The Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University seeks to fill a postdoctoral fellowship in November 2020 as part of a National Science Foundation (NSF) award to train a new scientist in data assimilation and machine learning techniques. Located at CIRA in Fort Collins, Colorado, this fellowship may last up to 3 years contingent upon NSF funding availability.
Recent work at CIRA has focused on non-Gaussian-based data assimilation systems that are mixed Gaussian-lognormal based. As part of a previous award, a second non-Gaussian distribution has been detected in the Lorenz 63 model, as well as early indication of this reverse lognormal distribution in the output from the Weather Research and Forecasting (WRF) model. The individual in this position will develop the theory of the reverse lognormal in both variational and ensemble data assimilation systems.
Specifically, the individual in this position will serve as a member of the CIRA data assimilation group and will test the robustness of machine learning techniques to identify the links between non-Gaussian distributions and different atmospheric scale dynamics, convert the hybrid version of WRF-GSI to have a non-Gaussian component, and assess the robustness of new non-Gaussian based ensemble systems along with advancing the development of a new version of the Maximum Likelihood Ensemble Smoother.
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