This is a collaborative project available under the University of Oxford NERC DTP, co-supervised by Magdalena Balmaseda, ECMWF
All weather and climate predictions are produced using computer simulators of the atmosphere. As a society, we rely on these predictions to make decisions, from considering whether to take an umbrella out with us today, to developing policies that will improve our society’s resilience to climate change. One of the foundational principles of the scientific method is the need to assess and quantify uncertainty. Weather and climate prediction are not exempt from this requirement, and forecasts should include an estimate of the uncertainty in the forecast.
Making a weather or climate forecast involves combining information about the current state of the Earth-system with a computer model which represents the equations of motion describing the system. The computer model propagates the state of the Earth-system forward into the future, while making any necessary assumptions about future forcing (e.g. greenhouse gas concentrations). Potential errors in the starting conditions, unknowable future forcing, and approximations made when building the computer model of the Earth-system can introduce uncertainty into the prediction. These uncertainties must be accounted for to produce the reliable probabilistic forecasts essential for policy makers, industry and the humanitarian sector.
This project focuses on the uncertainty introduced into the forecast due to limitations in the forecast model. This model uncertainty is a substantial source of uncertainty on both weather and climate timescales, so it is imperative that it is correctly accounted for in forecasts. However, despite its importance, there is no consensus on how to best account for model uncertainty. At best, model uncertainty is represented in a heuristic or ad hoc manner, while at worst it is ignored completely. There is also a disconnect between the weather and climate communities, with different approaches used for the same model on different timescales.
This project seeks to provide a new understanding of the nature of model error and how to best represent this as a source of uncertainty in weather and climate models. The student will benefit from working closely with scientists at the European Centre for Medium Range Weather Forecasting (ECMWF), the world leader in weather and seasonal forecasting.
This project will make use of the large database of forecasts archived at ECMWF. The database includes operational forecasts made at a range of resolutions (i.e. using different pixel sizes in the atmosphere and ocean). Through comparing the forecasts with observations at short lead times (a day or less) we can measure the error in the forecast, and seek to understand the statistics of this error. This project would pioneer combining model forecasts with observations in this way, allowing us to characterise the uncertainty in our forecast model.
Exploration and assessment of the database will allow us to characterise the nature of model error. Are current techniques for representing model uncertainty consistent with these characteristics? Can we rule out some techniques as not fit for purpose? Can we identify new approaches which better represent the statistics of model error? How does changing the degree of pixilation in our forecast model affect error growth in the model? Understanding the answer to these questions will enable us to improve the fidelity of our weather and climate models