Morning session
Understanding weather and climate datasets
Explore station observations, satellite products, reanalysis, operational forecasts and climate-model outputs. Compare their strengths, limitations, spatial coverage and temporal resolution.
Day 2 – Climate Data
1. Day 2 Overview
African Summer School · Day 2
Climate Data: From Observations to Model Outputs
Weather and climate applications depend on many different types of data. Today you will explore where these datasets come from, what they represent, how they are stored and how they can be prepared for analysis.
Day 2 programme
Afternoon session
Data formats, preprocessing and Python
Learn how weather and climate information is stored in CSV, NetCDF and GRIB files. Practise inspecting variables, dimensions, units, missing values and metadata before using the data in an AI workflow.
Learning outcomes
By the end of Day 2, you should be able to:
1. Identify data sources
Distinguish station, satellite, reanalysis, forecast and climate-model datasets.
2. Compare datasets
Explain differences in coverage, resolution, frequency, uncertainty and accessibility.
3. Recognise data formats
Recognise the basic characteristics of CSV, NetCDF and GRIB files.
4. Inspect data quality
Check variables, units, coordinates, missing values, time periods and metadata.
5. Prepare data for analysis
Describe the main preprocessing steps required before training an AI or ML model.
6. Explore data with Python
Open a dataset, examine its structure and create a simple visualisation.
Today’s data journey
Every successful weather or climate AI application begins with understanding the data before selecting a model.
1. Discover Find relevant data sources
2. Understand Read variables and metadata
3. Check Assess quality and gaps
4. Prepare Clean and transform
5. Explore Analyse and visualise
Opening reflection
Think about the data you currently use in your organisation or country.
Consider:
- Which weather or climate datasets do you use most frequently?
- Where do these datasets come from?
- What limitations or data gaps affect your work?
- Which additional datasets would improve your services?