WeatherNext models are global medium-range forecasting models with broad applications across industries. If your work relies on models such as ECMWF HRES/ENS/AIFS or NOAA GFS, WeatherNext offers a compelling alternative.
Benefits
Accuracy: WeatherNext 3 outperforms leading operational NWP systems across the majority of variables, lead times, and pressure levels. See the research and benchmarks page for detailed benchmarks.
Probabilistic forecasting: WeatherNext 3 generates 64-member ensembles to characterize predictive uncertainty and estimate risk. (For custom ensemble sizing on demand, WeatherNext 2 managed inference is available on Vertex AI Model Garden.)
Speed: A full 15-day global forecast can be generated in minutes on a single TPU, enabling rapid iteration and near real-time applications. For forecast availability details, see the dissemination schedule.
High resolution: WeatherNext 3 produces forecasts at up to 0.05° (~5 km) spatial resolution for station-calibrated surface variables (
station_head_temperature_2mandstation_head_dewpoint_temperature_2m), and 0.1° (~10 km) for core gridded surface fields (wind, pressure, precipitation, clouds, solar) with hourly timesteps. This provides a 2.5× to 5× resolution improvement over WeatherNext 2's 0.25° grid, resolving local features like valleys, coastlines, and topography.Improved precipitation: WeatherNext 3 trains against three distinct precipitation data sources (ECMWF reanalysis, NASA IMERG, and Google's satellite-radar reanalysis), achieving up to 50% reduction in Brier score and CRPS compared to numerical weather prediction baselines.
Clean energy parameters: WeatherNext 3 directly outputs 100-metre wind speeds, cloud cover, and solar radiation components (SSRD, FDIR) for renewable energy applications.
Downstream integration: WeatherNext outputs can serve as inputs to other systems and models, such as ensemble post-processing pipelines, flood forecasting models, and specialized regional downscaling models.
Limitations
Reanalysis targeting: WeatherNext models are trained against global reanalysis data (ERA5 / HRES-fc0), which has limited resolution and carries its own biases. Reanalysis does not always match what you would measure on the ground, particularly for localized variables. For applications requiring close agreement with surface observations, bias-correction techniques may be needed. WeatherNext 3 partially addresses this by also training on raw weather station observations for key surface variables.
Blurring: Deterministic ML models like WeatherNext Graph tend to produce forecasts that become progressively smoother at longer lead times. This happens because the model learns to predict the average of possible outcomes, which washes out fine-scale detail as uncertainty grows. Probabilistic ensemble models (WeatherNext 2 and 3) are less affected because individual ensemble members preserve sharper structures.
Precipitation data quality: Model precipitation outputs are influenced by the quality and biases of their training data. ERA5 precipitation has known limitations and is often excluded from headline evaluations (see research papers for details). WeatherNext 3 mitigates this by training against multiple precipitation sources including satellite-based observations.
Artifacts in predictions: As demonstrated in Alet et al. (2025), despite being trained to optimize marginal skill, WeatherNext models produce skillful predictions of joint structures. Nevertheless, some artifacts are visible in individual output samples, reflecting the underlying mesh structure of the model. These artifacts were noted already for WeatherNext 2 and are still present in WeatherNext 3. While visible to some degree in most variables, they are more severe in some, in particular the station output and precipitation.
In the experimental satellite-radar head output, individual samples show clear hexagonal patterns. The ensemble mean still shows these while they are less noticeable in the median. The experimental satellite-radar head shows these artifacts more strongly than the IMERG head. This is due to its distribution being sharper, which leads to overall better performance but also more evident artifacts in the individual members, compared to IMERG's smoother distribution (see research and benchmarks). Despite these unrealistic patterns, WeatherNext 3 still performs strongly on pooled metrics that take into account spatial correlations. This is important to make reliable probabilistic predictions of cumulative precipitation over larger areas, used for example in flood forecasting.
WeatherNext 3 produces outputs at time steps smaller than the outer 6-hour time step. For variables that are not auto-regressively predicted, such as the station head, distinct jumps can occur across the 6-hour windows. In addition, the station head also exhibits a per-sample bias, where one sample will tend to be warmer or colder globally compared to the ensemble mean. The global bias changes with each 6-hour time step as a new noise vector is drawn. This behaviour shows another way the model produces realistic marginal distributions while approximating the covariance structure. However, when looking at marginal statistics such as quantiles, the discontinuities are relatively minor. Since many applications rely mostly on ensemble statistics (e.g., the ensemble mean or exceedance probabilities), the forecasts are nevertheless widely usable.