Weather Forecasting

Use AI to deliver faster, more accurate, and cost-efficient weather forecasts worldwide

Business impact

• Forecast accuracy — AI models increase precision of weather predictions, reducing errors and uncertainty
• Forecast speed — AI reduces computation time from hours to minutes or seconds
• Operational efficiency — Lower computational costs and faster insights improve resource utilization
• Decision-making quality — More timely and accurate forecasts support better strategic and tactical decisions
• Computational cost — AI models require significantly less supercomputing power than traditional methods

Data requirements

• Satellite imagery (Image) — Provides global atmospheric observations for model input and validation
• Radar data (Numeric) — Captures precipitation and storm dynamics for short-term forecasting
• Weather station measurements (Numeric) — Offers ground truth data for temperature, humidity, and wind
• IoT sensor networks (Numeric) — Delivers hyper-local environmental data to enhance forecast resolution
• Historical weather records (Structured) — Used for training AI models to learn weather patterns and trends

AI methods and techniques

• Predictive AI — Forecasts future weather states by learning from historical and real-time data
• Generative AI — Generates ensemble forecast scenarios to capture uncertainty and variability
• Symbolic AI — Incorporates physical laws and domain knowledge to constrain model outputs

AI models and model families

FourCastNet, GenCast, Aardvark Weather, FastNet, YanTian, CorrDiff, Atlas, StormScope

Industries

Real-world evidence

10 documented case studies on record.

Companies using this: DeepMind, European Centre Medium Range Weather Forecasts, European Centre Medium Range Weather Forecasts ECMWF, G42, Google, Met Office, NVIDIA, Spire Global, Tomorrow, University Cambridge.