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学者姓名：林瀚
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The circulation of tropical cyclones (TCs) exerts a multifaceted influence on the spatial and temporal distribution of surface pollutants. This study investigates the response of surface ozone (O3) concentration to the TCs in Fujian Province from June to December 2022 by analyzing the contributions of atmospheric pollutants, meteorological conditions, and dynamical transports. Empirical orthogonal function (EOF) decomposition methods are used to analyze the spatio-temporal distribution patterns of affected O3, and a Gradient Boosting Regression Trees (GBRT) machine learning model is employed to estimate surface O3 concentration, quantifying the influence of each factor. The results indicate an anomaly increase in O3 concentration during this period, with photochemistry-related meteorological conditions being the primary influencer, accounting for 66.9% of O3 variations, elucidating the interpretability of the GBRT model for attributing changes in O3 concentration. Low relative humidity and high temperature conditions have been identified as pivotal factors influencing the rise in O3 concentrations. The presence of TC undermines this predominant influence, amplifying the role of transport factors and other atmospheric pollutants. In the case studies of TC (Muifa and Nanmadol, 2022), the slow or stagnant TCs triggered persistent downdrafts in its periphery and brought favorable meteorological conditions such as clear sky and warm temperature for photochemistry. TCs also enhances the impact of horizontal and vertical dynamic transport on O3 concentrations. This work provides vital insights into the complex interplay between TCs and surface O3 concentrations, highlighting the need for targeted environmental and air quality management strategies in regions frequently impacted by TCs.
Keyword ：
Attribution analysis Attribution analysis Gradient boosting regression trees Gradient boosting regression trees Surface ozone concentration Surface ozone concentration Tropical cyclone Tropical cyclone
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| GB/T 7714 | Wang, Keyue , Zhao, Rui , Wu, Qunyong et al. Responses of surface ozone under the tropical cyclone circulations: Case studies from Fujian Province, China [J]. | ATMOSPHERIC POLLUTION RESEARCH , 2025 , 16 (1) . |
| MLA | Wang, Keyue et al. "Responses of surface ozone under the tropical cyclone circulations: Case studies from Fujian Province, China" . | ATMOSPHERIC POLLUTION RESEARCH 16 . 1 (2025) . |
| APA | Wang, Keyue , Zhao, Rui , Wu, Qunyong , Li, Jun , Wang, Hong , Lin, Han . Responses of surface ozone under the tropical cyclone circulations: Case studies from Fujian Province, China . | ATMOSPHERIC POLLUTION RESEARCH , 2025 , 16 (1) . |
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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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经过半个世纪的发展,我国风云气象卫星已经成为全球观测系统的重要组成部分,为灾害天气监测和预警、数值天气预报、气候研究和预测、环境监测和预报等提供了关键观测资料.特别是最近10多年来,随着新一代风云气象卫星的发展,定量应用已成为业务应用的主要方式.本文综述了风云气象卫星在短时临近天气预报应用中的最新进展,并提出未来需要研究和解决的短时临近天气预报应用中的关键科学和技术问题.
Keyword ：
定量应用 定量应用 监测和预警 监测和预警 短时临近天气预报 短时临近天气预报 风云气象卫星 风云气象卫星
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| GB/T 7714 | 李俊 , 郑婧 , 闵敏 et al. 风云气象卫星观测在短时临近天气预报中的定量应用进展(特邀) [J]. | 光学学报 , 2024 , 44 (18) : 16-28 . |
| MLA | 李俊 et al. "风云气象卫星观测在短时临近天气预报中的定量应用进展(特邀)" . | 光学学报 44 . 18 (2024) : 16-28 . |
| APA | 李俊 , 郑婧 , 闵敏 , 李博 , 薛允恒 , 马亚宇 et al. 风云气象卫星观测在短时临近天气预报中的定量应用进展(特邀) . | 光学学报 , 2024 , 44 (18) , 16-28 . |
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Two groups of retrieval algorithms, physics based and machine learning (ML) based, each consisting of two independent approaches, have been developed to retrieve cloud base height (CBH) and its diurnal cycle from Himawari-8 geostationary satellite observations. Validations have been conducted using the joint CloudSat/Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) CBH products in 2017, ensuring independent assessments. Results show that the two ML-based algorithms exhibit markedly superior performance (the optimal method is with a correlation coefficient of R > 0.91 and an absolute bias of approximately 0.8 km) compared to the two physics-based algorithms. However, validations based on CBH data from the ground-based lidar at the Lijiang station in Yunnan Province and the cloud radar at the Nanjiao station in Beijing, China, explicitly present contradictory outcomes (R < 0.60). An identifiable issue arises with significant underestimations in the retrieved CBH by both ML-based algorithms, leading to an inability to capture the diurnal cycle characteristics of CBH. The strong consistence observed between CBH derived from ML-based algorithms and the spaceborne active sensors of CloudSat/CALIOP may be attributed to utilizing the same dataset for training and validation, sourced from the CloudSat/CALIOP products. In contrast, the CBH derived from the optimal physics-based algorithm demonstrates good agreement in diurnal variations in CBH with ground-based lidar/cloud radar observations during the daytime (with an R value of approximately 0.7). Therefore, the findings in this investigation from ground-based observations advocate for the more reliable and adaptable nature of physics-based algorithms in retrieving CBH from geostationary satellite measurements. Nevertheless, under ideal conditions, with an ample dataset of spaceborne cloud profiling radar observations encompassing the entire day for training purposes, the ML-based algorithms may hold promise for still delivering accurate CBH outputs.
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| GB/T 7714 | Wang, Mengyuan , Min, Min , Li, Jun et al. Technical note: Applicability of physics-based and machine-learning-based algorithms of a geostationary satellite in retrieving the diurnal cycle of cloud base height [J]. | ATMOSPHERIC CHEMISTRY AND PHYSICS , 2024 , 24 (24) : 14239-14256 . |
| MLA | Wang, Mengyuan et al. "Technical note: Applicability of physics-based and machine-learning-based algorithms of a geostationary satellite in retrieving the diurnal cycle of cloud base height" . | ATMOSPHERIC CHEMISTRY AND PHYSICS 24 . 24 (2024) : 14239-14256 . |
| APA | Wang, Mengyuan , Min, Min , Li, Jun , Lin, Han , Liang, Yongen , Chen, Binlong et al. Technical note: Applicability of physics-based and machine-learning-based algorithms of a geostationary satellite in retrieving the diurnal cycle of cloud base height . | ATMOSPHERIC CHEMISTRY AND PHYSICS , 2024 , 24 (24) , 14239-14256 . |
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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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