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.
Data description paper
| 
20 Jan 2023
Data description paper |
| 20 Jan 2023
| 20 Jan 2023
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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Lian Zong, Yuanjian Yang, Meng Gao, Hong Wang, Peng Wang, Hongliang Zhang, Linlin Wang, Guicai Ning, Chao Liu, Yubin Li, and Zhiqiu Gao
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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 751–756, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-751-2020, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-751-2020, 2020
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Linlin Wang, Junkai Liu, Zhiqiu Gao, Yubin Li, Meng Huang, Sihui Fan, Xiaoye Zhang, Yuanjian Yang, Shiguang Miao, Han Zou, Yele Sun, Yong Chen, and Ting Yang
Atmos. Chem. Phys., 19, 6949–6967, https://doi.org/10.5194/acp-19-6949-2019, https://doi.org/10.5194/acp-19-6949-2019, 2019
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Urban boundary layer (UBL) affects the physical and chemical processes of the pollutants, and UBL structure can also be altered by pollutants. This paper presents the interactions between air pollution and the UBL structure by using the field data mainly collected from a 325 m meteorology tower, as well as from a Doppler wind lidar, during a severe heavy pollution event that occurred during 1–4 December 2016 in Beijing.
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We have compiled data regarding stable precipitation isotopes from 842 sampling points throughout the Eurasian continent since 1961, accumulating a total of 51 753 data records. The collected data have undergone pre-processing and statistical analysis. We also analysed the spatiotemporal distribution of stable precipitation isotopes across the Eurasian continent and their interrelationships with meteorological elements.
Valentin Wiener, Marie-Laure Roussel, Christophe Genthon, Étienne Vignon, Jacopo Grazioli, and Alexis Berne
Earth Syst. Sci. Data, 16, 821–836, https://doi.org/10.5194/essd-16-821-2024, https://doi.org/10.5194/essd-16-821-2024, 2024
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This paper presents 7 years of data from a precipitation radar deployed at the Dumont d'Urville station in East Antarctica. The main characteristics of the dataset are outlined in a short statistical study. Interannual and seasonal variability are also investigated. Then, we extensively describe the processing method to retrieve snowfall profiles from the radar data. Lastly, a brief comparison is made with two climate models as an application example of the dataset.
Ling Yuan, Xuelong Chen, Yaoming Ma, Cunbo Han, Binbin Wang, and Weiqiang Ma
Earth Syst. Sci. Data, 16, 775–801, https://doi.org/10.5194/essd-16-775-2024, https://doi.org/10.5194/essd-16-775-2024, 2024
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Accurately monitoring and understanding the spatial–temporal variability of evapotranspiration (ET) components over the Tibetan Plateau (TP) remains difficult. Here, 37 years (1982–2018) of monthly ET component data for the TP was produced, and the data are consistent with measurements. The annual average ET for the TP was about 0.93 (± 0.037) × 103 Gt yr−1. The rate of increase of the ET was around 0.96 mm yr−1. The increase in the ET can be explained by warming and wetting of the climate.
Dominik Rains, Isabel Trigo, Emanuel Dutra, Sofia Ermida, Darren Ghent, Petra Hulsman, Jose Gómez-Dans, and Diego G. Miralles
Earth Syst. Sci. Data, 16, 567–593, https://doi.org/10.5194/essd-16-567-2024, https://doi.org/10.5194/essd-16-567-2024, 2024
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Land surface temperature and surface net radiation are vital inputs for many land surface and hydrological models. However, current remote sensing datasets of these variables come mostly at coarse resolutions, and the few high-resolution datasets available have large gaps due to cloud cover. Here, we present a continuous daily product for both variables across Europe for 2018–2019 obtained by combining observations from geostationary as well as polar-orbiting satellites.
Yaoming Ma, Zhipeng Xie, Yingying Chen, Shaoming Liu, Tao Che, Ziwei Xu, Lunyu Shang, Xiaobo He, Xianhong Meng, Weiqiang Ma, Baiqing Xu, Huabiao Zhao, Junbo Wang, Guangjian Wu, and Xin Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-9, https://doi.org/10.5194/essd-2024-9, 2024
Revised manuscript accepted for ESSD
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Current models and the satellites struggle to accurately represent the land-atmosphere (L-A) interactions over the Tibetan Plateau. We present the most extensive compilation of in-situ observations to date, comprising 17 years of data on L-A interactions across 12 sites. This quality-assured benchmark dataset provides independent validation to improve models and remote sensing for the region and it enables new investigations of fine-scale L-A processes and their mechanistic drivers
Zen Mariani, Sara Morris, Taneil Uttal, Elena Akish, Robert Crawford, Laura Huang, Jonathan Day, Johanna Tjernström, Øystein Godøy, Lara Ferrighi, Leslie Hartten, Jareth Holt, Christopher Cox, Ewan O'Connor, Roberta Pirazzini, Marion Maturilli, Giri Prakash, James Mather, Kimberly Strong, Pierre Fogal, Vasily Kustov, Gunilla Svensson, Michael Gallagher, and Brian Vasel
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-497, https://doi.org/10.5194/essd-2023-497, 2024
Revised manuscript accepted for ESSD
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During the Year of Polar Prediction (YOPP) we increased measurements in the polar regions and have made dedicated efforts to centralize and standardize all of the different types of data sets that have been collected to facilitate user uptake and model-observation comparisons. This paper is an overview of those efforts and a description of the novel standardized Merged Observation Data Files (MODF), including a description of the sites, data format, and instruments.
Hadleigh D. Thompson, Julie M. Thériault, Stephen J. Déry, Ronald E. Stewart, Dominique Boisvert, Lisa Rickard, Nicolas R. Leroux, Matteo Colli, and Vincent Vionnet
Earth Syst. Sci. Data, 15, 5785–5806, https://doi.org/10.5194/essd-15-5785-2023, https://doi.org/10.5194/essd-15-5785-2023, 2023
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The Saint John River experiment on Cold Season Storms was conducted in northwest New Brunswick, Canada, to investigate the types of precipitation that can lead to ice jams and flooding along the river. We deployed meteorological instruments, took precipitation measurements and photographs of snowflakes, and launched weather balloons. These data will help us to better understand the atmospheric conditions that can affect local communities and townships downstream during the spring melt season.
Raghavendra Krishnamurthy, Gabriel García Medina, Brian Gaudet, William I. Gustafson Jr., Evgueni I. Kassianov, Jinliang Liu, Rob K. Newsom, Lindsay M. Sheridan, and Alicia M. Mahon
Earth Syst. Sci. Data, 15, 5667–5699, https://doi.org/10.5194/essd-15-5667-2023, https://doi.org/10.5194/essd-15-5667-2023, 2023
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Our understanding and ability to observe and model air–sea processes has been identified as a principal limitation to our ability to predict future weather. Few observations exist offshore along the coast of California. To improve our understanding of the air–sea transition zone and support the wind energy industry, two buoys with state-of-the-art equipment were deployed for 1 year. In this article, we present details of the post-processing, algorithms, and analyses.
Baptiste Vandecrux, Jason E. Box, Andreas P. Ahlstrøm, Signe B. Andersen, Nicolas Bayou, William T. Colgan, Nicolas J. Cullen, Robert S. Fausto, Dominik Haas-Artho, Achim Heilig, Derek A. Houtz, Penelope How, Ionut Iosifescu Enescu, Nanna B. Karlsson, Rebecca Kurup Buchholz, Kenneth D. Mankoff, Daniel McGrath, Noah P. Molotch, Bianca Perren, Maiken K. Revheim, Anja Rutishauser, Kevin Sampson, Martin Schneebeli, Sandy Starkweather, Simon Steffen, Jeff Weber, Patrick J. Wright, Henry Jay Zwally, and Konrad Steffen
Earth Syst. Sci. Data, 15, 5467–5489, https://doi.org/10.5194/essd-15-5467-2023, https://doi.org/10.5194/essd-15-5467-2023, 2023
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The Greenland Climate Network (GC-Net) comprises stations that have been monitoring the weather on the Greenland Ice Sheet for over 30 years. These stations are being replaced by newer ones maintained by the Geological Survey of Denmark and Greenland (GEUS). The historical data were reprocessed to improve their quality, and key information about the weather stations has been compiled. This augmented dataset is available at https://doi.org/10.22008/FK2/VVXGUT (Steffen et al., 2022).
Giovanni Chellini, Rosa Gierens, Kerstin Ebell, Theresa Kiszler, Pavel Krobot, Alexander Myagkov, Vera Schemann, and Stefan Kneifel
Earth Syst. Sci. Data, 15, 5427–5448, https://doi.org/10.5194/essd-15-5427-2023, https://doi.org/10.5194/essd-15-5427-2023, 2023
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We present a comprehensive quality-controlled dataset of remote sensing observations of low-level mixed-phase clouds (LLMPCs) taken at the high Arctic site of Ny-Ålesund, Svalbard, Norway. LLMPCs occur frequently in the Arctic region, and substantially warm the surface. However, our understanding of microphysical processes in these clouds is incomplete. This dataset includes a comprehensive set of variables which allow for extensive investigation of such processes in LLMPCs at the site.
Emma L. Robinson, Chris Huntingford, Valyaveetil Shamsudheen Semeena, and James M. Bullock
Earth Syst. Sci. Data, 15, 5371–5401, https://doi.org/10.5194/essd-15-5371-2023, https://doi.org/10.5194/essd-15-5371-2023, 2023
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CHESS-SCAPE is a suite of high-resolution climate projections for the UK to 2080, derived from United Kingdom Climate Projections 2018 (UKCP18), designed to support climate impact modelling. It contains four realisations of four scenarios of future greenhouse gas levels (RCP2.6, 4.5, 6.0 and 8.5), with and without bias correction to historical data. The variables are available at 1 km resolution and a daily time step, with monthly, seasonal and annual means and 20-year mean-monthly time slices.
Motoshi Nishimura, Teruo Aoki, Masashi Niwano, Sumito Matoba, Tomonori Tanikawa, Tetsuhide Yamasaki, Satoru Yamaguchi, and Koji Fujita
Earth Syst. Sci. Data, 15, 5207–5226, https://doi.org/10.5194/essd-15-5207-2023, https://doi.org/10.5194/essd-15-5207-2023, 2023
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We presented the method of data quality checks and the dataset for two ground weather observations in northwest Greenland. We found that the warm and clear weather conditions in the 2015, 2019, and 2020 summers caused the snowmelt and the decline in surface reflectance of solar radiation at a low-elevated site (SIGMA-B; 944 m), but those were not seen at the high-elevated site (SIGMA-A; 1490 m). We hope that our data management method and findings will help climate scientists.
Shaomin Liu, Ziwei Xu, Tao Che, Xin Li, Tongren Xu, Zhiguo Ren, Yang Zhang, Junlei Tan, Lisheng Song, Ji Zhou, Zhongli Zhu, Xiaofan Yang, Rui Liu, and Yanfei Ma
Earth Syst. Sci. Data, 15, 4959–4981, https://doi.org/10.5194/essd-15-4959-2023, https://doi.org/10.5194/essd-15-4959-2023, 2023
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We present a suite of observational datasets from artificial and natural oases–desert systems that consist of long-term turbulent flux and auxiliary data, including hydrometeorological, vegetation, and soil parameters, from 2012 to 2021. We confirm that the 10-year, long-term dataset presented in this study is of high quality with few missing data, and we believe that the data will support ecological security and sustainable development in oasis–desert areas.
Gina C. Jozef, Robert Klingel, John J. Cassano, Björn Maronga, Gijs de Boer, Sandro Dahlke, and Christopher J. Cox
Earth Syst. Sci. Data, 15, 4983–4995, https://doi.org/10.5194/essd-15-4983-2023, https://doi.org/10.5194/essd-15-4983-2023, 2023
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Observations from the MOSAiC expedition relating to lower-atmospheric temperature, wind, stability, moisture, and surface radiation budget from radiosondes, a meteorological tower, radiation station, and ceilometer were compiled to create a dataset which describes the thermodynamic and kinematic state of the central Arctic lower atmosphere between October 2019 and September 2020. This paper describes the methods used to develop this lower-atmospheric properties dataset.
Karl-Göran Karlsson, Martin Stengel, Jan Fokke Meirink, Aku Riihelä, Jörg Trentmann, Tom Akkermans, Diana Stein, Abhay Devasthale, Salomon Eliasson, Erik Johansson, Nina Håkansson, Irina Solodovnik, Nikos Benas, Nicolas Clerbaux, Nathalie Selbach, Marc Schröder, and Rainer Hollmann
Earth Syst. Sci. Data, 15, 4901–4926, https://doi.org/10.5194/essd-15-4901-2023, https://doi.org/10.5194/essd-15-4901-2023, 2023
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This paper presents a global climate data record on cloud parameters, radiation at the surface and at the top of atmosphere, and surface albedo. The temporal coverage is 1979–2020 (42 years) and the data record is also continuously updated until present time. Thus, more than four decades of climate parameters are provided. Based on CLARA-A3, studies on distribution of clouds and radiation parameters can be made and, especially, investigations of climate trends and evaluation of climate models.
Boyang Jiao, Yucheng Su, Qingxiang Li, Veronica Manara, and Martin Wild
Earth Syst. Sci. Data, 15, 4519–4535, https://doi.org/10.5194/essd-15-4519-2023, https://doi.org/10.5194/essd-15-4519-2023, 2023
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This paper develops an observational integrated and homogenized global-terrestrial (except for Antarctica) SSRIH station. This is interpolated into a 5° × 5° SSRIH grid and reconstructed into a long-term (1955–2018) global land (except for Antarctica) 5° × 2.5° SSR anomaly dataset (SSRIH20CR) by an improved partial convolutional neural network deep-learning method. SSRIH20CR yields trends of −1.276 W m−2 per decade over the dimming period and 0.697 W m−2 per decade over the brightening period.
Lukas Frank, Marius Opsanger Jonassen, Teresa Remes, Florina Roana Schalamon, and Agnes Stenlund
Earth Syst. Sci. Data, 15, 4219–4234, https://doi.org/10.5194/essd-15-4219-2023, https://doi.org/10.5194/essd-15-4219-2023, 2023
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The Isfjorden Weather Information Network (IWIN) provides continuous meteorological near-surface observations from Isfjorden in Svalbard. The network combines permanent automatic weather stations on lighthouses along the coast line with mobile stations on board small tourist cruise ships regularly trafficking the fjord during spring to autumn. All data are available online in near-real time. Besides their scientific value, IWIN data crucially enhance the safety of field activities in the region.
Jingya Han, Chiyuan Miao, Jiaojiao Gou, Haiyan Zheng, Qi Zhang, and Xiaoying Guo
Earth Syst. Sci. Data, 15, 3147–3161, https://doi.org/10.5194/essd-15-3147-2023, https://doi.org/10.5194/essd-15-3147-2023, 2023
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Constructing a high-quality, long-term daily precipitation dataset is essential to current hydrometeorology research. This study aims to construct a long-term daily precipitation dataset with different spatial resolutions based on 2839 gauge observations. The constructed precipitation dataset shows reliable quality compared with the other available precipitation products and is expected to facilitate the advancement of drought monitoring, flood forecasting, and hydrological modeling.
Christian Borger, Steffen Beirle, and Thomas Wagner
Earth Syst. Sci. Data, 15, 3023–3049, https://doi.org/10.5194/essd-15-3023-2023, https://doi.org/10.5194/essd-15-3023-2023, 2023
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This study presents a long-term data set of monthly mean total column water vapour (TCWV) based on measurements of the Ozone Monitoring Instrument (OMI) covering the time range from January 2005 to December 2020. We describe how the TCWV values are retrieved from UV–Vis satellite spectra and demonstrate that the OMI TCWV data set is in good agreement with various different reference data sets. Moreover, we also show that it fulfills typical stability requirements for climate data records.
Jonathan Demaeyer, Jonas Bhend, Sebastian Lerch, Cristina Primo, Bert Van Schaeybroeck, Aitor Atencia, Zied Ben Bouallègue, Jieyu Chen, Markus Dabernig, Gavin Evans, Jana Faganeli Pucer, Ben Hooper, Nina Horat, David Jobst, Janko Merše, Peter Mlakar, Annette Möller, Olivier Mestre, Maxime Taillardat, and Stéphane Vannitsem
Earth Syst. Sci. Data, 15, 2635–2653, https://doi.org/10.5194/essd-15-2635-2023, https://doi.org/10.5194/essd-15-2635-2023, 2023
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A benchmark dataset is proposed to compare different statistical postprocessing methods used in forecasting centers to properly calibrate ensemble weather forecasts. This dataset is based on ensemble forecasts covering a portion of central Europe and includes the corresponding observations. Examples on how to download and use the data are provided, a set of evaluation methods is proposed, and a first benchmark of several methods for the correction of 2 m temperature forecasts is performed.
Dirk Nikolaus Karger, Stefan Lange, Chantal Hari, Christopher P. O. Reyer, Olaf Conrad, Niklaus E. Zimmermann, and Katja Frieler
Earth Syst. Sci. Data, 15, 2445–2464, https://doi.org/10.5194/essd-15-2445-2023, https://doi.org/10.5194/essd-15-2445-2023, 2023
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We present the first 1 km, daily, global climate dataset for climate impact studies. We show that the high-resolution data have a decreased bias and higher correlation with measurements from meteorological stations than coarser data. The dataset will be of value for a wide range of climate change impact studies both at global and regional level that benefit from using a consistent global dataset.
Elisa Adirosi, Federico Porcù, Mario Montopoli, Luca Baldini, Alessandro Bracci, Vincenzo Capozzi, Clizia Annella, Giorgio Budillon, Edoardo Bucchignani, Alessandra Lucia Zollo, Orietta Cazzuli, Giulio Camisani, Renzo Bechini, Roberto Cremonini, Andrea Antonini, Alberto Ortolani, Samantha Melani, Paolo Valisa, and Simone Scapin
Earth Syst. Sci. Data, 15, 2417–2429, https://doi.org/10.5194/essd-15-2417-2023, https://doi.org/10.5194/essd-15-2417-2023, 2023
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The paper describes the database of 1 min drop size distribution (DSD) of atmospheric precipitation collected by the Italian disdrometer network over the last 10 years. These data are useful for several applications that range from climatological, meteorological and hydrological uses to telecommunications, agriculture and conservation of cultural heritage exposed to precipitation. Descriptions of the processing and of the database organization, along with some examples, are provided.
Jinfang Yin, Xudong Liang, Yanxin Xie, Feng Li, Kaixi Hu, Lijuan Cao, Feng Chen, Haibo Zou, Feng Zhu, Xin Sun, Jianjun Xu, Geli Wang, Ying Zhao, and Juanjuan Liu
Earth Syst. Sci. Data, 15, 2329–2346, https://doi.org/10.5194/essd-15-2329-2023, https://doi.org/10.5194/essd-15-2329-2023, 2023
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A collection of regional reanalysis datasets has been produced. However, little attention has been paid to East Asia, and there are no long-term, physically consistent regional reanalysis data available. The East Asia Reanalysis System was developed using the WRF model and GSI data assimilation system. A 39-year (1980–2018) reanalysis dataset is available for the East Asia region, at a high temporal (of 3 h) and spatial resolution (of 12 km), for mesoscale weather and regional climate studies.
John Erik Engström, Lennart Wern, Sverker Hellström, Erik Kjellström, Chunlüe Zhou, Deliang Chen, and Cesar Azorin-Molina
Earth Syst. Sci. Data, 15, 2259–2277, https://doi.org/10.5194/essd-15-2259-2023, https://doi.org/10.5194/essd-15-2259-2023, 2023
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Newly digitized wind speed observations provide data from the time period from around 1920 to the present, enveloping one full century of wind measurements. The results of this work enable the investigation of the historical variability and trends in surface wind speed in Sweden for
the last century.
Ulrike Herzschuh, Thomas Böhmer, Chenzhi Li, Manuel Chevalier, Raphaël Hébert, Anne Dallmeyer, Xianyong Cao, Nancy H. Bigelow, Larisa Nazarova, Elena Y. Novenko, Jungjae Park, Odile Peyron, Natalia A. Rudaya, Frank Schlütz, Lyudmila S. Shumilovskikh, Pavel E. Tarasov, Yongbo Wang, Ruilin Wen, Qinghai Xu, and Zhuo Zheng
Earth Syst. Sci. Data, 15, 2235–2258, https://doi.org/10.5194/essd-15-2235-2023, https://doi.org/10.5194/essd-15-2235-2023, 2023
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Climate reconstruction from proxy data can help evaluate climate models. We present pollen-based reconstructions of mean July temperature, mean annual temperature, and annual precipitation from 2594 pollen records from the Northern Hemisphere, using three reconstruction methods (WA-PLS, WA-PLS_tailored, and MAT). Since no global or hemispheric synthesis of quantitative precipitation changes are available for the Holocene so far, this dataset will be of great value to the geoscientific community.
Aart Overeem, Else van den Besselaar, Gerard van der Schrier, Jan Fokke Meirink, Emiel van der Plas, and Hidde Leijnse
Earth Syst. Sci. Data, 15, 1441–1464, https://doi.org/10.5194/essd-15-1441-2023, https://doi.org/10.5194/essd-15-1441-2023, 2023
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EURADCLIM is a new precipitation dataset covering a large part of Europe. It is based on weather radar data to provide local precipitation information every hour and combined with rain gauge data to obtain good precipitation estimates. EURADCLIM provides a much better reference for validation of weather model output and satellite precipitation datasets. It also allows for climate monitoring and better evaluation of extreme precipitation events and their impact (landslides, flooding).
Alfonso Ferrone and Alexis Berne
Earth Syst. Sci. Data, 15, 1115–1132, https://doi.org/10.5194/essd-15-1115-2023, https://doi.org/10.5194/essd-15-1115-2023, 2023
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This article presents the datasets collected between November 2019 and February 2020 in the vicinity of the Belgian research base Princess Elisabeth Antarctica. Five meteorological radars, a multi-angle snowflake camera, three weather stations, and two radiometers have been deployed at five sites, up to a maximum distance of 30 km from the base. Their varied locations allow the study of spatial variability in snowfall and its interaction with the complex terrain in the region.
José Dias Neto, Louise Nuijens, Christine Unal, and Steven Knoop
Earth Syst. Sci. Data, 15, 769–789, https://doi.org/10.5194/essd-15-769-2023, https://doi.org/10.5194/essd-15-769-2023, 2023
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This paper describes a dataset from a novel experimental setup to retrieve wind speed and direction profiles, combining cloud radars and wind lidar. This setup allows retrieving profiles from near the surface to the top of clouds. The field campaign occurred in Cabauw, the Netherlands, between September 13th and October 3rd 2021. This paper also provides examples of applications of this dataset (e.g. studying atmospheric turbulence, validating numerical atmospheric models).
Peng Yuan, Geoffrey Blewitt, Corné Kreemer, William C. Hammond, Donald Argus, Xungang Yin, Roeland Van Malderen, Michael Mayer, Weiping Jiang, Joseph Awange, and Hansjörg Kutterer
Earth Syst. Sci. Data, 15, 723–743, https://doi.org/10.5194/essd-15-723-2023, https://doi.org/10.5194/essd-15-723-2023, 2023
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We developed a 5 min global integrated water vapour (IWV) product from 12 552 ground-based GPS stations in 2020. It contains more than 1 billion IWV estimates. The dataset is an enhanced version of the existing operational GPS IWV dataset from the Nevada Geodetic Laboratory. The enhancement is reached by using accurate meteorological information from ERA5 for the GPS IWV retrieval with a significantly higher spatiotemporal resolution. The dataset is recommended for high-accuracy applications.
Yaozhi Jiang, Kun Yang, Youcun Qi, Xu Zhou, Jie He, Hui Lu, Xin Li, Yingying Chen, Xiaodong Li, Bingrong Zhou, Ali Mamtimin, Changkun Shao, Xiaogang Ma, Jiaxin Tian, and Jianhong Zhou
Earth Syst. Sci. Data, 15, 621–638, https://doi.org/10.5194/essd-15-621-2023, https://doi.org/10.5194/essd-15-621-2023, 2023
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Our work produces a long-term (1979–2020) high-resolution (1/30°, daily) precipitation dataset for the Third Pole (TP) region by merging an advanced atmospheric simulation with high-density rain gauge (more than 9000) observations. Validation shows that the produced dataset performs better than the currently widely used precipitation datasets in the TP. This dataset can be used for hydrological, meteorological and ecological studies in the TP.
Yetang Wang, Xueying Zhang, Wentao Ning, Matthew A. Lazzara, Minghu Ding, Carleen H. Reijmer, Paul C. J. P. Smeets, Paolo Grigioni, Petra Heil, Elizabeth R. Thomas, David Mikolajczyk, Lee J. Welhouse, Linda M. Keller, Zhaosheng Zhai, Yuqi Sun, and Shugui Hou
Earth Syst. Sci. Data, 15, 411–429, https://doi.org/10.5194/essd-15-411-2023, https://doi.org/10.5194/essd-15-411-2023, 2023
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Here we construct a new database of Antarctic automatic weather station (AWS) meteorological records, which is quality-controlled by restrictive criteria. This dataset compiled all available Antarctic AWS observations, and its resolutions are 3-hourly, daily and monthly, which is very useful for quantifying spatiotemporal variability in weather conditions. Furthermore, this compilation will be used to estimate the performance of the regional climate models or meteorological reanalysis products.
Jun Qin, Weihao Pan, Min He, Ning Lu, Ling Yao, Hou Jiang, and Chenghu Zhou
Earth Syst. Sci. Data, 15, 331–344, https://doi.org/10.5194/essd-15-331-2023, https://doi.org/10.5194/essd-15-331-2023, 2023
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To enrich a glacial surface air temperature (SAT) product of a long time series, an ensemble learning model is constructed to estimate monthly SATs from satellite land surface temperatures at a spatial resolution of 1 km, and long-term glacial SATs from 1961 to 2020 are reconstructed using a Bayesian linear regression. This product reveals the overall warming trend and the spatial heterogeneity of warming on TP glaciers and helps to monitor glacier warming, analyze glacier evolution, etc.
Tao Zhang, Yuyu Zhou, Kaiguang Zhao, Zhengyuan Zhu, Gang Chen, Jia Hu, and Li Wang
Earth Syst. Sci. Data, 14, 5637–5649, https://doi.org/10.5194/essd-14-5637-2022, https://doi.org/10.5194/essd-14-5637-2022, 2022
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We generated a global 1 km daily maximum and minimum near-surface air temperature (Tmax and Tmin) dataset (2003–2020) using a novel statistical model. The average root mean square errors ranged from 1.20 to 2.44 °C for Tmax and 1.69 to 2.39 °C for Tmin. The gridded global air temperature dataset is of great use in a variety of studies such as the urban heat island phenomenon, hydrological modeling, and epidemic forecasting.
Benjamin Fersch, Andreas Wagner, Bettina Kamm, Endrit Shehaj, Andreas Schenk, Peng Yuan, Alain Geiger, Gregor Moeller, Bernhard Heck, Stefan Hinz, Hansjörg Kutterer, and Harald Kunstmann
Earth Syst. Sci. Data, 14, 5287–5307, https://doi.org/10.5194/essd-14-5287-2022, https://doi.org/10.5194/essd-14-5287-2022, 2022
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In this study, a comprehensive multi-disciplinary dataset for tropospheric water vapor was developed. Geodetic, photogrammetric, and atmospheric modeling and data fusion techniques were used to obtain maps of water vapor in a high spatial and temporal resolution. It could be shown that regional weather simulations for different seasons benefit from assimilating these maps and that the combination of the different observation techniques led to positive synergies.
Craig D. Smith, Eva Mekis, Megan Hartwell, and Amber Ross
Earth Syst. Sci. Data, 14, 5253–5265, https://doi.org/10.5194/essd-14-5253-2022, https://doi.org/10.5194/essd-14-5253-2022, 2022
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It is well understood that precipitation gauges underestimate the measurement of solid precipitation (snow) as a result of systematic bias caused by wind. Relationships between the wind speed and gauge catch efficiency of solid precipitation have been previously established and are applied to the hourly precipitation measurements made between 2001 and 2019 in the automated Environment and Climate Change Canada observation network. The adjusted data are available for download and use.
Zen Mariani, Laura Huang, Robert Crawford, Jean-Pierre Blanchet, Shannon Hicks-Jalali, Eva Mekis, Ludovick Pelletier, Peter Rodriguez, and Kevin Strawbridge
Earth Syst. Sci. Data, 14, 4995–5017, https://doi.org/10.5194/essd-14-4995-2022, https://doi.org/10.5194/essd-14-4995-2022, 2022
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Environment and Climate Change Canada (ECCC) commissioned two supersites in Iqaluit (64°N, 69°W) and Whitehorse (61°N, 135°W) to provide new and enhanced automated and continuous altitude-resolved meteorological observations as part of the Canadian Arctic Weather Science (CAWS) project. These observations are being used to test new technologies, provide recommendations to the optimal Arctic observing system, and evaluate and improve the performance of numerical weather forecast systems.
Eva Beele, Maarten Reyniers, Raf Aerts, and Ben Somers
Earth Syst. Sci. Data, 14, 4681–4717, https://doi.org/10.5194/essd-14-4681-2022, https://doi.org/10.5194/essd-14-4681-2022, 2022
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This paper presents crowdsourced data from the Leuven.cool network, a citizen science network of around 100 low-cost weather stations distributed across Leuven, Belgium. The temperature data have undergone a quality control (QC) and correction procedure. The procedure consists of three levels that remove implausible measurements while also correcting for between-station and station-specific temperature biases.
Auguste Gires, Jerry Jose, Ioulia Tchiguirinskaia, and Daniel Schertzer
Earth Syst. Sci. Data, 14, 3807–3819, https://doi.org/10.5194/essd-14-3807-2022, https://doi.org/10.5194/essd-14-3807-2022, 2022
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The Hydrology Meteorology and Complexity laboratory of École des Ponts ParisTech (https://hmco.enpc.fr) has made a data set of high-resolution atmospheric measurements (rainfall, wind, temperature, pressure, and humidity) available. It comes from a campaign carried out on a meteorological mast located on a wind farm in the framework of the Rainfall Wind Turbine or Turbulence project (RW-Turb; supported by the French National Research Agency – ANR-19-CE05-0022).
Bastian Kirsch, Cathy Hohenegger, Daniel Klocke, Rainer Senke, Michael Offermann, and Felix Ament
Earth Syst. Sci. Data, 14, 3531–3548, https://doi.org/10.5194/essd-14-3531-2022, https://doi.org/10.5194/essd-14-3531-2022, 2022
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Conventional observation networks are too coarse to resolve the horizontal structure of kilometer-scale atmospheric processes. We present the FESST@HH field experiment that took place in Hamburg (Germany) during summer 2020 and featured a dense network of 103 custom-built, low-cost weather stations. The data set is capable of providing new insights into the structure of convective cold pools and the nocturnal urban heat island and variations of local temperature fluctuations.
Fan Mei, Mikhail S. Pekour, Darielle Dexheimer, Gijs de Boer, RaeAnn Cook, Jason Tomlinson, Beat Schmid, Lexie A. Goldberger, Rob Newsom, and Jerome D. Fast
Earth Syst. Sci. Data, 14, 3423–3438, https://doi.org/10.5194/essd-14-3423-2022, https://doi.org/10.5194/essd-14-3423-2022, 2022
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This work focuses on an expanding number of data sets observed using ARM TBS (133 flights) and UAS (seven flights) platforms by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility. These data streams provide new perspectives on spatial variability of atmospheric and surface parameters, helping to address critical science questions in Earth system science research, such as the aerosol–cloud interaction in the boundary layer.
Falu Hong, Wenfeng Zhan, Frank-M. Göttsche, Zihan Liu, Pan Dong, Huyan Fu, Fan Huang, and Xiaodong Zhang
Earth Syst. Sci. Data, 14, 3091–3113, https://doi.org/10.5194/essd-14-3091-2022, https://doi.org/10.5194/essd-14-3091-2022, 2022
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Daily mean land surface temperature (LST) acquired from satellite thermal sensors is crucial for various applications such as global and regional climate change analysis. This study proposed a framework to generate global spatiotemporally seamless daily mean LST products (2003–2019). Validations show that the products outperform the traditional method with satisfying accuracy. Our further analysis reveals that the LST-based global land surface warming rate is 0.029 K yr−1 from 2003 to 2019.
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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