Remote Sensing of Clouds and the Atmosphere XXVIII最新文献

Significance of simultaneous observations of polarization and radiance with SGLI SGLI同时观测偏振和辐射的意义

Remote Sensing of Clouds and the Atmosphere XXVIII Pub Date : 2023-10-20 DOI: 10.1117/12.2679091

S. Mukai, S. Hioki, M. Nakata, T. Fujito, I. Sano

引用次数: 0

Determining background concentrations of major atmospheric pollutants using Sentinel-5P TROPOMI data 利用Sentinel-5P TROPOMI数据确定主要大气污染物的背景浓度

Remote Sensing of Clouds and the Atmosphere XXVIII Pub Date : 2023-10-20 DOI: 10.1117/12.2679839

Plamen Trenchev, Maria Dimitrova, Daniela Avetisyan

{"title":"Determining background concentrations of major atmospheric pollutants using Sentinel-5P TROPOMI data","authors":"Plamen Trenchev, Maria Dimitrova, Daniela Avetisyan","doi":"10.1117/12.2679839","DOIUrl":"https://doi.org/10.1117/12.2679839","url":null,"abstract":"The increase in concentrations of major atmospheric pollutants such as NO2, CO, CH4 as result of human activities is one of the main causes of the dynamic climate changes observed in recent years. These rapid changes have a strong influence on air quality at local and global levels and directly affect human health. This is one of the main reasons for faster global warming. The concentration of methane in the atmosphere is increasing at an accelerating rate. Three sectors are responsible for most anthropogenic CH4 emissions: fossil fuels, waste and agriculture. Locating, tracking and quantifying all these emissions is an important step towards a more accurate inventory. The use of satellite observations rises at a new label the monitoring process and improves the accuracy of emissions reporting. Medium-resolution satellite data, such as that provided by the TROPOMI sensor on the European Sentinel-5P satellite, is a powerful tool for detecting and tracking large emissions of air pollutants. The methodology presented here enables us to determine background concentrations of CH4, NO2, CO relatively quickly and efficiently. It improves our ability to quickly detect periodic or occasional emissions from unregulated sources, track seasonal and annual variations in concentrations of these air pollutants, etc. Hundreds of cases of high methane, NO2 and CO emissions in coal mining areas have been registered using this methodology. The method is also applicable to lower-intensity emission sources, such as landfills, agriculture or recording methane emissions from wetlands.","PeriodicalId":117988,"journal":{"name":"Remote Sensing of Clouds and the Atmosphere XXVIII","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133483459","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}

引用次数: 0

Spatiotemporal behavior of atmospheric pollutant ingredients over Bulgaria, based on open access GAMS data 基于开放获取GAMS数据的保加利亚上空大气污染物成分的时空行为

Remote Sensing of Clouds and the Atmosphere XXVIII Pub Date : 2023-10-20 DOI: 10.1117/12.2684037

Maria Dimitrova, Plamen Trenchev, Daniela Avetisyan

{"title":"Spatiotemporal behavior of atmospheric pollutant ingredients over Bulgaria, based on open access GAMS data","authors":"Maria Dimitrova, Plamen Trenchev, Daniela Avetisyan","doi":"10.1117/12.2684037","DOIUrl":"https://doi.org/10.1117/12.2684037","url":null,"abstract":"In recent years a steady trend of increasing concentrations of major air pollutants is observed. The nature and dynamics of this trend vary according to the type of pollutant, source of emissions, and location. Because of these differences, it is important to comprehensively analyze the spatial and temporal behavior of the most important air pollutants using satellite and ground-based measurement data. An important step in this process is locating, tracking, and quantifying the emissions. This paper presents the results of air pollution monitoring based on the analysis of data obtained from 32 Ground-based Automatic Measuring Stations (GAMS) located throughout Bulgaria. The spatial and temporal behavior of major air pollutants such as NO, NO2, SO2, CO, and benzene for the period 2015 - 2022 was investigated. However, not all GAMS have data for all types of pollutants. The largest amount of information is available for SO2 and NO2, while small numbers of GAMS provide data for CO. For pollutants such as NO2, SO2, and CO an analysis with satellite data from the European Sentinel-5P satellite was performed. Due to the uneven distribution of the available information from ground measurements, the spatial behavior of the pollutants studied is presented using a unified methodology for selected regions. Monthly and annual average data were also analyzed in our study.","PeriodicalId":117988,"journal":{"name":"Remote Sensing of Clouds and the Atmosphere XXVIII","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117122875","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}

引用次数: 0

Verification of reproducibility of biomass burning aerosol distribution by regional modeling 区域模拟验证生物质燃烧气溶胶分布的可重复性

Remote Sensing of Clouds and the Atmosphere XXVIII Pub Date : 2023-10-20 DOI: 10.1117/12.2679086

M. Nakata, S. Hioki, S. Mukai

{"title":"Verification of reproducibility of biomass burning aerosol distribution by regional modeling","authors":"M. Nakata, S. Hioki, S. Mukai","doi":"10.1117/12.2679086","DOIUrl":"https://doi.org/10.1117/12.2679086","url":null,"abstract":"Open burning of biomass occurs in many parts of the world and is a major environmental problem. This is because biomass combustion is a major source of greenhouse gases, reactive trace gases, and particulate matter emissions into the atmosphere. Emissions from combustion of biomass have the potential to impact local, regional, and global air quality issues and climate change. Satellite information on fire activity and vegetation productivity has been combined to create a data set of gas and aerosol emissions from fires. We used these emission data to obtain aerosol distributions of open burning origin by using a regional chemical transport model simulation. This study targets severe biomass burning aerosols in Sumatra Island in September 2019. We simulated the meteorological fields required for offline calculations of chemical transport models with the SCALE (Scalable Computing for Advanced Library and Environment) regional model. Simulation results were validated with biomass burning aerosol distributions derived from JAXA/GCOM-C/Second Generation Global Imager (SGLI) and aerosol optical thickness from the NASA/AErosol RObotic NETwork (AERONET). The biomass burning aerosol distribution was found to be well reproduced, but there was an underestimation in aerosol volume.","PeriodicalId":117988,"journal":{"name":"Remote Sensing of Clouds and the Atmosphere XXVIII","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121534568","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}

引用次数: 0

Machine learning clustering of cloud regimes using synergetic ground-based remote sensing observations 基于协同地面遥感观测的云区机器学习聚类

Remote Sensing of Clouds and the Atmosphere XXVIII Pub Date : 2023-10-20 DOI: 10.1117/12.2689207

Andreu Julián-Izquierdo, Patricia García-Pitarch, F. Scarlatti, Pedro C. Valdelomar, J. Gómez-Amo, Pilar Utrillas

{"title":"Machine learning clustering of cloud regimes using synergetic ground-based remote sensing observations","authors":"Andreu Julián-Izquierdo, Patricia García-Pitarch, F. Scarlatti, Pedro C. Valdelomar, J. Gómez-Amo, Pilar Utrillas","doi":"10.1117/12.2689207","DOIUrl":"https://doi.org/10.1117/12.2689207","url":null,"abstract":"Clouds are essential in climate, especially to evaluate the radiative balance in the Earth atmosphere and, their contribution depends on the type of cloud. In addition, cloud classification plays an important role in the development of different research and technological fields such as solar photovoltaic energy. We use ground-based zenith observations of Cloud Optical Depth (COD) and Cloud Base Height (CBH), at one-minute intervals, to develop a clustering algorithm. It is based on non-supervised machine learning using k-means function. Due to the intrinsic characteristics of the measuring instruments, high-altitude clouds with large COD are not accurately represented. For this reason, a classification into six categories is performed. Regarding to COD, our machine learning method detects three COD clusters separated at 3.2 and 24.5. On the other hand, the three CBH clusters well identify low-, mid- and high-clouds, with centroids around 1500 m, 5399-6240 m, and 9589 m, respectively. A slight increase in these CBH boundaries with COD is also observed. Our clustering method is consistent and robust since it does not present any sensitivity regarding to the temporal window used to perform the clustering. The resulting clusters are consistent and in line with the cloud classification established by the WMO.","PeriodicalId":117988,"journal":{"name":"Remote Sensing of Clouds and the Atmosphere XXVIII","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114923630","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}

引用次数: 0