Articles | Volume 15, issue 8
https://doi.org/10.5194/essd-15-3641-2023
© 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-15-3641-2023
© 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
| 
15 Aug 2023
Data description paper |
| 15 Aug 2023
| 15 Aug 2023
A monthly 1° resolution dataset of daytime cloud fraction over the Arctic during 2000–2020 based on multiple satellite products
Xinyan Liu
Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
now at: Aerospace Information Research Institute, Henan Academy of Sciences, Henan 450046, China
Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Shunlin Liang
Department of Geography, The University of Hong Kong, Hong Kong 999077, China
Ruibo Li
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Xiongxin Xiao
Institute of Geography and Oeschger Center for Climate Change Research, University of Bern, Bern 3012, Switzerland
Rui Ma
Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Yichuan Ma
Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Related authors
No articles found.
Bing Li, Shunlin Liang, Han Ma, Xiaobang Liu, Tao He, and Yufang Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-16, https://doi.org/10.5194/essd-2024-16, 2024
Revised manuscript under review for ESSD
Short summary
Short summary
This study describes 1-km all-weather instantaneous, and daily mean LST datasets for global scale during 2000–2020. It is the first attempt to synergistically estimate all-weather instantaneous and daily mean LST data on a long time-series, global scale. The generated datasets were evaluated by the observations in-situ stations and other LST datasets, and the evaluation indicated the dataset is sufficiently reliable.
Yufang Zhang, Shunlin Liang, Han Ma, Tao He, Qian Wang, Bing Li, Jianglei Xu, Guodong Zhang, Xiaobang Liu, and Changhao Xiong
Earth Syst. Sci. Data, 15, 2055–2079, https://doi.org/10.5194/essd-15-2055-2023, https://doi.org/10.5194/essd-15-2055-2023, 2023
Short summary
Short summary
Soil moisture observations are important for a range of earth system applications. This study generated a long-term (2000–2020) global seamless soil moisture product with both high spatial and temporal resolutions (1 km, daily) using an XGBoost model and multisource datasets. Evaluation of this product against dense in situ soil moisture datasets and microwave soil moisture products showed that this product has reliable accuracy and more complete spatial coverage.
Aolin Jia, Shunlin Liang, Dongdong Wang, Lei Ma, Zhihao Wang, and Shuo Xu
Earth Syst. Sci. Data, 15, 869–895, https://doi.org/10.5194/essd-15-869-2023, https://doi.org/10.5194/essd-15-869-2023, 2023
Short summary
Short summary
Satellites are now producing multiple global land surface temperature (LST) products; however, they suffer from data gaps caused by cloud cover, seriously restricting the applications, and few products provide gap-free global hourly LST. We produced global hourly, 5 km, all-sky LST data from 2011 to 2021 using geostationary and polar-orbiting satellite data. Based on the assessment, it has high accuracy and can be used to estimate evapotranspiration, drought, etc.
Sinan Li, Li Zhang, Jingfeng Xiao, Rui Ma, Xiangjun Tian, and Min Yan
Hydrol. Earth Syst. Sci., 26, 6311–6337, https://doi.org/10.5194/hess-26-6311-2022, https://doi.org/10.5194/hess-26-6311-2022, 2022
Short summary
Short summary
Accurate estimation for global GPP and ET is important in climate change studies. In this study, the GLASS LAI, SMOS, and SMAP datasets were assimilated jointly and separately in a coupled model. The results show that the performance of joint assimilation for GPP and ET is better than that of separate assimilation. The joint assimilation in water-limited regions performed better than in humid regions, and the global assimilation results had higher accuracy than other products.
Han Ma, Shunlin Liang, Changhao Xiong, Qian Wang, Aolin Jia, and Bing Li
Earth Syst. Sci. Data, 14, 5333–5347, https://doi.org/10.5194/essd-14-5333-2022, https://doi.org/10.5194/essd-14-5333-2022, 2022
Short summary
Short summary
The fraction of absorbed photosynthetically active radiation (FAPAR) is one of the essential climate variables. This study generated a global land surface FAPAR product with a 250 m resolution based on a deep learning model that takes advantage of the existing FAPAR products and MODIS time series of observation information. Direct validation and intercomparison revealed that our product better meets user requirements and has a greater spatiotemporal continuity than other existing products.
Rui Ma, Jingfeng Xiao, Shunlin Liang, Han Ma, Tao He, Da Guo, Xiaobang Liu, and Haibo Lu
Geosci. Model Dev., 15, 6637–6657, https://doi.org/10.5194/gmd-15-6637-2022, https://doi.org/10.5194/gmd-15-6637-2022, 2022
Short summary
Short summary
Parameter optimization can improve the accuracy of modeled carbon fluxes. Few studies conducted pixel-level parameterization because it requires a high computational cost. Our paper used high-quality spatial products to optimize parameters at the pixel level, and also used the machine learning method to improve the speed of optimization. The results showed that there was significant spatial variability of parameters and we also improved the spatial pattern of carbon fluxes.
Jianglei Xu, Shunlin Liang, and Bo Jiang
Earth Syst. Sci. Data, 14, 2315–2341, https://doi.org/10.5194/essd-14-2315-2022, https://doi.org/10.5194/essd-14-2315-2022, 2022
Short summary
Short summary
Land surface all-wave net radiation (Rn) is a key parameter in many land processes. Current products have drawbacks of coarse resolutions, large uncertainty, and short time spans. A deep learning method was used to obtain global surface Rn. A long-term Rn product was generated from 1981 to 2019 using AVHRR data. The product has the highest accuracy and a reasonable spatiotemporal variation compared to three other products. Our product will play an important role in long-term climate change.
Xueyuan Gao, Shunlin Liang, Dongdong Wang, Yan Li, Bin He, and Aolin Jia
Earth Syst. Dynam., 13, 219–230, https://doi.org/10.5194/esd-13-219-2022, https://doi.org/10.5194/esd-13-219-2022, 2022
Short summary
Short summary
Numerical experiments with a coupled Earth system model show that large-scale nighttime artificial lighting in tropical forests will significantly increase carbon sink, local temperature, and precipitation, and it requires less energy than direct air carbon capture for capturing 1 t of carbon, suggesting that it could be a powerful climate mitigation option. Side effects include CO2 outgassing after the termination of the nighttime lighting and impacts on local wildlife.
Xiaona Chen, Shunlin Liang, Lian He, Yaping Yang, and Cong Yin
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-279, https://doi.org/10.5194/essd-2021-279, 2021
Preprint withdrawn
Short summary
Short summary
The present study developed a 39 year consistent 8-day 0.05 degree gap-free SCE dataset over the NH for the period 1981–2019 as part of the Global LAnd Surface Satellite dataset (GLASS) product suite based on the NOAA AVHRR-SR CDR and several contributory datasets. Compared with published SCE datasets, GLASS SCE has several advantages in snow cover studies, including long time series, finer spatial resolution (especially for years before 2000), and complete spatial coverage.
Diyang Cui, Shunlin Liang, Dongdong Wang, and Zheng Liu
Earth Syst. Sci. Data, 13, 5087–5114, https://doi.org/10.5194/essd-13-5087-2021, https://doi.org/10.5194/essd-13-5087-2021, 2021
Short summary
Short summary
Large portions of the Earth's surface are expected to experience changes in climatic conditions. The rearrangement of climate distributions can lead to serious impacts on ecological and social systems. Major climate zones are distributed in a predictable pattern and are largely defined following the Köppen climate classification. This creates an urgent need to compile a series of Köppen climate classification maps with finer spatial and temporal resolutions and improved accuracy.
Yan Chen, Shunlin Liang, Han Ma, Bing Li, Tao He, and Qian Wang
Earth Syst. Sci. Data, 13, 4241–4261, https://doi.org/10.5194/essd-13-4241-2021, https://doi.org/10.5194/essd-13-4241-2021, 2021
Short summary
Short summary
This study used remotely sensed and assimilated data to estimate all-sky land surface air temperature (Ta) using a machine learning method, and developed an all-sky 1 km daily mean land Ta product for 2003–2019 over mainland China. Validation results demonstrated that this dataset has achieved satisfactory accuracy and high spatial resolution simultaneously, which fills the current dataset gap in this field and plays an important role in studies of climate change and the hydrological cycle.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
Short summary
Short summary
The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Diyang Cui, Shunlin Liang, Dongdong Wang, and Zheng Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-53, https://doi.org/10.5194/essd-2021-53, 2021
Preprint withdrawn
Short summary
Short summary
The Köppen-Geiger climate classification has been widely applied in climate change and ecology studies to characterize climatic conditions. We present a new 1-km global dataset of Köppen-Geiger climate classification and bioclimatic variables for historical and future climates. The new climate maps offer higher classification accuracy, correspond well with distributions of vegetation and topographic features, and demonstrate the ability to identify recent and future changes in climate zones.
Xiongxin Xiao, Shunlin Liang, Tao He, Daiqiang Wu, Congyuan Pei, and Jianya Gong
The Cryosphere, 15, 835–861, https://doi.org/10.5194/tc-15-835-2021, https://doi.org/10.5194/tc-15-835-2021, 2021
Short summary
Short summary
Daily time series and full space-covered sub-pixel snow cover area data are urgently needed for climate and reanalysis studies. Due to the fact that observations from optical satellite sensors are affected by clouds, this study attempts to capture dynamic characteristics of snow cover at a fine spatiotemporal resolution (daily; 6.25 km) accurately by using passive microwave data. We demonstrate the potential to use the passive microwave and the MODIS data to map the fractional snow cover area.
Jin Ma, Ji Zhou, Frank-Michael Göttsche, Shunlin Liang, Shaofei Wang, and Mingsong Li
Earth Syst. Sci. Data, 12, 3247–3268, https://doi.org/10.5194/essd-12-3247-2020, https://doi.org/10.5194/essd-12-3247-2020, 2020
Short summary
Short summary
Land surface temperature is an important parameter in the research of climate change and many land surface processes. This article describes the development and testing of an algorithm for generating a consistent global long-term land surface temperature product from 20 years of NOAA AVHRR radiance data. The preliminary validation results indicate good accuracy of this new long-term product, which has been designed to simplify applications and support the scientific research community.
Yi Zheng, Ruoque Shen, Yawen Wang, Xiangqian Li, Shuguang Liu, Shunlin Liang, Jing M. Chen, Weimin Ju, Li Zhang, and Wenping Yuan
Earth Syst. Sci. Data, 12, 2725–2746, https://doi.org/10.5194/essd-12-2725-2020, https://doi.org/10.5194/essd-12-2725-2020, 2020
Short summary
Short summary
Accurately reproducing the interannual variations in vegetation gross primary production (GPP) is a major challenge. A global GPP dataset was generated by integrating the regulations of several major environmental variables with long-term changes. The dataset can effectively reproduce the spatial, seasonal, and particularly interannual variations in global GPP. Our study will contribute to accurate carbon flux estimates at long timescales.
Han Liu, Peng Gong, Jie Wang, Nicholas Clinton, Yuqi Bai, and Shunlin Liang
Earth Syst. Sci. Data, 12, 1217–1243, https://doi.org/10.5194/essd-12-1217-2020, https://doi.org/10.5194/essd-12-1217-2020, 2020
Short summary
Short summary
We built the first set of 5 km resolution CDRs to record the annual dynamics of global land cover (GLASS-GLC) from 1982 to 2015. The average overall accuracy is 82 %. By conducting long-term change analysis, significant land cover changes and spatiotemporal patterns at various scales were found, which can improve our understanding of global environmental change and help achieve sustainable development goals. This will be further applied in Earth system modeling to facilitate relevant studies.
Aolin Jia, Shunlin Liang, Dongdong Wang, Bo Jiang, and Xiaotong Zhang
Atmos. Chem. Phys., 20, 881–899, https://doi.org/10.5194/acp-20-881-2020, https://doi.org/10.5194/acp-20-881-2020, 2020
Short summary
Short summary
The Tibetan Plateau (TP) plays a vital role in regional and global climate change due to its location and orography. After generating a long-term surface radiation (SR) dataset, we characterized the SR spatiotemporal variation along with temperature. Evidence from multiple data sources indicated that the TP dimming was primarily driven by increased aerosols from human activities, and the cooling effect of aerosol loading offsets TP surface warming, revealing the human impact on regional warming.
Xiongxin Xiao, Tingjun Zhang, Xinyue Zhong, Xiaodong Li, and Yuxing Li
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-300, https://doi.org/10.5194/tc-2019-300, 2019
Manuscript not accepted for further review
Short summary
Short summary
Seasonal snow cover is an important component of the climate system and global water cycle that stores large amounts of freshwater. Our research attempts to develop a long-term Northern Hemisphere daily snow depth and snow water equivalent product data using a new algorithm applying in historical passive microwave dataset from 1992 to 2016. Our further analysis showed that snow cover has a significant declining trend across the Northern Hemisphere, especially beginning in the new century.
Xiongxin Xiao, Tingjun Zhang, Xinyue Zhong, Xiaodong Li, and Yuxing Li
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-33, https://doi.org/10.5194/tc-2019-33, 2019
Revised manuscript not accepted
Short summary
Short summary
Seasonal snow cover is an important component of the climate system and global water cycle that stores large amounts of freshwater. Our research attempts to develop a long-term Northern Hemisphere daily snow depth and snow water equivalent products using a new algorithm applying in historical passive microwave data sets from 1992 to 2016. Our further analysis showed the snow cover has a significant declining trend across the Northern Hemisphere, especially beginning at the new century.
Tanguang Gao, Jie Liu, Tingjun Zhang, Yuantao Hu, Jianguo Shang, Shufa Wang, Xiongxin Xiao, Chuankun Liu, Shichang Kang, Mika Sillanpää, and Yulan Zhang
The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-176, https://doi.org/10.5194/tc-2017-176, 2017
Preprint retracted
Short summary
Short summary
Understanding the interactions between groundwater and surface water in permafrost regions is essential to the understanding of flood frequencies and river water quality of high latitude/altitude basins. Thus, we analyzed the interaction between surface water and groundwater in a permafrost region in the northern Tibetan Plateau by using heat tracing methods.
X. Xie, S. Meng, S. Liang, and Y. Yao
Hydrol. Earth Syst. Sci., 18, 3923–3936, https://doi.org/10.5194/hess-18-3923-2014, https://doi.org/10.5194/hess-18-3923-2014, 2014
Q. Shi and S. Liang
Atmos. Chem. Phys., 14, 5659–5677, https://doi.org/10.5194/acp-14-5659-2014, https://doi.org/10.5194/acp-14-5659-2014, 2014
N. F. Liu, Q. Liu, L. Z. Wang, S. L. Liang, J. G. Wen, Y. Qu, and S. H. Liu
Hydrol. Earth Syst. Sci., 17, 2121–2129, https://doi.org/10.5194/hess-17-2121-2013, https://doi.org/10.5194/hess-17-2121-2013, 2013
T. R. Xu, S. M. Liu, Z. W. Xu, S. Liang, and L. Xu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-10-3927-2013, https://doi.org/10.5194/hessd-10-3927-2013, 2013
Preprint withdrawn
Related subject area
Domain: ESSD – Atmosphere | Subject: Atmospheric chemistry and physics
IPB-MSA&SO4: a daily 0.25° resolution dataset of in situ-produced biogenic methanesulfonic acid and sulfate over the North Atlantic during 1998–2022 based on machine learning
Indicators of Global Climate Change 2023: annual update of key indicators of the state of the climate system and human influence
The Total Carbon Column Observing Network's GGG2020 data version
Global anthropogenic emissions (CAMS-GLOB-ANT) for the Copernicus Atmosphere Monitoring Service simulations of air quality forecasts and reanalyses
Deep Convective Microphysics Experiment (DCMEX) coordinated aircraft and ground observations: microphysics, aerosol, and dynamics during cumulonimbus development
High-resolution physicochemical dataset of atmospheric aerosols over the Tibetan Plateau and its surroundings
Introduction to the NJIAS Himawari-8/9 Cloud Feature Dataset for climate and typhoon research
The Tibetan Plateau space-based tropospheric aerosol climatology: 2007–2020
PalVol v1: a proxy-based semi-stochastic ensemble reconstruction of volcanic stratospheric sulfur injection for the last glacial cycle (140 000–50 BP)
Reconstructing long-term (1980–2022) daily ground particulate matter datasets in India (LongPMInd)
Large synthesis of in situ field measurements of the size distribution of mineral dust aerosols across their lifecycle
Ground- and ship-based microwave radiometer measurements during EUREC4A
Shortwave and longwave components of the surface radiation budget measured at the Thule High Arctic Atmospheric Observatory, Northern Greenland
Cloud condensation nuclei concentrations derived from the CAMS reanalysis
A merged continental planetary boundary layer height dataset based on high-resolution radiosonde measurements, ERA5 reanalysis, and GLDAS
12 years of continuous atmospheric O2, CO2 and APO data from Weybourne Atmospheric Observatory in the United Kingdom
CLAAS-3: the third edition of the CM SAF cloud data record based on SEVIRI observations
Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets
A Level 3 Monthly Gridded Ice Cloud Dataset Derived from a Decade of CALIOP Measurements
High-resolution aerosol data from the top 3.8 kyr of the East Greenland Ice coring Project (EGRIP) ice core
Visibility-derived aerosol optical depth over global land from 1980 to 2021
Characterizing clouds with the CCClim dataset, a machine learning cloud class climatology
A database of aircraft measurements of carbon monoxide (CO) with high temporal and spatial resolution during 2011–2021
A first global height-resolved cloud condensation nuclei data set derived from spaceborne lidar measurements
Network for the Detection of Atmospheric Composition Change (NDACC) Fourier transform infrared (FTIR) trace gas measurements at the University of Toronto Atmospheric Observatory from 2002 to 2020
Deconstruction of tropospheric chemical reactivity using aircraft measurements: the Atmospheric Tomography Mission (ATom) data
Spatial variability of Saharan dust deposition revealed through a citizen science campaign
Radiative sensitivity quantified by a new set of radiation flux kernels based on the ECMWF Reanalysis v5 (ERA5)
Updated observations of clouds by MODIS for global model assessment
An extensive database of airborne trace gas and meteorological observations from the Alpha Jet Atmospheric eXperiment (AJAX)
Two years of volatile organic compound online in situ measurements at the Site Instrumental de Recherche par Télédétection Atmosphérique (Paris region, France) using proton-transfer-reaction mass spectrometry
Global Ozone Monitoring Experiment-2 (GOME-2) daily and monthly level-3 products of atmospheric trace gas columns
Crowdsourced Doppler measurements of time standard stations demonstrating ionospheric variability
A machine learning approach to address air quality changes during the COVID-19 lockdown in Buenos Aires, Argentina
Version 2 of the global catalogue of large anthropogenic and volcanic SO2 sources and emissions derived from satellite measurements
World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series, 2022 update
Long-term ash dispersal dataset of the Sakurajima Taisho eruption for ashfall disaster countermeasure
Full-coverage 250 m monthly aerosol optical depth dataset (2000–2019) amended with environmental covariates by an ensemble machine learning model over arid and semi-arid areas, NW China
The polar mesospheric cloud dataset of the Balloon Lidar Experiment (BOLIDE)
Multiyear emissions of carbonaceous aerosols from cooking, fireworks, sacrificial incense, joss paper burning, and barbecue as well as their key driving forces in China
Impacts of the proposal of the CNG2020 strategy on aircraft emissions of China–foreign routes
Northern hemispheric atmospheric ethane trends in the upper troposphere and lower stratosphere (2006–2016) with reference to methane and propane
New contributions of measurements in Europe to the global inventory of the stable isotopic composition of methane
International Monitoring System infrasound data products for atmospheric studies and civilian applications
A benchmark dataset of diurnal- and seasonal-scale radiation, heat, and CO2 fluxes in a typical East Asian monsoon region
Attenuated atmospheric backscatter profiles measured by the CO2 Sounder lidar in the 2017 ASCENDS/ABoVE airborne campaign
Climatology of aerosol component concentrations derived from multi-angular polarimetric POLDER-3 observations using GRASP algorithm
Reconstructing 6-hourly PM2.5 datasets from 1960 to 2020 in China
A 10-year global monthly averaged terrestrial net ecosystem exchange dataset inferred from the ACOS GOSAT v9 XCO2 retrievals (GCAS2021)
Karam Mansour, Stefano Decesari, Darius Ceburnis, Jurgita Ovadnevaite, Lynn M. Russell, Marco Paglione, Laurent Poulain, Shan Huang, Colin O'Dowd, and Matteo Rinaldi
Earth Syst. Sci. Data, 16, 2717–2740, https://doi.org/10.5194/essd-16-2717-2024, https://doi.org/10.5194/essd-16-2717-2024, 2024
Short summary
Short summary
We propose and evaluate machine learning predictive algorithms to model freshly formed biogenic methanesulfonic acid and sulfate concentrations. The long-term constructed dataset covers the North Atlantic at an unprecedented resolution. The improved parameterization of biogenic sulfur aerosols at regional scales is essential for determining their radiative forcing, which could help further understand marine-aerosol–cloud interactions and reduce uncertainties in climate models
Piers M. Forster, Chris Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Bradley Hall, Mathias Hauser, Aurélien Ribes, Debbie Rosen, Nathan P. Gillett, Matthew D. Palmer, Joeri Rogelj, Karina von Schuckmann, Blair Trewin, Myles Allen, Robbie Andrew, Richard A. Betts, Alex Borger, Tim Boyer, Jiddu A. Broersma, Carlo Buontempo, Samantha Burgess, Chiara Cagnazzo, Lijing Cheng, Pierre Friedlingstein, Andrew Gettelman, Johannes Gütschow, Masayoshi Ishii, Stuart Jenkins, Xin Lan, Colin Morice, Jens Mühle, Christopher Kadow, John Kennedy, Rachel E. Killick, Paul B. Krummel, Jan C. Minx, Gunnar Myhre, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Carl-Friedrich Schleussner, Sonia I. Seneviratne, Sophie Szopa, Peter Thorne, Mahesh V. M. Kovilakam, Elisa Majamäki, Jukka-Pekka Jalkanen, Margreet van Marle, Rachel M. Hoesly, Robert Rohde, Dominik Schumacher, Guido van der Werf, Russell Vose, Kirsten Zickfeld, Xuebin Zhang, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 16, 2625–2658, https://doi.org/10.5194/essd-16-2625-2024, https://doi.org/10.5194/essd-16-2625-2024, 2024
Short summary
Short summary
This paper tracks some key indicators of global warming through time, from 1850 through to the end of 2023. It is designed to give an authoritative estimate of global warming to date and its causes. We find that in 2023, global warming reached 1.3 °C and is increasing at over 0.2 °C per decade. This is caused by all-time-high greenhouse gas emissions.
Joshua L. Laughner, Geoffrey C. Toon, Joseph Mendonca, Christof Petri, Sébastien Roche, Debra Wunch, Jean-Francois Blavier, David W. T. Griffith, Pauli Heikkinen, Ralph F. Keeling, Matthäus Kiel, Rigel Kivi, Coleen M. Roehl, Britton B. Stephens, Bianca C. Baier, Huilin Chen, Yonghoon Choi, Nicholas M. Deutscher, Joshua P. DiGangi, Jochen Gross, Benedikt Herkommer, Pascal Jeseck, Thomas Laemmel, Xin Lan, Erin McGee, Kathryn McKain, John Miller, Isamu Morino, Justus Notholt, Hirofumi Ohyama, David F. Pollard, Markus Rettinger, Haris Riris, Constantina Rousogenous, Mahesh Kumar Sha, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Yao Té, Voltaire A. Velazco, Steven C. Wofsy, Minqiang Zhou, and Paul O. Wennberg
Earth Syst. Sci. Data, 16, 2197–2260, https://doi.org/10.5194/essd-16-2197-2024, https://doi.org/10.5194/essd-16-2197-2024, 2024
Short summary
Short summary
This paper describes a new version, called GGG2020, of a data set containing column-integrated observations of greenhouse and related gases (including CO2, CH4, CO, and N2O) made by ground stations located around the world. Compared to the previous version (GGG2014), improvements have been made toward site-to-site consistency. This data set plays a key role in validating space-based greenhouse gas observations and in understanding the carbon cycle.
Antonin Soulie, Claire Granier, Sabine Darras, Nicolas Zilbermann, Thierno Doumbia, Marc Guevara, Jukka-Pekka Jalkanen, Sekou Keita, Cathy Liousse, Monica Crippa, Diego Guizzardi, Rachel Hoesly, and Steven J. Smith
Earth Syst. Sci. Data, 16, 2261–2279, https://doi.org/10.5194/essd-16-2261-2024, https://doi.org/10.5194/essd-16-2261-2024, 2024
Short summary
Short summary
Anthropogenic emissions are the result of transportation, power generation, industrial, residential and commercial activities as well as waste treatment and agriculture practices. This work describes the new CAMS-GLOB-ANT gridded inventory of 2000–2023 anthropogenic emissions of air pollutants and greenhouse gases. The methodology to generate the emissions is explained and the datasets are analysed and compared with publicly available global and regional inventories for selected world regions.
Declan L. Finney, Alan M. Blyth, Martin Gallagher, Huihui Wu, Graeme J. Nott, Michael I. Biggerstaff, Richard G. Sonnenfeld, Martin Daily, Dan Walker, David Dufton, Keith Bower, Steven Böing, Thomas Choularton, Jonathan Crosier, James Groves, Paul R. Field, Hugh Coe, Benjamin J. Murray, Gary Lloyd, Nicholas A. Marsden, Michael Flynn, Kezhen Hu, Navaneeth M. Thamban, Paul I. Williams, Paul J. Connolly, James B. McQuaid, Joseph Robinson, Zhiqiang Cui, Ralph R. Burton, Gordon Carrie, Robert Moore, Steven J. Abel, Dave Tiddeman, and Graydon Aulich
Earth Syst. Sci. Data, 16, 2141–2163, https://doi.org/10.5194/essd-16-2141-2024, https://doi.org/10.5194/essd-16-2141-2024, 2024
Short summary
Short summary
The DCMEX (Deep Convective Microphysics Experiment) project undertook an aircraft- and ground-based measurement campaign of New Mexico deep convective clouds during July–August 2022. The campaign coordinated a broad range of instrumentation measuring aerosol, cloud physics, radar signals, thermodynamics, dynamics, electric fields, and weather. The project's objectives included the utilisation of these data with satellite observations to study the anvil cloud radiative effect.
Jianzhong Xu, Xinghua Zhang, Wenhui Zhao, Lixiang Zhai, Miao Zhong, Jinsen Shi, Junying Sun, Yanmei Liu, Conghui Xie, Yulong Tan, Kemei Li, Xinlei Ge, Qi Zhang, and Shichang Kang
Earth Syst. Sci. Data, 16, 1875–1900, https://doi.org/10.5194/essd-16-1875-2024, https://doi.org/10.5194/essd-16-1875-2024, 2024
Short summary
Short summary
A comprehensive aerosol observation project was carried out in the Tibetan Plateau (TP) and its surroundings in recent years to investigate the properties and sources of atmospheric aerosols as well as their regional differences by performing multiple intensive field observations. The release of this dataset can provide basic and systematic data for related research in the atmospheric, cryospheric, and environmental sciences in this unique region.
Xiaoyong Zhuge, Xiaolei Zou, Lu Yu, Xin Li, Mingjian Zeng, Yilun Chen, Bing Zhang, Bin Yao, Fei Tang, Fengjiao Chen, and Wanlin Kan
Earth Syst. Sci. Data, 16, 1747–1769, https://doi.org/10.5194/essd-16-1747-2024, https://doi.org/10.5194/essd-16-1747-2024, 2024
Short summary
Short summary
The Himawari-8/9 level-2 operational cloud product has a low spatial resolution and is available only during the daytime. To supplement this official dataset, a new dataset named the NJIAS Himawari-8/9 Cloud Feature Dataset (HCFD) was constructed. The NJIAS HCFD provides a comprehensive description of cloud features over the East Asia and west North Pacific regions for the years 2016–2022 by 30 retrieved cloud variables. The NJIAS HCFD has been demonstrated to outperform the official dataset.
Honglin Pan, Jianping Huang, Jiming Li, Zhongwei Huang, Minzhong Wang, Ali Mamtimin, Wen Huo, Fan Yang, Tian Zhou, and Kanike Raghavendra Kumar
Earth Syst. Sci. Data, 16, 1185–1207, https://doi.org/10.5194/essd-16-1185-2024, https://doi.org/10.5194/essd-16-1185-2024, 2024
Short summary
Short summary
We applied several correction procedures and rigorously checked for data quality constraints during the long observation period spanning almost 14 years (2007–2020). Nevertheless, some uncertainties remain, mainly due to technical constraints and limited documentation of the measurements. Even though not completely accurate, this strategy is expected to at least reduce the inaccuracy of the computed characteristic value of aerosol optical parameters.
Julie Christin Schindlbeck-Belo, Matthew Toohey, Marion Jegen, Steffen Kutterolf, and Kira Rehfeld
Earth Syst. Sci. Data, 16, 1063–1081, https://doi.org/10.5194/essd-16-1063-2024, https://doi.org/10.5194/essd-16-1063-2024, 2024
Short summary
Short summary
Volcanic forcing of climate resulting from major explosive eruptions is a dominant natural driver of past climate variability. To support model studies of the potential impacts of explosive volcanism on climate variability across timescales, we present an ensemble reconstruction of volcanic stratospheric sulfur injection over the last 140 000 years that is based primarily on tephra records.
Shuai Wang, Mengyuan Zhang, Hui Zhao, Peng Wang, Sri Harsha Kota, Qingyan Fu, Cong Liu, and Hongliang Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-34, https://doi.org/10.5194/essd-2024-34, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Long-term, open-source, gap-free daily ground-level PM2.5 and PM10 datasets for India (LongPMInd) were reconstructed using a robust machine learning model to support health assessment and air quality management.
Paola Formenti and Claudia Di Biagio
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-481, https://doi.org/10.5194/essd-2023-481, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Particles from deserts and semi-vegetated areas (mineral dust) are important for the Earth climate, and the human health, notably depending on their size. In this paper we collect and made de synthesis of a body of those observations since 1972 in order to provide researchers modelling the Earth climate as well as researchers developing satellite observations from space a simple way of confronting their results and understanding their validity.
Sabrina Schnitt, Andreas Foth, Heike Kalesse-Los, Mario Mech, Claudia Acquistapace, Friedhelm Jansen, Ulrich Löhnert, Bernhard Pospichal, Johannes Röttenbacher, Susanne Crewell, and Bjorn Stevens
Earth Syst. Sci. Data, 16, 681–700, https://doi.org/10.5194/essd-16-681-2024, https://doi.org/10.5194/essd-16-681-2024, 2024
Short summary
Short summary
This publication describes the microwave radiometric measurements performed during the EUREC4A campaign at Barbados Cloud Observatory (BCO) and aboard RV Meteor and RV Maria S Merian. We present retrieved integrated water vapor (IWV), liquid water path (LWP), and temperature and humidity profiles as a unified, quality-controlled, multi-site data set on a 3 s temporal resolution for a core period between 19 January 2020 and 14 February 2020.
Daniela Meloni, Filippo Calì Quaglia, Virginia Ciardini, Annalisa Di Bernardino, Tatiana Di Iorio, Antonio Iaccarino, Giovanni Muscari, Giandomenico Pace, Claudio Scarchilli, and Alcide di Sarra
Earth Syst. Sci. Data, 16, 543–566, https://doi.org/10.5194/essd-16-543-2024, https://doi.org/10.5194/essd-16-543-2024, 2024
Short summary
Short summary
Solar and infrared radiation are key factors in determining Arctic climate. Only a few sites in the Arctic perform long-term measurements of the surface radiation budget (SRB). At the Thule High Arctic Atmospheric Observatory (THAAO, 76.5° N, 68.8° W) in Northern Greenland, solar and infrared irradiance measurements were started in 2009. These data are of paramount importance in studying the impact of the atmospheric (mainly clouds and aerosols) and surface (albedo) parameters on the SRB.
Karoline Block, Mahnoosh Haghighatnasab, Daniel G. Partridge, Philip Stier, and Johannes Quaas
Earth Syst. Sci. Data, 16, 443–470, https://doi.org/10.5194/essd-16-443-2024, https://doi.org/10.5194/essd-16-443-2024, 2024
Short summary
Short summary
Aerosols being able to act as condensation nuclei for cloud droplets (CCNs) are a key element in cloud formation but very difficult to determine. In this study we present a new global vertically resolved CCN dataset for various humidity conditions and aerosols. It is obtained using an atmospheric model (CAMS reanalysis) that is fed by satellite observations of light extinction (AOD). We investigate and evaluate the abundance of CCNs in the atmosphere and their temporal and spatial occurrence.
Jianping Guo, Jian Zhang, Jia Shao, Tianmeng Chen, Kaixu Bai, Yuping Sun, Ning Li, Jingyan Wu, Rui Li, Jian Li, Qiyun Guo, Jason B. Cohen, Panmao Zhai, Xiaofeng Xu, and Fei Hu
Earth Syst. Sci. Data, 16, 1–14, https://doi.org/10.5194/essd-16-1-2024, https://doi.org/10.5194/essd-16-1-2024, 2024
Short summary
Short summary
A global continental merged high-resolution (PBLH) dataset with good accuracy compared to radiosonde is generated via machine learning algorithms, covering the period from 2011 to 2021 with 3-hour and 0.25º resolution in space and time. The machine learning model takes parameters derived from the ERA5 reanalysis and GLDAS product as input, with PBLH biases between radiosonde and ERA5 as the learning targets. The merged PBLH is the sum of the predicted PBLH bias and the PBLH from ERA5.
Karina E. Adcock, Penelope A. Pickers, Andrew C. Manning, Grant L. Forster, Leigh S. Fleming, Thomas Barningham, Philip A. Wilson, Elena A. Kozlova, Marica Hewitt, Alex J. Etchells, and Andy J. Macdonald
Earth Syst. Sci. Data, 15, 5183–5206, https://doi.org/10.5194/essd-15-5183-2023, https://doi.org/10.5194/essd-15-5183-2023, 2023
Short summary
Short summary
We present a 12-year time series of continuous atmospheric measurements of O2 and CO2 at the Weybourne Atmospheric Observatory in the United Kingdom. These measurements are combined into the term atmospheric potential oxygen (APO), a tracer that is not influenced by land biosphere processes. The datasets show a long-term increasing trend in CO2 and decreasing trends in O2 and APO between 2010 and 2021.
Nikos Benas, Irina Solodovnik, Martin Stengel, Imke Hüser, Karl-Göran Karlsson, Nina Håkansson, Erik Johansson, Salomon Eliasson, Marc Schröder, Rainer Hollmann, and Jan Fokke Meirink
Earth Syst. Sci. Data, 15, 5153–5170, https://doi.org/10.5194/essd-15-5153-2023, https://doi.org/10.5194/essd-15-5153-2023, 2023
Short summary
Short summary
This paper describes CLAAS-3, the third edition of the Cloud property dAtAset using SEVIRI, which was created based on observations from geostationary Meteosat satellites. CLAAS-3 cloud properties are evaluated using a variety of reference datasets, with very good overall results. The demonstrated quality of CLAAS-3 ensures its usefulness in a wide range of applications, including studies of local- to continental-scale cloud processes and evaluation of climate models.
Sandip S. Dhomse and Martyn P. Chipperfield
Earth Syst. Sci. Data, 15, 5105–5120, https://doi.org/10.5194/essd-15-5105-2023, https://doi.org/10.5194/essd-15-5105-2023, 2023
Short summary
Short summary
There are no long-term stratospheric profile data sets for two very important greenhouse gases: methane (CH4) and nitrous oxide (N2O). Along with radiative feedback, these species play an important role in controlling ozone loss in the stratosphere. Here, we use machine learning to fuse satellite measurements with a chemical model to construct long-term gap-free profile data sets for CH4 and N2O. We aim to construct similar data sets for other important trace gases (e.g. O3, Cly, NOy species).
David Winker, Xia Cai, Mark Vaughan, Anne Garnier, Brian Magill, Melody Avery, and Brian Getzewich
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-373, https://doi.org/10.5194/essd-2023-373, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
Clouds play important roles in both weather and climate. We describe Version 1.0 of a monthly globally gridded ice cloud data product containing vertically resolved statistics on the occurrence and properties of ice clouds, built from the curtains of lidar profile data from the CALIPSO satellite acquired over a 10-year period. This product should provide significant value for cloud research and the evaluation of clouds simulated in weather and climate models.
Tobias Erhardt, Camilla Marie Jensen, Florian Adolphi, Helle Astrid Kjær, Remi Dallmayr, Birthe Twarloh, Melanie Behrens, Motohiro Hirabayashi, Kaori Fukuda, Jun Ogata, François Burgay, Federico Scoto, Ilaria Crotti, Azzurra Spagnesi, Niccoló Maffezzoli, Delia Segato, Chiara Paleari, Florian Mekhaldi, Raimund Muscheler, Sophie Darfeuil, and Hubertus Fischer
Earth Syst. Sci. Data, 15, 5079–5091, https://doi.org/10.5194/essd-15-5079-2023, https://doi.org/10.5194/essd-15-5079-2023, 2023
Short summary
Short summary
The presented paper provides a 3.8 kyr long dataset of aerosol concentrations from the East Greenland Ice coring Project (EGRIP) ice core. The data consists of 1 mm depth-resolution profiles of calcium, sodium, ammonium, nitrate, and electrolytic conductivity as well as decadal averages of these profiles. Alongside the data a detailed description of the measurement setup as well as a discussion of the uncertainties are given.
Hongfei Hao, Kaicun Wang, Chuanfeng Zhao, Guocan Wu, and Jing Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-447, https://doi.org/10.5194/essd-2023-447, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
In this study, we employed a machine learning technique to derive daily AOD from hourly visibility collected at more 5000 airports worldwide from 1980 to 2021 combined with reanalysis meteorological parameters.
Arndt Kaps, Axel Lauer, Rémi Kazeroni, Martin Stengel, and Veronika Eyring
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-424, https://doi.org/10.5194/essd-2023-424, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
CCClim displays observations of clouds in terms of cloud classes that have been in use for a long time. CCClim is a machine-learning-powered product based on multiple existing observational products from different satellites. We show that the cloud classes in CCClim are physically meaningful and can be used to study cloud characteristics in more detail. The goal of this is to make real-world clouds more easily understandable to eventually improve the simulation of clouds in climate models.
Chaoyang Xue, Gisèle Krysztofiak, Vanessa Brocchi, Stéphane Chevrier, Michel Chartier, Patrick Jacquet, Claude Robert, and Valéry Catoire
Earth Syst. Sci. Data, 15, 4553–4569, https://doi.org/10.5194/essd-15-4553-2023, https://doi.org/10.5194/essd-15-4553-2023, 2023
Short summary
Short summary
To understand tropospheric air pollution at regional and global scales, an infrared laser spectrometer called SPIRIT was used on aircraft to rapidly and accurately measure carbon monoxide (CO), an important indicator of air pollution, during the last decade. Measurements were taken for more than 200 flight hours over three continents. Levels of CO are mapped with 3D trajectories for each flight. Additionally, this can be used to validate model performance and satellite measurements.
Goutam Choudhury and Matthias Tesche
Earth Syst. Sci. Data, 15, 3747–3760, https://doi.org/10.5194/essd-15-3747-2023, https://doi.org/10.5194/essd-15-3747-2023, 2023
Short summary
Short summary
Aerosols in the atmosphere that can form liquid cloud droplets are called cloud condensation nuclei (CCN). Accurate measurements of CCN, especially CCN of anthropogenic origin, are necessary to quantify the effect of anthropogenic aerosols on the present-day as well as future climate. In this paper, we describe a novel global 3D CCN data set calculated from satellite measurements. We also discuss the potential applications of the data in the context of aerosol–cloud interactions.
Shoma Yamanouchi, Stephanie Conway, Kimberly Strong, Orfeo Colebatch, Erik Lutsch, Sébastien Roche, Jeffrey Taylor, Cynthia H. Whaley, and Aldona Wiacek
Earth Syst. Sci. Data, 15, 3387–3418, https://doi.org/10.5194/essd-15-3387-2023, https://doi.org/10.5194/essd-15-3387-2023, 2023
Short summary
Short summary
Nineteen years of atmospheric composition measurements made at the University of Toronto Atmospheric Observatory (TAO; 43.66° N, 79.40° W; 174 m.a.s.l.) are presented. These are retrieved from Fourier transform infrared (FTIR) solar absorption spectra recorded with a spectrometer from May 2002 to December 2020. The retrievals have been optimized for fourteen species: O3, HCl, HF, HNO3, CH4, C2H6, CO, HCN, N2O, C2H2, H2CO, CH3OH, HCOOH, and NH3.
Michael J. Prather, Hao Guo, and Xin Zhu
Earth Syst. Sci. Data, 15, 3299–3349, https://doi.org/10.5194/essd-15-3299-2023, https://doi.org/10.5194/essd-15-3299-2023, 2023
Short summary
Short summary
The Atmospheric Tomography Mission (ATom) measured the chemical composition in air parcels from 0–12 km altitude on 2 km horizontal by 80 m vertical scales for four seasons, resolving most scales of chemical heterogeneity. ATom is one of the first missions designed to calculate the chemical evolution of each parcel, providing semi-global diurnal budgets for ozone and methane. Observations covered the remote troposphere: Pacific and Atlantic Ocean basins, Southern Ocean, Arctic basin, Antarctica.
Marie Dumont, Simon Gascoin, Marion Réveillet, Didier Voisin, François Tuzet, Laurent Arnaud, Mylène Bonnefoy, Montse Bacardit Peñarroya, Carlo Carmagnola, Alexandre Deguine, Aurélie Diacre, Lukas Dürr, Olivier Evrard, Firmin Fontaine, Amaury Frankl, Mathieu Fructus, Laure Gandois, Isabelle Gouttevin, Abdelfateh Gherab, Pascal Hagenmuller, Sophia Hansson, Hervé Herbin, Béatrice Josse, Bruno Jourdain, Irene Lefevre, Gaël Le Roux, Quentin Libois, Lucie Liger, Samuel Morin, Denis Petitprez, Alvaro Robledano, Martin Schneebeli, Pascal Salze, Delphine Six, Emmanuel Thibert, Jürg Trachsel, Matthieu Vernay, Léo Viallon-Galinier, and Céline Voiron
Earth Syst. Sci. Data, 15, 3075–3094, https://doi.org/10.5194/essd-15-3075-2023, https://doi.org/10.5194/essd-15-3075-2023, 2023
Short summary
Short summary
Saharan dust outbreaks have profound effects on ecosystems, climate, health, and the cryosphere, but the spatial deposition pattern of Saharan dust is poorly known. Following the extreme dust deposition event of February 2021 across Europe, a citizen science campaign was launched to sample dust on snow over the Pyrenees and the European Alps. This campaign triggered wide interest and over 100 samples. The samples revealed the high variability of the dust properties within a single event.
Han Huang and Yi Huang
Earth Syst. Sci. Data, 15, 3001–3021, https://doi.org/10.5194/essd-15-3001-2023, https://doi.org/10.5194/essd-15-3001-2023, 2023
Short summary
Short summary
We present a newly generated set of ERA5-based radiative kernels and compare them with other published kernels for the top of the atmosphere and surface radiation budgets. For both, the discrepancies in sensitivity values are generally of small magnitude, except for temperature kernels for the surface, likely due to improper treatment in the perturbation experiments used for kernel computation. The kernel bias is not a major cause of the inter-GCM (general circulation model) feedback spread.
Robert Pincus, Paul A. Hubanks, Steven Platnick, Kerry Meyer, Robert E. Holz, Denis Botambekov, and Casey J. Wall
Earth Syst. Sci. Data, 15, 2483–2497, https://doi.org/10.5194/essd-15-2483-2023, https://doi.org/10.5194/essd-15-2483-2023, 2023
Short summary
Short summary
This paper describes a new global dataset of cloud properties observed by a specific satellite program created to facilitate comparison with a matching observational proxy used in climate models. Statistics are accumulated over daily and monthly timescales on an equal-angle grid. Statistics include cloud detection, cloud-top pressure, and cloud optical properties. Joint histograms of several variable pairs are also available.
Emma L. Yates, Laura T. Iraci, Susan S. Kulawik, Ju-Mee Ryoo, Josette E. Marrero, Caroline L. Parworth, Jason M. St. Clair, Thomas F. Hanisco, Thao Paul V. Bui, Cecilia S. Chang, and Jonathan M. Dean-Day
Earth Syst. Sci. Data, 15, 2375–2389, https://doi.org/10.5194/essd-15-2375-2023, https://doi.org/10.5194/essd-15-2375-2023, 2023
Short summary
Short summary
The Alpha Jet Atmospheric eXperiment (AJAX) flew scientific flights between 2011 and 2018 providing measurements of carbon dioxide, methane, ozone, formaldehyde, water vapor and meteorological parameters over California and Nevada, USA. AJAX was a multi-year, multi-objective, multi-instrument program with a variety of sampling strategies resulting in an extensive dataset of interest to a wide variety of users. AJAX measurements have been published at https://asdc.larc.nasa.gov/project/AJAX.
Leïla Simon, Valérie Gros, Jean-Eudes Petit, François Truong, Roland Sarda-Estève, Carmen Kalalian, Alexia Baudic, Caroline Marchand, and Olivier Favez
Earth Syst. Sci. Data, 15, 1947–1968, https://doi.org/10.5194/essd-15-1947-2023, https://doi.org/10.5194/essd-15-1947-2023, 2023
Short summary
Short summary
Long-term measurements of volatile organic compounds (VOCs) have been set up to better characterize the atmospheric chemistry at the SIRTA national facility (Paris area, France). Results obtained from the first 2 years (2020–2021) confirm the importance of local sources for short-lived compounds and the role played by meteorology and air mass origins in the long-term analysis of VOCs. They also point to a substantial influence of anthropogenic on the monoterpene loadings.
Ka Lok Chan, Pieter Valks, Klaus-Peter Heue, Ronny Lutz, Pascal Hedelt, Diego Loyola, Gaia Pinardi, Michel Van Roozendael, François Hendrick, Thomas Wagner, Vinod Kumar, Alkis Bais, Ankie Piters, Hitoshi Irie, Hisahiro Takashima, Yugo Kanaya, Yongjoo Choi, Kihong Park, Jihyo Chong, Alexander Cede, Udo Frieß, Andreas Richter, Jianzhong Ma, Nuria Benavent, Robert Holla, Oleg Postylyakov, Claudia Rivera Cárdenas, and Mark Wenig
Earth Syst. Sci. Data, 15, 1831–1870, https://doi.org/10.5194/essd-15-1831-2023, https://doi.org/10.5194/essd-15-1831-2023, 2023
Short summary
Short summary
This paper presents the theoretical basis as well as verification and validation of the Global Ozone Monitoring Experiment-2 (GOME-2) daily and monthly level-3 products.
Kristina Collins, John Gibbons, Nathaniel Frissell, Aidan Montare, David Kazdan, Darren Kalmbach, David Swartz, Robert Benedict, Veronica Romanek, Rachel Boedicker, William Liles, William Engelke, David G. McGaw, James Farmer, Gary Mikitin, Joseph Hobart, George Kavanagh, and Shibaji Chakraborty
Earth Syst. Sci. Data, 15, 1403–1418, https://doi.org/10.5194/essd-15-1403-2023, https://doi.org/10.5194/essd-15-1403-2023, 2023
Short summary
Short summary
This paper summarizes radio data collected by citizen scientists, which can be used to analyze the charged part of Earth's upper atmosphere. The data are collected from several independent stations. We show ways to look at the data from one station or multiple stations over different periods of time and how it can be combined with data from other sources as well. The code provided to make these visualizations will still work if some data are missing or when more data are added in the future.
Melisa Diaz Resquin, Pablo Lichtig, Diego Alessandrello, Marcelo De Oto, Darío Gómez, Cristina Rössler, Paula Castesana, and Laura Dawidowski
Earth Syst. Sci. Data, 15, 189–209, https://doi.org/10.5194/essd-15-189-2023, https://doi.org/10.5194/essd-15-189-2023, 2023
Short summary
Short summary
We explored the performance of the random forest algorithm to predict CO, NOx, PM10, SO2, and O3 air quality concentrations and comparatively assessed the monitored and modeled concentrations during the COVID-19 lockdown phases. We provide the first long-term O3 and SO2 observational dataset for an urban–residential area of Buenos Aires in more than a decade and study the responses of O3 to the reduction in the emissions of its precursors because of its relevance regarding emission control.
Vitali E. Fioletov, Chris A. McLinden, Debora Griffin, Ihab Abboud, Nickolay Krotkov, Peter J. T. Leonard, Can Li, Joanna Joiner, Nicolas Theys, and Simon Carn
Earth Syst. Sci. Data, 15, 75–93, https://doi.org/10.5194/essd-15-75-2023, https://doi.org/10.5194/essd-15-75-2023, 2023
Short summary
Short summary
Sulfur dioxide (SO2) measurements from three satellite instruments were used to update and extend the previously developed global catalogue of large SO2 emission sources. This version 2 of the global catalogue covers the period of 2005–2021 and includes a total of 759 continuously emitting point sources. The catalogue data show an approximate 50 % decline in global SO2 emissions between 2005 and 2021, although emissions were relatively stable during the last 3 years.
Jed O. Kaplan and Katie Hong-Kiu Lau
Earth Syst. Sci. Data, 14, 5665–5670, https://doi.org/10.5194/essd-14-5665-2022, https://doi.org/10.5194/essd-14-5665-2022, 2022
Short summary
Short summary
Global lightning strokes are recorded continuously by a network of ground-based stations. We consolidated these point observations into a map form and provide these as electronic datasets for research purposes. Here we extend our dataset to include lightning observations from 2021.
Haris Rahadianto, Hirokazu Tatano, Masato Iguchi, Hiroshi L. Tanaka, Tetsuya Takemi, and Sudip Roy
Earth Syst. Sci. Data, 14, 5309–5332, https://doi.org/10.5194/essd-14-5309-2022, https://doi.org/10.5194/essd-14-5309-2022, 2022
Short summary
Short summary
We simulated the Taisho (1914) eruption of Sakurajima volcano under various weather conditions to show how a similar eruption would affect contemporary Japan in a worst-case scenario. We provide the dataset of projected airborne ash concentration and deposit over all of Japan to support risk assessment and planning for disaster management. Our work extends previous analyses of local risks to cover distal locations in Japan where a large population could be exposed to devastating impacts.
Xiangyue Chen, Hongchao Zuo, Zipeng Zhang, Xiaoyi Cao, Jikai Duan, Chuanmei Zhu, Zhe Zhang, and Jingzhe Wang
Earth Syst. Sci. Data, 14, 5233–5252, https://doi.org/10.5194/essd-14-5233-2022, https://doi.org/10.5194/essd-14-5233-2022, 2022
Short summary
Short summary
Arid and semi-arid areas are data-scarce aerosol areas. We provide path-breaking, high-resolution, full coverage, and long time series AOD datasets (FEC AOD) to support the atmosphere and related studies in northwestern China. The FEC AOD effectively compensates for the deficiency and constraints of in situ observations and satellite AOD products. Meanwhile, FEC AOD products demonstrate a reliable accuracy and ability to capture long-term change information.
Natalie Kaifler, Bernd Kaifler, Markus Rapp, and David C. Fritts
Earth Syst. Sci. Data, 14, 4923–4934, https://doi.org/10.5194/essd-14-4923-2022, https://doi.org/10.5194/essd-14-4923-2022, 2022
Short summary
Short summary
We measured polar mesospheric clouds (PMCs), our Earth’s highest clouds at the edge of space, with a Rayleigh lidar from a stratospheric balloon. We describe how we derive the cloud’s brightness and discuss the stability of the gondola pointing and the sensitivity of our measurements. We present our high-resolution PMC dataset that is used to study dynamical processes in the upper mesosphere, e.g. regarding gravity waves, mesospheric bores, vortex rings, and Kelvin–Helmholtz instabilities.
Yi Cheng, Shaofei Kong, Liquan Yao, Huang Zheng, Jian Wu, Qin Yan, Shurui Zheng, Yao Hu, Zhenzhen Niu, Yingying Yan, Zhenxing Shen, Guofeng Shen, Dantong Liu, Shuxiao Wang, and Shihua Qi
Earth Syst. Sci. Data, 14, 4757–4775, https://doi.org/10.5194/essd-14-4757-2022, https://doi.org/10.5194/essd-14-4757-2022, 2022
Short summary
Short summary
This work establishes the first emission inventory of carbonaceous aerosols from cooking, fireworks, sacrificial incense, joss paper burning, and barbecue, using multi-source datasets and tested emission factors. These emissions were concentrated in specific periods and areas. Positive and negative correlations between income and emissions were revealed in urban and rural regions. The dataset will be helpful for improving modeling studies and modifying corresponding emission control policies.
Qiang Cui, Yilin Lei, and Bin Chen
Earth Syst. Sci. Data, 14, 4419–4433, https://doi.org/10.5194/essd-14-4419-2022, https://doi.org/10.5194/essd-14-4419-2022, 2022
Short summary
Short summary
This paper calculates the emissions of six kinds of emissions from China’s foreign routes from 2014 to 2019, enriching the existing database. This paper applies the improved BFFM2-FOA-FPM method and ICAO method to calculate the emissions, which can combine CO2 and non-CO2 emissions calculations and calculate the aircraft types' emission intensity.
Mengze Li, Andrea Pozzer, Jos Lelieveld, and Jonathan Williams
Earth Syst. Sci. Data, 14, 4351–4364, https://doi.org/10.5194/essd-14-4351-2022, https://doi.org/10.5194/essd-14-4351-2022, 2022
Short summary
Short summary
We present a northern hemispheric airborne measurement dataset of atmospheric ethane, propane and methane and temporal trends for the time period 2006–2016 in the upper troposphere and lower stratosphere. The growth rates of ethane, methane, and propane in the upper troposphere are -2.24, 0.33, and -0.78 % yr-1, respectively, and in the lower stratosphere they are -3.27, 0.26, and -4.91 % yr-1, respectively, in 2006–2016.
Malika Menoud, Carina van der Veen, Dave Lowry, Julianne M. Fernandez, Semra Bakkaloglu, James L. France, Rebecca E. Fisher, Hossein Maazallahi, Mila Stanisavljević, Jarosław Nęcki, Katarina Vinkovic, Patryk Łakomiec, Janne Rinne, Piotr Korbeń, Martina Schmidt, Sara Defratyka, Camille Yver-Kwok, Truls Andersen, Huilin Chen, and Thomas Röckmann
Earth Syst. Sci. Data, 14, 4365–4386, https://doi.org/10.5194/essd-14-4365-2022, https://doi.org/10.5194/essd-14-4365-2022, 2022
Short summary
Short summary
Emission sources of methane (CH4) can be distinguished with measurements of CH4 stable isotopes. We present new measurements of isotope signatures of various CH4 sources in Europe, mainly anthropogenic, sampled from 2017 to 2020. The present database also contains the most recent update of the global signature dataset from the literature. The dataset improves CH4 source attribution and the understanding of the global CH4 budget.
Patrick Hupe, Lars Ceranna, Alexis Le Pichon, Robin S. Matoza, and Pierrick Mialle
Earth Syst. Sci. Data, 14, 4201–4230, https://doi.org/10.5194/essd-14-4201-2022, https://doi.org/10.5194/essd-14-4201-2022, 2022
Short summary
Short summary
Sound waves with frequencies below the human hearing threshold can travel long distances through the atmosphere. A global network of sensors records such infrasound to detect clandestine nuclear tests in the atmosphere. These data are generally not public. This study provides four data products based on global infrasound signal detections to make infrasound data available to a broad community. This will advance the use of infrasound observations for scientific studies and civilian applications.
Zexia Duan, Zhiqiu Gao, Qing Xu, Shaohui Zhou, Kai Qin, and Yuanjian Yang
Earth Syst. Sci. Data, 14, 4153–4169, https://doi.org/10.5194/essd-14-4153-2022, https://doi.org/10.5194/essd-14-4153-2022, 2022
Short summary
Short summary
Land–atmosphere interactions over the Yangtze River Delta (YRD) in China are becoming more varied and complex, as the area is experiencing rapid land use changes. In this paper, we describe a dataset of microclimate and eddy covariance variables at four sites in the YRD. This dataset has potential use cases in multiple research fields, such as boundary layer parametrization schemes, evaluation of remote sensing algorithms, and development of climate models in typical East Asian monsoon regions.
Xiaoli Sun, Paul T. Kolbeck, James B. Abshire, Stephan R. Kawa, and Jianping Mao
Earth Syst. Sci. Data, 14, 3821–3833, https://doi.org/10.5194/essd-14-3821-2022, https://doi.org/10.5194/essd-14-3821-2022, 2022
Short summary
Short summary
We describe the measurement and data processing of the atmospheric backscatter profile data by our CO2 Sounder lidar from the 2017 ASCENDS/ABoVE airborne campaign. It is an additional data set from the column average CO2 mixing ratio measurements from laser sounding. It not only helps to interpret the CO2 mixing ratio measurement but also give a standalone data set for atmosphere backscattering study at 1572 nm wavelength.
Lei Li, Yevgeny Derimian, Cheng Chen, Xindan Zhang, Huizheng Che, Gregory L. Schuster, David Fuertes, Pavel Litvinov, Tatyana Lapyonok, Anton Lopatin, Christian Matar, Fabrice Ducos, Yana Karol, Benjamin Torres, Ke Gui, Yu Zheng, Yuanxin Liang, Yadong Lei, Jibiao Zhu, Lei Zhang, Junting Zhong, Xiaoye Zhang, and Oleg Dubovik
Earth Syst. Sci. Data, 14, 3439–3469, https://doi.org/10.5194/essd-14-3439-2022, https://doi.org/10.5194/essd-14-3439-2022, 2022
Short summary
Short summary
A climatology of aerosol composition concentration derived from POLDER-3 observations using GRASP/Component is presented. The conceptual specifics of the GRASP/Component approach are in the direct retrieval of aerosol speciation without intermediate retrievals of aerosol optical characteristics. The dataset of satellite-derived components represents scarce but imperative information for validation and potential adjustment of chemical transport models.
Junting Zhong, Xiaoye Zhang, Ke Gui, Jie Liao, Ye Fei, Lipeng Jiang, Lifeng Guo, Liangke Liu, Huizheng Che, Yaqiang Wang, Deying Wang, and Zijiang Zhou
Earth Syst. Sci. Data, 14, 3197–3211, https://doi.org/10.5194/essd-14-3197-2022, https://doi.org/10.5194/essd-14-3197-2022, 2022
Short summary
Short summary
Historical long-term PM2.5 records with high temporal resolution are essential but lacking for research and environmental management. Here, we reconstruct site-based and gridded PM2.5 datasets at 6-hour intervals from 1960 to 2020 that combine visibility, meteorological data, and emissions based on a machine learning model with extracted spatial features. These two PM2.5 datasets will lay the foundation of research studies associated with air pollution, climate change, and aerosol reanalysis.
Fei Jiang, Weimin Ju, Wei He, Mousong Wu, Hengmao Wang, Jun Wang, Mengwei Jia, Shuzhuang Feng, Lingyu Zhang, and Jing M. Chen
Earth Syst. Sci. Data, 14, 3013–3037, https://doi.org/10.5194/essd-14-3013-2022, https://doi.org/10.5194/essd-14-3013-2022, 2022
Short summary
Short summary
A 10-year (2010–2019) global monthly terrestrial NEE dataset (GCAS2021) was inferred from the GOSAT ACOS v9 XCO2 product. It shows strong carbon sinks over eastern N. America, the Amazon, the Congo Basin, Europe, boreal forests, southern China, and Southeast Asia. It has good quality and can reflect the impacts of extreme climates and large-scale climate anomalies on carbon fluxes well. We believe that this dataset can contribute to regional carbon budget assessment and carbon dynamics research.
Cited articles
Ackerman, S. A., Holz, R. E., Frey, R., Eloranta, E. W., Maddux, B. C., and
McGill, M.: Cloud detection with MODIS. Part II: Validation, J.
Atmos. Ocean. Tech., 25, 1073–1086, https://doi.org/10.1175/2007jtecha1053.1,
2008.
Beckerman, B. S., Jerrett, M., Serre, M., Martin, R. V., Lee, S.-J., van
Donkelaar, A., Ross, Z., Su, J., and Burnett, R. T.: A Hybrid Approach to
Estimating National Scale Spatiotemporal Variability of PM2.5 in the
Contiguous United States, Environ. Sci. Technol., 47,
7233–7241, https://doi.org/10.1021/es400039u, 2013.
Bogaert, P., Christakos, G., Jerrett, M., and Yu, H. L.: Spatiotemporal
modelling of ozone distribution in the State of California, Atmos.
Environ., 43, 2471–2480, https://doi.org/10.1016/j.atmosenv.2009.01.049, 2009.
Bojinski, S., Verstraete, M., Peterson, T. C., Richter, C., Simmons, A., and
Zemp, M.: The concept of essential climate variables in support of climate
research, applications, and policy, B. Am. Meteorol. Soc., 95, 1431–1443, https://doi.org/10.1175/bams-d-13-00047.1, 2014.
Brocca, L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, T., Wagner,
W., Dorigo, W., Matgen, P., Martínez-Fernández, J., Llorens, P.,
Latron, J., Martin, C., and Bittelli, M.: Soil moisture estimation through
ASCAT and AMSR-E sensors: An intercomparison and validation study across
Europe, Remote Sens. Environ., 115, 3390–3408, https://doi.org/10.1016/j.rse.2011.08.003, 2011.
Chatterjee, A., Michalak, A. M., Kahn, R. A., Paradise, S. R., Braverman, A.
J., and Miller, C. E.: A geostatistical data fusion technique for merging
remote sensing and ground-based observations of aerosol optical thickness,
J. Geophys. Res.-Atmos., 115, D20207, https://doi.org/10.1029/2009jd013765,
2010.
Christakos, G.: Modern Spatiotemporal Geostatistics, New York, NY: Oxford University Press, 2000.
Christakos, G.: Integrative problem-solving in a time of decadence, Springer
Science & Business Media, https://doi.org/10.1007/978-90-481-9890-0, 2010.
Christakos, G. and Serre, M. L.: BME analysis of spatiotemporal particulate matter
distributions in North Carolina, Atmos. Environ., 34, 3393–3406, 2000.
Christakos, G., Kolovos, A., Serre, M. L., and Vukovich, F.: Total ozone
mapping by integrating databases from remote sensing instruments and
empirical models, IEEE T. Geosci. Remote, 42,
991–1008, https://doi.org/10.1109/Tgrs.2003.822751, 2004.
Claudia, S., William, R., and Stefan, K.: Assessment of Global Cloud Data Sets
from Satellites A Project of the World Climate Research Programme Global
Energy and Water Cycle Experiment (GEWEX) Radiation Panel, World Climate
Research Program Proport, http://climate.org/documents/GEWEX_Cloud_Assessment_2012.pdf (last access: 2019), 2012.
Cressie, N.: Statistics for spatial data, John Wiley & Sons, https://doi.org/10.1002/9781119115151, 2015.
Danso, D. K., Anquetin, S., Diedhiou, A., Kouadio, K., and Kobea, A. T.: Daytime low-level clouds in West Africa – occurrence, associated drivers, and shortwave radiation attenuation, Earth Syst. Dynam., 11, 1133–1152, https://doi.org/10.5194/esd-11-1133-2020, 2020.
Doelling, D. R., Sun, M., Nguyen, L. T., Nordeen, M. L., Haney, C. O.,
Keyes, D. F., and Mlynczak, P. E.: Advances in Geostationary-Derived
Longwave Fluxes for the CERES Synoptic (SYN1deg) Product, J.
Atmos. Ocean. Tech., 33, 503–521, https://doi.org/10.1175/Jtech-D-15-0147.1,
2016.
Drusch, M.: Observation operators for the direct assimilation of TRMM
microwave imager retrieved soil moisture, Geophys. Res. Lett., 32, L15403, https://doi.org/10.1029/2005gl023623, 2005.
Eastman, R. and Warren, S. G.: Arctic Cloud Changes from Surface and
Satellite Observations, J. Climate, 23, 4233–4242, https://doi.org/10.1175/2010jcli3544.1, 2010.
English, J. M., Kay, J. E., Gettelman, A., Liu, X. H., Wang, Y., Zhang, Y. Y., and Chepfer, H.: Contributions of clouds, surface albedos, and mixed phase ice nucleation schemes to Arctic radiation biases in CAM5, J. Climate, 27, 5174–5197, https://doi.org/10.1175/JCLI-D-13-00608.1, 2014.
Enriquez-Alonso, A., Sanchez-Lorenzo, A., Calbo, J., Gonzalez, J. A., and
Norris, J. R.: Cloud cover climatologies in the Mediterranean obtained from
satellites, surface observations, reanalyses, and CMIP5 simulations:
validation and future scenarios, Clim. Dynam., 47, 249–269, https://doi.org/10.1007/s00382-015-2834-4, 2016.
Forbes, R. M. and Ahlgrimm, M.: On the Representation of High-Latitude
Boundary Layer Mixed-Phase Cloud in the ECMWF Global Model, Mon.
Weather Rev., 142, 3425–3445, https://doi.org/10.1175/MWR-D-13-00325.1,
2014.
Freeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C.,
Angel, W. E., Berry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W.,
Ji, Z., Lawrimore, J., Rayner, N. A., Rosenhagen, G., and Smith, S. R.:
ICOADS Release 3.0: a major update to the historical marine climate record,
Int. J. Climatol., 37, 2211–2232, https://doi.org/10.1002/joc.4775, 2017.
Fuentes, M. and Raftery, A. E.: Model evaluation and spatial interpolation
by Bayesian combination of observations with outputs from numerical models,
Biometrics, 61, 36–45, https://doi.org/10.1111/j.0006-341X.2005.030821.x, 2005.
Gao, F., Masek, J., Schwaller, M., and Hall, F.:
On the blending of the Landsat and MODIS surface reflectance: Predicting
daily Landsat surface reflectance, IEEE T. Geosci. Remote, 44, 2207–2218, 2006.
Griffith, D. A.: Statistics for spatial data – CRESSIE, N, Geogr.
Anal., 25, 271–275, 1993.
Hakuba, M. Z., Folini, D., Wild, M., Long, C. N., Schaepman-Strub, G., and
Stephens, G. L.: Cloud effects on atmospheric solar absorption in light of
most recent surface and satellite measurements, in: Radiation Processes in the Atmosphere and Ocean (IRS2016), Proceedings of the International Radiation Symposium (IRC/IAMAS), International Radiation Symposium (IRC/IAMAS), IRS 2016, Auckland, New Zealand, 16–22 April 2016, American Institute of Physics, https://doi.org/10.1063/1.4975543, 2017.
Harris, I., Jones, P. D., Osborn, T. J., and Lister, D. H.: Updated
high-resolution grids of monthly climatic observations – the CRU TS3.10
Dataset, Int. J. Climatol., 34, 623–642, https://doi.org/10.1002/joc.3711, 2014.
Harris, I., Osborn, T. J., Jones, P., and Lister, D.: Version 4 of the CRU
TS monthly high-resolution gridded multivariate climate dataset, Sci. Data,
7, 109, https://doi.org/10.1038/s41597-020-0453-3, 2020.
He, J. and Kolovos, A.: Bayesian maximum entropy approach and its
applications: a review, Stoch. Env. Re. Risk
A., 32, 859–877, https://doi.org/10.1007/s00477-017-1419-7, 2017.
Heidinger, A. K., Evan, A. T., Foster, M. J., and Walther, A.: A Naive
Bayesian Cloud-Detection Scheme Derived from CALIPSO and Applied within
PATMOS-x, J. Appl. Meteorol. Climatol., 51, 1129–1144, https://doi.org/10.1175/Jamc-D-11-02.1, 2012.
Heidinger, A. K., Foster, M. J., Walther, A., and Zhao, X. P.: The
Pathfinder Atmospheres-Extended Avhrr Climate Dataset, B.
Am. Meteorol. Soc., 95, 909–922, https://doi.org/10.1175/Bams-D-12-00246.1,
2014.
Hilker, T., Wulder, M. A., Coops, N. C., Linke, J., McDermid, G., Masek, J.
G., Gao, F., and White, J. C.: A new data fusion model for high spatial- and
temporal-resolution mapping of forest disturbance based on Landsat and
MODIS, Remote Sens. Environ., 113, 1613–1627, https://doi.org/10.1016/j.rse.2009.03.007, 2009.
Hollmann, R.: ESA Cloud_cci Product Validation and
Intercomparison Report (PVIR), https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V002, 2018.
Hollmann, R., Merchant, C. J., Saunders, R., Downy, C., Buchwitz, M.,
Cazenave, A., Chuvieco, E., Defourny, P., de Leeuw, G., Forsberg, R.,
Holzer-Popp, T., Paul, F., Sandven, S., Sathyendranath, S., van Roozendael,
M., and Wagner, W.: The ESA Climate Change Initiative Satellite Data Records
for Essential Climate Variables, B. Am. Meteorol. Soc., 94, 1541–1552, https://doi.org/10.1175/bams-d-11-00254.1, 2013.
Hu, M. and Xue, M.: Implementation and evaluation of cloud
analysis with WSR-88D reflectivity data for GSI and WRF-ARW, Geophys. Res. Lett., 34, L07808, https://doi.org/10.1029/2006GL028847, 2007.
Huang, Y. Y., Dong, X. Q., Xi, B. K., Dolinar, E. K., Stanfield, R. E., and
Qiu, S. Y.: Quantifying the Uncertainties of Reanalyzed Arctic Cloud and
Radiation Properties Using Satellite Surface Observations, J.
Climate, 30, 8007–8029, https://doi.org/10.1175/Jcli-D-16-0722.1, 2017.
Hunt, W. H., Winker, D. M., Vaughan, M. A., Powell, K. A., Lucker, P. L.,
and Weimer, C.: CALIPSO Lidar Description and Performance Assessment,
J. Atmos. Ocean. Tech., 26, 1214–1228, https://doi.org/10.1175/2009jtecha1223.1, 2009.
Jaynes, E. T.: Information theory and statistical mechanics, Phys. Rev., 106, 620, https://doi.org/10.1103/PhysRev.106.620,
1957.
Jin, W., Fu, R.-D., Ye, M., and Li, J.-X.: Meteorological Cloud Image Fusion
Using Contourlet Transform and Compressed Sensing, International Conference
on Ecological Protection of Lakes-Wetlands-Watershed and Application of 3S
Technology (EPLWW3S 2011), Nanchang, Peoples R China,
25–26 June 2011, WOS:000391516000097, 413–416, 2011.
Karlsson, K.-G. and Devasthale, A.: Inter-Comparison and Evaluation of the
Four Longest Satellite-Derived Cloud Climate Data Records: CLARA-A2, ESA
Cloud CCI V3, ISCCP-HGM, and PATMOS-x, Remote Sens.-Basel, 10, 1567, https://doi.org/10.3390/rs10101567, 2018.
Karlsson, K.-G. and Dybbroe, A.: Evaluation of Arctic cloud products from the EUMETSAT Climate Monitoring Satellite Application Facility based on CALIPSO-CALIOP observations, Atmos. Chem. Phys., 10, 1789–1807, https://doi.org/10.5194/acp-10-1789-2010, 2010.
Karlsson, K.-G. and Håkansson, N.: Characterization of AVHRR global cloud detection sensitivity based on CALIPSO-CALIOP cloud optical thickness information: demonstration of results based on the CM SAF CLARA-A2 climate data record, Atmos. Meas. Tech., 11, 633–649, https://doi.org/10.5194/amt-11-633-2018, 2018.
Karlsson, K.-G., Riihelä, A., Müller, R., Meirink, J. F., Sedlar, J., Stengel, M., Lockhoff, M., Trentmann, J., Kaspar, F., Hollmann, R., and Wolters, E.: CLARA-A1: a cloud, albedo, and radiation dataset from 28 yr of global AVHRR data, Atmos. Chem. Phys., 13, 5351–5367, https://doi.org/10.5194/acp-13-5351-2013, 2013.
Karlsson, K.-G., Anttila, K., Trentmann, J., Stengel, M., Fokke Meirink, J., Devasthale, A., Hanschmann, T., Kothe, S., Jääskeläinen, E., Sedlar, J., Benas, N., van Zadelhoff, G.-J., Schlundt, C., Stein, D., Finkensieper, S., Håkansson, N., and Hollmann, R.: CLARA-A2: the second edition of the CM SAF cloud and radiation data record from 34 years of global AVHRR data, Atmos. Chem. Phys., 17, 5809–5828, https://doi.org/10.5194/acp-17-5809-2017, 2017.
Kato, S., Loeb, N. G., Rutan, D. A., Rose, F. G., Sun-Mack, S., Miller, W.
F., and Chen, Y.: Uncertainty Estimate of Surface Irradiances Computed with
MODIS-, CALIPSO-, and CloudSat-Derived Cloud and Aerosol Properties, Surv.
Geophys., 33, 395-412, https://doi.org/10.1007/s10712-012-9179-x, 2012.
Kato, S., Rose, F. G., Rutan, D. A., Thorsen, T. J., Loeb, N. G., Doelling,
D. R., Huang, X., Smith, W. L., Su, W., and Ham, S.-H.: Surface Irradiances
of Edition 4.0 Clouds and the Earth's Radiant Energy System (CERES) Energy
Balanced and Filled (EBAF) Data Product, J. Climate, 31, 4501–4527, https://doi.org/10.1175/jcli-d-17-0523.1, 2018a.
Kato, S., Rose, F. G., Rutan, D. A., Thorsen, T. J., Loeb, N. G., Doelling,
D. R., Huang, X. L., Smith, W. L., Su, W. Y., and Ham, S. H.: Surface
Irradiances of Edition 4.0 Clouds and the Earth's Radiant Energy System
(CERES) Energy Balanced and Filled (EBAF) Data Product, J. Climate,
31, 4501–4527, https://doi.org/10.1175/Jcli-D-17-0523.1, 2018b.
Kato, S., Rose, F. G., Sun-Mack, S., Miller, W. F., Chen, Y., Rutan, D. A.,
Stephens, G. L., Loeb, N. G., Minnis, P., Wielicki, B. A., Winker, D. M.,
Charlock, T. P., Stackhouse, P. W., Xu, K.-M., and Collins, W. D.:
Improvements of top-of-atmosphere and surface irradiance computations with
CALIPSO-, CloudSat-, and MODIS-derived cloud and aerosol properties, J.
Geophys. Res., 116, D19209, https://doi.org/10.1029/2011jd016050, 2011.
Kennedy, A., Xi, B., Dong, X., and Zib, B. J.: Evaluation and
Intercomparison of Cloud Fraction and Radiative Fluxes in Recent Reanalyses
over the Arctic Using BSRN Surface Observations, J. Climate, 25, 2291–2305, https://doi.org/10.1175/jcli-d-11-00147.1, 2012.
Kenyon, J. S., Moninger, W. R., Smith, T. L., Peckham, S. E., Lin, H.,
Grell, G. A., Dowell, D. C., James, E. P., Olson, J. B., Smirnova, T. G.,
Alexander, C. R., Hu, M., Brown, J. M., Weygandt, S. S., Benjamin, S. G.,
and Manikin, G. S.: A North American Hourly Assimilation and Model Forecast
Cycle: The Rapid Refresh, Mon. Weather Rev., 144, 1669–1694, https://doi.org/10.1175/mwr-d-15-0242.1, 2016.
Kim, D. and Ramanathan, V.: Solar radiation budget and radiative forcing due
to aerosols and clouds, J. Geophys. Res., 113, D02203, https://doi.org/10.1029/2007jd008434, 2008.
Kotarba, A. Z.: Evaluation of ISCCP cloud amount with MODIS observations,
Atmos. Res., 153, 310–317, https://doi.org/10.1016/j.atmosres.2014.09.006, 2015.
Kotarba, A. Z.: Calibration of global MODIS cloud amount using CALIOP cloud profiles, Atmos. Meas. Tech., 13, 4995–5012, https://doi.org/10.5194/amt-13-4995-2020, 2020.
Li, A., Bo, Y., Zhu, Y., Guo, P., Bi, J., and He, Y.: Blending
multi-resolution satellite sea surface temperature (SST) products using
Bayesian maximum entropy method, Remote Sens. Environ., 135, 52–63, https://doi.org/10.1016/j.rse.2013.03.021, 2013.
Li, L., Shi, R., Zhang, L., Zhang, J., and Gao, W.: The data fusion of
aerosol optical thickness using universal kriging and stepwise regression in
East China, Conference on Remote Sensing and Modeling of Ecosystems for
Sustainability XI, San Diego, CA,
18–20 August 2014, WOS:000344548600027, https://doi.org/10.1117/12.2061764, 2014.
Li, S. and Yang, B.: Multifocus image fusion by combining curvelet and
wavelet transform, Pattern Recogn. Lett., 29, 1295–1301, https://doi.org/10.1016/j.patrec.2008.02.002, 2008.
Liu, X., He, T., Sun, L., Xiao, X., Liang, S., and Li, S.: Analysis of
Daytime Cloud Fraction Spatiotemporal Variation over the Arctic from 2000 to
2019 from Multiple Satellite Products, J. Climate, 35, 3995–4023, https://doi.org/10.1175/jcli-d-22-0007.1, 2022.
Liu, Y., Ackerman, S. A., Maddux, B. C., Key, J. R., and Frey, R. A.: Errors
in Cloud Detection over the Arctic Using a Satellite Imager and Implications
for Observing Feedback Mechanisms, J. Climate, 23, 1894–1907, https://doi.org/10.1175/2009jcli3386.1, 2010.
Liu, Y., Wu, W., Jensen, M. P., and Toto, T.: Relationship between cloud radiative forcing, cloud fraction and cloud albedo, and new surface-based approach for determining cloud albedo, Atmos. Chem. Phys., 11, 7155–7170, https://doi.org/10.5194/acp-11-7155-2011, 2011.
Liu, Y., Key, J. R., Liu, Z., Wang, X., and Vavrus, S. J.: A cloudier Arctic
expected with diminishing sea ice, Geophys. Res. Lett., 39,
https://doi.org/10.1029/2012gl051251, 2012.
Liu, Y., Liu, S., and Wang, Z.: A general framework for image fusion based
on multi-scale transform and sparse representation, Inform. Fusion, 24,
147–164, https://doi.org/10.1016/j.inffus.2014.09.004, 2015.
Liu, Y. H., Key, J. R., Ackerman, S. A., Mace, G. G., and Zhang, Q. Q.:
Arctic cloud macrophysical characteristics from CloudSat and CALIPSO, Remote
Sens. Environ., 124, 159–173, https://doi.org/10.1016/j.rse.2012.05.006, 2012.
Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I. J. M., McCabe, M. F., and Evans, J. P.: Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals, Hydrol. Earth Syst. Sci., 15, 425–436, https://doi.org/10.5194/hess-15-425-2011, 2011.
Loyola R, D. G., Thomas, W., Spurr, R., and Mayer, B.: Global patterns in
daytime cloud properties derived from GOME backscatter UV-VIS measurements,
Int. J. Remote Sens., 31, 4295–4318, https://doi.org/10.1080/01431160903246741, 2010.
Marchant, B., Platnick, S., Meyer, K., Arnold, G. T., and Riedi, J.: MODIS Collection 6 shortwave-derived cloud phase classification algorithm and comparisons with CALIOP, Atmos. Meas. Tech., 9, 1587–1599, https://doi.org/10.5194/amt-9-1587-2016, 2016.
Marchant, B., Platnick, S., Meyer, K., and Wind, G.: Evaluation of the MODIS Collection 6 multilayer cloud detection algorithm through comparisons with CloudSat Cloud Profiling Radar and CALIPSO CALIOP products, Atmos. Meas. Tech., 13, 3263–3275, https://doi.org/10.5194/amt-13-3263-2020, 2020.
Miao, Q. and Wang, B.: A Novel Image Fusion Method Using Contourlet
Transform, International Conference on Communications, Communications, Circuits and Systems, Guilin, China, 548–552, https://doi.org/10.1109/ICCCAS.2006.284696, 2006.
Minnis, P., Sun-Mack, S., Young, D. F., Heck, P. W., Garber, D. P., Chen,
Y., Spangenberg, D. A., Arduini, R. F., Trepte, Q. Z., Smith, W. L., Ayers,
J. K., Gibson, S. C., Miller, W. F., Hong, G., Chakrapani, V., Takano, Y.,
Liou, K. N., Xie, Y., and Yang, P.: CERES Edition-2 Cloud Property
Retrievals Using TRMM VIRS and Terra and Aqua MODIS Data-Part I: Algorithms,
IEEE T. Geosci. Remote, 49, 4374–4400, https://doi.org/10.1109/tgrs.2011.2144601, 2011.
Nazelle, A. D., Arunachalam, S., and Serre, M. L.: Bayesian maximum entropy
integration of ozone observations and model predictions: an application for
attainment demonstration in North Carolina, Environ. Sci. Technol., 44,
5707–5713, https://doi.org/10.1021/es100228w, 2010.
Nie, S., Wu, T., Luo, Y., Deng, X., Shi, X., Wang, Z., Liu, X., and Huang,
J.: A strategy for merging objective estimates of global daily precipitation
from gauge observations, satellite estimates, and numerical predictions,
Adv. Atmos. Sci., 33, 889–904, https://doi.org/10.1007/s00376-016-5223-y,
2016.
Paul, A. H.: Collection 6.1 Change Summary Document MODIS Atmosphere Level-3
Algorithm and Global Products, https://atmosphere-imager.gsfc.nasa.gov/sites/default/files/ModAtmo/L3_C61_Changes_v2.pdf (last access: 2021), 2017.
Philipp, D., Stengel, M., and Ahrens, B.: Analyzing the Arctic Feedback
Mechanism between Sea Ice and Low-Level Clouds Using 34 Years of Satellite
Observations, J. Climate, 33, 7479–7501, https://doi.org/10.1175/jcli-d-19-0895.1,
2020.
Poulsen, C. J., Tabor, C., and White, J.: Response to Comment on “Long-term
climate forcing by atmospheric oxygen concentrations”, Science, 353, 132, https://doi.org/10.1126/science.aad8550, 2016.
Qian, Y., Long, C. N., Wang, H., Comstock, J. M., McFarlane, S. A., and Xie, S.: Evaluation of cloud fraction and its radiative effect simulated by IPCC AR4 global models against ARM surface observations, Atmos. Chem. Phys., 12, 1785–1810, https://doi.org/10.5194/acp-12-1785-2012, 2012.
Ramanathan, V., Cess, R. D., Harrison, E. F., Minnis, P., Barkstrom, B. R.,
Ahmad, E., and Hartmann, D.: Cloud-Radiative Forcing and Climate – Results
from the Earth Radiation Budget Experiment, Science, 243, 57–63, https://doi.org/10.1126/science.243.4887.57, 1989.
Rossow, W. B. and Schiffer, R. A.: Advances in understanding clouds from
ISCCP, B. Am. Meteorol. Soc., 80, 2261–2287, https://doi.org/10.1175/1520-0477(1999)080<2261:Aiucfi>2.0.Co;2, 1999.
Savelyeva, E., Utkin, S., Kazakov, S., and Demyanov, V.: Modeling Spatial
Uncertainty for Locally Uncertain Data, 7th International Conference on
Geostatistics for Environmental Applications, Southampton, England, WOS:000288481100026, https://doi.org/10.1007/978-90-481-2322-3_26, 2010.
Shupe, M. D., Turner, D. D., Walden, V. P., Bennartz, R., Cadeddu, M. P.,
Castellani, B. B., Cox, C. J., Hudak, D. R., Kulie, M. S., Miller, N. B.,
Neely, R. R., Neff, W. D., and Rowe, P. M.: HIGH AND DRY New Observations of
Tropospheric and Cloud Properties above the Greenland Ice Sheet, B. Am.
Meteorol. Soc., 94, 169–186, https://doi.org/10.1175/Bams-D-11-00249.1, 2013.
Sledd, A. and L'Ecuyer, T. S.: Emerging Trends in Arctic Solar Absorption,
Geophys. Res. Lett., 48, e2021GL095813, https://doi.org/10.1029/2021gl095813, 2021.
Spadavecchia, L. and Williams, M.: Can spatio-temporal geostatistical
methods improve high resolution regionalisation of meteorological
variables?, Agr. Forest Meteorol., 149, 1105–1117, https://doi.org/10.1016/j.agrformet.2009.01.008, 2009.
Stengel, M., Stapelberg, S., Sus, O., Schlundt, C., Poulsen, C., Thomas, G., Christensen, M., Carbajal Henken, C., Preusker, R., Fischer, J., Devasthale, A., Willén, U., Karlsson, K.-G., McGarragh, G. R., Proud, S., Povey, A. C., Grainger, R. G., Meirink, J. F., Feofilov, A., Bennartz, R., Bojanowski, J. S., and Hollmann, R.: Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project, Earth Syst. Sci. Data, 9, 881–904, https://doi.org/10.5194/essd-9-881-2017, 2017.
Stubenrauch, C. J., Rossow, W. B., Kinne, S., Ackerman, S., Cesana, G.,
Chepfer, H., Di Girolamo, L., Getzewich, B., Guignard, A., Heidinger, A.,
Maddux, B. C., Menzel, W. P., Minnis, P., Pearl, C., Platnick, S., Poulsen,
C., Riedi, J., Sun-Mack, S., Walther, A., Winker, D., Zeng, S., and Zhao,
G.: Assessment of Global Cloud Datasets from Satellites: Project and
Database Initiated by the GEWEX Radiation Panel, B. Am.
Meteorol. Soc., 94, 1031–1049, https://doi.org/10.1175/bams-d-12-00117.1, 2013.
Sun, B. M., Free, M., Yoo, H. L., Foster, M. J., Heidinger, A., and
Karlsson, K. G.: Variability and Trends in U.S. Cloud Cover: ISCCP,
PATMOS-x, and CLARA-A1 Compared to Homogeneity-Adjusted Weather
Observations, J. Climate, 28, 4373–4389, https://doi.org/10.1175/jcli-d-14-00805.1,
2015.
Tang, Q., Bo, Y., and Zhu, Y.: Spatiotemporal fusion of multiple-satellite
aerosol optical depth (AOD) products using Bayesian maximum entropy method,
J. Geophys. Res.-Atmos., 121, 4034–4048, https://doi.org/10.1002/2015jd024571, 2016.
Tiedtke, M.: Representation of Clouds in Large-Scale Models, Mon. Weather
Rev., 121, 3040–3061, https://doi.org/10.1175/1520-0493(1993)121<3040:Rocils>2.0.Co;2, 1993.
Toll, V., Christensen, M., Quaas, J., and Bellouin, N.: Weak average
liquid-cloud-water response to anthropogenic aerosols, Nature, 572, 51–55, https://doi.org/10.1038/s41586-019-1423-9, 2019.
Trepte, Q. Z., Bedka, K. M., Chee, T. L., Minnis, P., Sun-Mack, S., Yost, C.
R., Chen, Y., Jin, Z., Hong, G., Chang, F.-L., and Smith, W. L.: Global
Cloud Detection for CERES Edition 4 Using Terra and Aqua MODIS Data, IEEE
T. Geosci. Remote, 57, 9410–9449, https://doi.org/10.1109/tgrs.2019.2926620, 2019.
Tzallas, V., Hatzianastassiou, N., Benas, N., Meirink, J. F., Matsoukas, C.,
Stackhouse, P., and Vardavas, I.: Evaluation of CLARA-A2 and ISCCP-H Cloud
Cover Climate Data Records over Europe with ECA&D Ground-Based
Measurements, Remote Sens.-Basel, 11, 212, https://doi.org/10.3390/rs11020212, 2019.
Van Tricht, K., Lhermitte, S., Lenaerts, J. T. M., Gorodetskaya, I. V.,
L'Ecuyer, T. S., Noel, B., van den Broeke, M. R., Turner, D. D., and van
Lipzig, N. P. M.: Clouds enhance Greenland ice sheet meltwater runoff,
Nat. Commun., 7, 10266,
https://doi.org/10.1038/ncomms10266, 2016.
Vaughan, M., Young, S., Winker, D., Powell, K., Omar, A., Liu, Z. Y., Hu, Y.
X., and Hostetler, C.: Fully automated analysis of space-based lidar data:
an overview of the CALIPSO retrieval algorithms and data products, Proc. SPIE,
5575, 16–30, https://doi.org/10.1117/12.572024, 2004.
Vaughan, M. A., Powell, K. A., Kuehn, R. E., Young, S. A., Winker, D. M.,
Hostetler, C. A., Hunt, W. H., Liu, Z. Y., McGill, M. J., and Getzewich, B.
J.: Fully Automated Detection of Cloud and Aerosol Layers in the CALIPSO
Lidar Measurements, J. Atmos. Ocean. Tech., 26,
2034–2050, https://doi.org/10.1175/2009jtecha1228.1, 2009.
Vignesh, P. P., Jiang, J. H., Kishore, P., Su, H., Smay, T., Brighton, N.,
and Velicogna, I.: Assessment of CMIP6 Cloud Fraction and Comparison with
Satellite Observations, Earth Space Sci., 7, e2019EA000975, https://doi.org/10.1029/2019ea000975,
2020.
Walsh, J. E., Chapman, W. L., and Portis, D. H.: Arctic Cloud Fraction and
Radiative Fluxes in Atmospheric Reanalyses, J. Climate, 22,
2316–2334, https://doi.org/10.1175/2008jcli2213.1, 2009.
Wang, D., Bi, S., Wang, B., and Yan, J.: Satellite cloud image fusion based
on regional feature with nonsubsampled contourlet transform, J.
Comput. Appl., 32, 2585–2587, 2012.
Winker, D. M., Hunt, W. H., and McGill, M. J.: Initial performance
assessment of CALIOP, Geophys. Res. Lett., 34, L19803,
https://doi.org/10.1029/2007gl030135, 2007.
Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y. X., Powell, K. A., Liu, Z.
Y., Hunt, W. H., and Young, S. A.: Overview of the CALIPSO Mission and
CALIOP Data Processing Algorithms, J. Atmos. Ocean.
Tech., 26, 2310–2323, https://doi.org/10.1175/2009jtecha1281.1, 2009.
Woodruff, S. D., Diaz, H. F., Worley, S. J., Reynolds, R. W., and Lubker, S.
J.: Early ship observational data and ICOADS, Climatic Change, 73, 169–194, https://doi.org/10.1007/s10584-005-3456-3, 2005.
Wu, W., Liu, Y. G., Jensen, M. P., Toto, T., Foster, M. J., and Long, C. N.:
A comparison of multiscale variations of decade-long cloud fractions from
six different platforms over the Southern Great Plains in the United States,
J. Geophys. Res.-Atmos., 119, 3438–3459, https://doi.org/10.1002/2013jd019813, 2014.
Xia, X., Zhao, B., Zhang, T., Wang, L., Gu, Y., Liou, K.-N., Mao, F., Liu,
B., Bo, Y., Huang, Y., Dong, J., Gong, W., and Zhu, Z.: Satellite-Derived
Aerosol Optical Depth Fusion Combining Active and Passive Remote Sensing
Based on Bayesian Maximum Entropy, IEEE T. Geosci.
Remote, 60, 1–13, https://doi.org/10.1109/tgrs.2021.3051799, 2022.
Xie, S. C., McCoy, R. B., Klein, S. A., Cederwall, R. T., Wiscombe, W. J.,
Clothiaux, E. E., Gaustad, K. L., Golaz, J. C., Hall, S. D., Jensen, M. P.,
Johnson, K. L., Lin, Y. L., Long, C. N., Mather, J. H., McCord, R. A.,
McFarlane, S. A., Palanisamy, G., Shi, Y., and Turner, D. D. D.: ARM CLIMATE
MODELING BEST ESTIMATE DATA A New Data Product for Climate Studies, B.
Am. Meteorol. Soc., 91, 13–20, https://doi.org/10.1175/2009bams2891.1,
2010.
Xu, S. and Cheng, J.: A new land surface temperature fusion strategy based
on cumulative distribution function matching and multiresolution Kalman
filtering, Remote Sens. Environ., 254, 112256, https://doi.org/10.1016/j.rse.2020.112256,
2021.
Xu, S., Cheng, J., and Zhang, Q.: Reconstructing All-Weather Land Surface
Temperature Using the Bayesian Maximum Entropy Method Over the Tibetan
Plateau and Heihe River Basin, IEEE J. Sel. Top. Appl., 12, 3307–3316, https://doi.org/10.1109/jstars.2019.2921924, 2019.
Yang, J. and Hu, M.: Filling the missing data gaps of daily MODIS AOD using
spatiotemporal interpolation, Sci. Total Environ., 633,
677–683, https://doi.org/10.1016/j.scitotenv.2018.03.202, 2018.
Yeo, H., Kim, M.-H., Son, S.-W., Jeong, J.-H., Yoon, J.-H., Kim, B.-M., and
Kim, S.-W.: Arctic cloud properties and associated radiative effects in the
three newer reanalysis datasets (ERA5, MERRA-2, JRA-55): Discrepancies and
possible causes, Atmos. Res., 270, 106080, https://doi.org/10.1016/j.atmosres.2022.106080,
2022.
Young, A. H., Knapp, K. R., Inamdar, A., Hankins, W., and Rossow, W. B.: The International Satellite Cloud Climatology Project H-Series climate data record product, Earth Syst. Sci. Data, 10, 583–593, https://doi.org/10.5194/essd-10-583-2018, 2018.
Yu, H.-L. and Wang, C.-H.: Retrospective prediction of intraurban
spatiotemporal distribution of PM2.5 in Taipei, Atmos. Environ., 44,
3053–3065, https://doi.org/10.1016/j.atmosenv.2010.04.030, 2010.
Zhang, C.-J., Chen, Y., Duanmu, C., and Feng, H.-J.: Multi-channel satellite
cloud image fusion in the tetrolet transform domain, Int. J. Remote Sens., 35,
8138–8168, https://doi.org/10.1080/01431161.2014.980918, 2014.
Zhang, Q., Cheng, J., and Liang, S.: Deriving high-quality surface
emissivity spectra from atmospheric infrared sounder data using cumulative
distribution function matching and principal component analysis regression,
Remote Sens. Environ., 211, 388–399, https://doi.org/10.1016/j.rse.2018.04.033,
2018.
Zhu, X., Chen, J., Gao, F., Chen, X., and Masek, J. G.: An enhanced spatial
and temporal adaptive reflectance fusion model for complex heterogeneous
regions, Remote Sens. Environ., 114, 2610–2623, https://doi.org/10.1016/j.rse.2010.05.032, 2010.
Short summary
We proposed a data fusion strategy that combines the complementary features of multiple-satellite cloud fraction (CF) datasets and generated a continuous monthly 1° daytime cloud fraction product covering the entire Arctic during the sunlit months in 2000–2020. This study has positive significance for reducing the uncertainties for the assessment of surface radiation fluxes and improving the accuracy of research related to climate change and energy budgets, both regionally and globally.
We proposed a data fusion strategy that combines the complementary features of...
Altmetrics
Final-revised paper
Preprint