2. Inauguration and Keynote

Day 1 · Morning Session

Inauguration and Keynote

Explore why artificial intelligence is becoming increasingly important for African weather forecasting, climate services, disaster-risk reduction and early warning.

Purpose of the opening session

The opening session introduces the Summer School objectives, facilitators and participants. It establishes the connection between artificial intelligence, operational meteorology, climate modelling, disaster-risk reduction and impact-based early warning.

Participants are encouraged to consider both the opportunities offered by AI and the scientific, technical and institutional conditions required for its effective and responsible use.

Keynote themes

Growing data volumes Satellite, station, reanalysis and forecast datasets are increasing in volume and complexity.
Local forecast needs Users require timely and locally relevant information for preparedness and decision-making.
Data-sparse regions AI may help combine multiple data sources where observations are incomplete or unevenly distributed.
Human expertise AI should complement physical understanding and professional forecasting judgement rather than replace them.

Watch

Opening keynote video

While watching, identify one opportunity for AI in your institution and one challenge that would need to be addressed before operational use.

A transcript or summary should also be provided for participants who cannot access the video.

While watching

  1. Identify one weather or climate service that could benefit from AI.
  2. Identify the intended users of that service.
  3. Note the datasets that might be required.
  4. Record one technical or institutional barrier.
  5. Consider the role that forecasters should continue to play.

Explore

Why is AI relevant to weather and climate services?

Processing complex datasets

AI methods can analyse large collections of station observations, satellite imagery, model forecasts and climate records more rapidly than would be possible through manual analysis alone.

Identifying patterns

Machine-learning models can identify statistical relationships in historical data that may be useful for prediction, classification or forecast correction.

Improving local information

AI may help produce more locally relevant information from coarse-resolution forecast and climate-model products.

Supporting operational decisions

AI can help organise, prioritise and present information, but qualified professionals should continue to interpret the evidence, communicate uncertainty and remain accountable for warnings.

Small-group discussion

Select one weather or climate challenge faced by your institution and discuss:

  • Why the challenge is important.
  • Who is affected by it.
  • What information is currently available.
  • Where AI might contribute.
  • What limitations or risks should be considered.

Keynote presentation

Review the presentation slides and use them to support the group discussion.

View the Keynote Slides