Zilu Meng's Academic Page

About Me

Hi, I am Zilu Meng, a Ph.D. student in Atmospheric Sciences at the University of Washington. I am interested in Paleoclimate, Machine learning, Climate dynamics, and Data assimilation. I am currently working on Paleoclimate Data Assimilation, Last Millennium Climate Dynamics. In my free time, I enjoy vedio games, reading, and hiking.

Publications

Meng, Zilu, Hakim, Gregory J. , Steig Eric J. (2024). Last Millennium Seasonal Reanalysis, in prep.
Meng, Zilu, & Hakim, Gregory J. (2024). Reconstructing the Tropical Pacific Upper Ocean using Online Data Assimilation with a Deep Learning model. Journal of Advances in Modeling Earth Systems, under review. https://arxiv.org/abs/2406.07063.
Meng, Z., & Li, T. (2024). Why is the Pacific meridional mode most pronounced in boreal spring? Climate Dynamics, 62(1), 459–471. https://doi.org/10.1007/s00382-023-06914-4.
Meng, Z., 2023: SACPY -- A Python Package for Statistical Analysis of Climate. https://doi.org/10.5281/ZENODO.8182410.
Zhu, F., J. Emile-Geay, K. J. Anchukaitis, N. P. McKay, S. Stevenson, and Z. Meng , 2023: A pseudoproxy emulation of the PAGES 2k database using a hierarchy of proxy system models. Sci Data, 10, 624, https://doi.org/10.1038/s41597-023-02489-1.
Meng, Z., Z. Hu, Z. Ai, Y. Zhang, and K. Shan, 2021: Research on Planar Double Compound Pendulum Based on RK-8 Algorithm. Journal on Big Data, 3, 11–20, https://doi.org/10.32604/jbd.2021.015208.

Education

Ph.D. Student in Atmospheric Sciences, University of Washington, 2023 - Now
Advisor: Gregory Hakim & Eric Steig
Research interests: Paleoclimate, Machine learning, Climate dynamics, Data assimilation
GPA: 3.99/4.0

B.S. in Atmospheric Science, Nanjing University of Information Science and Technology, 2019 - 2023
Advisor: Tim Li
Core Courses: Atmospheric (Fluids) dynamics, Atmospheric Physics, Principle of Method Synoptic Meteorology GPA: 95/100

Projects

Last Millennium Seasonal Reanalysis, Developed a seasonal reanalysis dataset for the last millennium using online data assimilation.

Deep Learning for tropical pacific reconstruction, developed a deep learning model to reconstruct tropical climate fields.Github

Sacpy, Built an efficient and useful Statistical Analysis module for Climate data in Python. Over 20000 people have used it so far on Github.

Deep Learning for ENSO, Deep learning and Grad-CAM are used to study the cause of El Nino (La Nina). Over 100,000 people have read articles on Zhihu. Github.

CFR, Participated in developing a universal framework for climate field reconstruction. Github

Conference Presentations

Nanjing Data Assimilation Workshop, Nanjing, China, 2024 June. Title: "Deep Learning for tropical pacific reconstruction". Github. Poster

GCC Meeting 2024, Seattle, WA, USA, 2024 May. Title: "Deep Learning for Data Assimilation".

AGU Fall Meeting, San Francisco, CA, USA, 2023 Dec. Title: "Sacpy: Python Package for Statistical Analysis of Climate". Github. Poster