My research encompasses theoretical, computational and applied aspects of data assimilation — the science of optimally combining numerical models and observations of physical systems. In the past, I have utilized data assimilation algorithms to improve the representation of atmospheric convection through the incorporation of ground-based remote sensors. More recently, my focus has shifted to the development of new data assimilation methods which capitalize on the ongoing AI revolution. Beyond data assimilation, my interests also extend to numerical weather prediction, atmospheric dynamics, and various topics within the data sciences.
Education:
2021-2023: ASP Postdoctoral Fellow, National Center for Atmospheric Research 2016-2021: PhD, Meteorology, University of Oklahoma 2012-2016: MMet, Meteorology and Climate, University of Reading (UK)
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Hristo's Google Scholar Citations