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学者姓名：林瀚
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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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Significance Severe local storms, hail, squall lines, and tornadoes significantly affect daily life, social activities, and economic development. Despite their importance, understanding the mechanisms of severe storms and improving their forecasts remains a challenging task. Nowcasting focuses on high-impact weather (HIW) events that develop rapidly and have short durations. After half a century of development, Fengyun meteorological satellites have become a crucial component of the global observation network. They provide essential data for monitoring severe weather, generating early warnings, and contributing to numerical weather forecasting, climate projections, environmental assessments, and predictive analyses. Notably, in the past decade, the advent of the new generation of Fengyun satellites has brought quantitative products to the forefront of operational use. We review the latest advances in the applications of Fengyun meteorological satellites in short-term weather nowcasting and highlight the principal scientific and technical challenges that future research endeavors need to address. Progress China has actively utilized the new generation Fengyun meteorological satellite data to improve near real-time (NRT) forecasting and nowcasting capabilities. The China Meteorological Administration (CMA) assimilates these observation data into numerical weather prediction (NWP) models to enhance short-range and middle-range weather forecasts. In addition, the National Satellite Meteorological Center (NSMC) of the CMA processes these data to produce and distribute quantitative information on the atmosphere, clouds, and precipitation. These quantitative products, delivered to users on time through advanced communication and data distribution technologies, are crucial for NRT nowcasting applications and have played a significant role in monitoring and early warning of HIW events. Besides operational Fengyun satellite products, progress has been made in developing new products and prediction models for 0-6 h forecasts, particularly using data from the Fengyun-4 series. 1 New Products and Applications 1) Radar composite reflectivity estimation (RCRE). Ground-based weather radar observations are commonly used to track convective storms; however, the radar network’s coverage is limited, especially in mountainous and marine areas. Fengyun-4 satellites provide extensive coverage and NRT observations, compensating for radar’s limitations. Since the physical properties of clouds can be reflected in both ground- based radar and satellite observations, a connection exists between the two. Using deep learning methods, Yang et al. developed the radar composite reflectivity estimation (RCRE) using Fengyun- 4A AGRI observations. Independent validation indicates that RCRE accurately reproduces radar echoes’ position, shape, and intensity. This RCRE product is operationally used by the National Meteorological Center (NMC) and provides synthetic radar data for nowcasting applications where ground- based radar is unavailable. 2) Automatic recognition of convection clouds. Monitoring convective clouds from satellites is vital for nowcasting. Traditional techniques rely on thresholds, such as using the 240 - 258 K range to identify convective clouds from 11 μm brightness temperature images. For rapidly changing convective systems, these methods are often regional, seasonal, and weather- dependent. To address this, the K- means clustering method is used to analyze cloud types over China from AGRI infrared band brightness temperature measurements. This method enables users to select regions of interest and automatically identify convective systems and other cloud types in NRT, improving quantitative precipitation estimation (QPE) from satellite IR data. For instance, this product can enhance convective cloud precipitation estimation and provide valuable information on convection coverage and intensity, especially in areas without radar observations. Figure 1 shows the automatic identification of convective clouds based on Fengyun- 4A on July 30, 2023. Due to the northward influence of the typhoon’s peripheral cloud system, the northern and central parts of Shanxi, Hebei, Beijing, and Tianjin are completely covered by large areas of convective clouds, with maximum hourly precipitation exceeding 40 mm/h. The convective clouds correspond well with the radar observations [Fig. 1(b)]. 3) Cloud- base height. The cloud top height (CTH) product is well- established and widely used, while cloud base height (CBH) is challenging to obtain due to weak signals in passive remote sensing observations. However, CBH is crucial for understanding vertical atmospheric motion, aviation safety, and weather analysis. The physical method for retrieving CBH involves converting cloud optical thickness into physical thickness and subtracting it from CTH. The uncertainty of optical thickness is the main error source for CBH retrieval using the physical method. To overcome this limitation, a machine learning model trained on satellite- based lidar (CALIOP from CALIPSO satellite) observations, which has good accuracy but limited coverage, has been used to derive CBH by combining NWP products and Fengyun- 4 AGRI observations as input. This algorithm provides a CBH product with the same coverage as CTH (AGRI full disk). Independent validation shows an overall root mean square error (RMSE) of 1.87 km. This CBH product, along with the traditional CTH product, offers valuable information on cloud structure and physical thickness, enhancing nowcasting applications. 2 Prediction Models Using Fengyun- 4 Data for Nowcasting 1) Storm- warning in pre- convection environment. Severe local storms typically have three stages: pre- convection, initiation, and development. Identifying the pre- convection environment is crucial for nowcasting and providing warnings before radar observations. By integrating high spatiotemporal resolution AGRI observations from the Fengyun- 4 series with CMA NWP products, key factors in the pre- convection environment can be analyzed. Li et al. developed the storm warning in the pre- convection environment version 2.0 (SWIPE2.0) model for China and surrounding areas using machine learning techniques. This model identifies potential convective systems and classifies cloud clusters into strong, medium, or weak convection. SWIPE2.0 predicts storm occurrence and intensity 0 - 2 h ahead of radar observations and is used in NRT applications by the NMC/CMA. For example, the SWIPE2.0 model issued a severe convective warning for a cloud mass located in the western part of Gansu province at 14: 30 on July 10, 2023 (Beijing time). At that time, the ground-based radar reflectivity of about 20 dBz or lower is mainly near the provincial boundary, while the satellite warning signals did not correspond to ground- based radar signals, indicating that precipitation had not yet occurred. At 14: 34, the red severe convective warning signal still existed, and its range expanded slightly to the southeast. As the cloud developed and moved towards the southeast, it produced precipitation greater than 1 mm/h between 15: 00 and 16: 00, with some local areas experiencing rainfall exceeding 5 mm/h. SWIPE2.0 provides early warnings for local convection before ground- based radar observations. 2) Satellite image extrapolation. Similar to radar extrapolation, satellite image extrapolation is essential for short- term forecasting and applications such as solar photovoltaic power generation. The rapid advancement of artificial intelligence has led to the adoption of data- driven machine learning methods in satellite image extrapolation. Xia et al. developed an hourly cloud cover prediction algorithm using high spatiotemporal resolution geostationary satellite images. This model predicts cloud images for the next 0 - 4 h and estimates cloud cover over photovoltaic stations. Independent validation shows reliable and stable performance in the first two hours, with an average correlation coefficient of nearly 0.9 between predicted and observed cloud cover. Compared to previous methods of only being able to perform 10 - 30 min of extrapolation, the new approach greatly improves accuracy and forecasting time, making it valuable for regional short-term warnings. Conclusions and Prospects As a key member of the global observing system, the Fengyun meteorological satellite system has significantly enhanced observation capabilities, short-term monitoring, and early warning. However, challenges remain in applying Fengyun satellite data for nowcasting, particularly in achieving low latency and high-quality products with high spatiotemporal resolution. With ongoing advancements in Fengyun satellite technology, quantitative nowcasting applications are entering a new era. The future direction involves combining Fengyun satellite quantitative products, NWP products, ground-based measurements including radar, and other multi-source data with artificial intelligence to improve the identification, monitoring, and early warning of severe weather events. © 2024 Chinese Optical Society. All rights reserved.
Keyword ：
Customer satisfaction Customer satisfaction Energy policy Energy policy Enterprise resource planning Enterprise resource planning Phase change memory Phase change memory Precipitation (meteorology) Precipitation (meteorology) Predictive analytics Predictive analytics Radar reflection Radar reflection Radar warning systems Radar warning systems Resource allocation Resource allocation Risk management Risk management Satellite communication systems Satellite communication systems Storms Storms Text processing Text processing Tornadoes Tornadoes Tropics Tropics Weather forecasting Weather forecasting Weather satellites Weather satellites
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| GB/T 7714 | Li, Jun , Zheng, Jing , Min, Min et al. Progress in Quantitative Applications of Fengyun Meteorological Satellite Observations in Weather Nowcasting (Invited) [J]. | Acta Optica Sinica , 2024 , 44 (18) . |
| MLA | Li, Jun et al. "Progress in Quantitative Applications of Fengyun Meteorological Satellite Observations in Weather Nowcasting (Invited)" . | Acta Optica Sinica 44 . 18 (2024) . |
| APA | Li, Jun , Zheng, Jing , Min, Min , Li, Bo , Xue, Yunheng , Ma, Yayu et al. Progress in Quantitative Applications of Fengyun Meteorological Satellite Observations in Weather Nowcasting (Invited) . | Acta Optica Sinica , 2024 , 44 (18) . |
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Monitoring and predicting highly localized weather events over a very short-term period, typically ranging from minutes to a few hours, are very important for decision makers and public action. Nowcasting these events usually relies on radar observations through monitoring and extrapolation. With advanced high-resolution imaging and sounding observations from weather satellites, nowcasting can be enhanced by combining radar, satellite, and other data, while quantitative applications of those data for nowcasting are advanced through using machine learning techniques. Those applications include monitoring the location, impact area, intensity, water vapor, atmospheric instability, precipitation, physical properties, and optical properties of the severe storm at different stages (pre-convection, initiation, development, and decaying), identification of storm types (wind, snow, hail, etc.), and predicting the occurrence and evolution of the storm. Satellite observations can provide information on the environmental characteristics in the preconvection stage and are very useful for situational awareness and storm warning. This paper provides an overview of recent progress on quantitative applications of satellite data in nowcasting and its challenges, and future perspectives are also addressed and discussed.
Keyword ：
nowcasting nowcasting pre-convection pre-convection quantitative applications quantitative applications weather satellite weather satellite
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| GB/T 7714 | Li, Jun , Zheng, Jing , Li, Bo et al. Quantitative Applications of Weather Satellite Data for Nowcasting: Progress and Challenges [J]. | JOURNAL OF METEOROLOGICAL RESEARCH , 2024 , 38 (3) : 399-413 . |
| MLA | Li, Jun et al. "Quantitative Applications of Weather Satellite Data for Nowcasting: Progress and Challenges" . | JOURNAL OF METEOROLOGICAL RESEARCH 38 . 3 (2024) : 399-413 . |
| APA | Li, Jun , Zheng, Jing , Li, Bo , Min, Min , Liu, Yanan , Liu, Chian-Yi et al. Quantitative Applications of Weather Satellite Data for Nowcasting: Progress and Challenges . | JOURNAL OF METEOROLOGICAL RESEARCH , 2024 , 38 (3) , 399-413 . |
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The effect of 'space-time compression' caused by 'space flow' breaks the independent allocation of resources between cities and drives the formation of regionally integrated development pattern, and the organizational structure and operation mechanism of the urban network cannot be separated from the inter-city relationship. Based on Baidu migration big data from October 2021 to September 2022, this paper constructs the intercity population flow network for 366 cities in China. At the node level, a population flow surpassing index is proposed to measure urban centrality and explore the spatial clustering characteristics of urban centrality. At the network community level, the monthly intercity population flow pattern and characteristics of 366 cities are analyzed. The results show that: (1) The population flow surpassing index considering flow direction meets the actual needs of intercity population mobility evaluation for measuring urban centrality and can effectively characterize the centrality of cities in the intercity population flow network. Using Baidu Migration big data from January 2023 to April 2023 after the end of the epidemic for comparison, we found that the central impact on national central city is small due to the prevention and control of COVID-19 transmission; (2) Cities in the intercity population flow network exhibit 'High-High (HH)' and 'Low-Low (LL)' agglomeration characteristics according to their centrality. HH clustering areas are formed in the eastern coastal and central regions, while LL clustering areas are mainly located at the edge of the Qinghai Tibet Plateau, the edge of the three northeastern provinces, and some areas in Hainan Island; (3) The intercity population flow pattern shows different characteristics in different months due to the influence of holidays, COVID-19 transmission, etc., generally in accordance with the first law of geography, and exhibits provincial differentiation characteristics; (4) The finding of urban cohesive subgroups shows that the intercity population flow patterns of Chengdu- Chongqing Urban Agglomeration, Greater Bay Area, Central Plains Urban Agglomeration, Guanzhong Plain Urban Agglomeration, Yangtze River Delta Urban Agglomeration, and other urban clusters are relatively stable, characterized by cross-provincial population flow integration. The Shandong Peninsula Urban Agglomeration and the Beijing- Tianjin-Hebei Urban Agglomeration have close connection in intercity population flow patterns, characterized by cross-urban cluster intercity population flow. The intercity population flow pattern within Zhejiang Province is gradually enhanced, and the urban clusters in middle reaches of Yangtze River and the west bank of the Taiwan Strait haven’t yet formed a stable population flow pattern across provincial borders. © 2024 Science Press. All rights reserved.
Keyword ：
Agglomeration Agglomeration Big data Big data Digital storage Digital storage Disease control Disease control Flow patterns Flow patterns Population dynamics Population dynamics Population statistics Population statistics
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| GB/T 7714 | Yin, Yanzhong , Wu, Qunyong , Lin, Han et al. Analysis of Urban Centrality and Community Patterns from the Perspective of 'Intercity Mobility Flow' in China [J]. | Journal of Geo-Information Science , 2024 , 26 (3) : 666-678 . |
| MLA | Yin, Yanzhong et al. "Analysis of Urban Centrality and Community Patterns from the Perspective of 'Intercity Mobility Flow' in China" . | Journal of Geo-Information Science 26 . 3 (2024) : 666-678 . |
| APA | Yin, Yanzhong , Wu, Qunyong , Lin, Han , Zhao, Zhiyuan . Analysis of Urban Centrality and Community Patterns from the Perspective of 'Intercity Mobility Flow' in China . | Journal of Geo-Information Science , 2024 , 26 (3) , 666-678 . |
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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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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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