AI weather system design
Frame the forecast problem, data requirements, spatial and temporal scales, evaluation criteria, and operational constraints before selecting a model or architecture.
Applied Meteorology & AI/ML
Weather AI & Meteorology
Scientific guidance and working technical delivery for organizations evaluating, building, or operating AI/ML weather systems—from data and baselines through verification, uncertainty, and production use.
AI weather forecastingApplied meteorologyForecast verificationNWP/ML systemsWeather & climate data
Capabilities
Weather Science for AI
A model can score well and still fail the forecast situation, geography, lead time, or operational decision that matters. Domain judgment stays connected to the engineering.
NWP and machine-learning integration
Connect numerical weather prediction, observations, reanalysis, and ML approaches—including GraphCast- and Pangu-Weather-class workflows—into a coherent system.
Forecast verification
Evaluate skill, bias, reliability, extremes, spatial displacement, and performance across lead times, regions, seasons, and decision thresholds.
Weather data engineering
Prepare model, satellite, radar, station, reanalysis, and climate data for training, inference, comparison, and downstream applications with traceable transformations.
Uncertainty and decision support
Translate ensembles, probabilistic output, confidence limits, and known model failure modes into information people and automated systems can act on within known limits.
Operational delivery
Move from research code or a promising prototype to documented, monitored, repeatable pipelines that fit the team, infrastructure, and mission.
How we evaluate weather AI
Beyond Forecast Accuracy
Modern AI weather forecasting can be powerful, but it still depends on the training data, reference analysis, grid, loss function, verification design, and conditions outside the model's strongest regime.
- Meteorological validity before model novelty
- Baselines and verification matched to the real decision
- GIS-aware handling of grids, projections, and spatial scale
- Explicit uncertainty, provenance, and operational limits
- Human review where consequences require scientific judgment
The geospatial connection
Aligned Data First
Training, inference, and verification often require regridding model fields, observations, and targets across different coordinate systems and resolutions.
Evaluating an AI weather product or moving a forecasting workflow toward production?
Discuss the system