Articles | Volume 12, issue 4
https://doi.org/10.5194/essd-12-2635-2020
© Author(s) 2020. 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-12-2635-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
31 Oct 2020
Data description paper |
| 31 Oct 2020
| 31 Oct 2020
A dataset of microclimate and radiation and energy fluxes from the Lake Taihu eddy flux network
Zhen Zhang
Yale-NUIST Center on Atmospheric Environment, International Joint
Laboratory on Climate and Environment Change (ILCEC), Nanjing University of
Information Science and Technology, Nanjing, Jiangsu Province, China
Key Laboratory of Meteorological Disaster, Ministry of Education and
Collaborative Innovation Center on Forecast and Evaluation of Meteorological
Disasters, Nanjing University of Information Science and Technology,
Nanjing, Jiangsu Province, China
Mi Zhang
Yale-NUIST Center on Atmospheric Environment, International Joint
Laboratory on Climate and Environment Change (ILCEC), Nanjing University of
Information Science and Technology, Nanjing, Jiangsu Province, China
Key Laboratory of Meteorological Disaster, Ministry of Education and
Collaborative Innovation Center on Forecast and Evaluation of Meteorological
Disasters, Nanjing University of Information Science and Technology,
Nanjing, Jiangsu Province, China
NUIST-Wuxi Research Institute, Wuxi, Jiangsu Province, China
Chang Cao
Yale-NUIST Center on Atmospheric Environment, International Joint
Laboratory on Climate and Environment Change (ILCEC), Nanjing University of
Information Science and Technology, Nanjing, Jiangsu Province, China
Key Laboratory of Meteorological Disaster, Ministry of Education and
Collaborative Innovation Center on Forecast and Evaluation of Meteorological
Disasters, Nanjing University of Information Science and Technology,
Nanjing, Jiangsu Province, China
Wei Wang
Yale-NUIST Center on Atmospheric Environment, International Joint
Laboratory on Climate and Environment Change (ILCEC), Nanjing University of
Information Science and Technology, Nanjing, Jiangsu Province, China
Key Laboratory of Meteorological Disaster, Ministry of Education and
Collaborative Innovation Center on Forecast and Evaluation of Meteorological
Disasters, Nanjing University of Information Science and Technology,
Nanjing, Jiangsu Province, China
Wei Xiao
Yale-NUIST Center on Atmospheric Environment, International Joint
Laboratory on Climate and Environment Change (ILCEC), Nanjing University of
Information Science and Technology, Nanjing, Jiangsu Province, China
Key Laboratory of Meteorological Disaster, Ministry of Education and
Collaborative Innovation Center on Forecast and Evaluation of Meteorological
Disasters, Nanjing University of Information Science and Technology,
Nanjing, Jiangsu Province, China
NUIST-Wuxi Research Institute, Wuxi, Jiangsu Province, China
Chengyu Xie
Yale-NUIST Center on Atmospheric Environment, International Joint
Laboratory on Climate and Environment Change (ILCEC), Nanjing University of
Information Science and Technology, Nanjing, Jiangsu Province, China
Key Laboratory of Meteorological Disaster, Ministry of Education and
Collaborative Innovation Center on Forecast and Evaluation of Meteorological
Disasters, Nanjing University of Information Science and Technology,
Nanjing, Jiangsu Province, China
Haoran Chu
Yale-NUIST Center on Atmospheric Environment, International Joint
Laboratory on Climate and Environment Change (ILCEC), Nanjing University of
Information Science and Technology, Nanjing, Jiangsu Province, China
Key Laboratory of Meteorological Disaster, Ministry of Education and
Collaborative Innovation Center on Forecast and Evaluation of Meteorological
Disasters, Nanjing University of Information Science and Technology,
Nanjing, Jiangsu Province, China
Jiao Wang
Yale-NUIST Center on Atmospheric Environment, International Joint
Laboratory on Climate and Environment Change (ILCEC), Nanjing University of
Information Science and Technology, Nanjing, Jiangsu Province, China
Key Laboratory of Meteorological Disaster, Ministry of Education and
Collaborative Innovation Center on Forecast and Evaluation of Meteorological
Disasters, Nanjing University of Information Science and Technology,
Nanjing, Jiangsu Province, China
Jiayu Zhao
Yale-NUIST Center on Atmospheric Environment, International Joint
Laboratory on Climate and Environment Change (ILCEC), Nanjing University of
Information Science and Technology, Nanjing, Jiangsu Province, China
Key Laboratory of Meteorological Disaster, Ministry of Education and
Collaborative Innovation Center on Forecast and Evaluation of Meteorological
Disasters, Nanjing University of Information Science and Technology,
Nanjing, Jiangsu Province, China
Lei Jia
Yale-NUIST Center on Atmospheric Environment, International Joint
Laboratory on Climate and Environment Change (ILCEC), Nanjing University of
Information Science and Technology, Nanjing, Jiangsu Province, China
Key Laboratory of Meteorological Disaster, Ministry of Education and
Collaborative Innovation Center on Forecast and Evaluation of Meteorological
Disasters, Nanjing University of Information Science and Technology,
Nanjing, Jiangsu Province, China
Qiang Liu
Yale-NUIST Center on Atmospheric Environment, International Joint
Laboratory on Climate and Environment Change (ILCEC), Nanjing University of
Information Science and Technology, Nanjing, Jiangsu Province, China
Key Laboratory of Meteorological Disaster, Ministry of Education and
Collaborative Innovation Center on Forecast and Evaluation of Meteorological
Disasters, Nanjing University of Information Science and Technology,
Nanjing, Jiangsu Province, China
Wenjing Huang
Yale-NUIST Center on Atmospheric Environment, International Joint
Laboratory on Climate and Environment Change (ILCEC), Nanjing University of
Information Science and Technology, Nanjing, Jiangsu Province, China
Key Laboratory of Meteorological Disaster, Ministry of Education and
Collaborative Innovation Center on Forecast and Evaluation of Meteorological
Disasters, Nanjing University of Information Science and Technology,
Nanjing, Jiangsu Province, China
Wenqing Zhang
Yale-NUIST Center on Atmospheric Environment, International Joint
Laboratory on Climate and Environment Change (ILCEC), Nanjing University of
Information Science and Technology, Nanjing, Jiangsu Province, China
Key Laboratory of Meteorological Disaster, Ministry of Education and
Collaborative Innovation Center on Forecast and Evaluation of Meteorological
Disasters, Nanjing University of Information Science and Technology,
Nanjing, Jiangsu Province, China
Yang Lu
Yale-NUIST Center on Atmospheric Environment, International Joint
Laboratory on Climate and Environment Change (ILCEC), Nanjing University of
Information Science and Technology, Nanjing, Jiangsu Province, China
Key Laboratory of Meteorological Disaster, Ministry of Education and
Collaborative Innovation Center on Forecast and Evaluation of Meteorological
Disasters, Nanjing University of Information Science and Technology,
Nanjing, Jiangsu Province, China
Yanhong Xie
Yale-NUIST Center on Atmospheric Environment, International Joint
Laboratory on Climate and Environment Change (ILCEC), Nanjing University of
Information Science and Technology, Nanjing, Jiangsu Province, China
Key Laboratory of Meteorological Disaster, Ministry of Education and
Collaborative Innovation Center on Forecast and Evaluation of Meteorological
Disasters, Nanjing University of Information Science and Technology,
Nanjing, Jiangsu Province, China
Yi Wang
Yale-NUIST Center on Atmospheric Environment, International Joint
Laboratory on Climate and Environment Change (ILCEC), Nanjing University of
Information Science and Technology, Nanjing, Jiangsu Province, China
Key Laboratory of Meteorological Disaster, Ministry of Education and
Collaborative Innovation Center on Forecast and Evaluation of Meteorological
Disasters, Nanjing University of Information Science and Technology,
Nanjing, Jiangsu Province, China
Yini Pu
Yale-NUIST Center on Atmospheric Environment, International Joint
Laboratory on Climate and Environment Change (ILCEC), Nanjing University of
Information Science and Technology, Nanjing, Jiangsu Province, China
Key Laboratory of Meteorological Disaster, Ministry of Education and
Collaborative Innovation Center on Forecast and Evaluation of Meteorological
Disasters, Nanjing University of Information Science and Technology,
Nanjing, Jiangsu Province, China
Yongbo Hu
Yale-NUIST Center on Atmospheric Environment, International Joint
Laboratory on Climate and Environment Change (ILCEC), Nanjing University of
Information Science and Technology, Nanjing, Jiangsu Province, China
Key Laboratory of Meteorological Disaster, Ministry of Education and
Collaborative Innovation Center on Forecast and Evaluation of Meteorological
Disasters, Nanjing University of Information Science and Technology,
Nanjing, Jiangsu Province, China
Zheng Chen
Yale-NUIST Center on Atmospheric Environment, International Joint
Laboratory on Climate and Environment Change (ILCEC), Nanjing University of
Information Science and Technology, Nanjing, Jiangsu Province, China
Key Laboratory of Meteorological Disaster, Ministry of Education and
Collaborative Innovation Center on Forecast and Evaluation of Meteorological
Disasters, Nanjing University of Information Science and Technology,
Nanjing, Jiangsu Province, China
Zhihao Qin
Yale-NUIST Center on Atmospheric Environment, International Joint
Laboratory on Climate and Environment Change (ILCEC), Nanjing University of
Information Science and Technology, Nanjing, Jiangsu Province, China
Key Laboratory of Meteorological Disaster, Ministry of Education and
Collaborative Innovation Center on Forecast and Evaluation of Meteorological
Disasters, Nanjing University of Information Science and Technology,
Nanjing, Jiangsu Province, China
Xuhui Lee
CORRESPONDING AUTHOR
School of the Environment, Yale University, New Haven, CT 06511, USA
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Tao Zhang, Yuyu Zhou, Kaiguang Zhao, Zhengyuan Zhu, Gang Chen, Jia Hu, and Li Wang
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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.
Jianquan Dong, Stefan Brönnimann, Tao Hu, Yanxu Liu, and Jian Peng
Earth Syst. Sci. Data, 14, 5651–5664, https://doi.org/10.5194/essd-14-5651-2022, https://doi.org/10.5194/essd-14-5651-2022, 2022
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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.
Minghu Ding, Xiaowei Zou, Qizhen Sun, Diyi Yang, Wenqian Zhang, Lingen Bian, Changgui Lu, Ian Allison, Petra Heil, and Cunde Xiao
Earth Syst. Sci. Data, 14, 5019–5035, https://doi.org/10.5194/essd-14-5019-2022, https://doi.org/10.5194/essd-14-5019-2022, 2022
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The PANDA automatic weather station (AWS) network consists of 11 stations deployed along a transect from the coast (Zhongshan Station) to the summit of the East Antarctic Ice Sheet (Dome A). It covers the different climatic and topographic units of East Antarctica. All stations record hourly air temperature, relative humidity, air pressure, wind speed and direction at two or three heights. The PANDA AWS dataset commences from 1989 and is planned to be publicly available into the future.
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.
Aart Overeem, Else van den Besselaar, Gerard van der Schrier, Jan Fokke Meirink, Emiel van der Plas, and Hidde Leijnse
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-334, https://doi.org/10.5194/essd-2022-334, 2022
Revised manuscript accepted for ESSD
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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 for better evaluation of extreme precipitation events and their impact (landslides, flooding).
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.
Vera Thiemig, Goncalo N. Gomes, Jon O. Skøien, Markus Ziese, Armin Rauthe-Schöch, Elke Rustemeier, Kira Rehfeldt, Jakub P. Walawender, Christine Kolbe, Damien Pichon, Christoph Schweim, and Peter Salamon
Earth Syst. Sci. Data, 14, 3249–3272, https://doi.org/10.5194/essd-14-3249-2022, https://doi.org/10.5194/essd-14-3249-2022, 2022
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EMO-5 is a free and open European high-resolution (5 km), sub-daily, multi-variable (precipitation, temperatures, wind speed, solar radiation, vapour pressure), multi-decadal meteorological dataset based on quality-controlled observations coming from almost 30 000 stations across Europe, and is produced in near real-time. EMO-5 (v1) covers the time period from 1990 to 2019. In this paper, we have provided insight into the source data, the applied methods, and the quality assessment of EMO-5.
Qian He, Ming Wang, Kai Liu, Kaiwen Li, and Ziyu Jiang
Earth Syst. Sci. Data, 14, 3273–3292, https://doi.org/10.5194/essd-14-3273-2022, https://doi.org/10.5194/essd-14-3273-2022, 2022
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We used three machine learning models and determined that Gaussian process regression (GPR) is best suited to the interpolation of air temperature data for China. The GPR-derived results were compared with that of traditional interpolation techniques and existing data sets and it was found that the accuracy of the GPR-derived data was better. Finally, we generated a gridded monthly air temperature data set with 1 km resolution and high accuracy for China (1951–2020) using the GPR model.
Yinghong Jing, Xinghua Li, and Huanfeng Shen
Earth Syst. Sci. Data, 14, 3137–3156, https://doi.org/10.5194/essd-14-3137-2022, https://doi.org/10.5194/essd-14-3137-2022, 2022
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Snow variation is a vital factor in global climate change. Satellite-based approaches are effective for large-scale environmental monitoring. Nevertheless, the high cloud fraction seriously impedes the remote-sensed investigation. Therefore, a recent 20-year cloud-free snow cover collection in China is generated for the first time. This collection can serve as a basic dataset for hydrological and climatic modeling to explore various critical environmental issues.
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.
Sébastien Doutreloup, Xavier Fettweis, Ramin Rahif, Essam Elnagar, Mohsen S. Pourkiaei, Deepak Amaripadath, and Shady Attia
Earth Syst. Sci. Data, 14, 3039–3051, https://doi.org/10.5194/essd-14-3039-2022, https://doi.org/10.5194/essd-14-3039-2022, 2022
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This data set provides historical (1980–2014) and future (2015–2100) weather data for 12 cities in Belgium. This data set is intended for architects or building or energy designers. In particular, it makes available to all users hourly open-access weather data according to certain standards to recreate a Typical and an Extreme Meteorological Year. In addition, it provides hourly data on heatwaves from 1980 to 2100. Weather data were produced from the outputs of the MAR model simulations.
Hannah C. Bloomfield, David J. Brayshaw, Matthew Deakin, and David Greenwood
Earth Syst. Sci. Data, 14, 2749–2766, https://doi.org/10.5194/essd-14-2749-2022, https://doi.org/10.5194/essd-14-2749-2022, 2022
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There is a global increase in renewable generation to meet carbon targets and reduce the impacts of climate change. Renewable generation and electricity demand depend on the weather. This means there is a need for high-quality weather data for energy system modelling. We present a new European-level, 70-year dataset which has been specifically designed to support the energy sector. We provide hourly, sub-national climate outputs and include the impacts of near-term climate change.
Yuan Qiu, Jinming Feng, Zhongwei Yan, and Jun Wang
Earth Syst. Sci. Data, 14, 2195–2208, https://doi.org/10.5194/essd-14-2195-2022, https://doi.org/10.5194/essd-14-2195-2022, 2022
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A high-resolution climate projection dataset in central Asia, named the HCPD-CA dataset, is derived from the dynamically downscaled results based on three bias-corrected global climate models and contains 4 geostatic variables and 10 meteorological elements that are widely used to drive ecological and hydrological models. This dataset can serve as a scientific basis for assessing the potential impacts of projected climate changes over central Asia on many sectors.
Eun-Gyeong Yang, Hyun Mee Kim, and Dae-Hui Kim
Earth Syst. Sci. Data, 14, 2109–2127, https://doi.org/10.5194/essd-14-2109-2022, https://doi.org/10.5194/essd-14-2109-2022, 2022
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The East Asia Regional Reanalysis (EARR) system is developed based on the advanced hybrid gain data assimilation method (AdvHG) using the Weather Research and Forecasting (WRF) model and conventional observations. Based on EARR, high-resolution regional reanalysis and reforecast fields are produced with 12 km horizontal resolution over East Asia for the period 2010–2019. Compared to ERA5, EARR represents precipitation better for January and July 2017 over East Asia.
Sandrine Bony, Marie Lothon, Julien Delanoë, Pierre Coutris, Jean-Claude Etienne, Franziska Aemisegger, Anna Lea Albright, Thierry André, Hubert Bellec, Alexandre Baron, Jean-François Bourdinot, Pierre-Etienne Brilouet, Aurélien Bourdon, Jean-Christophe Canonici, Christophe Caudoux, Patrick Chazette, Michel Cluzeau, Céline Cornet, Jean-Philippe Desbios, Dominique Duchanoy, Cyrille Flamant, Benjamin Fildier, Christophe Gourbeyre, Laurent Guiraud, Tetyana Jiang, Claude Lainard, Christophe Le Gac, Christian Lendroit, Julien Lernould, Thierry Perrin, Frédéric Pouvesle, Pascal Richard, Nicolas Rochetin, Kevin Salaün, Alfons Schwarzenboeck, Guillaume Seurat, Bjorn Stevens, Julien Totems, Ludovic Touzé-Peiffer, Gilles Vergez, Jessica Vial, Leonie Villiger, and Raphaela Vogel
Earth Syst. Sci. Data, 14, 2021–2064, https://doi.org/10.5194/essd-14-2021-2022, https://doi.org/10.5194/essd-14-2021-2022, 2022
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The French ATR42 research aircraft participated in the EUREC4A international field campaign that took place in 2020 over the tropical Atlantic, east of Barbados. We present the extensive instrumentation of the aircraft, the research flights and the different measurements. We show that the ATR measurements of humidity, wind, aerosols and cloudiness in the lower atmosphere are robust and consistent with each other. They will make it possible to advance understanding of cloud–climate interactions.
Janosch Michaelis, Amelie U. Schmitt, Christof Lüpkes, Jörg Hartmann, Gerit Birnbaum, and Timo Vihma
Earth Syst. Sci. Data, 14, 1621–1637, https://doi.org/10.5194/essd-14-1621-2022, https://doi.org/10.5194/essd-14-1621-2022, 2022
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A major goal of the Springtime Atmospheric Boundary Layer Experiment (STABLE) aircraft campaign was to observe atmospheric conditions during marine cold-air outbreaks (MCAOs) originating from the sea-ice-covered Arctic ocean. Quality-controlled measurements of several meteorological variables collected during 15 vertical aircraft profiles and by 22 dropsondes are presented. The comprehensive data set may be used for validating model results to improve the understanding of future trends in MCAOs.
Christophe Genthon, Dana E. Veron, Etienne Vignon, Jean-Baptiste Madeleine, and Luc Piard
Earth Syst. Sci. Data, 14, 1571–1580, https://doi.org/10.5194/essd-14-1571-2022, https://doi.org/10.5194/essd-14-1571-2022, 2022
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The surface atmosphere of the high Antarctic Plateau is very cold and clean. Such conditions favor water vapor supersaturation. A 3-year quasi-continuous series of atmospheric moisture in a ~40 m atmospheric layer at Dome C is reported that documents time variability, vertical profiles and occurrences of supersaturation. Supersaturation with respect to ice is frequently observed throughout the column, with relative humidities occasionally reaching values near liquid water saturation.
Aoqi Zhang, Chen Chen, Yilun Chen, Weibiao Li, Shumin Chen, and Yunfei Fu
Earth Syst. Sci. Data, 14, 1433–1445, https://doi.org/10.5194/essd-14-1433-2022, https://doi.org/10.5194/essd-14-1433-2022, 2022
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We constructed an event-based precipitation dataset with life cycle evolution based on coordinated application of observations from spaceborne active precipitation radar and geostationary satellites. The dataset provides both three-dimensional structures of the precipitation system and its corresponding life cycle evolution. The dataset greatly reduces the data size and avoids complex data processing algorithms for studying the life cycle evolution of precipitation microphysics.
Shu Fang, Kebiao Mao, Xueqi Xia, Ping Wang, Jiancheng Shi, Sayed M. Bateni, Tongren Xu, Mengmeng Cao, Essam Heggy, and Zhihao Qin
Earth Syst. Sci. Data, 14, 1413–1432, https://doi.org/10.5194/essd-14-1413-2022, https://doi.org/10.5194/essd-14-1413-2022, 2022
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Air temperature is an important parameter reflecting climate change, and the current method of obtaining daily temperature is affected by many factors. In this study, we constructed a temperature model based on weather conditions and established a correction equation. The dataset of daily air temperature (Tmax, Tmin, and Tavg) in China from 1979 to 2018 was obtained with a spatial resolution of 0.1°. Accuracy verification shows that the dataset has reliable accuracy and high spatial resolution.
Hauke Schulz
Earth Syst. Sci. Data, 14, 1233–1256, https://doi.org/10.5194/essd-14-1233-2022, https://doi.org/10.5194/essd-14-1233-2022, 2022
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Trade wind clouds are often organized on the mesoscale (O(100 km)), forming different cloud patterns. We present C3ONTEXT (a Common Consensus on Convective OrgaNizaTion during the EUREC4A eXperimenT), a dataset that contains information about the mesoscale cloud patterns identified during the EUREC4A (Elucidating the role of clouds–circulation coupling in climate) field campaign in January–February 2020 and thereby provide the mesoscale context for the campaign's measurements.
Karl Lapo, Anita Freundorfer, Antonia Fritz, Johann Schneider, Johannes Olesch, Wolfgang Babel, and Christoph K. Thomas
Earth Syst. Sci. Data, 14, 885–906, https://doi.org/10.5194/essd-14-885-2022, https://doi.org/10.5194/essd-14-885-2022, 2022
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The layer of air near the surface is poorly understood during conditions with weak winds. Further, it is even difficult to observe. In this experiment we used distributed temperature sensing to observe air temperature and wind speed at thousands of points simultaneously every couple of seconds. This incredibly rich data set can be used to examine and understand what drives the mixing between the atmosphere and surface during these weak-wind periods.
Qian Ma, Kaicun Wang, Yanyi He, Liangyuan Su, Qizhong Wu, Han Liu, and Youren Zhang
Earth Syst. Sci. Data, 14, 463–477, https://doi.org/10.5194/essd-14-463-2022, https://doi.org/10.5194/essd-14-463-2022, 2022
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Surface incident solar radiation plays a key role in atmospheric circulation, the water cycle, and ecological equilibrium on Earth. A homogenized century-long surface incident solar radiation dataset was obtained over Japan.
Claudia Acquistapace, Richard Coulter, Susanne Crewell, Albert Garcia-Benadi, Rosa Gierens, Giacomo Labbri, Alexander Myagkov, Nils Risse, and Jan H. Schween
Earth Syst. Sci. Data, 14, 33–55, https://doi.org/10.5194/essd-14-33-2022, https://doi.org/10.5194/essd-14-33-2022, 2022
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This publication describes the unprecedented high-resolution cloud and precipitation dataset collected by two radars deployed on the Maria S. Merian research vessel. The ship operated in the west Atlantic Ocean during the measurement campaign called EUREC4A, between 19 January and 19 February 2020. The data collected are crucial to investigate clouds and precipitation and understand how they form and change over the ocean, where it is so difficult to measure them.
Martin Hagen, Florian Ewald, Silke Groß, Lothar Oswald, David A. Farrell, Marvin Forde, Manuel Gutleben, Johann Heumos, Jens Reimann, Eleni Tetoni, Gregor Köcher, Eleni Marinou, Christoph Kiemle, Qiang Li, Rebecca Chewitt-Lucas, Alton Daley, Delando Grant, and Kashawn Hall
Earth Syst. Sci. Data, 13, 5899–5914, https://doi.org/10.5194/essd-13-5899-2021, https://doi.org/10.5194/essd-13-5899-2021, 2021
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The German polarimetric weather radar Poldirad was deployed for the international campaign EUREC4A on Barbados. The focus was monitoring clouds and precipitation in the trade wind region east of Barbados. Observations were with a temporal sequence of 5 min and a maximum range of 375 km. Examples of mesoscale precipitation patterns, rain rate accumulation, diurnal cycle, and vertical distribution show the potential for further studies on the life cycle of precipitating shallow cumulus clouds.
Mark W. Seefeldt, Taydra M. Low, Scott D. Landolt, and Thomas H. Nylen
Earth Syst. Sci. Data, 13, 5803–5817, https://doi.org/10.5194/essd-13-5803-2021, https://doi.org/10.5194/essd-13-5803-2021, 2021
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The Antarctic Precipitation System project deployed and maintained four sites across Antarctica from November 2017 to November 2019. The goals for the project included the collection of in situ observations of precipitation in Antarctica, an improvement in the understanding of precipitation in Antarctica, and the ability to validate precipitation data from atmospheric numerical models. The collected dataset represents some of the first year-round observations of precipitation in Antarctica.
Xuebo Li, Yongxiang Huang, Guohua Wang, and Xiaojing Zheng
Earth Syst. Sci. Data, 13, 5819–5830, https://doi.org/10.5194/essd-13-5819-2021, https://doi.org/10.5194/essd-13-5819-2021, 2021
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High-frequency observatory data (50 Hz 3D wind velocity, 50 Hz temperature and 1 Hz PM10) for studying the features of the fluid and dust field during sand and dust storms were presented. It is anticipated that data collected in this work will be of utility not only specifically for the boundary layer community in building a model for sand and dust storms but also broadly for communities studying the exchange of the dust and fluid field and energy transfer for the particle-laden two-phase flow.
Christophe Genthon, Dana Veron, Etienne Vignon, Delphine Six, Jean-Louis Dufresne, Jean-Baptiste Madeleine, Emmanuelle Sultan, and François Forget
Earth Syst. Sci. Data, 13, 5731–5746, https://doi.org/10.5194/essd-13-5731-2021, https://doi.org/10.5194/essd-13-5731-2021, 2021
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A 10-year dataset of observation in the atmospheric boundary layer at Dome C on the high Antarctic plateau is presented. This is obtained with sensors at six levels along a tower higher than 40 m. The temperature inversion can reach more than 25 °C along the tower in winter, while full mixing by convection can occur in summer. Different amplitudes of variability for wind and temperature at the different levels reflect different signatures of solar vs. synoptic forcing of the boundary layer.
Xiao Liu, Jiyao Xu, Jia Yue, You Yu, Paulo P. Batista, Vania F. Andrioli, Zhengkuan Liu, Tao Yuan, Chi Wang, Ziming Zou, Guozhu Li, and James M. Russell III
Earth Syst. Sci. Data, 13, 5643–5661, https://doi.org/10.5194/essd-13-5643-2021, https://doi.org/10.5194/essd-13-5643-2021, 2021
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Based on the gradient balance wind theory and the SABER observations, a dataset of monthly mean zonal wind has been developed at heights of 18–100 km and latitudes of 50° Sndash;50° N from 2002 to 2019. The dataset agrees with the zonal wind from models (MERRA2, UARP, HWM14) and observations by meteor radar and lidar at seven stations. The dataset can be used to study seasonal and interannual variations and can serve as a background for wave studies of tides and planetary waves.
Heike Konow, Florian Ewald, Geet George, Marek Jacob, Marcus Klingebiel, Tobias Kölling, Anna E. Luebke, Theresa Mieslinger, Veronika Pörtge, Jule Radtke, Michael Schäfer, Hauke Schulz, Raphaela Vogel, Martin Wirth, Sandrine Bony, Susanne Crewell, André Ehrlich, Linda Forster, Andreas Giez, Felix Gödde, Silke Groß, Manuel Gutleben, Martin Hagen, Lutz Hirsch, Friedhelm Jansen, Theresa Lang, Bernhard Mayer, Mario Mech, Marc Prange, Sabrina Schnitt, Jessica Vial, Andreas Walbröl, Manfred Wendisch, Kevin Wolf, Tobias Zinner, Martin Zöger, Felix Ament, and Bjorn Stevens
Earth Syst. Sci. Data, 13, 5545–5563, https://doi.org/10.5194/essd-13-5545-2021, https://doi.org/10.5194/essd-13-5545-2021, 2021
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The German research aircraft HALO took part in the research campaign EUREC4A in January and February 2020. The focus area was the tropical Atlantic east of the island of Barbados. We describe the characteristics of the 15 research flights, provide auxiliary information, derive combined cloud mask products from all instruments that observe clouds on board the aircraft, and provide code examples that help new users of the data to get started.
Geet George, Bjorn Stevens, Sandrine Bony, Robert Pincus, Chris Fairall, Hauke Schulz, Tobias Kölling, Quinn T. Kalen, Marcus Klingebiel, Heike Konow, Ashley Lundry, Marc Prange, and Jule Radtke
Earth Syst. Sci. Data, 13, 5253–5272, https://doi.org/10.5194/essd-13-5253-2021, https://doi.org/10.5194/essd-13-5253-2021, 2021
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Dropsondes measure atmospheric parameters such as temperature, pressure, humidity and horizontal winds. The EUREC4A field campaign deployed 1215 dropsondes during January–February 2020 in the north Atlantic trade-wind region in order to characterize the thermodynamic and the dynamic structure of the atmosphere, primarily at horizontal scales of ~ 200 km. We present JOANNE, the dataset that provides these dropsonde measurements and thereby a rich characterization of the trade-wind atmosphere.
Chengzhi Xing, Cheng Liu, Hongyu Wu, Jinan Lin, Fan Wang, Shuntian Wang, and Meng Gao
Earth Syst. Sci. Data, 13, 4897–4912, https://doi.org/10.5194/essd-13-4897-2021, https://doi.org/10.5194/essd-13-4897-2021, 2021
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Observations of atmospheric composition, especially vertical profile observations, remain sparse and rare on the Tibetan Plateau (TP), due to extremely high altitude, topographical heterogeneity and the grinding environment. This paper introduces a high-time-resolution (~ 15 min) vertical profile observational dataset of atmospheric composition (aerosols, NO2, HCHO and HONO) on the TP for more than 1 year (2017–2019) using a passive remote sensing technique.
Bernd Schalge, Gabriele Baroni, Barbara Haese, Daniel Erdal, Gernot Geppert, Pablo Saavedra, Vincent Haefliger, Harry Vereecken, Sabine Attinger, Harald Kunstmann, Olaf A. Cirpka, Felix Ament, Stefan Kollet, Insa Neuweiler, Harrie-Jan Hendricks Franssen, and Clemens Simmer
Earth Syst. Sci. Data, 13, 4437–4464, https://doi.org/10.5194/essd-13-4437-2021, https://doi.org/10.5194/essd-13-4437-2021, 2021
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In this study, a 9-year simulation of complete model output of a coupled atmosphere–land-surface–subsurface model on the catchment scale is discussed. We used the Neckar catchment in SW Germany as the basis of this simulation. Since the dataset includes the full model output, it is not only possible to investigate model behavior and interactions between the component models but also use it as a virtual truth for comparison of, for example, data assimilation experiments.
Bjorn Stevens, Sandrine Bony, David Farrell, Felix Ament, Alan Blyth, Christopher Fairall, Johannes Karstensen, Patricia K. Quinn, Sabrina Speich, Claudia Acquistapace, Franziska Aemisegger, Anna Lea Albright, Hugo Bellenger, Eberhard Bodenschatz, Kathy-Ann Caesar, Rebecca Chewitt-Lucas, Gijs de Boer, Julien Delanoë, Leif Denby, Florian Ewald, Benjamin Fildier, Marvin Forde, Geet George, Silke Gross, Martin Hagen, Andrea Hausold, Karen J. Heywood, Lutz Hirsch, Marek Jacob, Friedhelm Jansen, Stefan Kinne, Daniel Klocke, Tobias Kölling, Heike Konow, Marie Lothon, Wiebke Mohr, Ann Kristin Naumann, Louise Nuijens, Léa Olivier, Robert Pincus, Mira Pöhlker, Gilles Reverdin, Gregory Roberts, Sabrina Schnitt, Hauke Schulz, A. Pier Siebesma, Claudia Christine Stephan, Peter Sullivan, Ludovic Touzé-Peiffer, Jessica Vial, Raphaela Vogel, Paquita Zuidema, Nicola Alexander, Lyndon Alves, Sophian Arixi, Hamish Asmath, Gholamhossein Bagheri, Katharina Baier, Adriana Bailey, Dariusz Baranowski, Alexandre Baron, Sébastien Barrau, Paul A. Barrett, Frédéric Batier, Andreas Behrendt, Arne Bendinger, Florent Beucher, Sebastien Bigorre, Edmund Blades, Peter Blossey, Olivier Bock, Steven Böing, Pierre Bosser, Denis Bourras, Pascale Bouruet-Aubertot, Keith Bower, Pierre Branellec, Hubert Branger, Michal Brennek, Alan Brewer, Pierre-Etienne Brilouet, Björn Brügmann, Stefan A. Buehler, Elmo Burke, Ralph Burton, Radiance Calmer, Jean-Christophe Canonici, Xavier Carton, Gregory Cato Jr., Jude Andre Charles, Patrick Chazette, Yanxu Chen, Michal T. Chilinski, Thomas Choularton, Patrick Chuang, Shamal Clarke, Hugh Coe, Céline Cornet, Pierre Coutris, Fleur Couvreux, Susanne Crewell, Timothy Cronin, Zhiqiang Cui, Yannis Cuypers, Alton Daley, Gillian M. Damerell, Thibaut Dauhut, Hartwig Deneke, Jean-Philippe Desbios, Steffen Dörner, Sebastian Donner, Vincent Douet, Kyla Drushka, Marina Dütsch, André Ehrlich, Kerry Emanuel, Alexandros Emmanouilidis, Jean-Claude Etienne, Sheryl Etienne-Leblanc, Ghislain Faure, Graham Feingold, Luca Ferrero, Andreas Fix, Cyrille Flamant, Piotr Jacek Flatau, Gregory R. Foltz, Linda Forster, Iulian Furtuna, Alan Gadian, Joseph Galewsky, Martin Gallagher, Peter Gallimore, Cassandra Gaston, Chelle Gentemann, Nicolas Geyskens, Andreas Giez, John Gollop, Isabelle Gouirand, Christophe Gourbeyre, Dörte de Graaf, Geiske E. de Groot, Robert Grosz, Johannes Güttler, Manuel Gutleben, Kashawn Hall, George Harris, Kevin C. Helfer, Dean Henze, Calvert Herbert, Bruna Holanda, Antonio Ibanez-Landeta, Janet Intrieri, Suneil Iyer, Fabrice Julien, Heike Kalesse, Jan Kazil, Alexander Kellman, Abiel T. Kidane, Ulrike Kirchner, Marcus Klingebiel, Mareike Körner, Leslie Ann Kremper, Jan Kretzschmar, Ovid Krüger, Wojciech Kumala, Armin Kurz, Pierre L'Hégaret, Matthieu Labaste, Tom Lachlan-Cope, Arlene Laing, Peter Landschützer, Theresa Lang, Diego Lange, Ingo Lange, Clément Laplace, Gauke Lavik, Rémi Laxenaire, Caroline Le Bihan, Mason Leandro, Nathalie Lefevre, Marius Lena, Donald Lenschow, Qiang Li, Gary Lloyd, Sebastian Los, Niccolò Losi, Oscar Lovell, Christopher Luneau, Przemyslaw Makuch, Szymon Malinowski, Gaston Manta, Eleni Marinou, Nicholas Marsden, Sebastien Masson, Nicolas Maury, Bernhard Mayer, Margarette Mayers-Als, Christophe Mazel, Wayne McGeary, James C. McWilliams, Mario Mech, Melina Mehlmann, Agostino Niyonkuru Meroni, Theresa Mieslinger, Andreas Minikin, Peter Minnett, Gregor Möller, Yanmichel Morfa Avalos, Caroline Muller, Ionela Musat, Anna Napoli, Almuth Neuberger, Christophe Noisel, David Noone, Freja Nordsiek, Jakub L. Nowak, Lothar Oswald, Douglas J. Parker, Carolyn Peck, Renaud Person, Miriam Philippi, Albert Plueddemann, Christopher Pöhlker, Veronika Pörtge, Ulrich Pöschl, Lawrence Pologne, Michał Posyniak, Marc Prange, Estefanía Quiñones Meléndez, Jule Radtke, Karim Ramage, Jens Reimann, Lionel Renault, Klaus Reus, Ashford Reyes, Joachim Ribbe, Maximilian Ringel, Markus Ritschel, Cesar B. Rocha, Nicolas Rochetin, Johannes Röttenbacher, Callum Rollo, Haley Royer, Pauline Sadoulet, Leo Saffin, Sanola Sandiford, Irina Sandu, Michael Schäfer, Vera Schemann, Imke Schirmacher, Oliver Schlenczek, Jerome Schmidt, Marcel Schröder, Alfons Schwarzenboeck, Andrea Sealy, Christoph J. Senff, Ilya Serikov, Samkeyat Shohan, Elizabeth Siddle, Alexander Smirnov, Florian Späth, Branden Spooner, M. Katharina Stolla, Wojciech Szkółka, Simon P. de Szoeke, Stéphane Tarot, Eleni Tetoni, Elizabeth Thompson, Jim Thomson, Lorenzo Tomassini, Julien Totems, Alma Anna Ubele, Leonie Villiger, Jan von Arx, Thomas Wagner, Andi Walther, Ben Webber, Manfred Wendisch, Shanice Whitehall, Anton Wiltshire, Allison A. Wing, Martin Wirth, Jonathan Wiskandt, Kevin Wolf, Ludwig Worbes, Ethan Wright, Volker Wulfmeyer, Shanea Young, Chidong Zhang, Dongxiao Zhang, Florian Ziemen, Tobias Zinner, and Martin Zöger
Earth Syst. Sci. Data, 13, 4067–4119, https://doi.org/10.5194/essd-13-4067-2021, https://doi.org/10.5194/essd-13-4067-2021, 2021
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The EUREC4A field campaign, designed to test hypothesized mechanisms by which clouds respond to warming and benchmark next-generation Earth-system models, is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. It was the first campaign that attempted to characterize the full range of processes and scales influencing trade wind clouds.
Nina Škrk, Roberto Serrano-Notivoli, Katarina Čufar, Maks Merela, Zalika Črepinšek, Lučka Kajfež Bogataj, and Martín de Luis
Earth Syst. Sci. Data, 13, 3577–3592, https://doi.org/10.5194/essd-13-3577-2021, https://doi.org/10.5194/essd-13-3577-2021, 2021
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SLOCLIM is the first climatic reconstruction for Slovenia with a spatial resolution of 1 × 1 km, providing daily data of maximum and minimum temperature and precipitation from 1950 to 2018. This new daily gridded dataset contributes significantly to the climate description of the country and is expected to facilitate research activities in numerous scientific disciplines dealing with climate trends, environment, human and animal populations, agriculture, and forestry.
Miguel Sanchez Gomez, Julie K. Lundquist, Petra M. Klein, and Tyler M. Bell
Earth Syst. Sci. Data, 13, 3539–3549, https://doi.org/10.5194/essd-13-3539-2021, https://doi.org/10.5194/essd-13-3539-2021, 2021
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In July 2018, the International Society for Atmospheric Research using Remotely-piloted Aircraft (ISARRA) hosted a flight week to demonstrate unmanned aircraft systems' capabilities in sampling the atmospheric boundary layer. Three Doppler lidars were deployed during this week-long experiment. We use data from these lidars to estimate turbulence dissipation rate. We observe large temporal variability and significant differences in dissipation for lidars with different sampling techniques.
Cunbo Han, Yaoming Ma, Binbin Wang, Lei Zhong, Weiqiang Ma, Xuelong Chen, and Zhongbo Su
Earth Syst. Sci. Data, 13, 3513–3524, https://doi.org/10.5194/essd-13-3513-2021, https://doi.org/10.5194/essd-13-3513-2021, 2021
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Actual terrestrial evapotranspiration (ETa) is a key parameter controlling the land–atmosphere interaction processes and water cycle. However, the spatial distribution and temporal changes in ETa over the Tibetan Plateau (TP) remain very uncertain. Here we estimate the multiyear (2001–2018) monthly ETa and its spatial distribution on the TP by a combination of meteorological data and satellite products. Results have been validated at six eddy-covariance monitoring sites and show high accuracy.
Marie-Louise Zeller, Jannis-Michael Huss, Lena Pfister, Karl E. Lapo, Daniela Littmann, Johann Schneider, Alexander Schulz, and Christoph K. Thomas
Earth Syst. Sci. Data, 13, 3439–3452, https://doi.org/10.5194/essd-13-3439-2021, https://doi.org/10.5194/essd-13-3439-2021, 2021
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The boundary layer (BL) is well understood when convectively mixed, yet we lack this understanding when it becomes stable and no longer follows classic similarity theories. The NYTEFOX campaign collected a unique meteorological data set in the Arctic BL of Svalbard during polar night, where it tends to be highly stable. Using innovative fiber-optic distributed sensing, we are able to provide unique insight into atmospheric motions across large distances resolved continuously in space and time.
Pierre-Etienne Brilouet, Marie Lothon, Jean-Claude Etienne, Pascal Richard, Sandrine Bony, Julien Lernoult, Hubert Bellec, Gilles Vergez, Thierry Perrin, Julien Delanoë, Tetyana Jiang, Frédéric Pouvesle, Claude Lainard, Michel Cluzeau, Laurent Guiraud, Patrice Medina, and Theotime Charoy
Earth Syst. Sci. Data, 13, 3379–3398, https://doi.org/10.5194/essd-13-3379-2021, https://doi.org/10.5194/essd-13-3379-2021, 2021
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During the EUREC4A field experiment that took place over the tropical Atlantic Ocean east of Barbados, the French ATR 42 environment research aircraft of SAFIRE aimed to characterize the shallow cloud properties near cloud base and the turbulent structure of the subcloud layer. The high-frequency measurements of wind, temperature and humidity as well as their translation in terms of turbulent fluctuations, turbulent moments and characteristic length scales of turbulence are presented.
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Short summary
Inland lakes play an important role in regulating local climate. In this paper, we describe a dataset on microclimate and eddy covariance variables measured at a network of sites across Lake Taihu. The dataset, which appears to be the first of its kind for lake systems, can be used for validation of lake–air flux parameterizations, investigation of climatic controls on lake evaporation, evaluation of remote-sensing surface data products and global synthesis on lake–air interactions.
Inland lakes play an important role in regulating local climate. In this paper, we describe a...
