孙浩昊

博士后

邮箱: hhsun@nju.edu.cn
网页: https://hhsunda.github.io/
Curriculum Vitae: https://hhsunda.github.io/CV/cv.pdf

教育经历

• 2020 - 2026 南京大学大气科学学院直博

• 2016 - 2020 南京大学大气科学学院本科

工作经历

• 2026–至今南京大学大气科学学院博士后

研究方向

数据同化、集合Kalman滤波、古气候数据同化、气候场重建

科研项目

• 国家自然科学基金青年学生基础研究项目（博士研究生），重建过去千年气候状态的多尺度集合资料同化方法研究，2024-2026，在研，主持

代表性著作

Sun, H., L. Lei, Z. Tan, L. Ning, and Z. Liu, 2026: An ensemble Kalman smoother for online paleoclimate data assimilation. J. Adv. Model. Earth Syst., 18, e2025MS005730, https://doi.org/10.1029/2025MS005730.

L. Lei, H. Sun, Z. Tan, L. Ning, and Z. Liu, 2026: Strongly Coupled Data Assimilation for Paleoclimate Reconstruction Using Deep Learning-Based Models. J. Adv. Model. Earth Syst., 18, e2025MS005686, https://doi.org/10.1029/2025MS005686.

Sun, H., L. Lei, Z. Liu, L. Ning, and Z. Tan, 2025: An online paleoclimate data assimilation with a deep learning-based network. J. Adv. Model. Earth Syst., 17, e2024MS004675, https://doi.org/10.1029/2024MS004675.

Sun, H., L. Lei, Z. Liu, L. Ning, and Z. Tan, 2024: A hybrid gain analog offline EnKF for paleoclimate data assimilation. J. Adv. Model. Earth Syst., 16, e2022MS003414, https://doi.org/10.1029/2022MS003414.

Sun, H., L. Lei, Z. Liu, L. Ning, and Z. Tan, 2022: An analog offline EnKF for paleoclimate data assimilation. J. Adv. Model. Earth Syst., 14, e2021MS002674, https://doi.org/10.1029/2021MS002674.

Wang, Z.#, H. Sun#, L. Lei, Y. Zhang and Z. Tan, 2023: The importance of data assimilation components for initial conditions and subsequent error growth. Sci. China Earth Sci., 67, 105–116, https://doi.org/10.1007/s11430-023-1229-7.

#表示共同第一作者