5. AI Applications in Weather and Climate
Day 1 · From Concepts to Applications
AI Applications in Weather and Climate
Explore how artificial intelligence and machine learning can complement observations, physical models and expert judgement across weather forecasting, climate services and early warning.
Learning objectives
By the end of this chapter, you should be able to:
- Identify major AI applications in weather and climate services.
- Connect different applications with appropriate data sources.
- Explain how AI can complement numerical models and observations.
- Recognise where human expertise remains essential.
- Select a possible AI application for an institutional challenge.
AI complements existing forecasting systems
AI should not be viewed as a replacement for observations, numerical weather-prediction models, climate models or professional expertise. Its strongest role is often to combine information, identify patterns, correct systematic errors and produce information that is more useful for operational decisions.
Major application areas
Explore the applications
Forecast post-processing and bias correction
Numerical weather-prediction models can show systematic errors at particular locations, seasons or forecast lead times. A machine-learning model can learn these historical relationships and adjust new forecasts.
Example: A forecast model consistently predicts daily maximum temperature two degrees too low during the hot season. A correction model can estimate and reduce this cold bias.
Statistical downscaling
Global forecasts and climate projections are often too coarse to represent local terrain, coastlines, cities or station conditions. Statistical downscaling learns relationships between large-scale conditions and local observations.
Example: A regional climate model provides rainfall information on a large grid, while district planners require more local estimates.
Nowcasting
Nowcasting focuses on the immediate future, often from a few minutes to several hours. Machine-learning systems can analyse sequences of satellite or radar images to estimate movement and development.
Example: Satellite imagery is used to predict where rapidly developing convective clouds may move during the next two hours.
Extreme-event monitoring
AI can help identify combinations of variables associated with dangerous conditions. However, rare extremes may be poorly represented in the training data and require careful evaluation.
Example: A model estimates the probability that daily maximum temperature will exceed a dangerous threshold five days ahead.
Impact-based early warning
Hazard information alone does not describe the likely consequences. Impact-based warning combines the forecast hazard with information about exposed people, livelihoods, infrastructure and vulnerability.
Example: A heat forecast is combined with settlement location, population vulnerability and access to cooling or health services.
Connecting applications with data
| Application | Possible input data | Possible output |
|---|---|---|
| Bias correction | Historical forecasts and observations | Corrected temperature or rainfall forecast |
| Nowcasting | Satellite images, radar and recent observations | Short-term storm or rainfall prediction |
| Downscaling | Coarse model output, local observations and elevation | Higher-resolution local prediction |
| Extreme-heat monitoring | Temperature forecasts, observations and vulnerability data | Probability or warning category |
| Data quality control | Station records and neighbouring observations | Flags for suspicious or missing measurements |
Check your understanding
Match the challenge with an AI application
A global weather model consistently predicts temperatures that are too low at a local station.
Appropriate application: forecast post-processing or bias correction.
Satellite images show rapidly developing clouds, and guidance is needed for the next hour.
Appropriate application: satellite-based nowcasting.
Climate projections are available on a coarse grid, but planners need district-level information.
Appropriate application: statistical downscaling.
Health authorities need to know which communities face the greatest danger during an extreme-heat event.
Appropriate application: extreme-event monitoring combined with impact-based early warning.
The continuing role of forecasters
Forecasters bring physical understanding, local knowledge and operational experience that may not be represented fully in the training data.
- Review AI outputs alongside observations and physical models.
- Identify unusual situations outside the model’s experience.
- Interpret uncertainty and conflicting guidance.
- Communicate warnings in language appropriate to users.
- Remain accountable for operational decisions.
Group activity: select an institutional application
Select one weather or climate challenge faced by your institution. Discuss the following questions:
- What operational problem needs to be addressed?
- Who would use the resulting information?
- Which AI application is most appropriate?
- Which datasets would be required?
- What benefit could the system provide?
- What limitation or risk would need to be managed?
- What role would human experts continue to play?