Mr Nankervis became a qualified Meteorologist in 2005, with a 2.1 honours degree from the highly prized University of Reading department. Embarking on postgraduate studies at the University of Edinburgh in 2005, he has developed specialist experience in the field of Earth Observation and general working knowledge in the physical sciences. His studies involved reading and analysing remotely sensed satellite data from NASA, the ECMWF and NOAA - the principal inputs used for weather prediction.
In August 2010, his expertise and aptitude for solving problems led him to the world of business. With strong entrepreneurial skills, he embarked on a venture selling seasonal weather predictions to customers in the UK and Ireland. With a low start-up budget, he began to think much more creatively. He designed a basic framework to describe the long-term weather patterns experienced in Britain, based on a holistic method that is used in systems science.
With attention to detail, and a keen interest in analysing global forecast system outputs, he noticed a distinct rise in the frequency of blocking patterns. These high pressure systems appeared to govern the extremes of seasonal temperature and rainfall. Blocking high pressures "prefer", or at least have a tendency, to remain in certain positions for prolonged periods of time. Based upon this concept, a model was constructed to simulate the impact of blocking patterns of various sizes and locations on the long term patterns in the upper air stream. In combination with sea surface temperature data and monthly jet-stream climatologies, he was able to produce realistic forecasts of temperature, rainfall, storminess, air flow direction and energy demand. Furthermore, his modelling was reported on a regional space-scale.
Weather Logistics UK was born in August 2010, selling seasonal weather predictions to businesses in the retail, leisure, agricultural and tourism sectors. With a strong green-fingered policy, the company promoted climate science and its similarities to seasonal weather predictions. This was achieved by the use of probabilistic outputs, expressing the likelihood of different weather events or temperatures exceeding critical boundary conditions. The seasonal weather predictions have proved particularly successful for energy demand forecasting.
