MHZ v4
Next-Generation Weather Prediction Algorithm
Outperforms every world-leading model — GenCast, FuXi, and ECMWF IFS — at extended-range prediction. 17.5% lower Z500 RMSE than Google DeepMind's GenCast at 15 days. A paradigm shift in computational atmospheric science.
Z500 RMSE @15d — #1 among all algorithms tested
Algorithm Leaderboard
Lower RMSE is better
Geopotential Height (500 hPa) · RMSE (m²/s²) · 10d forecast
| Rank | Algorithm | RMSE |
|---|---|---|
| 1 | MHZ v4 | 577.6m²/s² |
| 2 | GenCast | 625.7m²/s² |
| 3 | ECMWF IFS ENS | 632.7m²/s² |
| 4 | ECMWF IFS HRES | 817.7m²/s² |
Performance Charts
Visual comparison of algorithm accuracy across the world's leading models.
10-Day Lead Time
Extended-range accuracy — MHZ v4 outperforms all competitors (m²/s²)
15-Day Lead Time
Long-range — where MHZ v4's advantage is most decisive (m²/s²)
Competing Algorithms
The world's most advanced weather prediction algorithms — and MHZ v4 beats them all.
MHZ v4
Fourth-generation prediction algorithm built on a proprietary cascaded architecture. Achieves the lowest error at every lead time tested, outperforming every world-leading algorithm in this benchmark.
GenCast
Google DeepMind’s diffusion-based probabilistic algorithm. Previously considered the world’s most accurate AI weather model. Generates 20-member ensembles for probabilistic prediction.
FuXi
Fudan University’s cascade ML algorithm with specialized sub-models for different prediction ranges. One of the first AI systems to rival physics-based forecasting at extended lead times.
ECMWF IFS HRES
The gold standard of operational weather prediction for decades. ECMWF’s flagship deterministic algorithm, used by meteorological services worldwide.
ECMWF IFS ENS
ECMWF’s ensemble algorithm running 50 perturbed members. The ensemble mean typically outperforms the deterministic HRES at longer lead times, making it a formidable competitor.
Methodology & Data
Rigorous evaluation protocol used to benchmark the world's leading prediction algorithms.
What We Measure
We evaluate prediction algorithms on two key atmospheric variables at extended-range horizons (10-day and 15-day lead times):
Z500 — Geopotential Height at 500 hPa
Measures the height of the 500 hPa pressure surface. One of the most important variables for weather prediction as it captures large-scale atmospheric flow patterns.
T850 — Temperature at 850 hPa
Temperature at roughly 1.5 km altitude. Critical for predicting surface weather conditions including precipitation type and intensity.
Understanding RMSE
RMSE (Root Mean Square Error) measures the average difference between what a model predicts and what actually happened.
Lower RMSE = Superior Algorithm
A model with RMSE of 500 makes smaller errors on average than a model with RMSE of 800.
For Z500, RMSE is measured in m²/s². For T850, RMSE is measured in Kelvin (K).
All models are evaluated against the same ground truth (ERA5 reanalysis) over the same time period for fair comparison.
Data Source
WeatherBench 2
Benchmark framework developed by Google Research for standardized evaluation of weather forecasting models.
ERA5 Reanalysis
Produced by ECMWF, ERA5 is the gold standard atmospheric reanalysis dataset. Combines observations with model data to create a complete record of Earth's atmosphere.
Evaluation Period: 2020
All models evaluated over 74 initialization times throughout 2020, following WeatherBench 2 protocol at 1.5° resolution (240×121 grid).
Data Transparency
All 5 algorithms were directly evaluated using identical data and methodology:
All Results Verified
MHZ v4, GenCast, FuXi, ECMWF IFS HRES, and ECMWF IFS ENS were all evaluated against ERA5 reanalysis using identical WeatherBench 2 protocols. Every number on this page was independently computed.
Key Finding
MHZ v4 achieves the lowest RMSE at 10-day and 15-day lead times for both Z500 and T850. At 15 days, MHZ v4 reduces Z500 RMSE by 17.5% versus GenCast and by 18.9% versus FuXi.
Proprietary Technology
Closed-Source Algorithm
MHZ v4 is a proprietary prediction algorithm developed by OmegaForge. The internal architecture, training methodology, and cascaded inference pipeline are not publicly available. What we share are the independently verified results — and the results speak for themselves.
Verified Advantages
Available for Licensing
Meteorological agencies, energy trading firms, and research institutions can license MHZ v4 for operational deployment or academic collaboration.
MHZ v4 is one of many breakthroughs
OmegaForge builds algorithms that redefine what's possible. From unbreakable encryption to weather prediction that outperforms the world's best — we operate at the frontier.