Tag Archive: Craig Bishop

Research brief: Inexpensive innovation improves Ensemble Kalman filter

October 22, 2019 1:42 pm Published by Comments Off on Research brief: Inexpensive innovation improves Ensemble Kalman filter

A known weakness of the Ensemble Kalman filter approach is that its ability to provide state estimates that closely match densely distributed observations is very limited. This paper describes a computationally inexpensive innovative variation on the technique that greatly ameliorates this difficulty.

Postgraduate opportunities at University of Melbourne

July 23, 2019 12:53 pm Published by Comments Off on Postgraduate opportunities at University of Melbourne

The CLEX node at University of Melbourne is offering several PhD scholarships on a competitive basis. Details of how to apply can be found on this page along with some example projects offered by our researchers.

Seminar: Using observations to improve ensemble-based climate projections and the Ensemble Dependence Transformation

March 18, 2019 8:00 am Published by Leave your thoughts

Craig Bishop (University of Melbourne) This seminar introduces the “replicate Earth” ensemble interpretation framework, based on theoretically derived statistical relationships between ensembles of perfect models (replicate Earths) and observations. We transform an ensemble of (imperfect) climate projections into an ensemble whose mean and variance have the same statistical relationship to observations as an ensemble of replicate Earths. We use a ‘perfect model’ approach to test whether this Ensemble Dependence Transformation (EDT) approach can improve 21st century CMIP projections. In these... View Article

Research brief: Improving assimilation of radiance observations by implementing model space localisation in an ensemble Kalman filter

February 12, 2019 10:30 am Published by Comments Off on Research brief: Improving assimilation of radiance observations by implementing model space localisation in an ensemble Kalman filter

New data assimilation method leads to large improvements in forecast accuracy when satellite observations of electromagnetic radiation emanating from the Earth were used to inform the data assimilation scheme.

Research Fellow in Data Assimilation

August 16, 2018 12:31 pm Published by Comments Off on Research Fellow in Data Assimilation

The Centre seeks a highly qualified and motivated individual to create new and innovative data assimilation algorithms for discovering model trajectories that closely track observations in the presence of multi-scale, non-linear and non-Gaussian uncertainties such as those associated with observations and forecasts of clouds, precipitation, aerosols, soil moisture, ice, or ocean colour.