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:<br /> <br /> 1. 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? <br /> <br /> 2. Decomposing predictability of onset metrics into different types of uncertainty (things we can and can’t predict). <br /> <br /> 3. 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).<br /> <br />

<p>By comparing multiple onset definitions, the research aims to identify formulations that are not only physically meaningful and agronomically relevant, but also inherently more predictable. In doing so, it will open the door to new approaches to forecasting onset that make the most of recent advances in remote sensing and machine learning algorithms.  <br /> A central component of the work is the decomposition of predictability. Onset timing is influenced by a complex mix of drivers operating across scales, from local land surface feedbacks to large-scale climate modes. This research disentangles these influences by quantifying different sources of uncertainty - distinguishing between variability that is fundamentally unpredictable and signals that can, in principle, be forecast skillfully. This framework provides a clearer understanding of where predictability arises and where current models fall short.<br /> Building on these insights, the project seeks to improve machine learning-based downscaling models for onset prediction. By incorporating physically informed predictors - such as soil moisture evolution, vegetation dynamics, and large-scale tropical modes of variability - the research aims to enhance the skill and robustness of forecasts at local scales. Ultimately, this work contributes to the development of more reliable, actionable onset predictions that are better aligned with agricultural decision-making and climate resilience in Africa.<br /> The project will include collaboration with colleagues in Africa, both at universities and within the National Meteorological Services. There are likely to be opportunities to visit and work with African partners.</p> <p>This project will be linked with the Cumulus project, which you can <a href="https://www.leeds.ac.uk/research-32/news/article/5920/ai-forecasting-strengthens-climate-resilience">read more about here</a></p> <p><br />  </p>

<p>Formal applications for research degree study should be made online through the <a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">University's website</a>. Please state clearly in the research information section that the research degree you wish to be considered for is ‘Novel approaches to understanding and forecasting rainfall in Africa’, as well as <a href="https://environment.leeds.ac.uk/see/staff/9387/dr-chetan-deva">Dr Chetan Deva</a> or <a href="https://environment.leeds.ac.uk/see/staff/12868/dr-caroline-wainwright">Dr Caroline Wainwright</a> as your proposed supervisor.</p> <p>If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements (below).</p> <p><em>As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</em></p>

The minimum entry requirements for PhD study is a 2.1 honours Bachelor degree, or equivalent, in a subject relating to your proposed area of research, or a good performance in a Master’s level course in a relevant subject. The successful candidate will have a strong quantitative background (e.g. computer science, maths, statistics, physics, meteorology) and possess good coding skills. Experience with machine learning is desirable though not essential. Over the course of the PhD you will have opportunity to attend courses on ML and statistics. The training you will receive will equip you to work in data science, climate tech or research after completion of the PhD.<br /> <br /> Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or PGR Admissions Team prior to making an application.<br />

The minimum English language entry requirement for postgraduate research study in the School of Earth, Environment, and Sustainability is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course to be valid.

<p>We are offering a fully funded scholarship to study the project ‘Novel approaches to understanding and forecasting rainfall in Africa’, at the School of Earth, Environment, and Sustainability, University of Leeds for one UK status candidate. The funding covers UK tuition fees as well as a UKRI matched maintenance stipend (currently £20,780 in 2025/26) per year, for three and a half years, subject to satisfactory progress.</p> <p>Eligibility Criteria <br /> •    Applicants must be eligible to pay fees at the Home (UK) rate.</p> <p>If you are unsure whether you are eligible for UK fees/funding, please see our<a href="https://www.leeds.ac.uk/undergraduate-fees/doc/fee-assessment"> fee assessment page.</a><br />  </p>

<p>For further information please initially email <a href="http://c.r.deva@leeds.ac.uk">Chetan Deva</a> or alternatively, the <a href="http://ENV-PGR@leeds.ac.uk">PGR Environment Admissions team</a></p>