The Data Assimilation and Ensemble Forecasting group researches improving estimates of past, present, and future atmospheric and oceanic states.
The largest improvements in weather forecasting over the last decade are attributable to data assimilation improvements. Data assimilation schemes generate initial conditions for forecasts by using observations to correct short-range forecasts. More research is needed.
For example, clouds are known to be a leading cause of weather and climate prediction error. Cloud observations could help fix these errors, but few cloud observations are assimilated. Furthermore, the goal of allowing observations to automatically improve physically constrained parameterisations of clouds has yet to be achieved.
Contact
For enquiries, please email Dr Craig Bishop - craig.bishop@unimelb.edu.au