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Establishing Links between Atmospheric Dynamics and Non-Gaussian Distributions and Quantifying Their Effects on Numerical Weather Prediction


Project Period: November 1, 2017 to October 31, 2020
Principal Investigator(s): Steve Fletcher
Co-Principal Investigator(s): Anton Kliewer, Andrew Jones, John Forsythe
Postdoc(s) and Student(s): Michael Goodliff
Sponsor(s): National Science Foundation (NSF)

 

This project is a continuation of a previous NSF-supported research project, “Analyzing the Impacts of Non-Gaussian Errors in Gaussian Data Assimilation Systems.” The goal of this project is to address several different aspects about how non-Gaussian distributed errors affect data assimilation and retrieval systems. Previously we have shown that it is possible to obtain better fits for the moisture and temperature channels from the Advanced Microwave Sounding Unit A and B by using a mixed Gaussian-lognormal based 1D variational microwave retrieval system for temperature and mixing ratio respectively. We also observed that it is possible for the flow to change probability density function by season and as such detection of these PDF changes is important to ensure that the DA system is consistent with the PDF at that time. To continue this previous research, two of the major goals of this project include linking the atmospheric dynamics to their respective conditional PDFs as well as developing an online kernel detection system.