Incorporating and analyzing data from automatic stations.

Incorporating and analyzing data from automatic stations.

Host Institution: African Mathematics Initiative (AMI)

Location: Maseno, Kenya. Contact: James K. Musyoka, School of Mathematics, Statistics and Actuarial Sciences, Maseno University. E-mail:

Supervisors :James K Musyoka, David Stern, IDEMS International, Reading, UK.

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.All countries in Africa now have climatic data to process from automatic stations.  They are often on a 10-minute, 30-minute or hourly basis. These data often remain unanalyzed. The intern will therefore have to learn how to process “within-day” data.  It will also link to homogenizing, because users often want to combine the records with a previous manual station.

A second area of application will be for the temperature records, where degree-day information is now usually calculated using hourly data.

The intern may also extend existing work on data homogenization and the infilling of data. Work in this area has started through a current AIMS Cameroon coop student, who is based at the Kenya Met Department (KMD).  This is an important area and the proposed topic can continue this work.  The proposed methods for infilling can use either data from neighbouring stations or satellite (or reanalysis) data, or a mixture.

 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: