Novel approaches to understanding and forecasting rainfall in Africa
Rainfall onset is a fundamental driver of agricultural decision-making for smallholder farmers across Africa. This PhD investigates the dynamics and predictability of rainfall onset in Africa. You will explore how vegetation “green-up” and changes in soil moisture relate to commonly used onset metrics, asking whether these biophysical indicators can help refine our understanding of when the rainy season truly begins. In this PhD you will explore the following research questions by analysing datasets and developing new machine learning models:
- What is the link between green up, soil moisture changes and onset metrics? Is it possible to find onset definitions that are inherently more predictable while remaining agronomically useful?
- Decomposing predictability of onset metrics into different types of uncertainty (things we can and can’t predict).
- How can we use the results of 1 and 2 to adapt and improve our current machine learning downscaling models for forecasting onset (e.g. inclusion of tropical modes).
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