Articles | Volume 15, issue 3
https://doi.org/10.5194/amt-15-735-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-735-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Feb 2022
Research article |
| 09 Feb 2022
| 09 Feb 2022
A high-resolution monitoring approach of canopy urban heat island using a random forest model and multi-platform observations
Shihan Chen
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
Collaborative Innovation Centre on Forecast and Evaluation of
Meteorological Disasters, School of Atmospheric Physics, Nanjing University
of Information Science & Technology, Nanjing 210044, China
Yuanjian Yang
CORRESPONDING AUTHOR
Collaborative Innovation Centre on Forecast and Evaluation of
Meteorological Disasters, School of Atmospheric Physics, Nanjing University
of Information Science & Technology, Nanjing 210044, China
Fei Deng
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
Yanhao Zhang
Collaborative Innovation Centre on Forecast and Evaluation of
Meteorological Disasters, School of Atmospheric Physics, Nanjing University
of Information Science & Technology, Nanjing 210044, China
Duanyang Liu
Key Laboratory of Transportation Meteorology, China Meteorological
Administration, Nanjing 210008, China
China Meteorological Administration, Nanjing Joint Institute For Atmospheric Sciences, Nanjing 210008, China
Chao Liu
Collaborative Innovation Centre on Forecast and Evaluation of
Meteorological Disasters, School of Atmospheric Physics, Nanjing University
of Information Science & Technology, Nanjing 210044, China
Zhiqiu Gao
Collaborative Innovation Centre on Forecast and Evaluation of
Meteorological Disasters, School of Atmospheric Physics, Nanjing University
of Information Science & Technology, Nanjing 210044, China
Related authors
No articles found.
Eui-Jong Kang, Byung-Ju Sohn, Hwan-Jin Song, Wonho Kim, and Chao Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-471, https://doi.org/10.5194/essd-2026-471, 2026
Preprint under review for ESSD
Short summary
Short summary
This study provides a new winter Arctic sea-ice dataset of penetration-aware emission properties from satellite microwave observations for 2012–2025. It offers physically interpretable information on how snow-covered sea ice emits microwave radiation, helping to describe the Arctic sea-ice environment as a multivariate geophysical system. The dataset can serve as reusable geophysical information for characterizing sea-ice variability and supporting remote sensing and modelling applications.
Fu Luo, Yuanjian Yang, Ting Sun, Zuofang Zheng, Xueyan Bi, Guoyu Ren, and Simone Lolli
EGUsphere, https://doi.org/10.5194/egusphere-2026-3392, https://doi.org/10.5194/egusphere-2026-3392, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Cities are hotter than surrounding countryside, and heatwaves intensify this contrast. How combined warming reshapes air above different urban areas remains unclear. Using a high-resolution model, we examined two 2020 heatwaves over Guangzhou, China, finding extra warming strongest above densely built neighbourhoods and weakest over open low-rise areas. Dense areas retain heat overnight, adding to next-day warming and showing how urban form shapes extreme heat from surface to lower atmosphere.
Wanju Li, Lifang Sheng, Xueyan Bi, Zehao Huang, Yali Luo, Shiqi Xiao, Chao Liu, Yang Yang, Jiandong Wang, Yuanjian Yang, and Simone Lolli
Atmos. Chem. Phys., 26, 11943–11965, https://doi.org/10.5194/acp-26-11943-2026, https://doi.org/10.5194/acp-26-11943-2026, 2026
Short summary
Short summary
Heavy rain in South China often strikes suddenly far from weather fronts and is hard to forecast. Using five years of radar wind data and rainfall records from Guangdong, we studied 226 events and found warning signs one to four hours before onset that differ by region. In the west, strengthening low-level winds drive the rain; in the Pearl River Delta, friction reshapes the surface wind; in the east, warm surface air stores energy that fuels the storm.
Jiesheng Xue, Yuanjian Yang, Guoyu Ren, and Simone Lolli
Atmos. Chem. Phys., 26, 11543–11560, https://doi.org/10.5194/acp-26-11543-2026, https://doi.org/10.5194/acp-26-11543-2026, 2026
Short summary
Short summary
With the reduction in inter-city distances, the wind carries heat downstream to neighboring cities. Focusing on the urban agglomeration area in China, we demonstrate the spatiotemporal variations of this heat transfer process, the moderating influence of meteorological factors, and quantify its effect on the intensity of the urban heat island in downstream cities. The results highlight the necessity of considering inter-city heat interactions in thermal risk management.
Jing Cai, Yongchun Liu, Feixue Zheng, Dian Ding, Wei Du, Liang Yuan, Yingjie Zou, Lazong Suolang, Rongxiang Lu, Wanwei Li, Chao Liu, Xiaolong Fan, Qiaozhi Zha, Mengxiao Luan, and Jiandong Wang
EGUsphere, https://doi.org/10.5194/egusphere-2026-4106, https://doi.org/10.5194/egusphere-2026-4106, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Characterizing organic aerosol (OA) molecular composition, sources and toxicity in the North China Plain remains difficult. Through comprehensive online and offline measurements in Weifang, a representative medium-sized city, we identify combustion-derived OA with a moderate oxidation state as the key drivers of oxidative potential (OP). These insights are critical for health risk assessments amidst the region’s ongoing reduction in particulate mass due to emission controls.
Wenjie Zhang, Mengxi Qu, Ranhao Sun, Jiarui Zhao, Guicai Ning, Aziz Inamov, Zokhid Mamatkulovd, Di Zhou, Bakhtiyor Pulatov, and Yuanjian Yang
EGUsphere, https://doi.org/10.5194/egusphere-2026-2290, https://doi.org/10.5194/egusphere-2026-2290, 2026
Short summary
Short summary
MODIS satellite temperature data lack full validation across China. Using 18 years of balloon data from 74 stations, we evaluated its accuracy, lower-atmosphere stability, and inversion detection. Nighttime accuracy exceeds daytime; upper layers outperform lower layers . A bias exists at 700 hPa. Stability retrieval is more reliable by day. Inversion detection rates are low: 6.6 % day, 13.1 % night. This guides appropriate use of MOD07_L2 in atmospheric science, hydrology, and ecology.
Yunhua Chang, Tianhao Ding, Haifeng Yu, Yuanjian Yang, Liang Zhu, Xiaozheng Liu, and Wen Tan
Atmos. Meas. Tech., 19, 1515–1528, https://doi.org/10.5194/amt-19-1515-2026, https://doi.org/10.5194/amt-19-1515-2026, 2026
Short summary
Short summary
We validate a novel chemical ionization mass spectrometer for simultaneous measurements of volatile organic and inorganic compounds. A field comparison with a Cavity Ring-Down Spectroscopy analyzer for NH3 validated its accuracy and superior time resolution for capturing transient pollution peaks. Versatility is demonstrated across three key applications: stationary urban monitoring, mobile source mapping, and industrial process control.
Daihao Yu, Qiuwei Xia, Saifen Yu, Yixiang Chen, Keyi Xu, Haobin Han, Jianjun Guo, Kexin Guo, Jiadong Hu, Zhen Zhang, Jing Cai, Yuanjian Yang, and Haiyun Xia
EGUsphere, https://doi.org/10.5194/egusphere-2025-6470, https://doi.org/10.5194/egusphere-2025-6470, 2026
Short summary
Short summary
Industrial parks are critical nodes in the global carbon cycle. This study combines high-resolution lidar observations and the WRF-GHG model to examine CO2 accumulation and dispersion in a semi-enclosed bay. Distinct diurnal patterns emerge: nighttime accumulation and daytime dispersion driven by terrain, atmospheric stability and sea breezes. WRF-GHG captures temporal trends, but it underestimates CO2 levels, indicating limitations in emission inventories and terrain-wind interactions.
Yuanjian Yang, Chenjie Qian, Minxuan Zhang, Chenchao Zhan, Zhenxin Liu, Pak Wai Chan, Xueyan Bi, Meng Gao, and Simone Lolli
Atmos. Meas. Tech., 19, 1345–1363, https://doi.org/10.5194/amt-19-1345-2026, https://doi.org/10.5194/amt-19-1345-2026, 2026
Short summary
Short summary
Up to ~ 40 % of ozone pollution in the Greater Bay Area of China is related to tropical cyclones. The O3 pollution was found to be transported from inland areas to coastal areas. The transport process can be roughly divided into three phases: downdraft control, horizontal transport, and vertical mixing.
Jialu Xu, Yingjie Zhang, Yuying Wang, Xing Yan, Bin Zhu, Chunsong Lu, Yuanjian Yang, Yele Sun, Junhui Zhang, Xiaofan Zuo, Zhanghanshu Han, and Rui Zhang
Atmos. Chem. Phys., 25, 18599–18616, https://doi.org/10.5194/acp-25-18599-2025, https://doi.org/10.5194/acp-25-18599-2025, 2025
Short summary
Short summary
We conducted a year-long study in Nanjing to explore how the height of the atmospheric boundary layer affects fine particle pollution. We found that low boundary layers in winter trap pollutants like nitrate and primary particles, while higher layers in summer help form secondary pollutants like sulfate and organic aerosols. These findings show that boundary layer dynamics are key to understanding and managing seasonal air pollution.
Junhui Zhang, Yuying Wang, Jialu Xu, Xiaofan Zuo, Chunsong Lu, Bin Zhu, Yuanjian Yang, Xing Yan, and Yele Sun
Atmos. Chem. Phys., 25, 17413–17428, https://doi.org/10.5194/acp-25-17413-2025, https://doi.org/10.5194/acp-25-17413-2025, 2025
Short summary
Short summary
We conducted a year-long study in Nanjing to understand how tiny airborne particles take up water, which affects air quality and climate. We found that particle water uptake varies by season and size, with lower values in summer due to more organic materials. Local pollution mainly influences smaller particles, while larger ones are shaped by air mass transport. These findings help improve climate models and support better air pollution control in fast-growing cities.
Tao Shi, Yuanjian Yang, Ping Qi, and Simone Lolli
Atmos. Chem. Phys., 25, 17069–17090, https://doi.org/10.5194/acp-25-17069-2025, https://doi.org/10.5194/acp-25-17069-2025, 2025
Short summary
Short summary
Using Beijing’s Fifth Ring Road, the team combined data and models. Heatwave results: canopy heat island was 91.3 % stronger day/52.7 % night. Day heat relied on building coverage, night on sky visibility. Tall buildings block sun by day, trap heat at night. Night ventilation cools, day winds spread heat. Urban design must consider day-night cycles to fight extreme heat, guiding risk reduction.
Chong Luo, Yongbo Zhou, Yubao Liu, Wei Han, Bin Yao, and Chao Liu
EGUsphere, https://doi.org/10.5194/egusphere-2025-4553, https://doi.org/10.5194/egusphere-2025-4553, 2025
Short summary
Short summary
We developed a new technique to assimilate satellite visible reflectance. By testing our technique on a heavy rainfall event, we found that it significantly reduces errors in cloud water estimates and enhances light precipitation forecasts. This data assimilation also better improved thin clouds. This advancement helps increase the accuracy of weather predictions in situations where clouds and rain play a major role.
Tao Shi, Yuanjian Yang, Gaopeng Lu, Zuofang Zheng, Yucheng Zi, Ye Tian, Lei Liu, and Simone Lolli
Atmos. Chem. Phys., 25, 9219–9234, https://doi.org/10.5194/acp-25-9219-2025, https://doi.org/10.5194/acp-25-9219-2025, 2025
Short summary
Short summary
The city significantly influences thunderstorm and lightning activity, yet the potential mechanisms remain largely unexplored. Our study has revealed that both city size and building density play pivotal roles in modulating thunderstorm and lightning activity. This research not only deepens our understanding of urban meteorology but also lays an important foundation for developing accurate and targeted urban thunderstorm risk prediction models.
Quanzhe Hou, Zhiqiu Gao, Zexia Duan, and Minghui Yu
Geosci. Model Dev., 18, 4625–4641, https://doi.org/10.5194/gmd-18-4625-2025, https://doi.org/10.5194/gmd-18-4625-2025, 2025
Short summary
Short summary
This study evaluates various machine learning and statistical methods for interpolating turbulent heat flux data over the Tibetan Plateau. The Transformer model showed the best performance, leading to the development of the Transformer_CNN model, which combines global and local attention mechanisms. Results show that Transformer_CNN outperforms the other models and was successfully applied to interpolate heat flux data from 2007 to 2016.
Tao Shi, Yuanjian Yang, Lian Zong, Min Guo, Ping Qi, and Simone Lolli
Atmos. Chem. Phys., 25, 4989–5007, https://doi.org/10.5194/acp-25-4989-2025, https://doi.org/10.5194/acp-25-4989-2025, 2025
Short summary
Short summary
Our study explored the daily temperature patterns in urban areas of the Yangtze River Delta, focusing on how weather and human activities impact these patterns. We found that temperatures were higher at night, and weather patterns had a bigger impact during the day, while human activities mattered more at night. This helps us understand and address urban overheating.
Zeyuan Tian, Jiandong Wang, Jiaping Wang, Chao Liu, Jia Xing, Jinbo Wang, Zhouyang Zhang, Yuzhi Jin, Sunan Shen, Bin Wang, Wei Nie, Xin Huang, and Aijun Ding
Atmos. Meas. Tech., 18, 1149–1162, https://doi.org/10.5194/amt-18-1149-2025, https://doi.org/10.5194/amt-18-1149-2025, 2025
Short summary
Short summary
The radiative effect of black carbon (BC) is substantially modulated by its mixing state, which is challenging to derive physically with a single-particle soot photometer. This study establishes a machine-learning-based inversion model which can accurately and efficiently acquire the BC mixing state. Compared to the widely used leading-edge-only method, our model utilizes a broader scattering signal coverage to more accurately capture diverse particle characteristics.
Yuzhi Jin, Jiandong Wang, Chao Liu, David C. Wong, Golam Sarwar, Kathleen M. Fahey, Shang Wu, Jiaping Wang, Jing Cai, Zeyuan Tian, Zhouyang Zhang, Jia Xing, Aijun Ding, and Shuxiao Wang
Atmos. Chem. Phys., 25, 2613–2630, https://doi.org/10.5194/acp-25-2613-2025, https://doi.org/10.5194/acp-25-2613-2025, 2025
Short summary
Short summary
Black carbon (BC) affects climate and the environment, and its aging process alters its properties. Current models, like WRF-CMAQ, lack full accounting for it. We developed the WRF-CMAQ-BCG model to better represent BC aging by introducing bare and coated BC species and their conversion. The WRF-CMAQ-BCG model introduces the capability to simulate BC mixing states and bare and coated BC wet deposition, and it improves the accuracy of BC mass concentration and aerosol optics.
Zhouyang Zhang, Jiandong Wang, Jiaping Wang, Nicole Riemer, Chao Liu, Yuzhi Jin, Zeyuan Tian, Jing Cai, Yueyue Cheng, Ganzhen Chen, Bin Wang, Shuxiao Wang, and Aijun Ding
Atmos. Chem. Phys., 25, 1869–1881, https://doi.org/10.5194/acp-25-1869-2025, https://doi.org/10.5194/acp-25-1869-2025, 2025
Short summary
Short summary
Black carbon (BC) exerts notable warming effects. We use a particle-resolved model to investigate the long-term behavior of the BC mixing state, revealing its compositions, coating thickness distribution, and optical properties all stabilize with a characteristic time of less than 1 d. This study can effectively simplify the description of the BC mixing state, which facilitates the precise assessment of the optical properties of BC aerosols in global and chemical transport models.
Fengjiao Chen, Yuanjian Yang, Lu Yu, Yang Li, Weiguang Liu, Yan Liu, and Simone Lolli
Atmos. Chem. Phys., 25, 1587–1601, https://doi.org/10.5194/acp-25-1587-2025, https://doi.org/10.5194/acp-25-1587-2025, 2025
Short summary
Short summary
The microphysical mechanisms of precipitation responsible for the varied impacts of aerosol particles on shallow precipitation remain unclear. This study reveals that coarse aerosol particles invigorate shallow rainfall through enhanced coalescence processes, whereas fine aerosol particles suppress shallow rainfall through intensified microphysical breaks. These impacts are independent of thermodynamic environments but are more significant in low-humidity conditions.
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech., 17, 7129–7141, https://doi.org/10.5194/amt-17-7129-2024, https://doi.org/10.5194/amt-17-7129-2024, 2024
Short summary
Short summary
This study introduces a cloud property retrieval method which integrates traditional radiative transfer simulations with a machine learning method. Retrievals from a machine learning algorithm are used to provide a priori states, and a radiative transfer model is used to create lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and it is applicable to both daytime and nighttime conditions.
Eui-Jong Kang, Byung-Ju Sohn, Sang-Woo Kim, Wonho Kim, Young-Cheol Kwon, Seung-Bum Kim, Hyoung-Wook Chun, and Chao Liu
Geosci. Model Dev., 17, 8553–8568, https://doi.org/10.5194/gmd-17-8553-2024, https://doi.org/10.5194/gmd-17-8553-2024, 2024
Short summary
Short summary
Sea surface temperature (SST) is vital in climate, weather, and ocean sciences because it influences air–sea interactions. Errors in the ECMWF model's scheme for predicting ocean skin temperature prompted a revision of the ocean mixed layer model. Validation against infrared measurements and buoys showed a good correlation with minimal deviations. The revised model accurately simulates SST variations and aligns with solar radiation distributions, showing promise for weather and climate models.
Tao Shi, Yuanjian Yang, Ping Qi, and Simone Lolli
Atmos. Chem. Phys., 24, 12807–12822, https://doi.org/10.5194/acp-24-12807-2024, https://doi.org/10.5194/acp-24-12807-2024, 2024
Short summary
Short summary
This paper explored the formation mechanisms of the amplified canopy urban heat island intensity (ΔCUHII) during heat wave (HW) periods in the megacity of Beijing from the perspectives of mountain–valley breeze and urban morphology. During the mountain breeze phase, high-rise buildings with lower sky view factors (SVFs) had a pronounced effect on the ΔCUHII. During the valley breeze phase, high-rise buildings exerted a dual influence on the ΔCUHII.
Chaman Gul, Shichang Kang, Yuanjian Yang, Xinlei Ge, and Dong Guo
EGUsphere, https://doi.org/10.5194/egusphere-2024-1144, https://doi.org/10.5194/egusphere-2024-1144, 2024
Preprint archived
Short summary
Short summary
Long-term variations in upper atmospheric temperature and water vapor in the selected domains of time and space are presented. The temperature during the past two decades showed a cooling trend and water vapor showed an increasing trend and had an inverse relation with temperature in selected domains of space and time. Seasonal temperature variations are distinct, with a summer minimum and a winter maximum. Our results can be an early warning indication for future climate change.
Yueyue Cheng, Chao Liu, Jiandong Wang, Jiaping Wang, Zhouyang Zhang, Li Chen, Dafeng Ge, Caijun Zhu, Jinbo Wang, and Aijun Ding
Atmos. Chem. Phys., 24, 3065–3078, https://doi.org/10.5194/acp-24-3065-2024, https://doi.org/10.5194/acp-24-3065-2024, 2024
Short summary
Short summary
Brown carbon (BrC), a light-absorbing aerosol, plays a pivotal role in influencing global climate. However, assessing BrC radiative effects remains challenging because the required observational data are hardly accessible. Here we develop a new BrC radiative effect estimation method combining conventional observations and numerical models. Our findings reveal that BrC absorbs up to a third of the sunlight at 370 nm that black carbon does, highlighting its importance in aerosol radiative effects.
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-36, https://doi.org/10.5194/amt-2024-36, 2024
Preprint withdrawn
Short summary
Short summary
This study introduces a cloud property retrieval method which integrates traditional radiative transfer simulations with a machine-learning method. Retrievals from a machine learning algorithm are used to provide initial guesses, and a radiative transfer model is used to create radiance lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and is applicable both daytime and nighttime conditions.
Yuan Wang, Qiangqiang Yuan, Tongwen Li, Yuanjian Yang, Siqin Zhou, and Liangpei Zhang
Earth Syst. Sci. Data, 15, 3597–3622, https://doi.org/10.5194/essd-15-3597-2023, https://doi.org/10.5194/essd-15-3597-2023, 2023
Short summary
Short summary
We propose a novel spatiotemporally self-supervised fusion method to establish long-term daily seamless global XCO2 and XCH4 products. Results show that the proposed method achieves a satisfactory accuracy that distinctly exceeds that of CAMS-EGG4 and is superior or close to those of GOSAT and OCO-2. In particular, our fusion method can effectively correct the large biases in CAMS-EGG4 due to the issues from assimilation data, such as the unadjusted anthropogenic emission for COVID-19.
Jianbin Zhang, Zexia Duan, Shaohui Zhou, Yubin Li, and Zhiqiu Gao
Atmos. Meas. Tech., 16, 2197–2207, https://doi.org/10.5194/amt-16-2197-2023, https://doi.org/10.5194/amt-16-2197-2023, 2023
Short summary
Short summary
In this paper, we used a random forest model to fill the observation gaps of the fluxes measured during 2015–2019. We found that the net radiation was the most important input variable. And we justified the reliability of the model. Further, it was revealed that the model performed better after relative humidity was removed from the input. Lastly, we compared the results of the model with those of three other machine learning models, and we found that the model outperformed all of them.
Yilin Chen, Yuanjian Yang, and Meng Gao
Atmos. Meas. Tech., 16, 1279–1294, https://doi.org/10.5194/amt-16-1279-2023, https://doi.org/10.5194/amt-16-1279-2023, 2023
Short summary
Short summary
The Guangdong–Hong Kong–Macao Greater Bay Area suffers from summertime air pollution events related to typhoons. The present study leverages machine learning to predict typhoon-associated air quality over the area. The model evaluation shows that the model performs excellently. Moreover, the change in meteorological drivers of air quality on typhoon days and non-typhoon days suggests that air pollution control strategies should have different focuses on typhoon days and non-typhoon days.
Hui Zhang, Ming Luo, Yongquan Zhao, Lijie Lin, Erjia Ge, Yuanjian Yang, Guicai Ning, Jing Cong, Zhaoliang Zeng, Ke Gui, Jing Li, Ting On Chan, Xiang Li, Sijia Wu, Peng Wang, and Xiaoyu Wang
Earth Syst. Sci. Data, 15, 359–381, https://doi.org/10.5194/essd-15-359-2023, https://doi.org/10.5194/essd-15-359-2023, 2023
Short summary
Short summary
We generate the first monthly high-resolution (1 km) human thermal index collection (HiTIC-Monthly) in China over 2003–2020, in which 12 human-perceived temperature indices are generated by LightGBM. The HiTIC-Monthly dataset has a high accuracy (R2 = 0.996, RMSE = 0.693 °C, MAE = 0.512 °C) and describes explicit spatial variations for fine-scale studies. It is freely available at https://zenodo.org/record/6895533 and https://data.tpdc.ac.cn/disallow/036e67b7-7a3a-4229-956f-40b8cd11871d.
Fan Wang, Gregory R. Carmichael, Jing Wang, Bin Chen, Bo Huang, Yuguo Li, Yuanjian Yang, and Meng Gao
Atmos. Chem. Phys., 22, 13341–13353, https://doi.org/10.5194/acp-22-13341-2022, https://doi.org/10.5194/acp-22-13341-2022, 2022
Short summary
Short summary
Unprecedented urbanization in China has led to serious urban heat island (UHI) issues, exerting intense heat stress on urban residents. We find diverse influences of aerosol pollution on urban heat island intensity (UHII) under different circulations. Our results also highlight the role of black carbon in aggravating UHI, especially during nighttime. It could thus be targeted for cooperative management of heat islands and aerosol pollution.
Zexia Duan, Zhiqiu Gao, Qing Xu, Shaohui Zhou, Kai Qin, and Yuanjian Yang
Earth Syst. Sci. Data, 14, 4153–4169, https://doi.org/10.5194/essd-14-4153-2022, https://doi.org/10.5194/essd-14-4153-2022, 2022
Short summary
Short summary
Land–atmosphere interactions over the Yangtze River Delta (YRD) in China are becoming more varied and complex, as the area is experiencing rapid land use changes. In this paper, we describe a dataset of microclimate and eddy covariance variables at four sites in the YRD. This dataset has potential use cases in multiple research fields, such as boundary layer parametrization schemes, evaluation of remote sensing algorithms, and development of climate models in typical East Asian monsoon regions.
Lian Zong, Yuanjian Yang, Haiyun Xia, Meng Gao, Zhaobin Sun, Zuofang Zheng, Xianxiang Li, Guicai Ning, Yubin Li, and Simone Lolli
Atmos. Chem. Phys., 22, 6523–6538, https://doi.org/10.5194/acp-22-6523-2022, https://doi.org/10.5194/acp-22-6523-2022, 2022
Short summary
Short summary
Heatwaves (HWs) paired with higher ozone (O3) concentration at surface level pose a serious threat to human health. Taking Beijing as an example, three unfavorable synoptic weather patterns were identified to dominate the compound HW and O3 pollution events. Under the synergistic stress of HWs and O3 pollution, public mortality risk increased, and synoptic patterns and urbanization enhanced the compound risk of events in Beijing by 33.09 % and 18.95 %, respectively.
You Zhao, Chao Liu, Di Di, Ziqiang Ma, and Shihao Tang
Atmos. Meas. Tech., 15, 2791–2805, https://doi.org/10.5194/amt-15-2791-2022, https://doi.org/10.5194/amt-15-2791-2022, 2022
Short summary
Short summary
A typhoon is a high-impact atmospheric phenomenon that causes most significant socioeconomic damage, and its precipitation observation is always needed for typhoon characteristics and disaster prevention. This study developed a typhoon precipitation fusion method to combine observations from satellite radiometers, rain gauges and reanalysis to provide much improved typhoon precipitation datasets.
Jiandong Wang, Jia Xing, Shuxiao Wang, Rohit Mathur, Jiaping Wang, Yuqiang Zhang, Chao Liu, Jonathan Pleim, Dian Ding, Xing Chang, Jingkun Jiang, Peng Zhao, Shovan Kumar Sahu, Yuzhi Jin, David C. Wong, and Jiming Hao
Atmos. Chem. Phys., 22, 5147–5156, https://doi.org/10.5194/acp-22-5147-2022, https://doi.org/10.5194/acp-22-5147-2022, 2022
Short summary
Short summary
Aerosols reduce surface solar radiation and change the photolysis rate and planetary boundary layer stability. In this study, the online coupled meteorological and chemistry model was used to explore the detailed pathway of how aerosol direct effects affect secondary inorganic aerosol. The effects through the dynamics pathway act as an equally or even more important route compared with the photolysis pathway in affecting secondary aerosol concentration in both summer and winter.
Shaohui Zhou, Yuanjian Yang, Zhiqiu Gao, Xingya Xi, Zexia Duan, and Yubin Li
Atmos. Meas. Tech., 15, 757–773, https://doi.org/10.5194/amt-15-757-2022, https://doi.org/10.5194/amt-15-757-2022, 2022
Short summary
Short summary
Our research has determined the possible relationship between Weibull natural wind mesoscale parameter c and shape factor k with height under the conditions of a desert steppe terrain in northern China, which has great potential in wind power generation. We have gained an enhanced understanding of the seasonal changes in the surface roughness of the desert grassland and the changes in the incoming wind direction.
Xinyan Li, Yuanjian Yang, Jiaqin Mi, Xueyan Bi, You Zhao, Zehao Huang, Chao Liu, Lian Zong, and Wanju Li
Atmos. Meas. Tech., 14, 7007–7023, https://doi.org/10.5194/amt-14-7007-2021, https://doi.org/10.5194/amt-14-7007-2021, 2021
Short summary
Short summary
A random forest (RF) model framework for Fengyun-4A (FY-4A) daytime and nighttime quantitative precipitation estimation (QPE) is established using FY-4A multi-band spectral information, cloud parameters, high-density precipitation observations and physical quantities from reanalysis data. The RF model of FY-4A QPE has a high accuracy in estimating precipitation at the heavy-rain level or below, which has advantages for quantitative estimation of summer precipitation over East Asia in future.
Cited articles
Akdemir, S. and Tagarakis, A.: Investigation of Spatial Variability of Air Temperature, Humidity and Velocity in Cold Stores by Using Management Zone Analysis, Tarim Bilim. Derg., 20, 175–186,
https://doi.org/10.1501/Tarimbil_0000001277, 2014.
Al-Ameri, A. Q., Lamit, H., Ossen, D., and Raja Shahminan, R. N.: Urban Heat
Island and Thermal Comfort Conditions at Micro-climate Scale in a Tropical
Planned City, Energ. Buildings, 133, 577–595, https://doi.org/10.1016/j.enbuild.2016.10.006, 2016.
Alonso, L. and Renard, F.: Integrating Satellite-Derived Data as Spatial
Predictors in Multiple Regression Models to Enhance the Knowledge of Air
Temperature Patterns, Urban Science, 3, 101, https://doi.org/10.3390/urbansci3040101, 2019.
Alonso, L. and Renard, F.: A New Approach for Understanding Urban
Microclimate by Integrating Complementary Predictors at Different Scales in
Regression and Machine Learning Models, Remote Sens., 12, 2434, https://doi.org/10.3390/rs12152434, 2020.
An, N., Dou, J., González-Cruz, J. E., Bornstein, R. D., Miao, S., and
Li, L.: An Observational Case Study of Synergies between an Intense Heat
Wave and the Urban Heat Island in Beijing, J. Appl. Meteorol. Clim., 59, 605–620, https://doi.org/10.1175/jamc-d-19-0125.1, 2020.
Argüeso, D., Evans, J. P., Fita, L., and Bormann, K. J.: Temperature
response to future urbanization and climate change, Clim. Dynam., 42,
2183–2199, https://doi.org/10.1007/s00382-013-1789-6, 2014.
Astsatryan, H., Grigoryan, H., Poghosyan, A., Abrahamyan, R., Asmaryan, S.,
Muradyan, V., Tepanosyan, G., Guigoz, Y., and Giuliani, G.: Air temperature
forecasting using artificial neural network for Ararat valley, Earth Sci.
Inform., 14, 1–12, https://doi.org/10.1007/s12145-021-00583-9, 2021.
Bassett, R., Cai, X., Chapman, L., Heaviside, C., Thornes, J. E., Muller, C.
L., Young, D. T., and Warren, E. L.: Observations of urban heat island
advection from a high-density monitoring network,
Q. J. Roy. Meteor. Soc., 142, 2434–2441, https://doi.org/10.1002/qj.2836, 2016.
Benali, A., Carvalho, A. C., Nunes, J. P., Carvalhais, N., and Santos, A.:
Estimating air surface temperature in Portugal using MODIS LST data, Remote
Sens. Environ., 124, 108–121, https://doi.org/10.1016/j.rse.2012.04.024, 2012.
Brandsma, T., Können, G. P., and Wessels, H. R. A.: Empirical Estimation Of The Effect Of
Urban Heat Advection On The Temperature Series Of De Bilt (The Netherlands),
Int. J. Climatol., 23, 829–845, https://doi.org/10.1002/joc.902, 2003.
Buyadi, S. N. A., Mohd, W. M. N. W., and Misni, A.: Green Spaces Growth
Impact on the Urban Microclimate, Procd. Soc. Behv.,
105, 547–557, https://doi.org/10.1016/j.sbspro.2013.11.058, 2013.
Cao, C., Lee, X., Liu, S., Schultz, N., Xiao, W., Zhang, M., and Zhao, L.:
Urban heat islands in China enhanced by haze pollution, Nat. Commun., 7,
12509, https://doi.org/10.1038/ncomms12509, 2016.
Cao, Q., Yu, D., Georgescu, M., Wu, J., and Wang, W.: Impacts of future
urban expansion on summer climate and heat-related human health in eastern
China, Environ. Int., 112, 134–146, https://doi.org/10.1016/j.envint.2017.12.027, 2018.
Caselles, V., López García, M. J., Meliá, J., and Pérez
Cueva, A. J.: Analysis of the heat-island effect of the city of Valencia,
Spain, through air temperature transects and NOAA satellite data,
Theor. Appl. Climatol., 43, 195–203, https://doi.org/10.1007/BF00867455, 1991.
Chakraborty, T. and Lee, X.: A simplified urban-extent algorithm to
characterize surface urban heat islands on a global scale and examine
vegetation control on their spatiotemporal variability, Int. J. Appl. Earth Obs., 74, 269–280, https://doi.org/10.1016/j.jag.2018.09.015, 2019.
Chakraborty, T., Hsu, A., Manya, D., and Sheriff, G.: A spatially explicit
surface urban heat island database for the United States: Characterization,
uncertainties, and possible applications, ISPRS J. Photogramm., 168, 74–88, https://doi.org/10.1016/j.isprsjprs.2020.07.021, 2020.
Chen, B. and Shi, G.: Estimation of the Distribution of Global Anthropogenic Heat Flux, Atmospheric and Oceanic Science Letters, 5, 108–112, https://doi.org/10.1080/16742834.2012.11446974, 2012.
Chen, B., Shi, G., Wang, B., Zhao, J., and Tan, S.: Estimation of the
anthropogenic heat release distribution in China from 1992 to 2009, Acta
Meteorol. Sin., 26, 507–515, https://doi.org/10.1007/s13351-012-0409-y, 2012.
Chen, B., Dong, L., Shi, G., Li, L.-J., and Chen, L.-F.: Anthropogenic Heat
Release: Estimation of Global Distribution and Possible Climate Effect,
J. Meteorol. Soc. Jpn. Ser. II, 92A, 157–165, https://doi.org/10.2151/jmsj.2014-A10, 2014.
Chen, X., Jeong, S., Park, H., Kim, J., and Park, C.-R.: Urbanization has
stronger impacts than regional climate change on wind stilling: a lesson
from South Korea, Environ. Res. Lett., 15, 054016, https://doi.org/10.1088/1748-9326/ab7e51, 2020.
Chen, Y., Sui, D. Z., Fung, T., and Dou, W.: Fractal analysis of the structure and dynamics of a satellite-detected urban heat island, Int. J.
Remote Sens., 28, 2359–2366, https://doi.org/10.1080/01431160500315485, 2007.
Ching, J., Mills, G., Bechtel, B., See, L., Feddema, J., Wang, X., Ren, C.,
Brousse, O., Martilli, A., Neophytou, M., Mouzourides, P., Stewart, I.,
Hanna, A., Ng, E., Foley, M., Alexander, P., Aliaga, D., Niyogi, D.,
Shreevastava, A., Bhalachandran, P., Masson, V., Hidalgo, J., Fung, J.,
Andrade, M., Baklanov, A., Dai, W., Milcinski, G., Demuzere, M., Brunsell,
N., Pesaresi, M., Miao, S., Mu, Q., Chen, F., and Theeuwes, N.: WUDAPT: An
Urban Weather, Climate, and Environmental Modeling Infrastructure for the
Anthropocene, B. Am. Meteorol. Soc., 99,
1907–1924, https://doi.org/10.1175/bams-d-16-0236.1, 2018.
Chuanyan, Z., Zhongren, N., and Guodong, C.: Methods for modelling of
temporal and spatial distribution of air temperature at landscape scale in
the southern Qilian mountains, China, Ecol. Model., 189, 209–220, https://doi.org/10.1016/j.ecolmodel.2005.03.016, 2005.
Chun, B. and Guldmann, J. M.: Spatial statistical analysis and simulation of
the urban heat island in high-density central cities, Landscape Urban
Plan., 125, 76–88, https://doi.org/10.1016/j.landurbplan.2014.01.016, 2014.
Computer Network Information Center: Landsat 8 OLI and TIRS Digital Products, Computer Network Information Center, Chinese Academy of Sciences [data set], http://www.gscloud.cn/sources/index?pid=263&rootid=1&label=Landsat8&sort=priority&page=1, last access: 10 April 2021a.
Computer Network Information Center: ASTER GDEM, Computer Network Information Center, Chinese Academy of Sciences [data set], http://www.gscloud.cn/sources/accessdata/310?pid=302, last access: 10 April 2021b.
Corburn, J.: Cities, Climate Change and Urban Heat Island Mitigation:
Localising Global Environmental Science, Urban Studies, 46, 413–427, https://doi.org/10.1177/0042098008099361, 2009.
Dewan, A., Kiselev, G., Botje, D., Mahmud, G. I., Bhuian, M. H., and Hassan,
Q. K.: Surface urban heat island intensity in five major cities of
Bangladesh: Patterns, drivers and trends, Sustain. Cities Soc.,
71, 102926, https://doi.org/10.1016/j.scs.2021.102926, 2021.
Droste, A. M., Steeneveld, G. J., and Holtslag, A. A. M.: Introducing the
urban wind island effect, Environ. Res. Lett., 13, 094007, https://doi.org/10.1088/1748-9326/aad8ef, 2018.
Estrada, F., Botzen, W. J. W., and Tol, R. S. J.: A global economic
assessment of city policies to reduce climate change impacts, Nat. Clim.
Change, 7, 403–406, https://doi.org/10.1038/nclimate3301, 2017.
Gallo, K. P., McNab, A. L., Karl, T. R., Brown, J. F., Hood, J. J., and
Tarpley, J. D.: The use of NOAA AVHRR data for assessment of the urban heat
sland effect, J. Appl. Meteorol. Clim., 32, 899–908, https://doi.org/10.1175/1520-0450(1993)032<0899:TUONAD>2.0.CO;2, 1993a.
Gallo, K. P., McNab, A. L., Karl, T. R., Brown, J. F., Hood, J. J., and
Tarpley, J. D.: The use of a vegetation index for assessment of the urban
heat island effect, Int. J. Remote Sens., 14, 2223–2230, https://doi.org/10.1080/01431169308954031, 1993b.
Hankey, S. and Marshall, J. D.: Land Use Regression Models of On-Road
Particulate Air Pollution (Particle Number, Black Carbon, PM2.5, Particle Size) Using Mobile Monitoring, Environ. Sci. Technol., 49,
9194–9202, https://doi.org/10.1021/acs.est.5b01209, 2015.
He, B.-J.: Potentials of meteorological characteristics and synoptic
conditions to mitigate urban heat island effects, Urban Climate, 24, 26–33, https://doi.org/10.1016/j.uclim.2018.01.004, 2018.
He, B. J., Wang, J., Liu, H., and Ulpiani, G.: Localized synergies between
heat waves and urban heat islands: Implications on human thermal comfort and
urban heat management, Environ. Res., 193, 110584, https://doi.org/10.1016/j.envres.2020.110584, 2021.
Heusinkveld, B. G., Steeneveld, G. J., van Hove, L. W. A., Jacobs, C. M. J.,
and Holtslag, A. A. M.: Spatial variability of the Rotterdam urban heat
island as influenced by urban land use, J. Geophys. Res.-Atmos., 119, 677–692, https://doi.org/10.1002/2012jd019399, 2014.
Ho, H. C., Knudby, A., Xu, Y., Hodul, M., and Aminipouri, M.: A comparison
of urban heat islands mapped using skin temperature, air temperature, and
apparent temperature (Humidex), for the greater Vancouver area, Sci. Total Environ., 544, 929–938, https://doi.org/10.1016/j.scitotenv.2015.12.021, 2016.
Hong, J.-S., Yeh, S.-W., and Seo, K.-H.: Diagnosing Physical Mechanisms
Leading to Pure Heat Waves Versus Pure Tropical Nights Over the Korean
Peninsula, J. Geophys. Res.-Atmos., 123, 7149007160, https://doi.org/10.1029/2018jd028360, 2018.
Hu, X.-M., Xue, M., Klein, P. M., Illston, B. G., and Chen, S.: Analysis of
Urban Effects in Oklahoma City using a Dense Surface Observing Network,
J. Appl. Meteorol. Clim., 55, 723–741, https://doi.org/10.1175/jamc-d-15-0206.1, 2016.
Janatian, N., Sadeghi, M., Sanaeinejad, S. H., Bakhshian, E., Farid, A.,
Hasheminia, S. M., and Ghazanfari, S.: A statistical framework for
estimating air temperature using MODIS land surface temperature data, Int. J. Climatol., 37, 1181–1194, https://doi.org/10.1002/joc.4766, 2016.
Kabano, P., Lindley, S., and Harris, A.: Evidence of urban heat island
impacts on the vegetation growing season length in a tropical city,
Landscape Urban Plan., 206, 103989, https://doi.org/10.1016/j.landurbplan.2020.103989, 2021.
Khan, H. S., Paolini, R., Santamouris, M., and Caccetta, P.: Exploring the
Synergies between Urban Overheating and Heatwaves (HWs) in Western Sydney,
Energies, 13, 470, https://doi.org/10.3390/en13020470, 2020.
Koken, P. J., Piver, W. T., Ye, F., Elixhauser, A., Olsen, L. M., and
Portier, C. J.: Temperature, air pollution, and hospitalization for
cardiovascular diseases among elderly people in Denver, Environ. Health
Perspect., 111, 1312–1317, https://doi.org/10.1289/ehp.5957, 2003.
Li, X., Zhou, Y., Asrar, G. R., Imhoff, M., and Li, X.: The surface urban
heat island response to urban expansion: A panel analysis for the
conterminous United States, Sci. Total Environ., 605–606,
426–435, https://doi.org/10.1016/j.scitotenv.2017.06.229, 2017.
Li, Y., Schubert, S., Kropp, J. P., and Rybski, D.: On the influence of
density and morphology on the Urban Heat Island intensity, Nat. Commun., 11,
2647, https://doi.org/10.1038/s41467-020-16461-9, 2020.
Li, Y., Zhou, B., Glockmann, M., Kropp, J. P., and Rybski, D.: Context
sensitivity of surface urban heat island at the local and regional scales,
Sustain. Cities Soc., 74, 103146, https://doi.org/10.1016/j.scs.2021.103146, 2021.
Liang, S.: Narrowband to broadband conversions of land surface albedo I:
Algorithms, Remote Sens. Environ., 76, 213–238, https://doi.org/10.1016/S0034-4257(00)00205-4, 2001.
Liu, L., Lin, Y., Liu, J., Wang, L., Wang, D., Shui, T., Chen, X., and Wu,
Q.: Analysis of local-scale urban heat island characteristics using an
integrated method of mobile measurement and GIS-based spatial interpolation,
Build. Environ., 117, 191–207, https://doi.org/10.1016/j.buildenv.2017.03.013,
2017.
Liu, W., Ji, C., Zhong, J., Jiang, X., and Zheng, Z.: Temporal
characteristics of the Beijing urban heat island, Theor. Appl. Climatol., 87, 213–221, https://doi.org/10.1007/s00704-005-0192-6, 2006.
Liu, X., Guo, J., Zhang, A., Zhou, J., Chu, Z., Zhou, Y., and Ren, G.:
Urbanization Effects on Observed Surface Air Temperature Trends in North
China, J. Climate, 21, 1333–1348, https://doi.org/10.1175/2007jcli1348.1, 2008.
Long, D., Yan, L., Bai, L., Zhang, C., Li, X., Lei, H., Yang, H., Tian, F.,
Zeng, C., Meng, X., and Shi, C.: Generation of MODIS-like land surface
temperatures under all-weather conditions based on a data fusion approach,
Remote Sens. Environ., 246, 111863, https://doi.org/10.1016/j.rse.2020.111863, 2020.
Malings, C., Pozzi, M., Klima, K., Bergés, M., Bou-Zeid, E., and
Ramamurthy, P.: Surface heat assessment for developed environments:
Probabilistic urban temperature modeling, Comput. Environ. Urban, 66, 53–64, https://doi.org/10.1016/j.compenvurbsys.2017.07.006, 2017.
Manoli, G., Fatichi, S., Schlapfer, M., Yu, K., Crowther, T. W., Meili, N.,
Burlando, P., Katul, G. G., and Bou-Zeid, E.: Magnitude of urban heat
islands largely explained by climate and population, Nature, 573, 55–60, https://doi.org/10.1038/s41586-019-1512-9, 2019.
Meili, N., Manoli, G., Burlando, P., Carmeliet, J., Chow, W. T. L., Coutts,
A. M., Roth, M., Velasco, E., Vivoni, E. R., and Fatichi, S.: Tree effects
on urban microclimate: Diurnal, seasonal, and climatic temperature
differences explained by separating radiation, evapotranspiration, and
roughness effects, Urban For. Urban Gree., 58, 1–13, https://doi.org/10.1016/j.ufug.2020.126970, 2021.
Merbitz, H., Fritz, S., and Schneider, C.: Mobile measurements and
regression modeling of the spatial particulate matter variability in an
urban area, Sci. Total Environ., 438, 389–403, https://doi.org/10.1016/j.scitotenv.2012.08.049, 2012.
Mira, M., Ninyerola, M., Batalla, M., Pesquer, L., and Pons, X.: Improving
Mean Minimum and Maximum Month-to-Month Air Temperature Surfaces Using
Satellite-Derived Land Surface Temperature, Remote Sens., 9, 1313, https://doi.org/10.3390/rs9121313, 2017.
Mirzaei, P. A.: Recent challenges in modeling of urban heat island,
Sustain. Cities Soc., 19, 200–206, https://doi.org/10.1016/j.scs.2015.04.001,
2015.
Mora, C., Dousset, B., Caldwell, I. R., Powell, F. E., Geronimo, R. C.,
Bielecki, Coral R., Counsell, C. W. W., Dietrich, B. S., Johnston, E. T.,
Louis, L. V., Lucas, M. P., McKenzie, M. M., Shea, A. G., Tseng, H.,
Giambelluca, T. W., Leon, L. R., Hawkins, E., and Trauernicht, C.: Global
risk of deadly heat, Nat. Clim. Change, 7, 501–506, https://doi.org/10.1038/nclimate3322, 2017.
Mutiibwa, D., Strachan, S., and Albright, T.: Land Surface Temperature and
Surface Air Temperature in Complex Terrain, IEEE J. Sel. Top. Appl., 8, 4762–4774, https://doi.org/10.1109/jstars.2015.2468594, 2015.
National Centers for Environmental Information: Version 4 DMSP-OLS Nighttime Lights Time Series, National Centers for Environmental Information [data set], http://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html, last access: 10 April 2021.
Nganyiyimana, J. N. J., Kim, I., Geun, M. S., and YunID, Y.: Synergies
between urban heat island and heat waves in Seoul: The role of wind speed
and land use characteristics, PLoS ONE, 15, e0243571, https://doi.org/10.1371/journal.pone.0243571, 2020.
Oh, J. W., Ngarambe, J., Duhirwe, P. N., Yun, G. Y., and Santamouris, M.:
Using deep-learning to forecast the magnitude and characteristics of urban
heat island in Seoul Korea, Sci. Rep., 10, 3559, https://doi.org/10.1038/s41598-020-60632-z, 2020.
Oke, T. R.: The energetic basis of the urban heat island, Q. J. Roy.
Meteor. Soc., 108, 1–24, https://doi.org/10.1002/qj.49710845502, 1982.
Pandey, A. K., Singh, S., Berwal, S., Kumar, D., Pandey, P., Prakash, A.,
Lodhi, N., Maithani, S., Jain, V. K., and Kumar, K.: Spatio – temporal
variations of urban heat island over Delhi, Urban Climate, 10, 119–133, https://doi.org/10.1016/j.uclim.2014.10.005, 2014.
Peng, S., Piao, S., Ciais, P., Friedlingstein, P., Ottle, C., Bréon,
F.-M., Nan, H., Zhou, L., and Myneni, R. B.: Surface Urban Heat Island
Across 419 Global Big Cities, Environ. Sci. Technol., 46,
696–703, https://doi.org/10.1021/es2030438, 2012.
Popovici, I. E., Goloub, P., Podvin, T., Blarel, L., Loisil, R., Unga, F., Mortier, A., Deroo, C., Victori, S., Ducos, F., Torres, B., Delegove, C., Choël, M., Pujol-Söhne, N., and Pietras, C.: Description and applications of a mobile system performing on-road aerosol remote sensing and in situ measurements, Atmos. Meas. Tech., 11, 4671–4691, https://doi.org/10.5194/amt-11-4671-2018, 2018.
Prihodko, L. and Goward, S. N.: Estimation of air temperature from remotely
sensed surface observations, Remote Sens. Environ., 60, 335–346,
https://doi.org/10.1016/S0034-4257(96)00216-7, 1997.
Qin, Z., Dall'Olmo, G., Karnieli, A., and Berliner, P.: Derivation of split
window algorithm and its sensitivity analysis for retrieving land surface
temperature from NOAA-advanced very high resolution radiometer data, J. Geophys. Res.-Atmos., 106, 22655–22670, https://doi.org/10.1029/2000JD900452, 2001.
Qiu, X. F., Gu, L. H., Zeng, Y., Jiang, A. J., and He, Y. J.: Study on Urban Heat Island Effect of Nanjing, Climatic and Environmental Research,
13, 807–814, https://doi.org/10.3878/j.issn.1006-9585.2008.06.12, 2008 (in Chinese).
Ren, Y. and Ren, G.: A Remote-Sensing Method of Selecting Reference Stations for Evaluating Urbanization Effect on Surface Air Temperature Trends, J.
Climate, 24, 3179–3189, https://doi.org/10.1175/2010JCLI3658.1, 2011.
Roth, M.: Review of urban climate research in (sub)tropical regions,
Int. J. Climatol., 27, 1859–1873, https://doi.org/10.1002/joc.1591, 2007.
Roth, M., Oke, T. R., and Emery, W. J.: Satellite-derived urban heat islands
from three coastal cities and the utilization of such data in urban
climatology, Int. J. Remote Sens., 10, 1699–1720, https://doi.org/10.1080/01431168908904002, 1989.
Salata, F., Golasi, I., Petitti, D., de Lieto Vollaro, E., Coppi, M., and de
Lieto Vollaro, A.: Relating microclimate, human thermal comfort and health
during heat waves: An analysis of heat island mitigation strategies through
a case study in an urban outdoor environment, Sustain. Cities Soc., 30, 79–96, https://doi.org/10.1016/j.scs.2017.01.006, 2017.
Scott, A. A., Waugh, D. W., and Zaitchik, B. F.: Reduced Urban Heat Island
intensity under warmer conditions, Environ. Res. Lett., 13, 064003, https://doi.org/10.1088/1748-9326/aabd6c, 2018.
Shahmohamadi, P., Che-Ani, A. I., Maulud, K. N. A., Tawil, N. M., and
Abdullah, N. A. G.: The Impact of Anthropogenic Heat on Formation of Urban
Heat Island and Energy Consumption Balance, Urban Studies Research, 2011,
1–9, https://doi.org/10.1155/2011/497524, 2011.
Shi, T., Huang, Y., Wang, H., Shi, C.-E., and Yang, Y.-J.: Influence of
urbanization on the thermal environment of meteorological station:
Satellite-observed evidence, Advances in Climate Change Research, 6, 7–15, https://doi.org/10.1016/j.accre.2015.07.001, 2015.
Son, N.-T., Chen, C.-F., Chen, C.-R., Thanh, B.-X., and Vuong, T.-H.:
Assessment of urbanization and urban heat islands in Ho Chi Minh City,
Vietnam using Landsat data, Sustain. Cities Soc., 30, 150–161, https://doi.org/10.1016/j.scs.2017.01.009, 2017.
Stisen, S., Sandholt, I., Nørgaard, A., Fensholt, R., and Eklundh, L.:
Estimation of diurnal air temperature using MSG SEVIRI data in West Africa,
Remote Sens. Environ., 110, 262–274, https://doi.org/10.1016/j.rse.2007.02.025,
2007.
Taleghani, M., Sailor, D., and Ban-Weiss, G. A.: Micrometeorological
simulations to predict the impacts of heat mitigation strategies on
pedestrian thermal comfort in a Los Angeles neighborhood, Environ. Res. Lett., 11, 024003, https://doi.org/10.1088/1748-9326/11/2/024003, 2016.
Valmassoi, A. and Keller, J. D.: How to visualize the Urban Heat Island in Gridded Datasets?, Adv. Sci. Res., 18, 41–49, https://doi.org/10.5194/asr-18-41-2021, 2021.
Vancutsem, C., Ceccato, P., Dinku, T., and Connor, S. J.: Evaluation of
MODIS land surface temperature data to estimate air temperature in different
ecosystems over Africa, Remote Sens. Environ., 114, 449–465, https://doi.org/10.1016/j.rse.2009.10.002, 2010.
Venter, Z. S., Brousse, O., Esau, I., and Meier, F.: Hyperlocal mapping of
urban air temperature using remote sensing and crowdsourced weather data,
Remote Sens. Environ., 242, 111791, https://doi.org/10.1016/j.rse.2020.111791, 2020.
Wang, L., Fan, S., Hu, F., Miao, S., Yang, A., Li, Y., Liu, J., Liu, C.,
Chen, S., Ho, H. C., Duan, Z., Gao, Z., and Yang, Y.: Vertical Gradient
Variations in Radiation Budget and Heat Fluxes in the Urban Boundary Layer:
A Comparison Study Between Polluted and Clean Air Episodes in Beijing During
Winter, J. Geophys. Res.-Atmos., 125, e32478, https://doi.org/10.1029/2020JD032478,
2020.
Wang, R., Hou, H., Murayama, Y., and Derdouri, A.: Spatiotemporal Analysis
of Land Use/Cover Patterns and Their Relationship with Land Surface
Temperature in Nanjing, China, Remote Sens., 12, 440, https://doi.org/10.3390/rs12030440, 2020.
Wang, W., Chen, B., Xu, Y., Zhou, W., and Wang, X.: Urban heat islands in
Hong Kong: Bonding with atmospheric stability, Atmos. Sci. Lett., 22, e1032, https://doi.org/10.1002/asl.1032, 2021.
Yang, J., Yin, P., Sun, J., Wang, B., Zhou, M., Li, M., Tong, S., Meng, B.,
Guo, Y., and Liu, Q.: Heatwave and mortality in 31 major Chinese cities:
Definition, vulnerability and implications, Sci. Total Environ., 649, 695–702, https://doi.org/10.1016/j.scitotenv.2018.08.332, 2019.
Yang, P., Ren, G., Yan, P., and Deng, J.: Tempospatial Pattern of Surface
Wind Speed and the “Urban Stilling Island” in Beijing City, J.
Meteorol. Res., 34, 986–996, https://doi.org/10.1007/s13351-020-9135-5, 2020.
Yang, Y., Zhang, M., Li, Q., Chen, B., Gao, Z., Ning, G., Liu, C., Li, Y.,
and Luo, M.: Modulations of surface thermal environment and agricultural
activity on intraseasonal variations of summer diurnal temperature range in
the Yangtze River Delta of China, Sci. Total Environ., 736,
139445, https://doi.org/10.1016/j.scitotenv.2020.139445, 2020a.
Yang, Y., Zheng, Z., Yim, S. Y. L., Roth, M., Ren, G., Gao, Z., Wang, T.,
Li, Q., Shi, C., Ning, G., and Li, Y.: PM2.5 Pollution Modulates Wintertime Urban Heat Island Intensity in the Beijing-Tianjin-Hebei Megalopolis, China, Geophys. Res. Lett., 47, e2019GL084288, https://doi.org/10.1029/2019gl084288, 2020b.
Yang, Y.-J., Wu, B.-W., Shi, C.-E., Zhang, J.-H., Li, Y.-B., Tang, W.-A.,
Wen, H.-Y., Zhang, H.-Q., and Shi, T.: Impacts of Urbanization and
Station-relocation on Surface Air Temperature Series in Anhui Province,
China, Pure Appl. Geophys., 170, 1969–1983, https://doi.org/10.1007/s00024-012-0619-9, 2012.
Yang, Y.-J., Gao, Z., Shi, T., Wang, H., Li, Y., Zhang, N., Zhang, H., and
Huang, Y.: Assessment of urban surface thermal environment using MODIS with
a population-weighted method: a case study, J. Spat. Sci., 64,
287–300, https://doi.org/10.1080/14498596.2017.1422155, 2018.
Yoo, C., Im, J., Park, S., and Quackenbush, L. J.: Estimation of daily
maximum and minimum air temperatures in urban landscapes using MODIS time
series satellite data, ISPRS J. Photogramm.,
137, 149–162, https://doi.org/10.1016/j.isprsjprs.2018.01.018, 2018.
Yu, R., Lyu, M., Lu, J., Yang, Y., Shen, G., and Li, F.: Spatial Coordinates
Correction Based on Multi-Sensor Low-Altitude Remote Sensing Image
Registration for Monitoring Forest Dynamics, IEEE Access, 8, 18483–18496, https://doi.org/10.1109/ACCESS.2020.2968335, 2020.
Zhang, N., Wang, X., Chen, Y., Dai, W., and Wang, X.: Numerical simulations
on influence of urban land cover expansion and anthropogenic heat release on
urban meteorological environment in Pearl River Delta, Theor. Appl. Climatol., 126, 469–479, https://doi.org/10.1007/s00704-015-1601-0, 2015.
Zhang, Y., Ning, G., Chen, S., and Yang, Y.: Impact of Rapid Urban Sprawl on
the Local Meteorological Observational Environment Based on Remote Sensing
Images and GIS Technology, Remote Sens., 13, 2624, https://doi.org/10.3390/rs13132624, 2021.
Zhao, J., Zhao, X., Liang, S., Zhou, T., Du, X., Xu, P., and Wu, D.:
Assessing the thermal contributions of urban land cover types, Landscape
Urban Plan., 204, 103927, https://doi.org/10.1016/j.landurbplan.2020.103927, 2020.
Zhao, L., Lee, X., Smith, R. B., and Oleson, K.: Strong contributions of
local background climate to urban heat islands, Nature, 511, 216–219, https://doi.org/10.1038/nature13462, 2014.
Zhao, M., Cai, H., Qiao, Z., and Xu, X.: Influence of urban expansion on the
urban heat island effect in Shanghai, Int. J. Geogr. Inf. Sci., 30, 2421–2441, https://doi.org/10.1080/13658816.2016.1178389, 2016.
Zheng, Z., Ren, G., Wang, H., Dou, J., Gao, Z., Duan, C., Li, Y.,
Ngarukiyimana, J. P., Zhao, C., Cao, C., Jiang, M., and Yang, Y.:
Relationship Between Fine-Particle Pollution and the Urban Heat Island in
Beijing, China: Observational Evidence, Bound.-Lay. Meteorol., 169,
93–113, https://doi.org/10.1007/s10546-018-0362-6, 2018.
Zheng, Z., Zhao, C., Lolli, S., Wang, X., Wang, Y., Ma, X., Li, Q., and
Yang, Y.: Diurnal variation of summer precipitation modulated by air
pollution: observational evidences in the beijing metropolitan area,
Environ. Res. Lett., 15, 094053, https://doi.org/10.1088/1748-9326/ab99fc, 2020.
Zhou, D., Zhao, S., Zhang, L., Sun, G., and Liu, Y.: The footprint of urban
heat island effect in China, Sci. Rep., 5, 11160, https://doi.org/10.1038/srep11160, 2015.
Zhou, Y., Weng, Q., Gurney, K. R., Shuai, Y., and Hu, X.: Estimation of the
relationship between remotely sensed anthropogenic heat discharge and
building energy use, ISPRS J. Photogramm., 67,
65–72, https://doi.org/10.1016/j.isprsjprs.2011.10.007, 2012.
Zhu, X., Zhang, Q., Xu, C.-Y., Sun, P., and Hu, P.: Reconstruction of high
spatial resolution surface air temperature data across China: A new
geo-intelligent multisource data-based machine learning technique, Sci. Total Environ., 665, 300–313, https://doi.org/10.1016/j.scitotenv.2019.02.077,
2019.
Zong, L., Liu, S., Yang, Y., Ren, G., Yu, M., Zhang, Y., and Li, Y.:
Synergistic Influence of Local Climate Zones and Wind Speeds on the Urban
Heat Island and Heat Waves in the Megacity of Beijing, China, Front.
Earth Sci., 9, 673786, https://doi.org/10.3389/feart.2021.673786, 2021.
Zou, J., Sun, J., Zhang, N., and Zhao, W.: Evaluation and
Parameter-Sensitivity Study of a Single-Layer Urban Canopy Model (SLUCM)
with Measurements in Nanjing, China, J. Hydrometeorol., 15,
1078–1090, https://doi.org/10.1175/jhm-d-13-0129.1, 2014.
Short summary
This paper proposes a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a random forest (RF) model. The spatial distribution of CUHII was evaluated at 30 m resolution based on the output of the RF model. The present RF model framework for real-time monitoring and assessment of high-resolution CUHII provides scientific support for studying the changes and causes of CUHII.
This paper proposes a method for evaluating canopy UHI intensity (CUHII) at high resolution by...
