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Evaluating the Impact of Data Assimilation on Atmospheric Tidal Modes

Brandon
diLorenzo
University of Colorado
Abstract text

This study addresses the challenge of accurately capturing the dynamics of Earth’s upper atmosphere by exploring how data assimilation of space-based far ultraviolet (FUV) dayglow observations influence the representation of tidal modes in a whole atmosphere model. This poster presents comparisons of Whole Atmosphere Model (WAM) modeling results with and without ensemble data assimilation of GOLD FUV disk measurements of N2 Lyman-Birge-Hopfield (LBH) bands and discusses how a prior work by Cantrall (PhD thesis, 2022) can be extended with the help of different covariance localization techniques.

Authors
Brandon diLorenzo, Geospace Data Science Lab, University of Colorado Boulder
Tomoko Matsuo, Geospace Data Science Lab, University of Colorado Boulder
Clayton Cantrall, Applied Physics Laboratory, Johns Hopkins University
Poster PDF
Student in poster competition
Poster category
DATA - Data Assimilation, Data Analytics, Methods and Management