付乃锋 (Naifeng Fu)

付乃锋 (Naifeng Fu)

理学博士，海洋科学与技术博士后，航空航天领域正高级工程师（2022年3月）。研究方向：GNSS-R/-RO技术设备研制及数据应用，数值优化，GNSS掩星，电离层建模及同化，大气反演，机器学习，计算机可视化。

Posts by Collection

portfolio

研究助理，中国科学院上海天文台

Published:

参与中国航天 503 所掩星数据仿真与反演系统、东方红公司电离层反掩星探测载荷仿真等横向项目，负责核心算法模块开发与软件交付。

publications

The analysis of assumptions’ error sources on assimilating ground-based/spaceborne ionospheric observations

Published in Journal of Atmospheric and Solar-Terrestrial Physics, 2020

Use Google Scholar for full citation

Recommended citation: **Naifeng Fu**, Peng Guo, Yanling Chen, Mengjie Wu, Yong Huang, Xiaogong Hu, Zhenjie Hong "The analysis of assumptions’ error sources on assimilating ground-based/spaceborne ionospheric observations." Journal of Atmospheric and Solar-Terrestrial Physics, 2020. https://doi.org/10.1016/j.jastp.2020.105354

Sea Surface Height Estimation by Ground-Based BDS GEO Satellite Reflectometry

Published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020

Use Google Scholar for full citation

Recommended citation: J. Wu, Y. Chen, F. Gao, P. Guo, X. Wang, X. Niu, M. Wu, **Naifeng Fu** "Sea Surface Height Estimation by Ground-Based BDS GEO Satellite Reflectometry." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020. https://doi.org/10.1109/JSTARS.2020.3024743

The Empirical Orthogonal Function Theory and Simulation Research for Spaceborne GNSS-R Sea Surface High Wind Speed Retrieval

Published in 2021 IEEE Specialist Meeting on Reflectometry using GNSS and other Signals of Opportunity (GNSS+R), 2021

Access paper here

Recommended citation: Jianming Wu, Yanling Chen, Peng Guo, Xiaoya Wang, Xiaogong Hu, Mengjie Wu, Fenghui Li, **Naifeng Fu** "The Empirical Orthogonal Function Theory and Simulation Research for Spaceborne GNSS-R Sea Surface High Wind Speed Retrieval." 2021 IEEE GNSS+R, 2021, 65-68. https://doi.org/10.1109/GNSSR53802.2021.9617720

Vertical characterization on global ionospheric variations during the magnetic storm in September 2017 with hierarchical subtraction method

Published in Advances in Space Research, 2021

摘要

Recommended citation: Xin Song, Rong Yang, Xingqun Zhan, **Naifeng Fu**, Zhe Yang, Xumin Yu "Vertical characterization on global ionospheric variations during the magnetic storm in September 2017 with hierarchical subtraction method." Advances in Space Research, 2022. https://www.sciencedirect.com/science/article/pii/S0273117721008838

Sea Surface Wind Speed Retrieval Based on Empirical Orthogonal Function Analysis Using 2019–2020 CYGNSS Data

Published in IEEE Transactions on Geoscience and Remote Sensing, 2022

Use Google Scholar for full citation

Recommended citation: Jianming Wu, YanLing Chen, Peng Guo, Xiaoya Wang, Xiaogong Hu, Mengjie Wu, Fenghui Li, **Naifeng Fu**, Yanzhen Hao "Sea Surface Wind Speed Retrieval Based on Empirical Orthogonal Function Analysis Using 2019–2020 CYGNSS Data." IEEE Transactions on Geoscience and Remote Sensing, 2022. https://doi.org/10.1109/TGRS.2022.3169832

The Yunyao LEO Satellite Constellation: Occultation Results of the Neutral Atmosphere Using Multi-System Global Navigation Satellites

Published in Remote Sensing, 2025

Access paper here

Recommended citation: Hengyi Yue, **Naifeng Fu**, Fenghui Li, Yan Cheng, Mengjie Wu, Peng Guo, Wenli Dong, Xiaogong Hu, Feixue Wang "The Yunyao LEO Satellite Constellation: Occultation Results of the Neutral Atmosphere Using Multi-System Global Navigation Satellites." Remote Sensing, 2025, 17(16), 2851. https://doi.org/10.3390/rs17162851

A Hybrid Data Assimilation of GNSS Ground-Based VTEC and Radio Occultation Ionospheric Measurements Using Kalman Filter and Deep Learning

Published in Journal of Geophysical Research: Space Physics, 2026

Access paper here

Recommended citation: M.J. Wu, P. Guo, **Naifeng Fu**, Y. Gao, J.C. Xue, B.Z. Pang, Y.H. Cai "A Hybrid Data Assimilation of GNSS Ground-Based VTEC and Radio Occultation Ionospheric Measurements Using Kalman Filter and Deep Learning." Journal of Geophysical Research: Space Physics, 2026, 131(5), e2025JA034893. https://doi.org/10.1029/2025JA034893

talks

Error Sources of Imaging the Ionosphere and Assimilating the Ionospheric Observations from Multi-constellation/system.

Published:

In this paper, using the International Reference Ionosphere model as the real field, ionospheric observational data from ground-based and spaceborne systems on Jan. 1st, 2008 were simulated. With the Nequick model as the background field,the global ionospheric density field was constructed by the Kalman-filter algorithm, and the subsequent work was processed. Various errors and influences in the ionospheric inversion, especially easily overlooked errors, were analyzed,and corresponding improvement methods were proposed and verified.The effects of constellation/system observations on the observation quality and spatial distribution configuration of the ionospheric inversion were analyzed.Then,the characteristics of the Kalman filter assimilation algorithm and Abel inversion algorithm were compared.

Through assimulation local areas in space were over corrected due to the effects of the top layer; these overcorrections should be reduced before processing,and the time/grid correction can effectively and steadily improve the assimilation accuracy. The introduction of COSMIC occultation data can effectively fill the void in the ocean data and increase the ionospheric observational density and horizontal accuracy significantly, and it was showed that the Kalman filter assimilation algorithm has higher accuracy,especially in ionospheric peak altitude.

The Empirical Orthogonal Function Theory and Simulation Research for Spaceborne GNSS-R Sea Surface High Wind Speed Retrieval

Published:

Use Google Scholar for full citation

Recommended citation: Jianming Wu, Yanlin Chen, Peng Guo, Xiaoya Wang, Xiaogong Hu, Mengjie Wu, Fenghui Li, **Naifeng Fu** "The Empirical Orthogonal Function Theory and Simulation Research for Spaceborne GNSS-R Sea Surface High Wind Speed Retrieval." 2021 IEEE Specialist Meeting on Reflectometry using GNSS and other Signals of Opportunity (GNSS+R), 2021.

teaching

tools

Markdown 转图片与 PDF

科研工具 · Markdown, Writing, Visualization

在浏览器中将 Markdown 渲染为矢量 PDF 或 PNG，支持 GFM 表格、KaTeX 公式与 Mermaid 图表