Topic: Maximising the value of observational data in ensemble data assimilation for hazardous weather prediction
Lead Supervisor: Sarah L Dance, University of Reading/National Centre for Earth Observation
Email: s.l.dance@reading.ac.uk
Co-supervisors: Joanne Waller, Met Office

In a changing climate, an improved ability to forecast hazardous weather such as storms and floods is key to the management of risk for society. In weather forecasting systems, large numerical models solve nonlinear equations describing physical processes in the atmosphere. Data assimilation is routinely used to improve weather predictions by combining billions of variables from these numerical model simulations with millions of observations of the atmosphere. Data assimilation can be thought of as a machine learning or mathematical optimization approach, where a cost function is minimized. The cost function is essentially a weighted measure of the distance from forecast states (numerical simulations) and the available observations over a fixed time window, weighted by the uncertainties (error statistics) in the data. Thus, weather forecast accuracy relies on accurate estimates of the uncertainty in weather observations. However, less than 5% of some key observation-types are assimilated, in part because these uncertainties cannot be properly quantified and accounted for. Thus, a key research question is how to characterize and treat observation uncertainty in an assimilation system. This project will investigate mathematical methods to approximate observation uncertainty that preserve observation information content while being sufficiently efficient for practical use in operational weather prediction.

Funding particulars:
This project is funded by the NERC SCENARIO DTP. It has additional co-sponsorship from the UK Met Office in the form of a CASE award. This will supply an additional £1000 per annum to the Research Training and Support Grant for three years and also funds travel and subsistence for the student to undertake a 3-month placement at the Met Office.

Application deadline: 20 January 2023

For more information and to apply visit

SCENARIO DTP