Hi, I am Zilu Meng, a Ph.D. student at the University of Washington, working under the guidance of Prof. Gregory J. Hakim and Prof. Eric J. Steig. Prior to this, I earned my B.S. with honors in Atmospheric Sciences from Nanjing University of Information Science and Technology (NUIST) (Nanjing Institute of Meteorology), where I conducted research on tropical dynamics with Prof. Tim Li.
My research interests include data assimilation, climate dynamics, paleoclimate, and machine learning. Currently, my work focuses on paleoclimate data assimilation and investigating climate dynamics over the last millennium. I develop statistical approaches and physical models to better understand past climate variability and dynamics, with the ultimate goal of improving the accuracy of weather and climate predictions. In my free time, I enjoy playing video games, reading, and hiking.
My Curriculum Vitae can be found here.
Using Deep Learning based AGCMs (Neural GCM, DLESyM, ACE2), we conducted AMIP-like experiments spanning 1890-2020. We investigate two key questions: (1) Can these models simulate atmospheric conditions outside their training range? (2) How well do these models capture climate sensitivity and physical consistency compared to the real world?
Paleoclimate data assimilation is a powerful technique for reconstructing past climate states before the instrumental era. Instrumental records are relatively short and heavily influenced by anthropogenic forcing, limiting our understanding of natural climate variability. Paleoclimate proxies (e.g., tree rings, ice cores, corals) provide valuable indirect information about past conditions. By integrating these proxies with climate models through data assimilation, we can generate comprehensive reconstructions of past climate fields (temperature, precipitation, etc.) and study phenomena like ENSO, PDO, and AMO over longer timescales.
I am currently developing the first seasonal reanalysis dataset covering the last millennium using an "online" or "cycling" data assimilation approach. This dataset will provide gridded climate fields, enabling detailed studies of past climate variability and change. For instance, the comparison (shown right) of the Nino3.4 Index from our reanalysis with the HadISST dataset highlights the potential to accurately capture ENSO behavior over the millennium. This allows for comparisons between past and present climate dynamics.
A manuscript detailing this work has been submitted to the Journal of Climate (preprint available here).
Summary of completed reviews.