Markov Chains, hydrology and wind roses
Host Institution: African Mathematics Initiative (AMI)
Location: Maseno, Kenya. Contact: James K. Musyoka, School of Mathematics, Statistics and Actuarial Sciences, Maseno University. E-mail: firstname.lastname@example.org
Supervisors: James K Musyoka, David Stern, IDEMS International, Reading, UK (http://www.idems.international/), Danny Parsons, IDEMS International, Reading, UK, Roger Stern, University of Reading and Stats4SD, Reading, UK (https://stats4sd.org/)
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.
On discussion with the supervisors the intern would work on one or more of the topics below:
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.
Adding hydrological products and applications. There are many overlapping methods of analysis for climatological and hydrological applications, e.g. streamflow. There is even an R task view devoted to hydrology. The intern would asses the key products and analysis needed from hydrology and add them to the R-Instat software. This may simply add menu items to the current climatic menu, or become a separate menu. The work would include discussions with the developers of the WMO-supported MCH software developed in Mexico.
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.
Project Expected Outcomes [Project 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 analysed. 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: January 20th, 2020 – 11:59 PM (EAT).
Any inquiries about these internships should be sent to: