Markov Chains, hydrology and wind roses.
1.Host Institution:
African Mathematics Initiative (AMI), Maseno, Kenya
2. Supervisors:
(1) James K Musyoka,
(2) David Stern, IDEMS International, Reading, UK
(3) Danny Parsons, IDEMS International, Reading, UK
(4) Roger Stern, University of Reading and Stats4SD, Reading, UK
3. Project Description:
The intern will join the AMI team of young staff who are working on a range of topics concerned with mathematics and its applications. They will mainly work on the topic below. They will also be given the opportunity to take part in occasional AMI initiatives many of which (like Maths camps), are designed to improve Mathematics Education at school and university levels.
More than wind roses: Met services are obsessed by wind roses as a way of presenting wind speed and wind direction together. There is much more to be done with these data, both in handling the circular nature of the data and particularly in handling extremes in wind speeds and in many other climatic elements. This could include being able to process pollution data, where available, using the R-Openair package.
Simplifying Markov chain modelling: Modelling daily rainfall data using Markov chains is made easy using the original Instat software. Recent MPhil and PhD research in Kenya and Ghana has used the same methods in R. These methods could be used more widely if at least part of the modelling could be added to R-Instat. One application of these methods is to quantify the ENSO effect on the patterns of rainfall.
4. Expected Outcomes:
Long-Term goals: Each country in Africa has a National Meteorological Service (NMS). The NMS provides their country with the short-term forecast, and also often provides a special service for the aviation sector. Many countries also provide a seasonal forecast. In addition, the NMS is usually the custodian of the long-term historical data for their country. Most countries claim their density of climatic stations is insufficient and the manual stations have recently been supplemented by sets of automatic stations. There are many important uses of these historical records, particularly with concerns about climate change, but most services would agree that their existing data are currently insufficiently analyzed. This work is designed to provide and illustrate the use of a tool that is a “game changer” in this field. One goal is for NMS staff and others interested in this area, to add the full exploitation of these data into their “comfort zone”, in the same way as they handle climatic forecasts.
Applications for this internship should be submitted via the online application system, stating clearly the title of the internship.
Deadline for applications: February 28th, 2021 at 11:59 PM Central Africa Time (CAT)
Any inquiries about these internships should be sent to:
aims-ms4cr.internship@nexteinstein.org.