Ramon Padullés, Estel Cardellach, Antía Paz, Santi Oliveras, Douglas C. Hunt, Sergey Sokolovskiy, Jan P. Weiss, Kuo-Nung Wang, F. Joe Turk, Chi O. Ao, Manuel de la Torre Juárez
Abstract. Polarimetric Radio Occultations (PRO) represent an augmentation of the standard Radio Occultation (RO) technique that provides precipitation and clouds vertical information along with the standard thermodynamic products. A combined dataset that contains both the PRO observable and the RO standard retrievals, the resPrf, has been developed with the aim to foster the use of these unique observations and to fully exploit the scientific implication of having information about vertical cloud structures with intrinsically collocated thermodynamic state of the atmosphere. This manuscript describes such dataset and provides detailed information on the processing of the observations. The procedure followed at UCAR to combine both H and V observations to generate the equivalent profiles as in standard RO missions is described in detail, and the obtained refractivity is shown to be of equivalent quality as that from TerraSAR-X. The steps of the processing of the PRO observations are detailed, derived products such as the top-of-the-signal are described, and validation is provided. Furthermore, the dataset contains the simulated ray-trajectories for the PRO observation, and co-located information with global satellite-based precipitation products, such as merged rain rate retrievals or passive microwave observations. These co-locations are used for further validation of the PRO observations and they are also provided within the resPrf profiles for additional use. It is also shown how accounting for external co-located information can improve significantly the effective PRO horizontal resolution, tackling one of the challenges of the technique.
摘要。极坐标无线电掩星（PRO）是标准无线电掩星（RO）技术的一种增强技术，可提供降水和云层垂直信息以及标准热力学产品。为了促进对这些独特观测数据的利用，并充分利用垂直云结构信息与大气热力学状态内在联系的科学意义，开发了一个包含 PRO 观测数据和 RO 标准检索数据的组合数据集 resPrf。本手稿介绍了这种数据集，并提供了有关观测数据处理的详细信息。详细描述了 UCAR 将 H 和 V 两种观测数据结合起来以生成与标准 RO 任务中的等效剖面图的程序，并表明所获得的折射率与 TerraSAR-X 的折射率质量相当。详细介绍了 PRO 观测数据的处理步骤，描述了信号顶等衍生产品，并进行了验证。此外，数据集还包含 PRO 观测的模拟射线轨迹，以及与全球卫星降水产品（如合并雨量检索或被动微波观测）的同位信息。这些共定位信息用于进一步验证 PRO 观测数据，同时也在 resPrf 资料中提供，以供其他用途。此外，还显示了考虑外部共定位信息如何能够显著提高 PRO 的有效水平分辨率，从而解决该技术面临的挑战之一。
{"title":"The PAZ Polarimetric Radio Occultation Research Dataset for Scientific Applications","authors":"Ramon Padullés, Estel Cardellach, Antía Paz, Santi Oliveras, Douglas C. Hunt, Sergey Sokolovskiy, Jan P. Weiss, Kuo-Nung Wang, F. Joe Turk, Chi O. Ao, Manuel de la Torre Juárez","doi":"10.5194/essd-2024-150","DOIUrl":"https://doi.org/10.5194/essd-2024-150","url":null,"abstract":"<strong>Abstract.</strong> Polarimetric Radio Occultations (PRO) represent an augmentation of the standard Radio Occultation (RO) technique that provides precipitation and clouds vertical information along with the standard thermodynamic products. A combined dataset that contains both the PRO observable and the RO standard retrievals, the <em>resPrf</em>, has been developed with the aim to foster the use of these unique observations and to fully exploit the scientific implication of having information about vertical cloud structures with intrinsically collocated thermodynamic state of the atmosphere. This manuscript describes such dataset and provides detailed information on the processing of the observations. The procedure followed at UCAR to combine both H and V observations to generate the equivalent profiles as in standard RO missions is described in detail, and the obtained refractivity is shown to be of equivalent quality as that from TerraSAR-X. The steps of the processing of the PRO observations are detailed, derived products such as the top-of-the-signal are described, and validation is provided. Furthermore, the dataset contains the simulated ray-trajectories for the PRO observation, and co-located information with global satellite-based precipitation products, such as merged rain rate retrievals or passive microwave observations. These co-locations are used for further validation of the PRO observations and they are also provided within the <em>resPrf</em> profiles for additional use. It is also shown how accounting for external co-located information can improve significantly the effective PRO horizontal resolution, tackling one of the challenges of the technique.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"100 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141246551","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}
Pub Date : 2024-06-04DOI: 10.5194/essd-16-2669-2024
Simon Schulte, Arthur Jakobs, Stefan Pauliuk
Abstract. Global multi-regional input–output (GMRIO) analysis is the standard tool to calculate consumption-based carbon accounts at the macro level. Recent inter-database comparisons have exposed discrepancies in GMRIO-based results, pinpointing greenhouse gas (GHG) emission accounts as the primary source of variation. A few studies have analysed the robustness of GHG emission accounts, using Monte Carlo simulations to understand how uncertainty from raw data propagates to the final GHG emission accounts. However, these studies often make simplistic assumptions about raw data uncertainty and ignore correlations between disaggregated variables. Here, we compile GHG emission accounts for the year 2015 according to the resolution of EXIOBASE V3, covering CO2, CH4 and N2O emissions. We propagate uncertainty from the raw data, i.e. the United Nations Framework Convention on Climate Change (UNFCCC) and EDGAR inventories, to the GHG emission accounts and then further to the GHG footprints. We address both limitations from previous studies. First, instead of making simplistic assumptions, we utilise authoritative raw data uncertainty estimates from the National Inventory Reports (NIRs) submitted to the UNFCCC and a recent study on uncertainty of the EDGAR emission inventory. Second, we account for inherent correlations due to data disaggregation by sampling from a Dirichlet distribution. Our results show a median coefficient of variation (CV) for GHG emission accounts at the country level of 4 % for CO2, 12 % for CH4 and 33 % for N2O. For CO2, smaller economies with significant international aviation or shipping sectors show CVs as high as 94 %, as seen in Malta. At the sector level, uncertainties are higher, with median CVs of 94 % for CO2, 100 % for CH4 and 113 % for N2O. Overall, uncertainty decreases when propagated from GHG emission accounts to GHG footprints, likely due to the cancelling-out effects caused by the distribution of emissions and their uncertainties across global supply chains. Our GHG emission accounts generally align with official EXIOBASE emission accounts and OECD data at the country level, though discrepancies at the sectoral level give cause for further examination. We provide our GHG emission accounts with associated uncertainties and correlations at https://doi.org/10.5281/zenodo.10041196 (Schulte et al., 2023) to complement the official EXIOBASE emission accounts for users interested in estimating the uncertainties of their results.
{"title":"Estimating the uncertainty of the greenhouse gas emission accounts in global multi-regional input–output analysis","authors":"Simon Schulte, Arthur Jakobs, Stefan Pauliuk","doi":"10.5194/essd-16-2669-2024","DOIUrl":"https://doi.org/10.5194/essd-16-2669-2024","url":null,"abstract":"Abstract. Global multi-regional input–output (GMRIO) analysis is the standard tool to calculate consumption-based carbon accounts at the macro level. Recent inter-database comparisons have exposed discrepancies in GMRIO-based results, pinpointing greenhouse gas (GHG) emission accounts as the primary source of variation. A few studies have analysed the robustness of GHG emission accounts, using Monte Carlo simulations to understand how uncertainty from raw data propagates to the final GHG emission accounts. However, these studies often make simplistic assumptions about raw data uncertainty and ignore correlations between disaggregated variables. Here, we compile GHG emission accounts for the year 2015 according to the resolution of EXIOBASE V3, covering CO2, CH4 and N2O emissions. We propagate uncertainty from the raw data, i.e. the United Nations Framework Convention on Climate Change (UNFCCC) and EDGAR inventories, to the GHG emission accounts and then further to the GHG footprints. We address both limitations from previous studies. First, instead of making simplistic assumptions, we utilise authoritative raw data uncertainty estimates from the National Inventory Reports (NIRs) submitted to the UNFCCC and a recent study on uncertainty of the EDGAR emission inventory. Second, we account for inherent correlations due to data disaggregation by sampling from a Dirichlet distribution. Our results show a median coefficient of variation (CV) for GHG emission accounts at the country level of 4 % for CO2, 12 % for CH4 and 33 % for N2O. For CO2, smaller economies with significant international aviation or shipping sectors show CVs as high as 94 %, as seen in Malta. At the sector level, uncertainties are higher, with median CVs of 94 % for CO2, 100 % for CH4 and 113 % for N2O. Overall, uncertainty decreases when propagated from GHG emission accounts to GHG footprints, likely due to the cancelling-out effects caused by the distribution of emissions and their uncertainties across global supply chains. Our GHG emission accounts generally align with official EXIOBASE emission accounts and OECD data at the country level, though discrepancies at the sectoral level give cause for further examination. We provide our GHG emission accounts with associated uncertainties and correlations at https://doi.org/10.5281/zenodo.10041196 (Schulte et al., 2023) to complement the official EXIOBASE emission accounts for users interested in estimating the uncertainties of their results.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"25 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141246618","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}
Pub Date : 2024-06-04DOI: 10.5194/essd-16-2605-2024
Charles E. Miller, Peter C. Griffith, Elizabeth Hoy, Naiara S. Pinto, Yunling Lou, Scott Hensley, Bruce D. Chapman, Jennifer Baltzer, Kazem Bakian-Dogaheh, W. Robert Bolton, Laura Bourgeau-Chavez, Richard H. Chen, Byung-Hun Choe, Leah K. Clayton, Thomas A. Douglas, Nancy French, Jean E. Holloway, Gang Hong, Lingcao Huang, Go Iwahana, Liza Jenkins, John S. Kimball, Tatiana Loboda, Michelle Mack, Philip Marsh, Roger J. Michaelides, Mahta Moghaddam, Andrew Parsekian, Kevin Schaefer, Paul R. Siqueira, Debjani Singh, Alireza Tabatabaeenejad, Merritt Turetsky, Ridha Touzi, Elizabeth Wig, Cathy J. Wilson, Paul Wilson, Stan D. Wullschleger, Yonghong Yi, Howard A. Zebker, Yu Zhang, Yuhuan Zhao, Scott J. Goetz
Abstract. Permafrost-affected ecosystems of the Arctic–boreal zone in northwestern North America are undergoing profound transformation due to rapid climate change. NASA's Arctic Boreal Vulnerability Experiment (ABoVE) is investigating characteristics that make these ecosystems vulnerable or resilient to this change. ABoVE employs airborne synthetic aperture radar (SAR) as a powerful tool to characterize tundra, taiga, peatlands, and fens. Here, we present an annotated guide to the L-band and P-band airborne SAR data acquired during the 2017, 2018, 2019, and 2022 ABoVE airborne campaigns. We summarize the ∼80 SAR flight lines and how they fit into the ABoVE experimental design (Miller et al., 2023; https://doi.org/10.3334/ORNLDAAC/2150). The Supplement provides hyperlinks to extensive maps, tables, and every flight plan as well as individual flight lines. We illustrate the interdisciplinary nature of airborne SAR data with examples of preliminary results from ABoVE studies including boreal forest canopy structure from TomoSAR data over Delta Junction, AK, and the Boreal Ecosystem Research and Monitoring Sites (BERMS) area in northern Saskatchewan and active layer thickness and soil moisture data product validation. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band airborne SAR data (https://uavsar.jpl.nasa.gov/cgi-bin/data.pl).
摘要。由于气候变化迅速，北美西北部北极-北方地区受永冻土影响的生态系统正在发生深刻的变化。美国国家航空航天局（NASA）的北极寒带脆弱性实验（ABoVE）正在研究这些生态系统在这种变化中的脆弱性或复原力特征。ABoVE 利用机载合成孔径雷达 (SAR) 这一强大工具来描述苔原、针叶林、泥炭地和沼泽地的特征。在此，我们介绍了在2017、2018、2019和2022年ABoVE机载活动中获取的L波段和P波段机载合成孔径雷达数据的注释指南。我们总结了 ∼80条合成孔径雷达飞行线路以及它们如何与ABoVE实验设计相匹配（Miller等人，2023；https://doi.org/10.3334/ORNLDAAC/2150）。补编提供了大量地图、表格和每份飞行计划以及单条飞行线路的超链接。我们以 ABoVE 研究的初步结果为例，说明了机载合成孔径雷达数据的跨学科性质，这些初步结果包括通过 TomoSAR 数据对阿拉斯加州三角洲交界处和萨斯喀彻温省北部的北方生态系统研究与监测点 (BERMS) 地区的北方森林冠层结构进行的研究，以及活动层厚度和土壤水分数据产品的验证。本文将作为一份指南，帮助感兴趣的读者全面了解 ABoVE L 波段和 P 波段机载合成孔径雷达数据 (https://uavsar.jpl.nasa.gov/cgi-bin/data.pl)。
{"title":"The ABoVE L-band and P-band airborne synthetic aperture radar surveys","authors":"Charles E. Miller, Peter C. Griffith, Elizabeth Hoy, Naiara S. Pinto, Yunling Lou, Scott Hensley, Bruce D. Chapman, Jennifer Baltzer, Kazem Bakian-Dogaheh, W. Robert Bolton, Laura Bourgeau-Chavez, Richard H. Chen, Byung-Hun Choe, Leah K. Clayton, Thomas A. Douglas, Nancy French, Jean E. Holloway, Gang Hong, Lingcao Huang, Go Iwahana, Liza Jenkins, John S. Kimball, Tatiana Loboda, Michelle Mack, Philip Marsh, Roger J. Michaelides, Mahta Moghaddam, Andrew Parsekian, Kevin Schaefer, Paul R. Siqueira, Debjani Singh, Alireza Tabatabaeenejad, Merritt Turetsky, Ridha Touzi, Elizabeth Wig, Cathy J. Wilson, Paul Wilson, Stan D. Wullschleger, Yonghong Yi, Howard A. Zebker, Yu Zhang, Yuhuan Zhao, Scott J. Goetz","doi":"10.5194/essd-16-2605-2024","DOIUrl":"https://doi.org/10.5194/essd-16-2605-2024","url":null,"abstract":"Abstract. Permafrost-affected ecosystems of the Arctic–boreal zone in northwestern North America are undergoing profound transformation due to rapid climate change. NASA's Arctic Boreal Vulnerability Experiment (ABoVE) is investigating characteristics that make these ecosystems vulnerable or resilient to this change. ABoVE employs airborne synthetic aperture radar (SAR) as a powerful tool to characterize tundra, taiga, peatlands, and fens. Here, we present an annotated guide to the L-band and P-band airborne SAR data acquired during the 2017, 2018, 2019, and 2022 ABoVE airborne campaigns. We summarize the ∼80 SAR flight lines and how they fit into the ABoVE experimental design (Miller et al., 2023; https://doi.org/10.3334/ORNLDAAC/2150). The Supplement provides hyperlinks to extensive maps, tables, and every flight plan as well as individual flight lines. We illustrate the interdisciplinary nature of airborne SAR data with examples of preliminary results from ABoVE studies including boreal forest canopy structure from TomoSAR data over Delta Junction, AK, and the Boreal Ecosystem Research and Monitoring Sites (BERMS) area in northern Saskatchewan and active layer thickness and soil moisture data product validation. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band airborne SAR data (https://uavsar.jpl.nasa.gov/cgi-bin/data.pl).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"100 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141246527","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}
Abstract. Processed and analyzed sea surface wave characteristics derived from an up-looking acoustic Doppler current profiler (ADCP) for the period 2016–2022 are presented as a dataset available from the public open-access repository of SEA scieNtific Open data Edition (SEANOE) at https://doi.org/10.17882/96904 (Haim et al., 2022). The collected data include full two-dimensional wave fields, along with computed bulk parameters, such as wave heights, periods, and directions of propagation. The ADCP was mounted on the submerged Deep Levantine (DeepLev) mooring station located 50 km off the Israeli coast to the west of Haifa (bottom depth ∼1470 m). It meets the need for accurate and reliable in situ measurements in the eastern Mediterranean Sea as the area significantly lacks wave data compared to other Mediterranean sub-basins. The developed long-term time series of wave parameters contribute to the monitoring and analysis of the region's wave climate and the quality of wind–wave forecasting models.
摘要从上视声学多普勒海流剖面仪（ADCP）获得的 2016-2022 年期间海面波浪特征的处理和分析数据集，可从 https://doi.org/10.17882/96904 的 SEA scieNtific Open data Edition (SEANOE)（海姆等人，2022 年）公共开放存取数据库中获取。收集的数据包括完整的二维波场，以及计算得出的体参数，如波高、周期和传播方向。ADCP 安装在海法以西距以色列海岸 50 公里处的深海黎凡特（DeepLev）水下系泊站上（海底深度为 1470 米）。与其他地中海分流域相比，该地区严重缺乏波浪数据，因此该站满足了对地中海东部精确、可靠的现场测量的需求。开发的波浪参数长期时间序列有助于监测和分析该地区的波浪气候，提高风浪预报模型的质量。
{"title":"Multiyear surface wave dataset from the subsurface “DeepLev” eastern Levantine moored station","authors":"Nir Haim, Vika Grigorieva, Rotem Soffer, Boaz Mayzel, Timor Katz, Ronen Alkalay, Eli Biton, Ayah Lazar, Hezi Gildor, Ilana Berman-Frank, Yishai Weinstein, Barak Herut, Yaron Toledo","doi":"10.5194/essd-16-2659-2024","DOIUrl":"https://doi.org/10.5194/essd-16-2659-2024","url":null,"abstract":"Abstract. Processed and analyzed sea surface wave characteristics derived from an up-looking acoustic Doppler current profiler (ADCP) for the period 2016–2022 are presented as a dataset available from the public open-access repository of SEA scieNtific Open data Edition (SEANOE) at https://doi.org/10.17882/96904 (Haim et al., 2022). The collected data include full two-dimensional wave fields, along with computed bulk parameters, such as wave heights, periods, and directions of propagation. The ADCP was mounted on the submerged Deep Levantine (DeepLev) mooring station located 50 km off the Israeli coast to the west of Haifa (bottom depth ∼1470 m). It meets the need for accurate and reliable in situ measurements in the eastern Mediterranean Sea as the area significantly lacks wave data compared to other Mediterranean sub-basins. The developed long-term time series of wave parameters contribute to the monitoring and analysis of the region's wave climate and the quality of wind–wave forecasting models.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"65 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141246543","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}
Abstract. Under a warming climate, occurrences of wildfires have been increasingly more frequent in boreal and arctic forests during the last few decades. Wildfires can cause radical changes in the forest ecosystems and permafrost environment, such as irreversible degradation of permafrost, successions of boreal forests, rapid and massive losses of soil carbon stock, and increased periglacial geohazards. Since 2016, we have gradually and more systematically established a network for studying soil nutrients and monitoring the hydrothermal state of the active layer and near-surface permafrost in the northern Da Xing’anling (Hinggan) Mountains in Northeast China. The dataset of soil moisture content (0–9.4 m in depth), soil organic carbon (0–3.6 m), total nitrogen (0–3.6 m), and total phosphorus and potassium (0–3.6 m) have been obtained by field sampling and ensuing laboratory tests. Long-term datasets (2017–2022) of ground temperatures (0–20 m) and active layer thickness have been observed by thermistor cables permanently installed in boreholes. The present data can be used to simulate changes in permafrost features under a changing climate and wildfire disturbances and to explore the changing interactive mechanisms of the fire-permafrost-carbon system in the hemiboreal forest. Furthermore, can provide baseline data for studies and action plans to support the carbon neutralization initiative and assessment of ecological safety and management of the permafrost environment. This dataset can be easily accessed from the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Cryos.tpdc.300933, Li and Jin, 2024).
摘要在气候变暖的情况下，过去几十年来，北方和北极森林中的野火发生得越来越频繁。野火会使森林生态系统和冻土环境发生剧烈变化，如冻土不可逆转的退化、北方森林的演替、土壤碳储量的快速和大量损失以及冰川周围地质灾害的增加等。自2016年以来，我们在中国东北大兴安岭（兴安岭）北部逐步建立了较为系统的土壤养分研究和活动层及近表层冻土水热状态监测网络。土壤含水量（0-9.4 米）、土壤有机碳（0-3.6 米）、全氮（0-3.6 米）、全磷和全钾（0-3.6 米）数据集是通过野外采样和随后的实验室测试获得的。通过永久安装在钻孔中的热敏电阻电缆观测到了地温（0-20 米）和活性层厚度的长期数据集（2017-2022 年）。目前的数据可用于模拟在不断变化的气候和野火干扰下永久冻土特征的变化，并探索半寒带森林中火灾-永久冻土-碳系统不断变化的互动机制。此外，还可为研究和行动计划提供基准数据，以支持碳中和倡议以及永冻土环境的生态安全和管理评估。该数据集可从国家青藏高原/第三极环境数据中心轻松获取（https://doi.org/10.11888/Cryos.tpdc.300933, Li and Jin, 2024）。
{"title":"An integrated dataset of ground hydrothermal regimes and soil nutrients monitored during 2016–2022 in some previously burned areas in hemiboreal forests in Northeast China","authors":"Xiaoying Li, Huijun Jin, Qi Feng, Qingbai Wu, Hongwei Wang, Ruixia He, Dongliang Luo, Xiaoli Chang, Raul-David Șerban, Tao Zhan","doi":"10.5194/essd-2024-187","DOIUrl":"https://doi.org/10.5194/essd-2024-187","url":null,"abstract":"<strong>Abstract.</strong> Under a warming climate, occurrences of wildfires have been increasingly more frequent in boreal and arctic forests during the last few decades. Wildfires can cause radical changes in the forest ecosystems and permafrost environment, such as irreversible degradation of permafrost, successions of boreal forests, rapid and massive losses of soil carbon stock, and increased periglacial geohazards. Since 2016, we have gradually and more systematically established a network for studying soil nutrients and monitoring the hydrothermal state of the active layer and near-surface permafrost in the northern Da Xing’anling (Hinggan) Mountains in Northeast China. The dataset of soil moisture content (0–9.4 m in depth), soil organic carbon (0–3.6 m), total nitrogen (0–3.6 m), and total phosphorus and potassium (0–3.6 m) have been obtained by field sampling and ensuing laboratory tests. Long-term datasets (2017–2022) of ground temperatures (0–20 m) and active layer thickness have been observed by thermistor cables permanently installed in boreholes. The present data can be used to simulate changes in permafrost features under a changing climate and wildfire disturbances and to explore the changing interactive mechanisms of the fire-permafrost-carbon system in the hemiboreal forest. Furthermore, can provide baseline data for studies and action plans to support the carbon neutralization initiative and assessment of ecological safety and management of the permafrost environment. This dataset can be easily accessed from the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Cryos.tpdc.300933, Li and Jin, 2024).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"4 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141235930","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}
Thomas Dirnböck, Michael Bahn, Eugenio Diaz-Pines, Ika Djukic, Michael Englisch, Karl Gartner, Günther Gollobich, Armin Hofbauer, Johannes Ingrisch, Barbara Kitzler, Karl Knaebel, Johannes Kobler, Andreas Maier, Christoph Wohner, Ivo Offenthaler, Johannes Peterseil, Gisela Pröll, Sarah Venier, Sophie Zechmeister, Anita Zolles, Stephan Glatzel
Abstract. Seven long-term observation sites have been established in six regions across Austria, covering major ecosystem types such as forests, grasslands and wetlands across a wide bioclimatic range. The purpose of these observations is to measure key ecosystem parameters serving as baselines for assessing the impacts of extreme climate events on the carbon cycle. The data sets collected include meteorological variables, soil microclimate, CO2 fluxes and tree stem growth, all recorded at high temporal resolution between 2019 and 2021 (including one year of average climate conditions and two comparatively dry years). The DOIs of the dataset can be found in the data availability chapter. The sites will be integrated into the European Research Infrastructure for Integrated European Long-Term Ecosystem, Critical Zone, and Socio-Ecological Research (eLTER RI). Subsequently, new data covering the variables presented here will be continuously available through its data integration portal. This step will allow the data to reach its full potential for research on drought-related ecosystem carbon cycling.
摘要在奥地利的六个地区建立了七个长期观测点，涵盖森林、草原和湿地等主要生态系统类型，生物气候范围广泛。这些观测点的目的是测量关键的生态系统参数，作为评估极端气候事件对碳循环影响的基线。所收集的数据集包括气象变量、土壤微气候、二氧化碳通量和树干生长，所有数据均以高时间分辨率记录，时间跨度为 2019 年至 2021 年（包括一年平均气候条件和两年相对干旱的年份）。数据集的 DOIs 见数据可用性一章。这些站点将被纳入欧洲生态系统、临界区和社会生态综合研究基础设施（eLTER RI）。随后，将通过其数据集成门户网站不断提供涵盖本文所述变量的新数据。这一步骤将使数据在与干旱相关的生态系统碳循环研究中充分发挥其潜力。
{"title":"High-resolution Carbon cycling data from 2019 to 2021 measured at six Austrian LTER sites","authors":"Thomas Dirnböck, Michael Bahn, Eugenio Diaz-Pines, Ika Djukic, Michael Englisch, Karl Gartner, Günther Gollobich, Armin Hofbauer, Johannes Ingrisch, Barbara Kitzler, Karl Knaebel, Johannes Kobler, Andreas Maier, Christoph Wohner, Ivo Offenthaler, Johannes Peterseil, Gisela Pröll, Sarah Venier, Sophie Zechmeister, Anita Zolles, Stephan Glatzel","doi":"10.5194/essd-2024-110","DOIUrl":"https://doi.org/10.5194/essd-2024-110","url":null,"abstract":"<strong>Abstract.</strong> Seven long-term observation sites have been established in six regions across Austria, covering major ecosystem types such as forests, grasslands and wetlands across a wide bioclimatic range. The purpose of these observations is to measure key ecosystem parameters serving as baselines for assessing the impacts of extreme climate events on the carbon cycle. The data sets collected include meteorological variables, soil microclimate, CO<sub>2</sub> fluxes and tree stem growth, all recorded at high temporal resolution between 2019 and 2021 (including one year of average climate conditions and two comparatively dry years). The DOIs of the dataset can be found in the data availability chapter. The sites will be integrated into the European Research Infrastructure for Integrated European Long-Term Ecosystem, Critical Zone, and Socio-Ecological Research (eLTER RI). Subsequently, new data covering the variables presented here will be continuously available through its data integration portal. This step will allow the data to reach its full potential for research on drought-related ecosystem carbon cycling.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"127 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141236031","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}
Israel Silber, Jennifer M. Comstock, Michael R. Kieburtz, Lynn M. Russell
Abstract. Ground-based instruments offer unique capabilities such as detailed atmospheric thermodynamic, cloud, and aerosol profiling at a high temporal sampling rate. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility provides comprehensive datasets from key locations around the globe, facilitating long-term characterization and process-level understanding of clouds, aerosol, and aerosol-cloud interactions. However, as with other ground-based datasets, the fixed (Eulerian) nature of these measurements often introduces a knowledge gap in relating those observations with airmass hysteresis. Here, we describe ARMTRAJ, a set of multi-purpose trajectory datasets that helps close this gap in ARM deployments. Each dataset targets a different aspect of atmospheric research, including the analysis of surface, planetary boundary layer, distinct liquid-bearing cloud layers, and (primary) cloud decks. Trajectories are calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model informed by the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis dataset at its highest spatial resolution (0.25 degrees) and are initialized using ARM datasets. The trajectory datasets include information about airmass coordinates and state variables extracted from ERA5 before and after the ARM site overpass. Ensemble runs generated for each model initialization enhance trajectory consistency, while ensemble variability serves as a valuable uncertainty metric for those reported airmass coordinates and state variables. Following the description of dataset processing and structure, we demonstrate applications of ARMTRAJ to a case study and a few bulk analyses of observations collected during ARM’s Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) field deployment. ARMTRAJ is expected to become a near real-time product accompanying new ARM deployments and an augmenting product to ongoing and previous deployments, promoting reaching science goals of research relying on ARM observations.
摘要。地基仪器具有独特的功能，例如以高时间采样率进行详细的大气热力学、云层和气溶胶剖面分析。美国能源部大气辐射测量（ARM）用户设施提供来自全球主要地点的综合数据集，有助于对云、气溶胶和气溶胶-云相互作用进行长期特征描述和过程级理解。然而，与其他地基数据集一样，这些测量数据的固定（欧拉）性质往往会在将这些观测数据与气云滞后联系起来方面带来知识差距。在此，我们将介绍 ARMTRAJ，这是一套多用途轨迹数据集，有助于缩小 ARM 部署中的这一差距。每个数据集都针对大气研究的不同方面，包括对表面、行星边界层、独特的含液云层和（主要）云层的分析。轨迹使用混合单粒子拉格朗日综合轨迹（HYSPLIT）模型进行计算，该模型以最高空间分辨率（0.25 度）的欧洲中期天气预报中心 ERA5 再分析数据集为依据，并使用 ARM 数据集进行初始化。轨迹数据集包括从ERA5提取的气团坐标和状态变量信息，这些信息在ARM站点覆盖前后都有。为每个模式初始化生成的集合运行增强了轨迹一致性，而集合变异性则可作为报告的气团坐标和状态变量的重要不确定性指标。在介绍了数据集处理和结构之后，我们演示了 ARMTRAJ 在案例研究中的应用，以及对 ARM 的东太平洋云层气溶胶降水实验（EPCAPE）实地部署期间收集的观测数据进行的一些批量分析。ARMTRAJ 预计将成为伴随新的 ARM 部署的近实时产品，以及正在进行的和以前部署的增强产品，从而促进实现依靠 ARM 观测进行研究的科学目标。
{"title":"ARMTRAJ: A Set of Multi-Purpose Trajectory Datasets Augmenting the Atmospheric Radiation Measurement (ARM) User Facility Measurements","authors":"Israel Silber, Jennifer M. Comstock, Michael R. Kieburtz, Lynn M. Russell","doi":"10.5194/essd-2024-127","DOIUrl":"https://doi.org/10.5194/essd-2024-127","url":null,"abstract":"<strong>Abstract.</strong> Ground-based instruments offer unique capabilities such as detailed atmospheric thermodynamic, cloud, and aerosol profiling at a high temporal sampling rate. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility provides comprehensive datasets from key locations around the globe, facilitating long-term characterization and process-level understanding of clouds, aerosol, and aerosol-cloud interactions. However, as with other ground-based datasets, the fixed (Eulerian) nature of these measurements often introduces a knowledge gap in relating those observations with airmass hysteresis. Here, we describe ARMTRAJ, a set of multi-purpose trajectory datasets that helps close this gap in ARM deployments. Each dataset targets a different aspect of atmospheric research, including the analysis of surface, planetary boundary layer, distinct liquid-bearing cloud layers, and (primary) cloud decks. Trajectories are calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model informed by the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis dataset at its highest spatial resolution (0.25 degrees) and are initialized using ARM datasets. The trajectory datasets include information about airmass coordinates and state variables extracted from ERA5 before and after the ARM site overpass. Ensemble runs generated for each model initialization enhance trajectory consistency, while ensemble variability serves as a valuable uncertainty metric for those reported airmass coordinates and state variables. Following the description of dataset processing and structure, we demonstrate applications of ARMTRAJ to a case study and a few bulk analyses of observations collected during ARM’s Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) field deployment. ARMTRAJ is expected to become a near real-time product accompanying new ARM deployments and an augmenting product to ongoing and previous deployments, promoting reaching science goals of research relying on ARM observations.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"24 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141182426","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}
Pub Date : 2024-05-30DOI: 10.5194/essd-16-2525-2024
Francesca Lappin, Gijs de Boer, Petra Klein, Jonathan Hamilton, Michelle Spencer, Radiance Calmer, Antonio R. Segales, Michael Rhodes, Tyler M. Bell, Justin Buchli, Kelsey Britt, Elizabeth Asher, Isaac Medina, Brian Butterworth, Leia Otterstatter, Madison Ritsch, Bryony Puxley, Angelina Miller, Arianna Jordan, Ceu Gomez-Faulk, Elizabeth Smith, Steven Borenstein, Troy Thornberry, Brian Argrow, Elizabeth Pillar-Little
Abstract. The main goal of the TRacking Aerosol Convection interactions ExpeRiment (TRACER) project was to further understand the role that regional circulations and aerosol loading play in the convective cloud life cycle across the greater Houston, Texas, area. To accomplish this goal, the United States Department of Energy and research partners collaborated to deploy atmospheric observing systems across the region. Cloud and precipitation radars, radiosondes, and air quality sensors captured atmospheric and cloud characteristics. A dense lower-atmospheric dataset was developed using ground-based remote sensors, a tethersonde, and uncrewed aerial systems (UASs). TRACER-UAS is a subproject that deployed two UAS platforms to gather high-resolution observations in the lower atmosphere between 1 June and 30 September 2022. The University of Oklahoma CopterSonde and the University of Colorado Boulder RAAVEN (Robust Autonomous Aerial Vehicle – Endurant Nimble) were flown at two coastal locations between the Gulf of Mexico and Houston. The University of Colorado Boulder RAAVEN gathered measurements of atmospheric thermodynamic state, winds and turbulence, and aerosol size distribution. Meanwhile, the University of Oklahoma CopterSonde system operated on a regular basis to resolve the vertical structure of the thermodynamic and kinematic state. Together, a complementary dataset of over 200 flight hours across 61 d was generated, and data from each platform proved to be in strong agreement. In this paper, the platforms and respective data collection and processing are described. The dataset described herein provides information on boundary layer evolution, the sea breeze circulation, conditions prior to and nearby deep convection, and the vertical structure and evolution of aerosols. The quality-controlled TRACER-UAS observations from the CopterSonde and RAAVEN can be found at https://doi.org/10.5439/1969004 (Lappin, 2023) and https://doi.org/10.5439/1985470 (de Boer, 2023), respectively.
{"title":"Data collected using small uncrewed aircraft systems during the TRacking Aerosol Convection interactions ExpeRiment (TRACER)","authors":"Francesca Lappin, Gijs de Boer, Petra Klein, Jonathan Hamilton, Michelle Spencer, Radiance Calmer, Antonio R. Segales, Michael Rhodes, Tyler M. Bell, Justin Buchli, Kelsey Britt, Elizabeth Asher, Isaac Medina, Brian Butterworth, Leia Otterstatter, Madison Ritsch, Bryony Puxley, Angelina Miller, Arianna Jordan, Ceu Gomez-Faulk, Elizabeth Smith, Steven Borenstein, Troy Thornberry, Brian Argrow, Elizabeth Pillar-Little","doi":"10.5194/essd-16-2525-2024","DOIUrl":"https://doi.org/10.5194/essd-16-2525-2024","url":null,"abstract":"Abstract. The main goal of the TRacking Aerosol Convection interactions ExpeRiment (TRACER) project was to further understand the role that regional circulations and aerosol loading play in the convective cloud life cycle across the greater Houston, Texas, area. To accomplish this goal, the United States Department of Energy and research partners collaborated to deploy atmospheric observing systems across the region. Cloud and precipitation radars, radiosondes, and air quality sensors captured atmospheric and cloud characteristics. A dense lower-atmospheric dataset was developed using ground-based remote sensors, a tethersonde, and uncrewed aerial systems (UASs). TRACER-UAS is a subproject that deployed two UAS platforms to gather high-resolution observations in the lower atmosphere between 1 June and 30 September 2022. The University of Oklahoma CopterSonde and the University of Colorado Boulder RAAVEN (Robust Autonomous Aerial Vehicle – Endurant Nimble) were flown at two coastal locations between the Gulf of Mexico and Houston. The University of Colorado Boulder RAAVEN gathered measurements of atmospheric thermodynamic state, winds and turbulence, and aerosol size distribution. Meanwhile, the University of Oklahoma CopterSonde system operated on a regular basis to resolve the vertical structure of the thermodynamic and kinematic state. Together, a complementary dataset of over 200 flight hours across 61 d was generated, and data from each platform proved to be in strong agreement. In this paper, the platforms and respective data collection and processing are described. The dataset described herein provides information on boundary layer evolution, the sea breeze circulation, conditions prior to and nearby deep convection, and the vertical structure and evolution of aerosols. The quality-controlled TRACER-UAS observations from the CopterSonde and RAAVEN can be found at https://doi.org/10.5439/1969004 (Lappin, 2023) and https://doi.org/10.5439/1985470 (de Boer, 2023), respectively.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"35 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141177230","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}
Abstract. This paper presents an observational dataset on submesoscale eddies obtained from high–resolution chlorophyll–a data captured by GOCI I. Our methodology involves a combination of digital image processing, filtering, and object detection techniques, along with specific chlorophyll–a image enhancement procedure to extract essential information about submesoscale eddies. This information includes their time, polarity, geographical coordinates of the eddy center, eddy radius, coordinates of the upper left and lower right corners of the prediction box, area of the eddy's inner ellipse, and confidence score. The dataset spans eight time intervals, ranging from 00:00 to 08:00 (UTC) daily, covering the period from April 1, 2011, to March 31, 2021. A total of 19,136 anticyclonic eddies and 93,897 cyclonic eddies were identified with a confidence minimum of 0.2. The mean radius of anticyclonic eddies is 24.44 km (range 2.5 km to 44.25 km), while that of cyclonic eddies is 12.34 km (range 1.75 km to 44 km). This unprecedented hourly resolution dataset on submesoscale eddies offers valuable insights into their distribution, morphology, and energy dissipation. It significantly contributes to our understanding of marine environments, ecosystems and the improvement of climate model predictions. The dataset is available at https://doi.org/10.5281/zenodo.7694115 (Wang and Yang, 2023).
{"title":"A Submesoscale Eddy Identification Dataset in the Northwest Pacific Ocean Derived from GOCI I Chlorophyll–a Data based on Deep Learning","authors":"Yan Wang, Jie Yang, Ge Chen","doi":"10.5194/essd-2024-188","DOIUrl":"https://doi.org/10.5194/essd-2024-188","url":null,"abstract":"<strong>Abstract.</strong> This paper presents an observational dataset on submesoscale eddies obtained from high–resolution chlorophyll–a data captured by GOCI I. Our methodology involves a combination of digital image processing, filtering, and object detection techniques, along with specific chlorophyll–a image enhancement procedure to extract essential information about submesoscale eddies. This information includes their time, polarity, geographical coordinates of the eddy center, eddy radius, coordinates of the upper left and lower right corners of the prediction box, area of the eddy's inner ellipse, and confidence score. The dataset spans eight time intervals, ranging from 00:00 to 08:00 (UTC) daily, covering the period from April 1, 2011, to March 31, 2021. A total of 19,136 anticyclonic eddies and 93,897 cyclonic eddies were identified with a confidence minimum of 0.2. The mean radius of anticyclonic eddies is 24.44 km (range 2.5 km to 44.25 km), while that of cyclonic eddies is 12.34 km (range 1.75 km to 44 km). This unprecedented hourly resolution dataset on submesoscale eddies offers valuable insights into their distribution, morphology, and energy dissipation. It significantly contributes to our understanding of marine environments, ecosystems and the improvement of climate model predictions. The dataset is available at https://doi.org/10.5281/zenodo.7694115 (Wang and Yang, 2023).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"72 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141177309","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}
Abstract. Accurate long-term daily cloud-gap-filled fractional snow cover products are essential for climate change and snow hydrological studies in the Asian Water Tower (AWT) region, but existing Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are not sufficient. In this study, the multiple-endmember spectral mixture analysis algorithm based on automatic endmember extraction (MESMA-AGE) and the multistep spatiotemporal interpolation algorithm (MSTI) are used to produce the MODIS daily cloud-gap-filled fractional snow cover product over the AWT region (AWT MODIS FSC). The AWT MODIS FSC products have a spatial resolution of 0.005° and span from 2000 to 2022. The 2745 scenes of Landsat-8 images are used for the areal-scale accuracy assessment. The fractional snow cover accuracy metrics, including the coefficient of determination (R2), root mean squared error (RMSE) and mean absolute error (MAE), are 0.80, 0.16 and 0.10, respectively. The binarized identification accuracy metrics, including overall accuracy (OA), producer's accuracy (PA) and user's accuracy (UA), are 95.17 %, 97.34 % and 97.59 %, respectively. Snow depth data observed at 175 meteorological stations are used to evaluate accuracy at the point scale, yielding the following accuracy metrics: an OA of 93.26 %, a PA of 84.41 %, a UA of 82.14 % and a Cohen kappa (CK) value of 0.79. Snow depth observations from meteorological stations are also used to assess the fractional snow cover resulting from different weather conditions, with an OA of 95.36 % (88.96 %), a PA of 87.75 % (82.26 %), a UA of 86.86 % (78.86 %) and a CK of 0.84 (0.72) under the MODIS clear-sky observations (spatiotemporal reconstruction based on the MSTI algorithm). The AWT MODIS FSC product can provide quantitative spatial distribution information on snowpacks for mountain hydrological models, land surface models and numerical weather prediction in the Asian Water Tower region. This dataset is freely available from the National Tibetan Plateau Data Center at https://doi.org/10.11888/Cryos.tpdc.272503 (Jiang et al., 2022) or from the Zenodo platform at https://doi.org/10.5281/zenodo.10005826 (Jiang et al., 2023a).
{"title":"MODIS daily cloud-gap-filled fractional snow cover dataset of the Asian Water Tower region (2000–2022)","authors":"Fangbo Pan, Lingmei Jiang, Gongxue Wang, Jinmei Pan, Jinyu Huang, Cheng Zhang, Huizhen Cui, Jianwei Yang, Zhaojun Zheng, Shengli Wu, Jiancheng Shi","doi":"10.5194/essd-16-2501-2024","DOIUrl":"https://doi.org/10.5194/essd-16-2501-2024","url":null,"abstract":"Abstract. Accurate long-term daily cloud-gap-filled fractional snow cover products are essential for climate change and snow hydrological studies in the Asian Water Tower (AWT) region, but existing Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are not sufficient. In this study, the multiple-endmember spectral mixture analysis algorithm based on automatic endmember extraction (MESMA-AGE) and the multistep spatiotemporal interpolation algorithm (MSTI) are used to produce the MODIS daily cloud-gap-filled fractional snow cover product over the AWT region (AWT MODIS FSC). The AWT MODIS FSC products have a spatial resolution of 0.005° and span from 2000 to 2022. The 2745 scenes of Landsat-8 images are used for the areal-scale accuracy assessment. The fractional snow cover accuracy metrics, including the coefficient of determination (R2), root mean squared error (RMSE) and mean absolute error (MAE), are 0.80, 0.16 and 0.10, respectively. The binarized identification accuracy metrics, including overall accuracy (OA), producer's accuracy (PA) and user's accuracy (UA), are 95.17 %, 97.34 % and 97.59 %, respectively. Snow depth data observed at 175 meteorological stations are used to evaluate accuracy at the point scale, yielding the following accuracy metrics: an OA of 93.26 %, a PA of 84.41 %, a UA of 82.14 % and a Cohen kappa (CK) value of 0.79. Snow depth observations from meteorological stations are also used to assess the fractional snow cover resulting from different weather conditions, with an OA of 95.36 % (88.96 %), a PA of 87.75 % (82.26 %), a UA of 86.86 % (78.86 %) and a CK of 0.84 (0.72) under the MODIS clear-sky observations (spatiotemporal reconstruction based on the MSTI algorithm). The AWT MODIS FSC product can provide quantitative spatial distribution information on snowpacks for mountain hydrological models, land surface models and numerical weather prediction in the Asian Water Tower region. This dataset is freely available from the National Tibetan Plateau Data Center at https://doi.org/10.11888/Cryos.tpdc.272503 (Jiang et al., 2022) or from the Zenodo platform at https://doi.org/10.5281/zenodo.10005826 (Jiang et al., 2023a).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"49 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141177400","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}
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