, HRES, and ERA5 (columns). The 72 and 96 h panels capture Storm Éowyn over the British Isles and the North Sea. ERA5 is the ground-truth reanalysis.

Fig. 7: Time series of 2 m temperature forecasts at the Horns Rev offshore wind farm, Denmark (

55.5^{\circ}

7.8^{\circ}

E) over the 0–168 h horizon. Point values are bilinearly interpolated from each model’s native grid to the site; Aries and AIFS are output at 6-hourly steps, HRES and ERA5 at hourly steps, and the curves are interpolated between steps with an Akima spline. — Aries, — AIFS, — HRES, - - ERA5 (ground truth).

Fig. 8: Time series of 10 m wind speed forecasts at the Horns Rev offshore wind farm, Denmark (

55.5^{\circ}

7.8^{\circ}

E) over the 0–168 h horizon. The wind maximum around day 3.5 corresponds to the passage of Storm Éowyn. Point values are bilinearly interpolated from each model’s native grid to the site; Aries and AIFS are output at 6-hourly steps, HRES and ERA5 at hourly steps, and the curves are interpolated between steps with an Akima spline. — Aries, — AIFS, — HRES, - - ERA5 (ground truth).

Weather Forecasting

Weather forecasting predicts future atmospheric conditions, from local storms to global patterns, using physical models, observations, and machine learning.

See all 178 papers

Otter Weather: Skillful and Computationally Efficient Medium-Range Weather Forecasting
Jun 24, 2026Cristiana Diaconu, Jonas Scholz, Aliaksandra Shysheya +4Time Series Forecasting Probabilistic Forecasting
University of Cambridge
AIFS-SUBS: Extending Data-Driven Forecasting to Sub-Seasonal Timescales
Jul 6, 2026Jakob Schloer, Steffen Tietsche, Christopher D. Roberts +6Probabilistic Forecasting Weather Forecasting
1European Centre for Medium-Range Weather Forecasts (ECMWF)
PhysMetrics.Weather: An Evaluation Framework for Physical Consistency in ML Weather Models
Jun 9, 2026Emma Kasteleyn, Timo Maier, Axel Lauer +3Forecasting Benchmarks Physics-Informed ML
University of Amsterdam, Amsterdam, The Netherlands · Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany · University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany +1

Jun 24, 2026J/K move · Enter open · S save

Otter Weather: Skillful and Computationally Efficient Medium-Range Weather Forecasting

Cristiana Diaconu, Jonas Scholz, Aliaksandra Shysheya, Stratis Markou+3

University of Cambridge

Time Series Forecasting Probabilistic Forecasting Spatiotemporal Forecasting Weather Forecasting

Open paper AskPDF arXiv

State-of-the-art medium-range AI weather models can outperform traditional Numerical Weather Prediction (NWP) but require massive training budgets. This restricts usage for under-resourced groups and severely limits fast model iteration. Here we develop Otter Weather, a highly efficient spatiotemporal forecasting model designed to democratise high-performance weather prediction with AI. Evaluated on ERA5 reanalysis data at 1.5° resolution using standard WeatherBench protocols, the Otter family significantly advances the skill-compute Pareto frontier. The deterministic version outperforms the best NWP baseline by 9.6% at a 24-hour lead time while requiring fewer than 3.5 A100-days for training. It provides a 2x efficiency gain over lightweight AI models and a 100-fold reduction in compute compared to resource-intensive frontier architectures. We extend these efficiency gains into probabilistic forecasting by training via the Continuous Ranked Probability Score (CRPS). Scaling to a larger architecture, Otter-XL achieves a 9.7% CRPS improvement over the IFS ENS baseline. This yields an almost two-fold increase in predictive skill over comparable lightweight models at similar compute budgets. Otter-XL also outperforms frontier architectures like GenCast by over 2%, while using an order of magnitude less compute. Finally, Otter is applied out-of-the-box to a complex acoustic scattering PDE task where it outperforms a state-of-the-art foundation modelling approach, suggesting that the advances made here might apply across a range of scientific domains.

Abstract

Weather Forecasting

Explore similar work

Otter Weather: Skillful and Computationally Efficient Medium-Range Weather Forecasting

AIFS-SUBS: Extending Data-Driven Forecasting to Sub-Seasonal Timescales

PhysMetrics.Weather: An Evaluation Framework for Physical Consistency in ML Weather Models