CV

Education

• M.S. in Meterology, Northwest Insititude of Eco-Environment and Resources, Chinese Academy of Sciences, 2022 - Now
  • Advisor: Siqiong Luo
  • Research interests: Applications of Machine Learning in Meteorology
• B.S. in Atmospheric Science, Chengdu University of Information Technology, 2018 - 2022
  • Advisor: Xinyuan Feng
  • Core Courses: Mechanics of the Atmospheric Fluids, Atmospheric Physics, Dynamic Meteorology, Principles of Synoptic Meteorology

Skills

• Familiar with Linux environment, proficient in Python, and familiar with scientific computing libraries such as NumPy and SciPy.
• Solid foundation in mathematics, including calculus, linear algebra, and probability statistics.
• Mastery of basic deep learning theories, familiarity with fundamental neural networks such as CNN, LSTM, Unet, ResNet, GAN, and Transformer, and proficient in using PyTorch.

Publications

• Zhuoqun Li, Siqiong Luo, Donghang Shao. (2024). An advanced deep learning time series Python package provides forecasting and imputation for land surface variables. Environmental Modelling & Software, under review.
• Zhuoqun Li, Siqiong Luo, Xiaoqing Tan, Xiaohua Hao .(2024). Qinghai–Tibet Plateau 5-layer 0.1-degree daily soil moisture dataset from 2000 to 2021. Scientific Data, under review.
• [Dataset] Zhuoqun Li, Siqiong Luo, Xiaoqing Tan, Xiaohua Hao . The dataset of daily soil moisture at 0.1-degree resolution over five layers on the Qinghai-Tibet Plateau from 2000 to 2021.. National Cryosphere Desert Data Center(http://www.ncdc.ac.cn), 2024. https://www.doi.org/10.12072/ncdc.nieer.db6463.2024.
• Zhuoqun Li,Xinyuan Feng. Characteristics of 0cm Surface Temperature in Winter in Sichuan-Chongqing Region from 1980 to 2018 [J]. Plateau and Mountain Meteorology Research, 2023, 43 (02): 96-104. (available here, in Chinese)
• Zhuoqun Li,Xingcai Liu. Variations of high temperature from 1961 to 2019 in Liaoning Province，China[J]. Chinese Jouranl of Applied Ecology, 2021, 32 (11): 4059-4067. DOI:10.13287/j.1001-9332.202111.038. (available here, in Chinese)

Projects

• Deep Learning Models for Climsim, machine learning models that accurately emulate subgrid-scale atmospheric physics in an operational climate model, (GitHub)
• LSTS (land surface time sereies), LSTS is an open-source tool written in Python, utilizing advanced deep learning algorithms such as RNN-based LSTM and EALSTM, MLP-based DLinear, CNN-based TimesNet, and Transformer-based iTransformer and PatchTST models. It provides univariate short- and longterm forecasting and imputation tasks for land surface variables such as soil temperature, soil moisture, soil suction, snow depth, snow water equivalent, air temperature, and surface temperature. The advantages of our software include speed, lightweight, accuracy, and universality. (Github, Huggingface, Pypi)
• WeatherLearn, Implementation of the PyTorch version of the Weather Deep Learning Model Zoo. (Github)

Competitions

(Rank 35 / 693, Sliver Medalist (Team Leader)) Jerry Lin, Zeyuan Hu, Sungduk Yu, Mike Pritchard, Ritwik Gupta, Tian Zheng, Walter Hannah, Laura Mansfield, Yongquan Qu, Margarita Geleta, Molly Lopez, Maja Rudolph, Ashley Chow, Walter Reade. (2024). LEAP - Atmospheric Physics using AI (ClimSim). Kaggle. https://kaggle.com/competitions/leap-atmospheric-physics-ai-climsim