Articles | Volume 15, issue 1
https://doi.org/10.5194/essd-15-359-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-359-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020
Hui Zhang
School of Geography and Planning, and Guangdong Key Laboratory for
Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510006,
China
School of Geography and Planning, and Guangdong Key Laboratory for
Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510006,
China
Institute of Environment, Energy and Sustainability, The Chinese
University of Hong Kong, Hong Kong SAR, China
Yongquan Zhao
CORRESPONDING AUTHOR
School of Geospatial Engineering and Science, Sun Yat-sen University,
and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Zhuhai 519082, China
Lijie Lin
School of Management, Guangdong University of Technology, Guangzhou
510520, China
Erjia Ge
Dalla Lana School of Public Health, University of Toronto, Toronto,
Ontario M5T 3M7, Canada
Yuanjian Yang
School of Atmospheric Physics, Nanjing
University of Information Science & Technology, Nanjing 210044, China
Guicai Ning
Institute of Environment, Energy and Sustainability, The Chinese
University of Hong Kong, Hong Kong SAR, China
Jing Cong
Tianjin Municipal Meteorological Observatory, Tianjin 300074, China
Zhaoliang Zeng
State Key Laboratory of Severe Weather, Chinese Academy of
Meteorological Sciences, Beijing 100081, China
Ke Gui
State Key Laboratory of Severe Weather (LASW) and Key Laboratory of
Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences,
Beijing 100081, China
Jing Li
College of Resources and Environment, Fujian Agriculture and Forest
University, Fuzhou 35002, China
Ting On Chan
School of Geography and Planning, and Guangdong Key Laboratory for
Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510006,
China
Xiang Li
School of Geography and Planning, and Guangdong Key Laboratory for
Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510006,
China
Sijia Wu
School of Geography and Planning, and Guangdong Key Laboratory for
Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510006,
China
Peng Wang
School of Geography and Planning, and Guangdong Key Laboratory for
Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510006,
China
Xiaoyu Wang
School of Geography and Planning, and Guangdong Key Laboratory for
Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510006,
China
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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
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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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-3184, https://doi.org/10.5194/egusphere-2025-3184, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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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
EGUsphere, https://doi.org/10.5194/egusphere-2025-3186, https://doi.org/10.5194/egusphere-2025-3186, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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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
EGUsphere, https://doi.org/10.5194/egusphere-2025-2785, https://doi.org/10.5194/egusphere-2025-2785, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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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.
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
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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.
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
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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.
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
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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.
Yuezhen Cai, Linyuan Xia, and Ting On Chan
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-2024, 37–42, https://doi.org/10.5194/isprs-archives-XLVIII-1-2024-37-2024, https://doi.org/10.5194/isprs-archives-XLVIII-1-2024-37-2024, 2024
Ting On Chan, Yibo Ling, Yuli Wang, Kin Sum Li, and Jing Shen
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-2024, 19–25, https://doi.org/10.5194/isprs-annals-X-1-2024-19-2024, https://doi.org/10.5194/isprs-annals-X-1-2024-19-2024, 2024
Yibo Ling, Yuli Wang, and Ting On Chan
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-2024, 145–151, https://doi.org/10.5194/isprs-annals-X-1-2024-145-2024, https://doi.org/10.5194/isprs-annals-X-1-2024-145-2024, 2024
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
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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.
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
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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.
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
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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.
X. Peng and T. O. Chan
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-W2-2022, 103–110, https://doi.org/10.5194/isprs-archives-XLVIII-2-W2-2022-103-2022, https://doi.org/10.5194/isprs-archives-XLVIII-2-W2-2022-103-2022, 2022
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
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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
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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.
Lei Li, Yevgeny Derimian, Cheng Chen, Xindan Zhang, Huizheng Che, Gregory L. Schuster, David Fuertes, Pavel Litvinov, Tatyana Lapyonok, Anton Lopatin, Christian Matar, Fabrice Ducos, Yana Karol, Benjamin Torres, Ke Gui, Yu Zheng, Yuanxin Liang, Yadong Lei, Jibiao Zhu, Lei Zhang, Junting Zhong, Xiaoye Zhang, and Oleg Dubovik
Earth Syst. Sci. Data, 14, 3439–3469, https://doi.org/10.5194/essd-14-3439-2022, https://doi.org/10.5194/essd-14-3439-2022, 2022
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A climatology of aerosol composition concentration derived from POLDER-3 observations using GRASP/Component is presented. The conceptual specifics of the GRASP/Component approach are in the direct retrieval of aerosol speciation without intermediate retrievals of aerosol optical characteristics. The dataset of satellite-derived components represents scarce but imperative information for validation and potential adjustment of chemical transport models.
Junting Zhong, Xiaoye Zhang, Ke Gui, Jie Liao, Ye Fei, Lipeng Jiang, Lifeng Guo, Liangke Liu, Huizheng Che, Yaqiang Wang, Deying Wang, and Zijiang Zhou
Earth Syst. Sci. Data, 14, 3197–3211, https://doi.org/10.5194/essd-14-3197-2022, https://doi.org/10.5194/essd-14-3197-2022, 2022
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Historical long-term PM2.5 records with high temporal resolution are essential but lacking for research and environmental management. Here, we reconstruct site-based and gridded PM2.5 datasets at 6-hour intervals from 1960 to 2020 that combine visibility, meteorological data, and emissions based on a machine learning model with extracted spatial features. These two PM2.5 datasets will lay the foundation of research studies associated with air pollution, climate change, and aerosol reanalysis.
Ke Gui, Wenrui Yao, Huizheng Che, Linchang An, Yu Zheng, Lei Li, Hujia Zhao, Lei Zhang, Junting Zhong, Yaqiang Wang, and Xiaoye Zhang
Atmos. Chem. Phys., 22, 7905–7932, https://doi.org/10.5194/acp-22-7905-2022, https://doi.org/10.5194/acp-22-7905-2022, 2022
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This study investigates the aerosol optical and radiative properties and meteorological drivers during two mega SDS events over Northern China in March 2021. The MODIS-retrieved DOD data registered these two events as the most intense episode in the same period in history over the past 20 years. These two extreme SDS events were associated with both atmospheric circulation extremes and local meteorological anomalies that favor enhanced dust emissions in the Gobi Desert.
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
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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.
Yu Zheng, Huizheng Che, Yupeng Wang, Xiangao Xia, Xiuqing Hu, Xiaochun Zhang, Jun Zhu, Jibiao Zhu, Hujia Zhao, Lei Li, Ke Gui, and Xiaoye Zhang
Atmos. Meas. Tech., 15, 2139–2158, https://doi.org/10.5194/amt-15-2139-2022, https://doi.org/10.5194/amt-15-2139-2022, 2022
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Ground-based observations of aerosols and aerosol data verification is important for satellite and climate model modification. Here we present an evaluation of aerosol microphysical, optical and radiative properties measured using a multiwavelength photometer with a highly integrated design and smart control performance. The validation of this product is discussed in detail using AERONET as a reference. This work contributes to reducing AOD uncertainties in China and combating climate change.
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
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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.
Shihan Chen, Yuanjian Yang, Fei Deng, Yanhao Zhang, Duanyang Liu, Chao Liu, and Zhiqiu Gao
Atmos. Meas. Tech., 15, 735–756, https://doi.org/10.5194/amt-15-735-2022, https://doi.org/10.5194/amt-15-735-2022, 2022
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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.
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
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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.
Ke Gui, Huizheng Che, Yu Zheng, Hujia Zhao, Wenrui Yao, Lei Li, Lei Zhang, Hong Wang, Yaqiang Wang, and Xiaoye Zhang
Atmos. Chem. Phys., 21, 15309–15336, https://doi.org/10.5194/acp-21-15309-2021, https://doi.org/10.5194/acp-21-15309-2021, 2021
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This study utilized the globally gridded aerosol extinction data from CALIOP during 2007–2019 to investigate the 3D climatology, trends, and meteorological drivers of tropospheric type-dependent aerosols. Results revealed that the planetary boundary layer (PBL) and the free troposphere contribute 62.08 % and 37.92 %, respectively, of the global tropospheric TAOD. Trends in
CALIOP-derived aerosol loading, in particular those partitioned in the PBL, can be explained to a large extent by meteorology.
Debing Kong, Guicai Ning, Shigong Wang, Jing Cong, Ming Luo, Xiang Ni, and Mingguo Ma
Atmos. Chem. Phys., 21, 14493–14505, https://doi.org/10.5194/acp-21-14493-2021, https://doi.org/10.5194/acp-21-14493-2021, 2021
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This study provides the first attempt to examine the diurnal cycles of day-to-day temperature change and reveals their impacts on air quality forecasting in mountain-basin areas. Three different diurnal cycles of the preceding day-to-day temperature change are identified and exhibit notably distinct effects on the air quality evolutions. The mechanisms of the identified diurnal cycles' effects on air quality are also revealed, which exhibit promising potential for air quality forecasting.
Lian Zong, Yuanjian Yang, Meng Gao, Hong Wang, Peng Wang, Hongliang Zhang, Linlin Wang, Guicai Ning, Chao Liu, Yubin Li, and Zhiqiu Gao
Atmos. Chem. Phys., 21, 9105–9124, https://doi.org/10.5194/acp-21-9105-2021, https://doi.org/10.5194/acp-21-9105-2021, 2021
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In recent years, summer O3 pollution over eastern China has become more serious, and it is even the case that surface O3 and PM2.5 pollution can co-occur. However, the synoptic weather pattern (SWP) related to this compound pollution remains unclear. Regional PM2.5 and O3 compound pollution is characterized by various SWPs with different dominant factors. Our findings provide insights into the regional co-occurring high PM2.5 and O3 levels via the effects of certain meteorological factors.
Cited articles
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., and Hegewisch, K. C.:
TerraClimate, a high-resolution global dataset of monthly climate and
climatic water balance from 1958-2015, Sci. Data, 5, 170191,
https://doi.org/10.1038/sdata.2017.191, 2018.
Arias, P., Bellouin, N., Coppola, E., Jones, R., Krinner, G., Marotzke, J.,
Naik, V., Palmer, M., Plattner, G.-K., and Rogelj, J.: Climate Change 2021:
The Physical Science Basis. Contribution of Working Group14 I to the Sixth
Assessment Report of the Intergovernmental Panel on Climate Change,
Technical Summary, https://doi.org/10.1017/9781009157896.002, 2021.
Blazejczyk, K.: New climatological-and-physiological model of the human heat
balance outdoor (MENEX) and its applications in bioclimatological studies in
different scales, Zeszyty IgiPZ PAN, 28, 27–58, 1994.
Bolton, D.: The computation of equivalent potential temperature, Mon.
Weather Rev., 108, 1046–1053, 1980.
Brake, R. and Bates, G.: A valid method for comparing rational and empirical
heat stress indices, Ann. Occup. Hyg., 46, 165–174,
https://doi.org/10.1093/annhyg/mef030, 2002.
Brimicombe, C., Di Napoli, C., Cornforth, R., Pappenberger, F., Petty, C.,
and Cloke, H. L.: Borderless Heat Hazards With Bordered Impacts, Earth's
Future, 9, e2021EF002064, https://doi.org/10.1029/2021ef002064, 2021.
Budhathoki, N. K. and Zander, K. K.: Socio-Economic Impact of and Adaptation
to Extreme Heat and Cold of Farmers in the Food Bowl of Nepal,
Int. J. Environ. Res. Pub. He., 16, 1578, https://doi.org/10.3390/ijerph16091578, 2019.
Candido, C., Blanco, A. C., Medina, J., Gubatanga, E., Santos, A., Ana, R.
S., and Reyes, R. B.: Improving the consistency of multi-temporal land cover
mapping of Laguna lake watershed using light gradient boosting machine
(LightGBM) approach, change detection analysis, and Markov chain,
Remote Sensing Applications: Society and Environment, 23, 100565, https://doi.org/10.1016/j.rsase.2021.100565, 2021.
Cho, D., Yoo, C., Im, J., and Cha, D. H.: Comparative Assessment of Various
Machine Learning-Based Bias Correction Methods for Numerical Weather
Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas, Earth
Space Sci., 7, e2019EA000740, https://doi.org/10.1029/2019ea000740,
2020.
Copernicus Climate Change Service: ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate, https://cds.climate.copernicus.eu/cdsapp#!/home, last access: 5 January 2022.
Di Napoli, C., Pappenberger, F., and Cloke, H. L.: Assessing heat-related
health risk in Europe via the Universal Thermal Climate Index (UTCI),
Int. J. Biometeorol., 62, 1155–1165,
https://doi.org/10.1007/s00484-018-1518-2, 2018.
Di Napoli, C., Barnard, C., Prudhomme, C., Cloke, H. L., and Pappenberger,
F.: ERA5-HEAT: A global gridded historical dataset of human thermal comfort
indices from climate reanalysis, Geosci. Data J., 8, 2–10,
https://doi.org/10.1002/gdj3.102, 2020.
Djongyang, N., Tchinda, R., and Njomo, D.: Thermal comfort: A review paper,
Renewable and Sustainable Energy Reviews, 14, 2626–2640,
https://doi.org/10.1016/j.rser.2010.07.040, 2010.
Enander, A. E. and Hygge, S.: Thermal stress and human performance,
Scand. J.
Work Env. Hea., 16, 44–50, https://doi.org/10.5271/sjweh.1823, 1990.
Epstein, Y. and Moran, D. S.: Thermal comfort and the heat stress indices,
Ind. Health, 44, 388–398, https://doi.org/10.2486/indhealth.44.388, 2006.
Fan, J., Ma, X., Wu, L., Zhang, F., Yu, X., and Zeng, W.: Light Gradient
Boosting Machine: An efficient soft computing model for estimating daily
reference evapotranspiration with local and external meteorological data,
Agr. Water Manage., 225, 105758,
https://doi.org/10.1016/j.agwat.2019.105758, 2019.
Fang, C. and Yu, D.: China's new urbanization, Berlin and Beijing, Springer, https://doi.org/10.1007/978-3-662-49448-6, 2016.
Fanger, P. O.: Thermal comfort. Analysis and applications in environmental
engineering, Copenhagen, Danish Technical Press, 1970.
Gagge, A. and Nishi, Y.: Physical indices of the thermal environment, edited by: Ashrae,
J., United States, 18, 47–51, 1976.
Gagge, A., Stolwijk, J. A., and Nishi, Y.: An effective temperature scale
based on a simple model of human physiological regulatiry response, Memoirs
of the Faculty of Engineering, Hokkaido University, 13, 21–36, 1972.
Gaughan, A. E., Stevens, F. R., Linard, C., Jia, P., and Tatem, A. J.: High
resolution population distribution maps for Southeast Asia in 2010 and 2015,
PLoS One, 8, e55882, https://doi.org/10.1371/journal.pone.0055882, 2013.
Gong, P., Li, X., Wang, J., Bai, Y., Chen, B., Hu, T., Liu, X., Xu, B.,
Yang, J., Zhang, W., and Zhou, Y.: Annual maps of global artificial
impervious area (GAIA) between 1985 and 2018, Remote Sens. Environ.,
236, 111510, https://doi.org/10.1016/j.rse.2019.111510, 2020.
Haines, A. and Ebi, K.: The Imperative for Climate Action to Protect Health,
The New England Jornal of Medicine, 380, 263–273, https://doi.org/10.1056/NEJMra1807873, 2019.
He, Q., Wang, M., Liu, K., Li, K., and Jiang, Z.: GPRChinaTemp1km: a high-resolution monthly air temperature data set for China (1951–2020) based on machine learning, Earth Syst. Sci. Data, 14, 3273–3292, https://doi.org/10.5194/essd-14-3273-2022, 2022.
Hong, F., Zhan, W., Göttsche, F.-M., Liu, Z., Dong, P., Fu, H., Huang, F., and Zhang, X.: A global dataset of spatiotemporally seamless daily mean land surface temperatures: generation, validation, and analysis, Earth Syst. Sci. Data, 14, 3091–3113, https://doi.org/10.5194/essd-14-3091-2022, 2022.
Höppe, P.: The physiological equivalent temperature–a universal index
for the biometeorological assessment of the thermal environment,
Int. J. Biometeorol., 43, 71–75, 1999.
Houghton, F. C. and Yaglou, C. P.: Determining equal comfortlines, ASHVE
Trans., 29, 165–176, 1923.
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2391 pp., 2021.
Kang, S. and Eltahir, E. A. B.: North China Plain threatened by deadly
heatwaves due to climate change and irrigation, Nat. Commun., 9,
2894, https://doi.org/10.1038/s41467-018-05252-y, 2018.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu,
T.-Y.: Lightgbm: A highly efficient gradient boosting decision tree,
Adv. Neur. In., 30, 2017.
Krzysztof, B., Pavol, N., Oleh, S., Agnieszka, H., Olesya, S., Anna, B., and
Katarina, M.: Influence of geographical factors on thermal stress in
northern Carpathians, Int. J. Biometeorol., 65,
1553–1566, https://doi.org/10.1007/s00484-020-02011-x, 2021.
Kuchcik, M.: Mortality and thermal environment (UTCI) in Poland-long-term,
multi-city study, Int. J. Biometeorol. 65, 1529–1541,
https://doi.org/10.1007/s00484-020-01995-w, 2021.
Lazaro, P. and Momayez, M.: Heat Stress in Hot Underground Mines: a Brief
Literature Review, Mining, Metallurgy & Exploration, 38, 497–508,
https://doi.org/10.1007/s42461-020-00324-4, 2020.
Li, J., Chen, Y. D., Gan, T. Y., and Lau, N.-C.: Elevated increases in
human-perceived temperature under climate warming, Nature Climate Change, 8,
43–47, https://doi.org/10.1038/s41558-017-0036-2, 2018.
Li, Q., Liu, X., Zhang, H., Thomas C, P., and David R, E.: Detecting and
adjusting temporal inhomogeneity in Chinese mean surface air temperature
data, Adv. Atmos. Sci., 21, 260–268, https://doi.org/10.1007/bf02915712, 2004.
Li, W., Hao, X., Wang, L., Li, Y., Li, J., Li, H., and Han, T.: Detection
and Attribution of Changes in Thermal Discomfort over China during
1961–2014 and Future Projections, Adv. Atmos. Sci., 39,
456–470, https://doi.org/10.1007/s00376-021-1168-x, 2022.
Li, Y., Li, M., Li, C., and Liu, Z.: Forest aboveground biomass estimation
using Landsat 8 and Sentinel-1A data with machine learning algorithms, Sci.
Rep.-UK, 10, 9952, https://doi.org/10.1038/s41598-020-67024-3,
2020.
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.
Los, H., Mendes, G. S., Cordeiro, D., Grosso, N., Costa, H., Benevides, P.,
and Caetano, M.: Evaluation of Xgboost and Lgbm Performance in Tree Species
Classification with Sentinel-2 Data, 2021 IEEE International Geoscience and
Remote Sensing Symposium IGARSS, Brussels, Belgium, 11–16 July 2021, 5803–5806,
https://doi.org/10.1109/igarss47720.2021.9553031, 2021.
Luo, M. and Lau, N.-C.: Characteristics of summer heat stress in China
during 1979–2014: climatology and long-term trends, Clim. Dynam., 53,
5375–5388, https://doi.org/10.1007/s00382-019-04871-5, 2019.
Luo, M. and Lau, N. C.: Increasing Human-Perceived Heat Stress Risks
Exacerbated by Urbanization in China: A Comparative Study Based on Multiple
Metrics, Earth's Future, 9, e2020EF001848,
https://doi.org/10.1029/2020ef001848, 2021.
Luo, M., Lau, N. C., Liu, Z., Wu, S., and Wang, X.: An Observational
Investigation of Spatiotemporally Contiguous Heatwaves in China From a 3D
Perspective, Geophys. Res. Lett., 49, e2022GL097714, https://doi.org/10.1029/2022gl097714, 2022.
Masterton, J. M. and Richardson, F. A.: Humidex: A Method of Quantifying Human Discomfort Due to Excessive Heat and Humidity, Downsview, Ont.: Environment Canada, Atmospheric Environment, 1979.
McCarty, D. A., Kim, H. W., and Lee, H. K.: Evaluation of Light Gradient
Boosted Machine Learning Technique in Large Scale Land Use and Land Cover
Classification, Environments, 7, 84, https://doi.org/10.3390/environments7100084, 2020.
Mistry, M. N.: A High Spatiotemporal Resolution Global Gridded Dataset of
Historical Human Discomfort Indices, Atmosphere, 11, 835, https://doi.org/10.3390/atmos11080835, 2020.
Moda, H. M., Filho, W. L., and Minhas, A.: Impacts of Climate Change on
Outdoor Workers and their Safety: Some Research Priorities, Int. J. Environ. Res. Pub. He., 16, 3458, https://doi.org/10.3390/ijerph16183458, 2019.
Moran, D., Shapiro, Y., Epstein, Y., Matthew, W., and Pandolf, K.: A modified discomfort index (MDI) as an alternative to the wet bulb globe temperature (WBGT), Environmental Ergonomics VIII, edited by: Hodgdon, J. A., Heaney, J. H., and Buono, M. J., 77–80, 1998.
Nastos, P. T. and Matzarakis, A.: The effect of air temperature and human
thermal indices on mortality in Athens, Greece, Theor. Appl.
Climatol., 108, 591–599, https://doi.org/10.1007/s00704-011-0555-0, 2011.
NWS: Meteorological Conversions and Calculations: Heat Index Calculator, https://www.wpc.ncep.noaa.gov/html/heatindexbody_txt.html (last access: 1 October 2021),
2011.
Osczevski, R. and Bluestein, M.: The New Wind Chill Equivalent Temperature
Chart, B. Am. Meteorol. Soc., 86, 1453–1458,
https://doi.org/10.1175/bams-86-10-1453, 2005.
Patz, J. A., Campbell-Lendrum, D., Holloway, T., and Foley, J. A.: Impact of
regional climate change on human health, Nature, 438, 310–317, https://doi.org/10.1038/nature04188, 2005.
Peng, S., Ding, Y., Liu, W., and Li, Z.: 1 km monthly temperature and precipitation dataset for China from 1901 to 2017, Earth Syst. Sci. Data, 11, 1931–1946, https://doi.org/10.5194/essd-11-1931-2019, 2019.
Periard, J. D., Eijsvogels, T. M. H., and Daanen, H. A. M.: Exercise under
heat stress: thermoregulation, hydration, performance implications, and
mitigation strategies, Physiol. Rev., 101, 1873–1979, https://doi.org/10.1152/physrev.00038.2020, 2021.
Rahman, M. A., Franceschi, E., Pattnaik, N., Moser-Reischl, A., Hartmann,
C., Paeth, H., Pretzsch, H., Rotzer, T., and Pauleit, S.: Spatial and
temporal changes of outdoor thermal stress: influence of urban land cover
types, Sci. Rep.-UK, 12, 1–13, https://doi.org/10.1038/s41598-021-04669-8, 2022.
Raymond, C., Matthews, T., and Horton, R. M.: The emergence of heat and
humidity too severe for human tolerance, Sci. Adv., 6, eaaw1838,
https://doi.org/10.1126/sciadv.aaw1838, 2020.
Ren, Z., Fu, Y., Dong, Y., Zhang, P., and He, X.: Rapid urbanization and
climate change significantly contribute to worsening urban human thermal
comfort: A national 183-city, 26-year study in China, Urban Climate, 43,
101154, https://doi.org/10.1016/j.uclim.2022.101154, 2022.
Rice, J. A.: Mathematical statistics and data analysis, Cengage Learning,
2006.
Rogers, C. D. W., Ting, M., Li, C., Kornhuber, K., Coffel, E. D., Horton, R.
M., Raymond, C., and Singh, D.: Recent Increases in Exposure to Extreme
Humid-Heat Events Disproportionately Affect Populated Regions, Geophys.
Res. Lett., 48, e2021GL094183, https://doi.org/10.1029/2021gl094183,
2021.
Roghanchi, P. and Kocsis, K. C.: Challenges in Selecting an Appropriate Heat
Stress Index to Protect Workers in Hot and Humid Underground Mines, Saf.
Health Work, 9, 10–16, https://doi.org/10.1016/j.shaw.2017.04.002, 2018.
Rothfusz, L. P. and Headquarters, N. S. R.: The heat index equation (or,
more than you ever wanted to know about heat index), Fort Worth, Texas:
National Oceanic and Atmospheric Administration, National Weather Service,
Office of Meteorology, 9023, 1990.
Rustemeyer, N. and Howells, M.: Excess Mortality in England during the 2019
Summer Heatwaves, Climate, 9, 14,
https://doi.org/10.3390/cli9010014, 2021.
Schwingshackl, C., Sillmann, J., Vicedo-Cabrera, A. M., Sandstad, M., and
Aunan, K.: Heat Stress Indicators in CMIP6: Estimating Future Trends and
Exceedances of Impact-Relevant Thresholds, Earth's Future, 9, e2020EF001885, https://doi.org/10.1029/2020ef001885, 2021.
Sohar, E., Adar, R., and Kaly, J.: Comparison of the environmental heat load in various parts of Israel, Israel J. Exp. Med., 10, 111–115, 1963.
Staiger, H., Laschewski, G., and Matzarakis, A.: Selection of Appropriate
Thermal Indices for Applications in Human Biometeorological Studies,
Atmosphere, 10, 18, https://doi.org/10.3390/atmos10010018, 2019.
Steadman, R. G.: The assessment of sultriness. Part I: A
temperature-humidity index based on human physiology and clothing science,
J. Appl. Meteorol. Clim., 18, 861–873, 1979.
Steadman, R. G.: A universal scale of apparent temperature, J.
Appl. Meteorol. Clim., 23, 1674–1687, 1984.
Stolwijk, J.: Heat exchangers between body and environment, Bibl.
Radiol., 144–150, 1975.
Stull, R.: Wet-Bulb Temperature from Relative Humidity and Air Temperature,
J. Appl. Meteorol. Clim., 50, 2267–2269,
https://doi.org/10.1175/jamc-d-11-0143.1, 2011.
Su, H., Wang, A., Zhang, T., Qin, T., Du, X., and Yan, X.-H.:
Super-resolution of subsurface temperature field from remote sensing
observations based on machine learning,
Int. J. Appl. Earth Obs., 102,
https://doi.org/10.1016/j.jag.2021.102440, 2021.
Su, Y.: Prediction of air quality based on Gradient Boosting Machine Method,
2020 International Conference on Big Data and Informatization Education
(ICBDIE), Zhangjiajie, China, 23–25 April 2020, 395–397, https://doi.org/10.1109/icbdie50010.2020.00099, 2020.
Sulla-Menashe, D. and Friedl, M.: MCD12Q1 MODIS/Terra+ Aqua Land Cover
Type Yearly L3 Global 500m SIN Grid V006, NASA EOSDIS Land Processes DAAC:
Sioux Falls, SD, USA,
https://doi.org/10.5067/MODIS/MCD12Q1.006, 2019.
Sun, Q., Miao, C., Hanel, M., Borthwick, A. G. L., Duan, Q., Ji, D., and Li,
H.: Global heat stress on health, wildfires, and agricultural crops under
different levels of climate warming, Environ. Int., 128,
125–136, https://doi.org/10.1016/j.envint.2019.04.025, 2019.
Szer, I., Lipecki, T., Szer, J., and Czarnocki, K.: Using meteorological
data to estimate heat stress of construction workers on scaffolds for
improved safety standards, Automat. Constr., 134, 104079, https://doi.org/10.1016/j.autcon.2021.104079, 2022.
Tamiminia, H., Salehi, B., Mahdianpari, M., Beier, C. M., Johnson, L., and
Phoenix, D. B.: A Comparison of Random Forest and Light Gradient Boosting
Machine for Forest above-Ground Biomass Estimation Using a Combination of
Landsat, Alos Palsar, and Airborne Lidar Data, Int. Arch. Photogramm.,
XLIV-M-3-2021, 163–168, https://doi.org/10.5194/isprs-archives-XLIV-M-3-2021-163-2021, 2021.
Tian, H., Zhao, Y., Luo, M., He, Q., Han, Y., and Zeng, Z.: Estimating PM2.5
from multisource data: A comparison of different machine learning models in
the Pearl River Delta of China, Urban Climate, 35, 100740, https://doi.org/10.1016/j.uclim.2020.100740, 2021.
Tian, P., Lu, H., Li, D., and Guan, Y.: Quantifying the effects of
meteorological change between neighboring days on human thermal comfort in
China, Theor. Appl. Climatol., 147, 1345–1357, https://doi.org/10.1007/s00704-021-03908-2, 2022.
Tong, S., Prior, J., McGregor, G., Shi, X., and Kinney, P.: Urban heat: an
increasing threat to global health, BMJ, 375, n2467, https://doi.org/10.1136/bmj.n2467, 2021.
Tuholske, C., Caylor, K., Funk, C., Verdin, A., Sweeney, S., Grace, K.,
Peterson, P., and Evans, T.: Global urban population exposure to extreme
heat, P. Natl. Acad. Sci. USA, 118, e2024792118, https://doi.org/10.1073/pnas.2024792118, 2021.
Uddin, M. G., Nash, S., Mahammad Diganta, M. T., Rahman, A., and Olbert, A.
I.: Robust machine learning algorithms for predicting coastal water quality
index, J. Environ. Manage., 321, 115923, https://doi.org/10.1016/j.jenvman.2022.115923, 2022.
United Nations: World population prospects, Multimedia Library, 2017.
Varentsov, M., Shartova, N., Grischenko, M., and Konstantinov, P.: Spatial
Patterns of Human Thermal Comfort Conditions in Russia: Present Climate and
Trends, Weather Clim. Soc., 12, 629–642,
https://doi.org/10.1175/wcas-d-19-0138.1, 2020.
Wang, C., Zhan, W., Liu, Z., Li, J., Li, L., Fu, P., Huang, F., Lai, J.,
Chen, J., Hong, F., and Jiang, S.: Satellite-based mapping of the Universal
Thermal Climate Index over the Yangtze River Delta urban agglomeration,
J. Clean. Prod., 277, 123830, https://doi.org/10.1016/j.jclepro.2020.123830, 2020.
Wang, F., Duan, K., and Zou, L.: Urbanization Effects on Human-Perceived
Temperature Changes in the North China Plain, Sustainability, 11, https://doi.org/10.3390/su11123413, 2019.
Wang, P., Luo, M., Liao, W., Xu, Y., Wu, S., Tong, X., Tian, H., Xu, F., and
Han, Y.: Urbanization contribution to human perceived temperature changes in
major urban agglomerations of China, Urban Climate, 38, 100910, https://doi.org/10.1016/j.uclim.2021.100910, 2021.
Wu, J., Fang, H., Qin, W., Wang, L., Song, Y., Su, X., and Zhang, Y.:
Constructing High-Resolution (10 km) Daily Diffuse Solar Radiation Dataset
across China during 1982–2020 through Ensemble Model, Remote Sensing, 14,
3695, https://doi.org/10.3390/rs14153695, 2022.
Xu, W., Li, Q., Wang, X. L., Yang, S., Cao, L., and Feng, Y.: Homogenization
of Chinese daily surface air temperatures and analysis of trends in the
extreme temperature indices, J. Geophys. Res.-Atmos.,
118, 9708–9720, https://doi.org/10.1002/jgrd.50791, 2013.
Yaglou, C. and Minaed, D.: Control of heat casualties at military training
centers, Arch. Indust. Health, 16, 302–316, 1957.
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F.,
Neal, J. C., Sampson, C. C., Kanae, S., and Bates, P. D.: A high-accuracy
map of global terrain elevations, Geophys. Res. Lett., 44,
5844–5853, https://doi.org/10.1002/2017gl072874, 2017.
Yan, Y., Xu, Y., and Yue, S.: A high-spatial-resolution dataset of human
thermal stress indices over South and East Asia, Sci. Data, 8, 1–14,
https://doi.org/10.1038/s41597-021-01010-w, 2021.
Yan, Y. Y.: Human Thermal Climates in China, Phys. Geogr., 26,
163–176, https://doi.org/10.2747/0272-3646.26.3.163, 2013.
Zeng, Z., Ziegler, A. D., Searchinger, T., Yang, L., Chen, A., Ju, K., Piao,
S., Li, L. Z. X., Ciais, P., Chen, D., Liu, J., Azorin-Molina, C., Chappell,
A., Medvigy, D., and Wood, E. F.: A reversal in global terrestrial stilling
and its implications for wind energy production, Nat. Clim. Change, 9,
979–985, https://doi.org/10.1038/s41558-019-0622-6, 2019.
Zeng, Z., Gui, K., Wang, Z., Luo, M., Geng, H., Ge, E., An, J., Song, X.,
Ning, G., Zhai, S., and Liu, H.: Estimating hourly surface PM2.5
concentrations across China from high-density meteorological observations by
machine learning, Atmos. Res., 254, 105516, https://doi.org/10.1016/j.atmosres.2021.105516, 2021.
Zhang, G., Azorin-Molina, C., Chen, D., McVicar, T. R., Guijarro, J. A.,
Kong, F., Minola, L., Deng, K., and Shi, P.: Uneven Warming Likely
Contributed to Declining Near-Surface Wind Speeds in Northern China Between
1961 and 2016, J. Geophys. Res.-Atmos., 126, e2020JD033637, https://doi.org/10.1029/2020jd033637, 2021.
Zhang, H., Luo, M., Zhao, Y., Lin, L., Ge, E., Yang, Y., Ning, G., Zeng, Z.,
Gui, K., Li, J., Chen, T. O., Li, X., Wu, S., Wang, P., and Wang, X.:
HiTIC-Monthly: A Monthly High Spatial Resolution (1 km) Human Thermal Index
Collection over China during 2003–2020 (1.0), Zenodo [data set],
https://doi.org/10.5281/zenodo.6895533, 2022a.
Zhang, T., Zhou, Y., Zhu, Z., Li, X., and Asrar, G. R.: A global seamless 1 km resolution daily land surface temperature dataset (2003–2020), Earth Syst. Sci. Data, 14, 651–664, https://doi.org/10.5194/essd-14-651-2022, 2022b.
Zhao, B., Mao, K., Cai, Y., Shi, J., Li, Z., Qin, Z., Meng, X., Shen, X., and Guo, Z.: A combined Terra and Aqua MODIS land surface temperature and meteorological station data product for China from 2003 to 2017, Earth Syst. Sci. Data, 12, 2555–2577, https://doi.org/10.5194/essd-12-2555-2020, 2020.
Zhao, C., Liu, B., Piao, S., Wang, X., Lobell, D. B., Huang, Y., Huang, M.,
Yao, Y., Bassu, S., Ciais, P., Durand, J. L., Elliott, J., Ewert, F.,
Janssens, I. A., Li, T., Lin, E., Liu, Q., Martre, P., Muller, C., Peng, S.,
Penuelas, J., Ruane, A. C., Wallach, D., Wang, T., Wu, D., Liu, Z., Zhu, Y.,
Zhu, Z., and Asseng, S.: Temperature increase reduces global yields of major
crops in four independent estimates, P. Natl. Acad. Sci. USA, 114,
9326–9331, https://doi.org/10.1073/pnas.1701762114, 2017.
Zhao, Y. and Zhu, Z.: ASI: An artificial surface Index for Landsat 8
imagery, Int. J. Appl. Earth Obs., 107, 102703, https://doi.org/10.1016/j.jag.2022.102703,
2022.
Zhou, C., Chen, D., Wang, K., Dai, A., and Qi, D.: Conditional Attribution
of the 2018 Summer Extreme Heat over Northeast China: Roles of Urbanization,
Global Warming, and Warming-Induced Circulation Changes, B.
Am. Meteorol. Soc., 101, S71–S76, https://doi.org/10.1175/bams-d-19-0197.1, 2020.
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.
We generate the first monthly high-resolution (1 km) human thermal index collection...
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