Xing Yan, Z. Zang, Zhanqing Li, N. Luo, Chen Zuo, Yize Jiang, Dan Li, Yushan Guo, Wenji Zhao, W. Shi, M. Cribb
Abstract. The aerosol fine-mode fraction (FMF) is potentially valuable for discriminating natural aerosols from anthropogenic ones. However, most current satellite-based FMF products are highly unreliable. Here, we developed a new satellite-based global land daily FMF dataset (Phy-DL FMF) by synergizing the advantages of physical and deep learning methods at a 1° spatial resolution by covering the period from 2001 to 2020. The Phy-DL FMF dataset is comparable to Aerosol Robotic Network (AERONET) measurements, based on the analysis of 361,089 data samples from 1170 AERONET sites around the world. Overall, Phy-DL FMF showed a root-mean-square error of 0.136 and correlation coefficient of 0.68, and the proportion of results that fell within the ±20 % expected error window was 79.15 %. Phy-DL FMF showed superior performance over alternate deep learning or physical approaches (such as the spectral deconvolution algorithm presented in our previous studies), particularly for forests, grasslands, croplands, and urban and barren land types. As a long-term dataset, Phy-DL FMF is able to show an overall significant decreasing trend (at a 95 % significance level) over global land areas. Based on the trend analysis of Phy-DL FMF for different countries, the upward trend in the FMFs was particularly strong over India and the western USA. Overall, this study provides a new FMF dataset for global land areas that can help improve our understanding of spatiotemporal fine- and coarse-mode aerosol changes. The datasets can be downloaded from https://doi.org/10.5281/zenodo.5105617 (Yan, 2021).
{"title":"A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches","authors":"Xing Yan, Z. Zang, Zhanqing Li, N. Luo, Chen Zuo, Yize Jiang, Dan Li, Yushan Guo, Wenji Zhao, W. Shi, M. Cribb","doi":"10.5194/essd-2021-326","DOIUrl":"https://doi.org/10.5194/essd-2021-326","url":null,"abstract":"Abstract. The aerosol fine-mode fraction (FMF) is potentially valuable for discriminating natural aerosols from anthropogenic ones. However, most current satellite-based FMF products are highly unreliable. Here, we developed a new satellite-based global land daily FMF dataset (Phy-DL FMF) by synergizing the advantages of physical and deep learning methods at a 1° spatial resolution by covering the period from 2001 to 2020. The Phy-DL FMF dataset is comparable to Aerosol Robotic Network (AERONET) measurements, based on the analysis of 361,089 data samples from 1170 AERONET sites around the world. Overall, Phy-DL FMF showed a root-mean-square error of 0.136 and correlation coefficient of 0.68, and the proportion of results that fell within the ±20 % expected error window was 79.15 %. Phy-DL FMF showed superior performance over alternate deep learning or physical approaches (such as the spectral deconvolution algorithm presented in our previous studies), particularly for forests, grasslands, croplands, and urban and barren land types. As a long-term dataset, Phy-DL FMF is able to show an overall significant decreasing trend (at a 95 % significance level) over global land areas. Based on the trend analysis of Phy-DL FMF for different countries, the upward trend in the FMFs was particularly strong over India and the western USA. Overall, this study provides a new FMF dataset for global land areas that can help improve our understanding of spatiotemporal fine- and coarse-mode aerosol changes. The datasets can be downloaded from https://doi.org/10.5281/zenodo.5105617 (Yan, 2021).\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122302624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Doughty, T. Kurosu, N. Parazoo, P. Köhler, Yujie Wang, Ying Sun, C. Frankenberg
Abstract. The retrieval of solar induced chlorophyll fluorescence (SIF) from space is a relatively new advance in Earth observation science, having only become feasible within the last decade. Interest in SIF data has grown exponentially, and the retrieval of SIF and the provision of SIF data products has become an important and formal component of spaceborne Earth observation missions. Here, we describe the global Level 2 SIF Lite data products for the Greenhouse Gases Observing Satellite (GOSAT), the Orbiting Carbon Observatory-2 (OCO-2), and OCO-3 platforms, which are provided for each platform in daily netCDF files. We also outline the methods used to retrieve SIF and estimate uncertainty, describe all the data fields, and provide users the background information necessary for the proper use and interpretation of the data, such as considerations of retrieval noise, sun-sensor geometry, the indirect relationship between SIF and photosynthesis, and differences among the three platforms and their respective data products. OCO-2 and OCO-3 have the highest spatial resolution spaceborne SIF retrievals to date, and the target and snapshot area mode observation modes of OCO-2 and OCO-3 are unique. These modes provide hundreds to thousands of SIF retrievals at biologically diverse global target sites during a single overpass, and provide an opportunity to better inform our understanding of canopy-scale vegetation SIF emission across biomes.
{"title":"Global GOSAT, OCO-2 and OCO-3 Solar Induced Chlorophyll Fluorescence Datasets","authors":"R. Doughty, T. Kurosu, N. Parazoo, P. Köhler, Yujie Wang, Ying Sun, C. Frankenberg","doi":"10.5194/essd-2021-237","DOIUrl":"https://doi.org/10.5194/essd-2021-237","url":null,"abstract":"Abstract. The retrieval of solar induced chlorophyll fluorescence (SIF) from space is a relatively new advance in Earth observation science, having only become feasible within the last decade. Interest in SIF data has grown exponentially, and the retrieval of SIF and the provision of SIF data products has become an important and formal component of spaceborne Earth observation missions. Here, we describe the global Level 2 SIF Lite data products for the Greenhouse Gases Observing Satellite (GOSAT), the Orbiting Carbon Observatory-2 (OCO-2), and OCO-3 platforms, which are provided for each platform in daily netCDF files. We also outline the methods used to retrieve SIF and estimate uncertainty, describe all the data fields, and provide users the background information necessary for the proper use and interpretation of the data, such as considerations of retrieval noise, sun-sensor geometry, the indirect relationship between SIF and photosynthesis, and differences among the three platforms and their respective data products. OCO-2 and OCO-3 have the highest spatial resolution spaceborne SIF retrievals to date, and the target and snapshot area mode observation modes of OCO-2 and OCO-3 are unique. These modes provide hundreds to thousands of SIF retrievals at biologically diverse global target sites during a single overpass, and provide an opportunity to better inform our understanding of canopy-scale vegetation SIF emission across biomes.","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126099557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Jessell, Jiateng Guo, Yunqiang Li, M. Lindsay, R. Scalzo, J. Giraud, G. Pirot, E. Cripps, V. Ogarko
Abstract. Unlike some other well-known challenges such as facial recognition, where Machine Learning and Inversion algorithms are widely developed, the geosciences suffer from a lack of large, labelled datasets that can be used to validate or train robust Machine Learning and inversion schemes. Publicly available 3D geological models are far too restricted in both number and the range of geological scenarios to serve these purposes. With reference to inverting geophysical data this problem is further exacerbated as in most cases real geophysical observations result from unknown 3D geology, and synthetic test datasets are often not particularly geological, nor geologically diverse. To overcome these limitations, we have used the Noddy modelling platform to generate one million models, which represent the first publicly accessible massive training set for 3D geology and resulting gravity and magnetic datasets. This model suite can be used to train Machine Learning systems, and to provide comprehensive test suites for geophysical inversion. We describe the methodology for producing the model suite, and discuss the opportunities such a model suit affords, as well as its limitations, and how we can grow and access this resource.
{"title":"Into the Noddyverse: A massive data store of 3D geological models for Machine Learning & inversion applications","authors":"M. Jessell, Jiateng Guo, Yunqiang Li, M. Lindsay, R. Scalzo, J. Giraud, G. Pirot, E. Cripps, V. Ogarko","doi":"10.5194/essd-2021-304","DOIUrl":"https://doi.org/10.5194/essd-2021-304","url":null,"abstract":"Abstract. Unlike some other well-known challenges such as facial recognition, where Machine Learning and Inversion algorithms are widely developed, the geosciences suffer from a lack of large, labelled datasets that can be used to validate or train robust Machine Learning and inversion schemes. Publicly available 3D geological models are far too restricted in both number and the range of geological scenarios to serve these purposes. With reference to inverting geophysical data this problem is further exacerbated as in most cases real geophysical observations result from unknown 3D geology, and synthetic test datasets are often not particularly geological, nor geologically diverse. To overcome these limitations, we have used the Noddy modelling platform to generate one million models, which represent the first publicly accessible massive training set for 3D geology and resulting gravity and magnetic datasets. This model suite can be used to train Machine Learning systems, and to provide comprehensive test suites for geophysical inversion. We describe the methodology for producing the model suite, and discuss the opportunities such a model suit affords, as well as its limitations, and how we can grow and access this resource.\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"142 3-4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114022194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Baojun Zhang, Zemin Wang, J. An, Tingting Liu, H. Geng
Abstract. A long-term time series of ice sheet surface elevation change (SEC) is important for study of ice sheet variation and its response to climate change. In this study, we used an updated plane-fitting least-squares regression strategy to generate a 30 year surface elevation time series for the Greenland Ice Sheet (GrIS) at monthly temporal resolution and 5 × 5 km grid spatial resolution using ERS‐1, ERS‐2, Envisat, and CryoSat‐2 satellite radar altimeter observations obtained between August 1991 and December 2020. The accuracy and reliability of the time series are effectively guaranteed by application of sophisticated corrections for intermission bias and interpolation based on empirical orthogonal function reconstruction. Validation using both airborne laser altimeter observations and the European Space Agency GrIS Climate Change Initiative (CCI) product indicated that our merged surface elevation time series is reliable. The accuracy and dispersion of errors of SECs of our results were 19.3 % and 8.9 % higher, respectively, than those of CCI SECs, and even 30.9 % and 19.0 % higher, respectively, in periods from 2006–2010 to 2010–2014. Further analysis showed that our merged time series could provide detailed insight into GrIS SEC on multiple temporal (up to 30 years) and spatial scales, thereby providing opportunity to explore potential associations between ice sheet change and climatic forcing. The merged surface elevation time series data are available at http://dx.doi.org/10.11888/Glacio.tpdc.271658 (Zhang et al., 2021).
摘要冰盖表面高程变化(SEC)的长期时间序列对于研究冰盖变化及其对气候变化的响应具有重要意义。在这项研究中，我们使用更新的平面拟合最小二乘回归策略，利用1991年8月至2020年12月期间获得的ERS‐1、ERS‐2、Envisat和CryoSat‐2卫星雷达高度计观测数据，以月时间分辨率和5 × 5 km网格空间分辨率生成了格陵兰冰盖(GrIS)的30年地表高程时间序列。采用基于经验正交函数重构的复杂的间隔偏差校正和插值，有效地保证了时间序列的准确性和可靠性。使用机载激光高度计观测和欧洲航天局GrIS气候变化倡议(CCI)产品的验证表明，我们合并的地表高程时间序列是可靠的。在2006-2010年至2010-2014年期间，我们的结果的SECs的准确度和误差离散度分别比CCI SECs高19.3%和8.9%，甚至比CCI SECs高30.9%和19.0%。进一步分析表明，我们合并的时间序列可以在多个时间(长达30年)和空间尺度上详细了解GrIS SEC，从而为探索冰盖变化与气候强迫之间的潜在关联提供了机会。合并后的地表高程时间序列数据可在http://dx.doi.org/10.11888/Glacio.tpdc.271658获取(Zhang et al.， 2021)。
{"title":"A 30 year monthly 5 km gridded surface elevation time series for the Greenland Ice Sheet from multiple satellite radar altimeters","authors":"Baojun Zhang, Zemin Wang, J. An, Tingting Liu, H. Geng","doi":"10.5194/essd-2021-293","DOIUrl":"https://doi.org/10.5194/essd-2021-293","url":null,"abstract":"Abstract. A long-term time series of ice sheet surface elevation change (SEC) is important for study of ice sheet variation and its response to climate change. In this study, we used an updated plane-fitting least-squares regression strategy to generate a 30 year surface elevation time series for the Greenland Ice Sheet (GrIS) at monthly temporal resolution and 5 × 5 km grid spatial resolution using ERS‐1, ERS‐2, Envisat, and CryoSat‐2 satellite radar altimeter observations obtained between August 1991 and December 2020. The accuracy and reliability of the time series are effectively guaranteed by application of sophisticated corrections for intermission bias and interpolation based on empirical orthogonal function reconstruction. Validation using both airborne laser altimeter observations and the European Space Agency GrIS Climate Change Initiative (CCI) product indicated that our merged surface elevation time series is reliable. The accuracy and dispersion of errors of SECs of our results were 19.3 % and 8.9 % higher, respectively, than those of CCI SECs, and even 30.9 % and 19.0 % higher, respectively, in periods from 2006–2010 to 2010–2014. Further analysis showed that our merged time series could provide detailed insight into GrIS SEC on multiple temporal (up to 30 years) and spatial scales, thereby providing opportunity to explore potential associations between ice sheet change and climatic forcing. The merged surface elevation time series data are available at http://dx.doi.org/10.11888/Glacio.tpdc.271658 (Zhang et al., 2021).\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129617176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Groos, Janik Niederhauser, B. Lemma, Mekbib Fekadu, W. Zech, Falk Hänsel, Luise Wraase, N. Akçar, H. Veit
Abstract. Tropical mountains and highlands in Africa are under pressure because of anthropogenic climate and land-use change. To determine the impacts of global climate change on the afro-alpine environment and to assess the potential socio-economic consequences, the monitoring of essential climate and environmental variables at high elevation is fundamental. However, long-term climate observations on the continent above 3,000 m are very rare. Here we present a consistent multinannual ground temperature dataset for the BaleMountains in the southern Ethiopian Highlands, which comprise Africa's largest tropical alpine area. 29 ground temperature data loggers have been installed at 16 sites since 2017 to characterise and continuously monitor the mountain climate and ecosystem of the Bale Mountains along an elevation gradient from 3493 to 4377 m. At five sites above ∼ 3900 m, the monitoring will be continued to trace long-term changes. The generated time series provide insights in the spatio temporal ground temperature variations at high elevation, the energy exchange between the ground surface and atmosphere, as well as the impact of vegetation and slope orientation on the thermal dynamics of the ground. To promote the further use of the ground temperature dataset by the wider research community dealing with the climate and geo-ecology of tropical mountains in Eastern Africa, it is made freely available via the open-access repository Zenodo: https://doi.org/10.5281/zenodo.5172002 (Groos et al., 2021b).
摘要由于人为气候和土地利用变化，非洲的热带山地和高地正面临压力。为了确定全球气候变化对非洲-高山环境的影响并评估其潜在的社会经济后果，对高海拔地区基本气候和环境变量的监测至关重要。然而，在海拔3000米以上的大陆上进行长期气候观测是非常罕见的。在这里，我们提供了埃塞俄比亚南部高原balemmountains多年来一致的地温数据集，该数据集包括非洲最大的热带高山地区。自2017年以来，已在16个地点安装了29台地温数据记录器，以描述和持续监测贝尔山脉海拔梯度从3493米至4377米的山地气候和生态系统。在海拔约3900米以上的5个地点，将继续监测长期变化。生成的时间序列可以揭示高海拔地温的时空变化、地表与大气之间的能量交换以及植被和坡向对地面热动力学的影响。为了促进研究东非热带山区气候和地质生态的更广泛的研究界进一步使用地温数据集，该数据集通过开放存取库Zenodo: https://doi.org/10.5281/zenodo.5172002免费提供(Groos et al.， 2021b)。
{"title":"A multiannual ground temperature dataset covering sixteen high elevation sites (3493–4377 m a.s.l.) in the Bale Mountains, Ethiopia","authors":"A. Groos, Janik Niederhauser, B. Lemma, Mekbib Fekadu, W. Zech, Falk Hänsel, Luise Wraase, N. Akçar, H. Veit","doi":"10.5194/essd-2021-268","DOIUrl":"https://doi.org/10.5194/essd-2021-268","url":null,"abstract":"Abstract. Tropical mountains and highlands in Africa are under pressure because of anthropogenic climate and land-use change. To determine the impacts of global climate change on the afro-alpine environment and to assess the potential socio-economic consequences, the monitoring of essential climate and environmental variables at high elevation is fundamental. However, long-term climate observations on the continent above 3,000 m are very rare. Here we present a consistent multinannual ground temperature dataset for the BaleMountains in the southern Ethiopian Highlands, which comprise Africa's largest tropical alpine area. 29 ground temperature data loggers have been installed at 16 sites since 2017 to characterise and continuously monitor the mountain climate and ecosystem of the Bale Mountains along an elevation gradient from 3493 to 4377 m. At five sites above ∼ 3900 m, the monitoring will be continued to trace long-term changes. The generated time series provide insights in the spatio temporal ground temperature variations at high elevation, the energy exchange between the ground surface and atmosphere, as well as the impact of vegetation and slope orientation on the thermal dynamics of the ground. To promote the further use of the ground temperature dataset by the wider research community dealing with the climate and geo-ecology of tropical mountains in Eastern Africa, it is made freely available via the open-access repository Zenodo: https://doi.org/10.5281/zenodo.5172002 (Groos et al., 2021b).\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131531934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Moran, Peter-Paul Pichler, Heran Zheng, H. Muri, Jan Klenner, Diogo Kramel, Johannes Többen, H. Weisz, T. Wiedmann, Annemie Wyckmans, A. Strømman, K. Gurney
Abstract. City-level CO2 emissions inventories are foundational for supporting the EU’s decarbonization goals. Inventories are essential for priority setting and for estimating impacts from the decarbonization transition. Here we present a new CO2 emissions inventory for 116,572 municipal and local government units in Europe. The inventory spatially disaggregates the national reported emissions, using 9 spatialization methods to distribute the 167 line items detailed in the UN's Common Reporting Framework. The novel contribution of this model is that results are provided per administrative jurisdiction at multiple administrative levels using a new spatialization approach. All data from this study is available along with an interactive map of results at https://openghgmap.net
{"title":"Estimating CO2 Emissions for 108,000 European Cities","authors":"D. Moran, Peter-Paul Pichler, Heran Zheng, H. Muri, Jan Klenner, Diogo Kramel, Johannes Többen, H. Weisz, T. Wiedmann, Annemie Wyckmans, A. Strømman, K. Gurney","doi":"10.5194/essd-2021-299","DOIUrl":"https://doi.org/10.5194/essd-2021-299","url":null,"abstract":"Abstract. City-level CO2 emissions inventories are foundational for supporting the EU’s decarbonization goals. Inventories are essential for priority setting and for estimating impacts from the decarbonization transition. Here we present a new CO2 emissions inventory for 116,572 municipal and local government units in Europe. The inventory spatially disaggregates the national reported emissions, using 9 spatialization methods to distribute the 167 line items detailed in the UN's Common Reporting Framework. The novel contribution of this model is that results are provided per administrative jurisdiction at multiple administrative levels using a new spatialization approach. All data from this study is available along with an interactive map of results at https://openghgmap.net\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120979515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Richter, M. Palm, C. Weinzierl, H. Griesche, P. Rowe, J. Notholt
Abstract. A dataset of microphysical cloud parameters from optically thin clouds, retrieved from infrared spectral radiances measured in summer 2017 in the Arctic, is presented. Measurements were conducted using a mobile Fourier-transform infrared (FTIR) spectrometer which was carried by the RV Polarstern. This dataset contains retrieved optical depths and effective radii of ice and water, from which the liquid water path and ice water path are calculated. These water paths and the effective radii are compared with derived quantities from a combined cloud radar, lidar and microwave radiometer measurement synergy retrieval, called Cloudnet. Comparing the liquid water paths from the infrared retrieval and Cloudnet shows significant correlations with a standard deviation of 8.60 g · m−2. Although liquid water path retrievals from microwave radiometer data come with a uncertainty of at least 20 g · m−2, a significant correlation and a standard deviation of 5.32 g · m−2 between the results of clouds with a liquid water path of at most 20 g · m−2 retrieved from infrared spectra and results from Cloudnet can be seen. Therefore, despite its large uncertainty, the comparison with data retrieved from infrared spectra shows that optically thin clouds of the measurement campaign in summer 2017 can be observed well using microwave radiometers within the Cloudnet framework. Apart from this, the dataset of microphysical cloud properties presented here allows to perform calculations of the cloud radiative effects, when the Cloudnet data from the campaign are not available, which was from the 22nd July 2017 until the 19th August 2017. The dataset is published at Pangaea (Richter et al., 2021).
摘要本文介绍了2017年夏季北极地区红外光谱辐射测得的光学薄云微物理云参数数据集。测量采用RV Polarstern携带的移动式傅里叶变换红外光谱仪(FTIR)进行。该数据集包含检索到的冰和水的光学深度和有效半径，并以此计算液态水路径和冰水路径。这些水路径和有效半径与云雷达、激光雷达和微波辐射计测量协同检索(称为Cloudnet)的导出量进行比较。通过比较红外数据和Cloudnet数据得到的液态水路径，其标准差为8.60 g·m−2。虽然从微波辐射计数据中检索到的液态水路径的不确定性至少为20 g·m−2，但从红外光谱中检索到的液态水路径最多为20 g·m−2的云的结果与Cloudnet的结果之间存在显著的相关性和5.32 g·m−2的标准偏差。因此，尽管存在很大的不确定性，但与红外光谱数据的比较表明，使用Cloudnet框架内的微波辐射计可以很好地观测到2017年夏季测量活动的光学薄云。除此之外，这里提供的微物理云属性数据集允许在2017年7月22日至2017年8月19日期间无法获得Cloudnet活动数据的情况下，对云辐射效应进行计算。该数据集发表于Pangaea (Richter et al.， 2021)。
{"title":"A dataset of microphysical cloud parameters, retrieved from Emission-FTIR spectra measured in Arctic summer 2017","authors":"P. Richter, M. Palm, C. Weinzierl, H. Griesche, P. Rowe, J. Notholt","doi":"10.5194/essd-2021-284","DOIUrl":"https://doi.org/10.5194/essd-2021-284","url":null,"abstract":"Abstract. A dataset of microphysical cloud parameters from optically thin clouds, retrieved from infrared spectral radiances measured in summer 2017 in the Arctic, is presented. Measurements were conducted using a mobile Fourier-transform infrared (FTIR) spectrometer which was carried by the RV Polarstern. This dataset contains retrieved optical depths and effective radii of ice and water, from which the liquid water path and ice water path are calculated. These water paths and the effective radii are compared with derived quantities from a combined cloud radar, lidar and microwave radiometer measurement synergy retrieval, called Cloudnet. Comparing the liquid water paths from the infrared retrieval and Cloudnet shows significant correlations with a standard deviation of 8.60 g · m−2. Although liquid water path retrievals from microwave radiometer data come with a uncertainty of at least 20 g · m−2, a significant correlation and a standard deviation of 5.32 g · m−2 between the results of clouds with a liquid water path of at most 20 g · m−2 retrieved from infrared spectra and results from Cloudnet can be seen. Therefore, despite its large uncertainty, the comparison with data retrieved from infrared spectra shows that optically thin clouds of the measurement campaign in summer 2017 can be observed well using microwave radiometers within the Cloudnet framework. Apart from this, the dataset of microphysical cloud properties presented here allows to perform calculations of the cloud radiative effects, when the Cloudnet data from the campaign are not available, which was from the 22nd July 2017 until the 19th August 2017. The dataset is published at Pangaea (Richter et al., 2021).\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122741015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Runmei Ma, J. Ban, Qing Wang, Yayi Zhang, Yang Yang, Shenshen Li, Wen-Qiang Shi, Tiantian Li
Abstract. The health risks of fine particulate matter (PM2.5) and ambient ozone (O3) have been widely recognized in recent years. An accurate estimate of PM2.5 and O3 exposures is important for supporting health risk analysis and environmental policy-making. The aim of our study was to construct random forest models with high-performance, and estimate daily average PM2.5 concentration and O3 daily maximum 8 h average concentration (O3-8hmax) of China in 2005–2017 at a spatial resolution of 1 km×1 km. The model variables included meteorological variables, satellite data, chemical transport model output, geographic variables and socioeconomic variables. Random forest model based on ten-fold cross validation was established, and spatial and temporal validations were performed to evaluate the model performance. According to our sample-based division method, the daily, monthly and yearly simulations of PM2.5 gave average model fitting R2 values of 0.85, 0.88 and 0.90, respectively; these R2 values were 0.77, 0.77, and 0.69 for O3-8hmax, respectively. The meteorological variables and their lagged values can significantly affect both PM2.5 and O3-8hmax simulations. During 2005–2017, PM2.5 exhibited an overall downward trend, while ambient O3 experienced an upward trend. Whilst the spatial patterns of PM2.5 and O3-8hmax barely changed between 2005 and 2017, the temporal trend had spatial characteristic. The dataset is accessible to the public at https://doi.org/10.5281/zenodo.4009308 , and the shared data set of Chinese Environmental Public Health Tracking: CEPHT ( https://cepht.niehs.cn:8282/developSDS3.html ).
{"title":"Full-coverage 1 km daily ambient PM 2.5 and O 3 concentrations of China in 2005–2017 based on multi-variable random forest model","authors":"Runmei Ma, J. Ban, Qing Wang, Yayi Zhang, Yang Yang, Shenshen Li, Wen-Qiang Shi, Tiantian Li","doi":"10.5281/ZENODO.4009308","DOIUrl":"https://doi.org/10.5281/ZENODO.4009308","url":null,"abstract":"Abstract. The health risks of fine particulate matter (PM2.5) and ambient ozone (O3) have been widely recognized in recent years. An accurate estimate of PM2.5 and O3 exposures is important for supporting health risk analysis and environmental policy-making. The aim of our study was to construct random forest models with high-performance, and estimate daily average PM2.5 concentration and O3 daily maximum 8 h average concentration (O3-8hmax) of China in 2005–2017 at a spatial resolution of 1 km×1 km. The model variables included meteorological variables, satellite data, chemical transport model output, geographic variables and socioeconomic variables. Random forest model based on ten-fold cross validation was established, and spatial and temporal validations were performed to evaluate the model performance. According to our sample-based division method, the daily, monthly and yearly simulations of PM2.5 gave average model fitting R2 values of 0.85, 0.88 and 0.90, respectively; these R2 values were 0.77, 0.77, and 0.69 for O3-8hmax, respectively. The meteorological variables and their lagged values can significantly affect both PM2.5 and O3-8hmax simulations. During 2005–2017, PM2.5 exhibited an overall downward trend, while ambient O3 experienced an upward trend. Whilst the spatial patterns of PM2.5 and O3-8hmax barely changed between 2005 and 2017, the temporal trend had spatial characteristic. The dataset is accessible to the public at https://doi.org/10.5281/zenodo.4009308 , and the shared data set of Chinese Environmental Public Health Tracking: CEPHT ( https://cepht.niehs.cn:8282/developSDS3.html ).","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125561131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mengze Li, A. Pozzer, J. Lelieveld, Jonathan Williams
Abstract. Methane, ethane and propane are among the most abundant hydrocarbons in the atmosphere. These compounds have many emission sources in common and are all primarily removed through OH oxidation. Their mixing ratios and long-term trends in the upper troposphere and stratosphere are rarely reported due to the paucity of measurements. In this study, we present long-term (2006–2016) global ethane, propane, and methane data from airborne observation in the Upper Troposphere - Lower Stratosphere (UTLS) region, combined with atmospheric model simulations for ethane at the same times and locations, to focus on global ethane trends. The model uses the Copernicus emission inventory CAMS-GLOB and distinguishes 12 ethane emission sectors (natural and anthropogenic): BIO (biogenic emission), BIB (biomass burning), AWB (agricultural waste burning), ENE (power generation), FEF (fugitives), IND (industrial processes), RES (residential energy use), SHP (ships), SLV (solvents), SWD (solid waste and waste water), TNR (off-road transportation), and TRO (road transportation). The results from the model simulations were compared with observational data and further optimized. The Northern Hemispheric (NH) upper tropospheric and stratospheric ethane trends were 0.33 ± 0.27 %/yr and −3.6 ± 0.3 %/yr, respectively, in 2006–2016. The global ethane emission for this decade was estimated to be 19.28 Tg/yr. Trends of methane and propane, and of the 12 model sectors provided more insights on the variation of ethane trends. FEF, RES, TRO, SWD and BIB are the top five contributing sectors to the observed ethane trends. An ethane plume for NH upper troposphere and stratosphere in 2010–2011 was identified to be due to fossil fuel related emissions, likely from oil and gas exploitation. The discrepancy between model results and observations suggests that the current ethane emission inventories must be improved and higher temporal-spatial resolution data of ethane are needed. This dataset is of value to future global ethane budget estimates and the optimization of current ethane inventories. The data are public accessible at https://doi.org/10.5281/zenodo.5112059 (Li et al., 2021b).
摘要甲烷、乙烷和丙烷是大气中最丰富的碳氢化合物。这些化合物有许多共同的排放源，并且都主要通过OH氧化去除。由于缺乏测量，它们在对流层上层和平流层的混合比率和长期趋势很少报告。在本研究中，我们提供了对流层上层-平流层下层(UTLS)区域的长期(2006-2016)全球乙烷、丙烷和甲烷的航空观测数据，并结合大气模式在同一时间和地点对乙烷的模拟，以关注全球乙烷的趋势。该模型使用哥白尼排放清单CAMS-GLOB，并区分了12个乙烷排放部门(自然和人为):BIO(生物源排放)、BIB(生物质燃烧)、AWB(农业废弃物燃烧)、ENE(发电)、FEF(废气)、IND(工业过程)、RES(住宅能源使用)、SHP(船舶)、SLV(溶剂)、SWD(固体废物和废水)、TNR(非公路运输)和TRO(公路运输)。将模型模拟结果与观测数据进行对比，并进一步优化。2006-2016年，北半球对流层上层和平流层乙烷变化趋势分别为0.33±0.27% /年和- 3.6±0.3% /年。这十年的全球乙烷排放量估计为19.28 Tg/年。甲烷和丙烷的趋势，以及12个模型部门提供了更多关于乙烷趋势变化的见解。FEF、RES、TRO、SWD和BIB是对观测到的乙烷趋势贡献最大的五个行业。2010-2011年北半球对流层上层和平流层的乙烷羽流被确定为与化石燃料相关的排放，可能来自石油和天然气开采。模型结果与观测值之间的差异表明，必须改进现有的乙烷排放清单，并需要更高时空分辨率的乙烷数据。该数据集对未来全球乙烷预算估计和当前乙烷库存优化具有价值。这些数据可在https://doi.org/10.5281/zenodo.5112059上公开获取(Li et al.， 2021b)。
{"title":"Global atmospheric ethane, propane and methane trends (2006–2016)","authors":"Mengze Li, A. Pozzer, J. Lelieveld, Jonathan Williams","doi":"10.5194/ESSD-2021-246","DOIUrl":"https://doi.org/10.5194/ESSD-2021-246","url":null,"abstract":"Abstract. Methane, ethane and propane are among the most abundant hydrocarbons in the atmosphere. These compounds have many emission sources in common and are all primarily removed through OH oxidation. Their mixing ratios and long-term trends in the upper troposphere and stratosphere are rarely reported due to the paucity of measurements. In this study, we present long-term (2006–2016) global ethane, propane, and methane data from airborne observation in the Upper Troposphere - Lower Stratosphere (UTLS) region, combined with atmospheric model simulations for ethane at the same times and locations, to focus on global ethane trends. The model uses the Copernicus emission inventory CAMS-GLOB and distinguishes 12 ethane emission sectors (natural and anthropogenic): BIO (biogenic emission), BIB (biomass burning), AWB (agricultural waste burning), ENE (power generation), FEF (fugitives), IND (industrial processes), RES (residential energy use), SHP (ships), SLV (solvents), SWD (solid waste and waste water), TNR (off-road transportation), and TRO (road transportation). The results from the model simulations were compared with observational data and further optimized. The Northern Hemispheric (NH) upper tropospheric and stratospheric ethane trends were 0.33 ± 0.27 %/yr and −3.6 ± 0.3 %/yr, respectively, in 2006–2016. The global ethane emission for this decade was estimated to be 19.28 Tg/yr. Trends of methane and propane, and of the 12 model sectors provided more insights on the variation of ethane trends. FEF, RES, TRO, SWD and BIB are the top five contributing sectors to the observed ethane trends. An ethane plume for NH upper troposphere and stratosphere in 2010–2011 was identified to be due to fossil fuel related emissions, likely from oil and gas exploitation. The discrepancy between model results and observations suggests that the current ethane emission inventories must be improved and higher temporal-spatial resolution data of ethane are needed. This dataset is of value to future global ethane budget estimates and the optimization of current ethane inventories. The data are public accessible at https://doi.org/10.5281/zenodo.5112059 (Li et al., 2021b).","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126408505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Among the multitude of magnitude scales developed to measure the size of an earthquake, the surface wave magnitude MS is the only magnitude type that can be computed since the dawn of modern observational seismology (beginning of the 20th century) for most shallow earthquakes worldwide. This is possible thanks to the work of station operators, analysts and researchers that performed measurements of surface wave amplitudes and periods on analogue instruments well before the development of recent digital seismological practice. As a result of a monumental undertaking to digitize such pre-1971 measurements from printed bulletins and integrate them in parametric data form into the database of the International Seismo- logical Centre (ISC, www.isc.ac.uk, last access: August 2021), we are able to recompute MS using a large set of stations and obtain it for the first time for several hundred earthquakes. We summarize the work started at the ISC in 2010 which aims to provide the seismological and broader geoscience community with a revised MS dataset (i.e., catalogue as well as the underlying station data) starting from December 1904 up to the last complete year reviewed by the ISC (currently 2018). This MS dataset is available at the ISC Dataset Repository at https://doi.org/10.31905/0N4HOS2D.
{"title":"100+ years of recomputed surface wave magnitude of shallow earthquakes","authors":"D. Di Giacomo, D. Storchak","doi":"10.5194/essd-2021-266","DOIUrl":"https://doi.org/10.5194/essd-2021-266","url":null,"abstract":"Abstract. Among the multitude of magnitude scales developed to measure the size of an earthquake, the surface wave magnitude MS is the only magnitude type that can be computed since the dawn of modern observational seismology (beginning of the 20th century) for most shallow earthquakes worldwide. This is possible thanks to the work of station operators, analysts and researchers that performed measurements of surface wave amplitudes and periods on analogue instruments well before the development of recent digital seismological practice. As a result of a monumental undertaking to digitize such pre-1971 measurements from printed bulletins and integrate them in parametric data form into the database of the International Seismo- logical Centre (ISC, www.isc.ac.uk, last access: August 2021), we are able to recompute MS using a large set of stations and obtain it for the first time for several hundred earthquakes. We summarize the work started at the ISC in 2010 which aims to provide the seismological and broader geoscience community with a revised MS dataset (i.e., catalogue as well as the underlying station data) starting from December 1904 up to the last complete year reviewed by the ISC (currently 2018). This MS dataset is available at the ISC Dataset Repository at https://doi.org/10.31905/0N4HOS2D.\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115428022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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