Earth System Science Data最新文献

{"title":"High-spatiotemporal reconstruction of biogeochemical dynamics in Australia integrating satellites products and in-situ observations (2000–2022)","authors":"Xiaohan Zhang, Lizhe Wang, Jining Yan, Sheng Wang","doi":"10.5194/essd-2024-219","DOIUrl":"https://doi.org/10.5194/essd-2024-219","url":null,"abstract":"<strong>Abstract.</strong> The marine biogeochemical time-series products, which include total alkalinity, inorganic carbon, nitrate, phosphate, silicate, and pH, constitute a foundational support mechanism for the ongoing surveillance of oceanic biogeochemical changes. These products play a critical role in facilitating research focused on dynamic monitoring of marine ecosystems and fostering sustainable oceanic development. However, existing monitoring methodologies are hampered by inherent limitations, notably the paucity of observational products that simultaneously offer high spatial and temporal resolutions. Furthermore, the interpolation methods typically employed in these contexts frequently prove low-effective on a large scale, resulting in data with extensive temporal and spatial expanses that are difficulty for applications aimed at monitoring large-scale ocean dynamics. A novel integration of the CANYON-B and Random Forest regression methods was explored to address these challenges in reconstructing key marine biogeochemical parameters. This work reconstructs the concentrations of these marine biogeochemicals at the sea surface within Australia's Exclusive Economic Zone over the period from 2000 to 2022 on a 1-kilometre scale. The approach involves the amalgamation of multi-source in-situ ocean chemistry time-series observations with MODIS Terra ocean reflectance imagery and ocean water colour product distributions. This research highlights the substantial capabilities of machine learning for the large-scale reconstruction of ocean chemistry data, introducing a new, viable method for utilising in-situ measurements and optical imagery in reconstructing marine biogeochemical elements, thereby significantly enhancing our ability to monitor large-scale ocean dynamics. The datasets generated and analysed in this study are available on Science Data Bank (https://doi.org/10.57760/sciencedb.09331) (Zhang et al., 2024)","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"31 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141489476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Permafrost temperature baseline at 15 meters depth in the Qinghai-Tibet Plateau (2010–2019)","authors":"Defu Zou, Lin Zhao, Guojie Hu, Erji Du, Guangyue Liu, Chong Wang, Wangping Li","doi":"10.5194/essd-2024-114","DOIUrl":"https://doi.org/10.5194/essd-2024-114","url":null,"abstract":"<strong>Abstract.</strong> The ground temperature at a fixed depth is a crucial boundary condition for understanding the properties of deep permafrost. However, the commonly used mean annual ground temperature at the depth of the zero annual amplitude (MAGT_dzaa) has application limitations due to large spatial heterogeneity in observed depths. In this study, we utilized 231 borehole records of mean annual ground temperature at a depth of 15 meters (MAGT_15m) from 2010 to 2019 and employed support vector regression (SVR) to predict gridded MAGT_15m data at a spatial resolution of nearly 1 km across the Qinghai-Tibet Plateau (QTP). SVR predictions demonstrated a R² value of 0.48 with a negligible negative overestimation (-0.01 °C). The average MAGT_15m of the QTP permafrost was -1.85 °C (±1.58 °C), with 90% of values ranging from -5.1 °C to -0.1 °C and 51.2% exceeding -1.5 °C. The freezing degree days (FDD) was the most significant predictor (p&lt;0.001) of MAGT_15m, followed by thawing degree days (TDD), mean annual precipitation (MAP), and soil bulk density (BD) (p&lt;0.01). Overall, the MAGT_15m increased from northwest to southeast and decreased with elevation. Lower MAGT_15m values are prevail in high mountainous areas with steep slopes. The MAGT_15m was the lowest in the basins of the Amu Darya, Indus, and Tarim rivers (-2.7 to -2.9 °C) and the highest in the Yangtze and Yellow River basins (-0.8 to -0.9 °C). The baseline dataset of MAGT_15m during 2010–2019 for the QTP permafrost will facilitates simulations of deep permafrost characteristics and provides fundamental data for permafrost model validation and improvement.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"30 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141475253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A global forest burn severity dataset from Landsat imagery (2003–2016)","authors":"Kang He, Xinyi Shen, Emmanouil N. Anagnostou","doi":"10.5194/essd-16-3061-2024","DOIUrl":"https://doi.org/10.5194/essd-16-3061-2024","url":null,"abstract":"Abstract. Forest fires, while destructive and dangerous, are important to the functioning and renewal of ecosystems. Over the past 2 decades, large-scale, severe forest fires have become more frequent globally, and the risk is expected to increase as fire weather and drought conditions intensify. To improve quantification of the intensity and extent of forest fire damage, we have developed a 30 m resolution global forest burn severity (GFBS) dataset of the degree of biomass consumed by fires from 2003 to 2016. To develop this dataset, we used the Global Fire Atlas product to determine when and where forest fires occurred during that period and then we overlaid the available Landsat surface reflectance products to obtain pre-fire and post-fire normalized burn ratios (NBRs) for each burned pixel, designating the difference between them as dNBR and the relative difference as RdNBR. We compared the GFBS dataset against the Canada Landsat Burned Severity (CanLaBS) product, showing better agreement than the existing Moderate Resolution Imaging Spectrometer (MODIS)-based global burn severity dataset (MOdis burn SEVerity, MOSEV) in representing the distribution of forest burn severity over Canada. Using the in situ burn severity category data available for the 2013 wildfires in southeastern Australia, we demonstrated that GFBS could provide burn severity estimation with clearer differentiation between the high-severity and moderate-/low-severity classes, while such differentiation among the in situ burn severity classes is not captured in the MOSEV product. Using the CONUS-wide composite burn index (CBI) as a ground truth, we showed that dNBR from GFBS was more strongly correlated with CBI (r=0.63) than dNBR from MOSEV (r=0.28). RdNBR from GFBS also exhibited better agreement with CBI (r=0.56) than RdNBR from MOSEV (r=0.20). On a global scale, while the dNBR and RdNBR spatial patterns extracted by GFBS are similar to those of MOSEV, MOSEV tends to provide higher burn severity levels than GFBS. We attribute this difference to variations in reflectance values and the different spatial resolutions of the two satellites. The GFBS dataset provides a more precise and reliable assessment of burn severity than existing available datasets. These enhancements are crucial for understanding the ecological impacts of forest fires and for informing management and recovery efforts in affected regions worldwide. The GFBS dataset is freely accessible at https://doi.org/10.5281/zenodo.10037629 (He et al., 2023).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"27 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141475230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SAR Image Semantic Segmentation of Typical Oceanic and Atmospheric Phenomena","authors":"Quankun Li, Xue Bai, Lizhen Hu, Liangsheng Li, Yaohui Bao, Xupu Geng, Xiao-Hai Yan","doi":"10.5194/essd-2024-222","DOIUrl":"https://doi.org/10.5194/essd-2024-222","url":null,"abstract":"<strong>Abstract.</strong> The ocean surface exhibits a variety of oceanic and atmospheric phenomena. Automatically detecting and identifying these phenomena is crucial for understanding oceanic dynamics and ocean-atmosphere interactions. In this study, we select 2,383 Sentinel-1 WV mode images and 2,628 IW mode sub-images to construct a semantic segmentation dataset that includes 12 typical oceanic and atmospheric phenomena. Each phenomenon is represented by approximately 400 sub-images, resulting in a total of 5,011 images. The images in this dataset have a resolution of 100 meters and dimensions of 256 × 256 pixels. We propose a modified Segformer model to segment semantically these multiple categories of oceanic and atmospheric phenomena. Experimental results show that the modified Segformer model achieves an average Dice coefficient of 80.98 %, an average IoU of 70.32 %, and an overall accuracy of 87.13 %, demonstrating robust segmentation performance of typical oceanic and atmospheric phenomena in SAR images.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"61 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141475207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physical, social, and biological attributes for improved understanding and prediction of wildfires: FPA FOD-Attributes dataset","authors":"Yavar Pourmohamad, John T. Abatzoglou, Erin J. Belval, Erica Fleishman, Karen Short, Matthew C. Reeves, Nicholas Nauslar, Philip E. Higuera, Eric Henderson, Sawyer Ball, Amir AghaKouchak, Jeffrey P. Prestemon, Julia Olszewski, Mojtaba Sadegh","doi":"10.5194/essd-16-3045-2024","DOIUrl":"https://doi.org/10.5194/essd-16-3045-2024","url":null,"abstract":"Abstract. Wildfires are increasingly impacting social and environmental systems in the United States (US). The ability to mitigate the adverse effects of wildfires increases with understanding of the social, physical, and biological conditions that co-occurred with or caused the wildfire ignitions and contributed to the wildfire impacts. To this end, we developed the FPA FOD-Attributes dataset, which augments the sixth version of the Fire Program Analysis Fire-Occurrence Database (FPA FOD v6) with nearly 270 attributes that coincide with the date and location of each wildfire ignition in the US. FPA FOD v6 contains information on location, jurisdiction, discovery time, cause, and final size of >2.3×106 wildfires in the US between 1992 and 2020 . For each wildfire, we added physical (e.g., weather, climate, topography, and infrastructure), biological (e.g., land cover and normalized difference vegetation index), social (e.g., population density and social vulnerability index), and administrative (e.g., national and regional preparedness level and jurisdiction) attributes. This publicly available dataset can be used to answer numerous questions about the covariates associated with human- and lightning-caused wildfires. Furthermore, the FPA FOD-Attributes dataset can support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including the development of machine learning models. The FPA FOD-Attributes dataset is available at https://doi.org/10.5281/zenodo.8381129 (Pourmohamad et al., 2023).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"8 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dataset of spatially extensive long-term quality-assured land–atmosphere interactions over the Tibetan Plateau","authors":"Yaoming Ma, Zhipeng Xie, Yingying Chen, Shaomin Liu, Tao Che, Ziwei Xu, Lunyu Shang, Xiaobo He, Xianhong Meng, Weiqiang Ma, Baiqing Xu, Huabiao Zhao, Junbo Wang, Guangjian Wu, Xin Li","doi":"10.5194/essd-16-3017-2024","DOIUrl":"https://doi.org/10.5194/essd-16-3017-2024","url":null,"abstract":"Abstract. The climate of the Tibetan Plateau (TP) has experienced substantial changes in recent decades as a result of the location's susceptibility to global climate change. The changes observed across the TP are closely associated with regional land–atmosphere interactions. Current models and satellites struggle to accurately depict the interactions; therefore, critical field observations on land–atmosphere interactions outlined here provide necessary independent validation data and fine-scale process insights for constraining reanalysis products, remote sensing retrievals, and land surface model parameterizations. Scientific data sharing is crucial for the TP since in situ observations are rarely available under these harsh conditions. However, field observations are currently dispersed among individuals or groups and have not yet been integrated for comprehensive analysis. This has prevented a better understanding of the interactions, the unprecedented changes they generate, and the substantial ecological and environmental consequences they bring about. In this study, we collaborated with different agencies and organizations to present a comprehensive dataset for hourly measurements of surface energy balance components, soil hydrothermal properties, and near-surface micrometeorological conditions spanning up to 17 years (2005–2021). This dataset, derived from 12 field stations covering a variety of typical TP landscapes, provides the most extensive in situ observation data available for studying land–atmosphere interactions on the TP to date in terms of both spatial coverage and duration. Three categories of observations are provided in this dataset: meteorological gradient data (met), soil hydrothermal data (soil), and turbulent flux data (flux). To assure data quality, a set of rigorous data-processing and quality control procedures are implemented for all observation elements (e.g., wind speed and direction at different height) in this dataset. The operational workflow and procedures are individually tailored to the varied types of elements at each station, including automated error screening, manual inspection, diagnostic checking, adjustments, and quality flagging. The hourly raw data series; the quality-assured data; and supplementary information, including data integrity and the percentage of correct data on a monthly scale, are provided via the National Tibetan Plateau Data Center (https://doi.org/10.11888/Atmos.tpdc.300977, Ma et al., 2023a). With the greatest number of stations covered, the fullest collection of meteorological elements, and the longest duration of observations and recordings to date, this dataset is the most extensive hourly land–atmosphere interaction observation dataset for the TP. It will serve as the benchmark for evaluating and refining land surface models, reanalysis products, and remote sensing retrievals, as well as for characterizing fine-scale land–atmosphere interaction processes of the TP and underlying ","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"88 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MUDA: dynamic geophysical and geochemical MUltiparametric DAtabase","authors":"Marco Massa, Andrea Luca Rizzo, Davide Scafidi, Elisa Ferrari, Sara Lovati, Lucia Luzi, the MUDA working group","doi":"10.5194/essd-2024-185","DOIUrl":"https://doi.org/10.5194/essd-2024-185","url":null,"abstract":"<strong>Abstract.</strong> In this paper, the new dynamic geophysical and geochemical MUltiparametric DAtabase (MUDA) is presented. MUDA is a new infrastructure of the National Institute of Geophysics and Volcanology (INGV), published on-line in December 2023, with the aim of archiving and disseminating multiparametric data collected by multidisciplinary monitoring networks. MUDA is a <em>MySQL </em>relational database with a web interface developed in <em>php,</em> aimed at investigating in quasi real time possible correlations between seismic phenomena and variations in endogenous and environmental parameters. At present, MUDA collects data from different types of sensors such as hydrogeochemical probes for physical-chemical parameters in waters, meteorological stations, detectors of air Radon concentration, diffusive flux of carbon dioxide (CO₂) and seismometers belonging both to the National Seismic Network of INGV and to temporary networks installed in the framework of multidisciplinary research projects. MUDA daily publishes data updated to the previous day and offers the chance to view and download multiparametric time series selected for different time periods. The resultant dataset provides broad perspectives in the framework of future high frequency and continuous multiparametric monitorings as a starting point to identify possible seismic precursors for short-term earthquake forecasting. MUDA is now quoted with the Digital Object Identifier https://doi.org/10.13127/muda (Massa et al., 2023).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"68 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterizing clouds with the CCClim dataset, a machine learning cloud class climatology","authors":"Arndt Kaps, Axel Lauer, Rémi Kazeroni, Martin Stengel, Veronika Eyring","doi":"10.5194/essd-16-3001-2024","DOIUrl":"https://doi.org/10.5194/essd-16-3001-2024","url":null,"abstract":"Abstract. We present the new Cloud Class Climatology (CCClim) dataset, quantifying the global distribution of established morphological cloud types over 35 years. CCClim combines active and passive sensor data with machine learning (ML) and provides a new opportunity for improving the understanding of clouds and their related processes. CCClim is based on cloud property retrievals from the European Space Agency's (ESA) Cloud_cci dataset, adding relative occurrences of eight major cloud types, designed to be similar to those defined by the World Meteorological Organization (WMO) at 1° resolution. The ML framework used to obtain the cloud types is trained on data from multiple satellites in the afternoon constellation (A-Train). Using multiple spaceborne sensors reduces the impact of single-sensor problems like the difficulty of passive sensors to detect thin cirrus or the small footprint of active sensors. We leverage this to generate sufficient labeled data to train supervised ML models. CCClim's global coverage being almost gapless from 1982 to 2016 allows for performing process-oriented analyses of clouds on a climatological timescale. Similarly, the moderate spatial and temporal resolutions make it a lightweight dataset while enabling straightforward comparison to climate models. CCClim creates multiple opportunities to study clouds, of which we sketch out a few examples. Along with the cloud-type frequencies, CCClim contains the cloud properties used as inputs to the ML framework, such that all cloud types can be associated with relevant physical quantities. CCClim can also be combined with other datasets such as reanalysis data to assess the dynamical regime favoring the occurrence of a specific cloud type in association with its properties. Additionally, we show an example of how to evaluate a global climate model by comparing CCClim with cloud types obtained by applying the same ML method used to create CCClim to output from the icosahedral nonhydrostatic atmosphere model (ICON-A). CCClim can be accessed via the following digital object identifier: https://doi.org/10.5281/zenodo.8369202 (Kaps et al., 2023b).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"28 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141461882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ChatEarthNet: A Global-Scale Image-Text Dataset Empowering Vision-Language Geo-Foundation Models","authors":"Zhenghang Yuan, Zhitong Xiong, Lichao Mou, Xiao Xiang Zhu","doi":"10.5194/essd-2024-140","DOIUrl":"https://doi.org/10.5194/essd-2024-140","url":null,"abstract":"<strong>Abstract.</strong> The rapid development of remote sensing technology has led to an exponential growth in satellite images, yet their inherent complexity often makes them difficult for non-expert users to understand. Natural language, as a carrier of human knowledge, can bridge common users and complicated satellite imagery. Additionally, when paired with visual data, natural language can be utilized to train large vision-language foundation models, significantly improving performance in various tasks. Despite these advancements, the remote sensing community still faces a challenge due to the lack of large- scale, high-quality vision-language datasets for satellite images. To address this challenge, we introduce a new image-text dataset, providing high-quality natural language descriptions for global-scale satellite data. Specifically, we utilize Sentinel-2 data for its global coverage as the foundational image source, employing semantic segmentation labels from the European Space Agency’s WorldCover project to enrich the descriptions of land covers. By conducting in-depth semantic analysis, we formulate detailed prompts to elicit rich descriptions from ChatGPT. We then include a manual verification process to enhance the dataset’s quality further. This step involves manual inspection and correction to refine the dataset. Finally, we offer the community ChatEarthNet, a large-scale image-text dataset characterized by global coverage, high quality, wide-ranging diversity, and detailed descriptions. ChatEarthNet consists of 163,488 image-text pairs with captions generated by ChatGPT3.5 and an additional 10,000 image-text pairs with captions generated by ChatGPT-4V(ision). This dataset has significant potential for both training and evaluating vision-language geo-foundation models for remote sensing. The code is publicly available at https://doi.org/10.5281/zenodo.11004358 (Yuan et al., 2024b), and the ChatEarthNet dataset is at https://doi.org/10.5281/zenodo.11003436 (Yuan et al., 2024c).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"62 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141461965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Atmospheric Radiation Measurement (ARM) airborne field campaign data products between 2013 and 2018","authors":"Fan Mei, Jennifer M. Comstock, Mikhail S. Pekour, Jerome D. Fast, Beat Schmid, Krista L. Gaustad, Shuaiqi Tang, Damao Zhang, John E. Shilling, Jason Tomlinson, Adam C. Varble, Jian Wang, L. Ruby Leung, Lawrence Kleinman, Scot Martin, Sebastien C. Biraud, Brian D. Ermold, Kenneth W. Burk","doi":"10.5194/essd-2024-97","DOIUrl":"https://doi.org/10.5194/essd-2024-97","url":null,"abstract":"<strong>Abstract.</strong> Airborne measurements are pivotal for providing detailed, spatiotemporally resolved information about atmospheric parameters, and aerosol and cloud properties, thereby enhancing our understanding of dynamic atmospheric processes. For 30 years, the U.S. Department of Energy (DOE) Office of Science supported an instrumented Gulfstream-1 (G-1) aircraft for atmospheric field campaigns. Data from the final decade of G-1 operations were archived by the Atmospheric Radiation Measurement (ARM) user facility Data Center and made publicly available at no cost to all registered users. To ensure a consistent data format and to improve the accessibility of the ARM airborne data, an integrated dataset was recently developed covering the final six years of G-1 operations (2013 to 2018). The integrated dataset includes data collected from 236 flights (766.4 hours), which covered the Arctic, the U.S. Southern Great Plains (SGP), the U.S. West Coast, the Eastern North Atlantic (ENA), the Amazon Basin in Brazil, and the Sierras de Córdoba range in Argentina. These comprehensive data streams provide much-needed insight into spatiotemporal variability of thermodynamic quantities, aerosol and cloud states and properties for addressing essential science questions in Earth system process studies. This manuscript describes the DOE ARM merged G-1 datasets, including information on the acquisition, collection, and quality control processes. It further illustrates the usage of this merged dataset to evaluate the Energy Exascale Earth System Model (E3SM) with the Earth System Model Aerosol-Cloud Diagnostics (ESMAC Diags) package.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"29 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}