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.
Tag Archive: Craig Bishop
The Climate Variability program has seen an extraordinary amount of activity over the past four months with new arrivals, a clutch of thesis submissions, awards, research voyages and a wealth of research.
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 TransformationMarch 18, 2019 8:00 am 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
This research shows how to optimise the observational uncertainty description for data assimilation schemes in the special case of state dependent observational uncertainty.
Research brief: Improving assimilation of radiance observations by implementing model space localisation in an ensemble Kalman filterFebruary 12, 2019 10:30 am 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.
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.