Zilu Meng's Academic Page

e-mail: zilumeng@uw.edu; Github; Google Scholar; ResearchGate; LinkedIn; Zhihu; X (twitter);

About Me

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. in Atmospheric Sciences from Nanjing University of Information Science and Technology (Nanjing Institute of Meteorology), where I conducted research on tropical dynamics with Prof. Tim Li. My research interests include paleoclimate, machine learning, climate dynamics, and data assimilation. Currently, I am focusing on paleoclimate data assimilation and exploring climate dynamics during the last millennium. In my free time, I enjoy playing video games, reading, and hiking.

Zilu Meng

Curriculum Vitae

My Curriculum Vitae

Recent Research

Paleoclimate Data Assimilation

Paleo data assimilation is a powerful tool to reconstruct past climate fields. Before the instrumental era, the climate system was not well observed. And the instrumental data is not long enough and strongly forced by human activities. This makes it difficult to study the earth climate variability. However, there are many paleoclimate proxies that can represent the past climate variability, like tree rings, ice cores, and corals. By combining these proxies with climate models, we can reconstruct the past climate fields, like temperature, precipitation, and wind fields and study the past climate variability like ENSO, PDO and AMO.

I am currently working on developing the first seasonal reanalysis dataset for the last millennium using "cycling" data assimilation. The reanalysis dataset will provide a gridded climate field for the last millennium, which can be used to study the past climate variability and the climate change. For example, the right figure shows the Nino3.4 Index (a measure of El Niño and Southern Oscillation) from the our reanalysis comparing with the HadISST dataset. The reanalysis dataset can provide a accurate ENSO variability for the last millennium. In this case, we can study the ENSO variability in the past and compare it with the present. Additionally, the reanalysis dataset can also be used to study the climate change in the past and compare it with the present.

The paper for this work has been submitted to Journal of Climate. The code of this work will be released on Github after the paper is published. The poster for this work can be found here.

Seasonal Reanalysis DAcycle

Publications

  1. Meng, Zilu, Hakim, Gregory J., Steig Eric J. (2024). Coupled Seasonal Data Assimilation of Sea Ice, Ocean, and Atmospheric Dynamics over the Last Millennium. Journal of Climate, submitted, poster.
  2. 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, 16, e2024MS004422. https://doi.org/10.1029/2024MS004422.
  3. 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.
  4. 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.
  5. 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 Climate and Atmospheric Sciences, University of Washington, 2023 - Now
 Advisor: Gregory J. Hakim & Eric J. Steig
 Research interests: Paleoclimate, Machine learning, Climate dynamics, Data assimilation
 GPA: 3.99/4.0

B.S. in Atmospheric Science with Honors, Nanjing University of Information Science and Technology, 2019 - 2023
 Advisor: Tim Li
 Core Courses: Atmospheric (Fluids) dynamics, Atmospheric Physics, Synoptic Meteorology
 GPA: 95/100 (Ranked 1st/50 in the class with honors)
 Thesis: Why is the Pacific meridional mode most pronounced in boreal spring? [Paper in English, Thesis in Chinese]

Projects

  1. CFR, Participated in developing a universal framework for climate field reconstruction. Github
  2. 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.
  3. Sacpy, Built an efficient and useful Statistical Analysis module for Climate data in Python. Over 20000 people have used it so far on Github. Poster.
  4. Deep Learning for tropical pacific reconstruction, developed a deep learning model to reconstruct tropical climate fields. Github. Paper. Poster.
  5. Last Millennium Seasonal Reanalysis, Developed a seasonal reanalysis dataset for the last millennium using online data assimilation. Poster.
  6. AMIP experiments on the Deap Learning GCMs. Conducted AMIP experiments on the Deep Learning GCMs to study the climate sensitivity.

Conference Presentations

AGU Fall Meeting, Washington, D.C., USA, 2024 Dec. Oral Presentation. Title: "Reconstructing the Tropical Pacific Upper Ocean using Online Data Assimilation with a Deep Learning model". Paper.

Graduate Climate Conference, Seattle, WA, USA, 2024 Oct. Title: "Last Millennium Seasonal Reanalysis". Poster

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

PEER REVIEW SUMMARY

Earth System Science Data (2)

Plos One (3)

Climate Dynamics (1)

JGR Machine Learning and Computation (1)

Atmosphere (1)

Radio Science (1)

Frontiers in Earth Science (1)