I have just come back from the European Meteorological Society 2017 conference in Dublin, where I was co-convenor for a session on Data Assimilation. It’s theme was Serving Society with better Weather and Climate Information. A key challenge for the meteorological communities is how best to harness the wealth of data now available – both observational and modelled – to generate and communicate effectively relevant, tailored and timely information ensuring the highest quality support to users’ decision-making. The conference produced some highlight videos that sum up the activities better than I could!
Even though my undergraduate degree and PhD were in Applied Mathematics, I don’t tend to go to many Mathematics conferences. I often meet with fellow data assimilation practitioners at Meteorology conferences instead. So it was great to see people proving data assimilation related theorems, applying data assimilation in different applications like neuroscience and cancer treatment, and of course to get some new ideas from dynamical systems approaches that have potential to be applied in different ways. I particularly enjoyed Mary Silber’s talk on using Landsat data to understand vegetation pattern formation in the drylands of Africa
Gowda/Silber’s work on African drylands. This image shows shrublands in Somalia from high above. Two images – from 1952 (purple) and 2006 (green) – are overlaid here for comparison. The colors highlight the large communities of shrubs and grasses which grow in bands along this sloping landscape. Over the fifty years shown here, all the vegetation has moved uphill – the green bands of modern plant growth are further up the hillside than the purple bands from 1952.
Data assimilation is an emerging mathematical technique for improving predictions from large and complex forecasting models, by combining uncertain model predictions with a diverse set of observational data in a dynamic feedback loop. The project will use advanced data assimilation to combine a range of advanced sensors with state-of-the-art computational models and produce a step-change in the skill of forecasts of urban natural hazards such as floods, snow, ice and heat stress. For more information about the research programme click here.
The Fellowship is held by Dr Sarah L. Dance at the University of Reading and she is working together with a team of other researchers and stakeholders. The Fellowship will influence the future research agenda for how digital technologies can be applied in new and transformative ways to help the human and natural environment be more resilient and adaptable to climate change. In addition to an innovative research programme, Dr Dance is acting as a Champion for this area, developing outreach activities to other researchers, policy makers and industry through workshops, networks and other mechanisms.