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学者姓名：林瀚
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Abstract ：
城市网络的组织结构与运行机制离不开城市间的关联关系。本文基于2021年10月至2022年9月的百度迁徙大数据，构建了中国366个城市的城际人口流动网络。在节点层面，提出了城际人口流动超越指数衡量城市中心性，探索城市中心性的空间聚类特征；在网络社区层面，分析了中国366个城市的月际城际人口流动特征及社群格局。结果表明：(1)城际人口流动超越指数能够有效表征城际人口流动网络中各城市的中心性；(2)城际人口流动网络中各城市根据其中心性形成“高高”集聚分布和“低低”集聚分布的特征；(3)城际人口流动集聚格局受节假日因素、新型冠状病毒感染等综合影响，在不同月份表现出不同的特征，总体上符合地理学第一定律，并呈现省际分异特征；(4)城市凝聚子群发现结果表明，成渝、大湾区、中原、关中平原、长三角等城市群人口流动格局较为稳定，具有跨省人口流动融合特征；山东半岛城市群与京津冀城市群的人口流动格局联系密切，出现跨城市群人口流动特征；浙江省省域内人口流动特征逐渐加强；长江中游、海峡西岸城市群仍未形成跨越省界的稳定人口流动社群格局。
Keyword ：
区域协调发展 区域协调发展 城市中心性 城市中心性 城市相互作用 城市相互作用 城市群 城市群 城际人口流动网络格局 城际人口流动网络格局 城际人口流动超越指数 城际人口流动超越指数 百度迁徙数据 百度迁徙数据 社区检测 社区检测
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| GB/T 7714 | 尹延中 , 邬群勇 , 林瀚 et al. “人口流动”视域下中国城市中心性和社群格局分析 [J]. | 地球信息科学学报 , 2024 , 26 (03) : 666-678 . |
| MLA | 尹延中 et al. "“人口流动”视域下中国城市中心性和社群格局分析" . | 地球信息科学学报 26 . 03 (2024) : 666-678 . |
| APA | 尹延中 , 邬群勇 , 林瀚 , 赵志远 . “人口流动”视域下中国城市中心性和社群格局分析 . | 地球信息科学学报 , 2024 , 26 (03) , 666-678 . |
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Abstract ：
Clouds play an important role in the Earth's climate system; however, various observational methods describe clouds differently, leading to cloud products being described with different characteristics, and affecting our understanding of cloud effects. To address this problem, this study integrates different cloud products into the transfer-learning procedure of a deep-learning model and determines the cloud effective radius (CER), cloud optical thickness (COT), and cloud top height (CTH) from Himawari-8 thermal infrared measurements. The retrieval results were independently evaluated against the moderate-resolution imaging spectroradiometer science products and further compared with Himawari-8 operational products during the day. The root mean squared errors (RMSEs) of the model for the CER, COT, and CTH were 4.490 mu m , 11.198, and 1.904 km, respectively, which are lower than those of Himawari-8 operational products (RMSE: 11.172 mu m , 14.755, and 2.860 km). Moreover, validation results against active sensors show that the model performs slightly better during the day than at night, and both are generally better than the Himawari-8 operational product. Overall, the model maintains stable performance during both day and night, and its accuracy is higher than that of Himawari-8 operational products.
Keyword ：
Brightness temperature Brightness temperature Cloud properties Cloud properties Clouds Clouds Data models Data models Himawari-8 Himawari-8 MODIS MODIS Optical sensors Optical sensors Remote sensing Remote sensing Satellites Satellites Small Attention-UNet (SmaAt-UNet) Small Attention-UNet (SmaAt-UNet) transfer-learning transfer-learning
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| GB/T 7714 | Li, Jingwei , Zhang, Feng , Li, Wenwen et al. Transfer-Learning-Based Approach to Retrieve the Cloud Properties Using Diverse Remote Sensing Datasets [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2023 , 61 . |
| MLA | Li, Jingwei et al. "Transfer-Learning-Based Approach to Retrieve the Cloud Properties Using Diverse Remote Sensing Datasets" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 61 (2023) . |
| APA | Li, Jingwei , Zhang, Feng , Li, Wenwen , Tong, Xuan , Pan, Baoxiang , Li, Jun et al. Transfer-Learning-Based Approach to Retrieve the Cloud Properties Using Diverse Remote Sensing Datasets . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2023 , 61 . |
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This paper introduces a novel back propagation (BP) neural network method to accurately characterize optical properties of liquid cloud droplets, including black carbon. The model establishes relationships between black carbon volume fraction, wavelength, cloud effective radius, and optical properties. Evaluated on a test set, the value of the root mean square error (RMSE) of the asymmetry factor, extinction coefficient, single-scattering albedo, and the first 4 moments of the Legendre expansion of the phase function are less than 0.003, with the maximum mean relative error (MRE) reaching 0.2%, which are all better than the traditional method that only uses polynomials to fit the relationship between the effective radius and optical properties. Notably, the BP neural network significantly compresses the optical property database size by 37,800 times. Radiative transfer simulations indicate that mixing black carbon particles in water clouds reduces the top-of-atmosphere (TOA) reflectance and heats the atmosphere. However, if the volume fraction of black carbon is less than 10-6, the black carbon mixed in the water cloud has a tiny effect on the simulated TOA reflectance.
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| GB/T 7714 | Li, Jun , Hang, Feng , Liu, Jia et al. Parameterization of optical properties for liquid cloud droplets containing black carbon based on neural network [J]. | OPTICS EXPRESS , 2023 , 31 (24) : 40124-40141 . |
| MLA | Li, Jun et al. "Parameterization of optical properties for liquid cloud droplets containing black carbon based on neural network" . | OPTICS EXPRESS 31 . 24 (2023) : 40124-40141 . |
| APA | Li, Jun , Hang, Feng , Liu, Jia , Li, Wenwen , Wu, Kun , Hu, Shuai et al. Parameterization of optical properties for liquid cloud droplets containing black carbon based on neural network . | OPTICS EXPRESS , 2023 , 31 (24) , 40124-40141 . |
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