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Large-scale synoptic and weather drivers for the co-occurrence of fine particulate matter and ozone extremes over China

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science China Earth sciences 2025年第5期68卷 1448-1457页

作者： Wei WANG Yiting ZHOU Mengmeng LI Yuting LU Xinyi JIANG Tijian WANG Bingliang ZHUANG Shu LI Chenchao ZHAN Xiaoyun SUN Xin HUANG School of Atmospheric Sciences Nanjing University Nanjing-Helsinki Institute in Atmospheric and Earth System Sciences Nanjing University Frontiers Science Center for Critical Earth Material Cycling School of Atmospheric Physics Nanjing University of Information Science and Technology Anhui Province Key Laboratory of Atmospheric Science and Satellite Remote Sensing Anhui Institute of Meteorological Sciences

The co-occurrence of extreme weather events and air pollution events, such as elevated fine particulate matter(PM2.5)and ozone（O3）, poses a significant threat to human health, a concern that is anticipated to be exacerbated under climate *** a 9-year dataset of surface air pollutant observations and reanalysis data, this study investigated the large-scale synoptic and weather drivers behind the co-occurrence of PM2.5and O3extremes in China. These co-occurrences are mostly observed during the warm season（March to October） and are particularly prevalent(25.8 days yr-1) in cleaner regions situated in South China. Such co-occurrences are usually associated with elevated air temperatures（～27.7°C）, lower wind speeds(～1.4 ms-1) and atmospheric humidity（～51.9%）. Notably, a substantial proportion of these co-occurrences（up to 76.4%） coincide with extreme weather linked to stagnant weather and extreme heat conditions. By employing an objective clustering approach, we identified the typical synoptic systems conducive to the widespread co-occurrence of PM2.5and O3extremes, including the Western Pacific Subtropical High, Northeast Blocking High, Mongolian High, and typhoons. These findings underscore a robust correlation between the co-occurrence of PM2.5and O3extremes and the annual variations in extreme weather and prevailing weather patterns, suggesting an increased likelihood of concurrent air pollution and extreme weather episodes under a warming climate across China.

关键词： Co-occurrence pollution Ozone PM2.5 Extreme weather T-PCA

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Super-resolution Imaging of Telescopic Systems based on Optical-neural Network Joint Optimization

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Research in Astronomy and Astrophysics 2024年第9期24卷 167-177页

作者： You-Hong Sun Tao Zhang Hao-Dong Shi Qiang Fu Jia-Nan Liu Kai-Kai Wang Chao Wang Research Institute of Chongqing Changchun University of Science and TechnologyChangchun 130022China National and Local Joint Engineering Research Center for Space Photoelectric Technology Changchun University of Science and TechnologyChangchun 130022China The Institute of Remote Sensing Satellites China Academy of Space Technology(CAST)Beijing 100094China

Optical telescopes are an important tool for acquiring optical information about distant objects,and resolution is an important indicator that measures the ability to observe object ***,due to the effects of system aberration,atmospheric seeing,and other factors,the observed image of ground-based telescopes is often degraded,resulting in reduced *** paper proposes an optical-neural network joint optimization method to improve the resolution of the observed image by co-optimizing the point-spread function(PSF)of the telescopic system and the image super-resolution(SR)*** improve the speed of image reconstruction,we designed a generative adversarial net(LCR-GAN)with light parameters,which is much faster than the latest unsupervised *** reconstruct the PSF trained by the network in the optical path,a phase mask is *** improves the image reconstruction effect of LCR-GAN by reconstructing the PSF that best matches the *** results of simulation and verification experiments show that compared with the pure deep learning method,the SR image reconstructed by this method is rich in detail and it is easier to distinguish stars or stripes.

关键词： Techniques:image processing Telescopes Stars:imaging

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An improved wind quality control for the China-France Oceanography Satellite (CFOSAT) scatterometer

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Acta Oceanologica Sinica 2024年第5期43卷 100-109页

作者： Xiaoheng Mou Wenming Lin School of Marine Sciences Nanjing University of Information Science and TechnologyNanjing 210044China Key Laboratory of Space Ocean Remote Sensing and Application Ministry of Natural ResourcesBeijing 100081China

Quality control(QC)is an essential procedure in scatterometer wind retrieval,which is used to distinguish good-quality data from poor-quality wind vector cells(WVCs)for the sake of wind *** current wind processor of the China-France Oceanography Satellite(CFOSAT)scatterometer(CSCAT)adopts a maximum likelihood estimator(MLE)-based QC method to filter WVCs affected by geophysical noise,such as rainfall and wind *** the first Ku-band rotating fan-beam scatterometer,CSCAT can acquire up to 16 observations over a single WVC,giving abundant information with diverse incidence/azimuth angles,as such its MLE statistical characteristics may be different from the previous *** this study,several QC indicators,including MLE,its spatially averaged value(MLE_(m)),and the singularity exponents(SE),are analyzed using the collocated Global Precipitation Mission rainfall data as well as buoy data,to compare their sensitivity to rainfall and wind *** results show that wind error characteristics of CSCAT under different QC methods are similar to those of other Ku-band scatterometers,i.e.,SE is more suitable than other parameters for the wind QC at outer-swath and nadir regions,while MLE_(m) is the best QC indicator for the sweet region ***,SE is much more favorable than others at high wind *** combining different indicators,an improved QC method is developed for *** validation with the collocated buoy data shows that it accepts more WVCs,and in turn,improves the quality control of CSCAT wind data.

关键词： China-France Oceanography Satellite(CFOSAT) scatterometer winds quality control(QC) singularity exponent(SE)

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Estimation of Sea Ice Thickness Using FY-3E Data Based on Random Forest Method

Estimation of Sea Ice Thickness Using FY-3E Data Based on Ra...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Li, Hongying Yan, Qingyun Zhen, Yinqing Nanjing University of Information Science and Technology School of Remote Sensing and Geomatics Engineering Nanjing210044 China

ISBN: (纸本)9798350375909

Sea ice is a fundamental component of the cryosphere, necessitating a comprehensive understanding of its parameters. Notably, sea ice thickness (SIT) poses a significant challenge for accurate measurement. In this study, we employ a machine learning approach, specifically the Random Forest (RF) method, to estimate SIT by combining data from the Fengyun-3E (FY-3E) satellite and Soil Moisture Ocean Salinity (SMOS) datasets. This method relies on four input parameters: the incidence angle (θ), reflectivity (Γ), sea ice salinity (S), and sea ice temperature (T). The former two parameters are derived from FY-3E data, while the latter two are extracted from SMOS data. Moreover, FY-3E has the capability to receive signals reflected from both the Global Positioning System (GPS) and the Beidou Navigation Satellite System (BDS). Our investigation is centered on the Arctic polar region. An evaluation spanning from October 2022 to April 2023 demonstrates that the RF model trained on GPS data achieves a high coefficient of determination (r) of 0.98 for the training set, accompanied by a mean absolute error (MAE) of 0.021 m. For the test set, r is 0.95, and the MAE is 0.033 m. The RF model trained on BDS data exhibits a coefficient r of 0.98, with a corresponding MAE of 0.024 m. For the test set, r is 0.94, and the MAE is 0.038 m. These results underscore the efficacy of machine learning methodologies and the benefits of integrating FY-3E data with SMOS sea ice parameters for SIT estimation. © 2024 IEEE.

关键词： Global positioning system

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Using FY-3E Data to Independently Retrieve Sea Ice Thickness

Using FY-3E Data to Independently Retrieve Sea Ice Thickness

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Xie, Yunjian Zhen, Yinqing Yan, Qingyun Nanjing University of Information Science and Technology School of Remote Sensing and Geomatics Engineering Nanjing210044 China

ISBN: (纸本)9798350375909

Scientific studies worldwide have long put an emphasis on Arctic sea ice, viewing sea ice thickness (SIT) as a crucial determinant for forecasting local oceanic conditions, climate fluctuations, and assuring maritime transport safety. The present research pivots on the extraction of SIT, deploying a well-established two-layer (sea ice-seawater) model of Global Navigation Satellite System-Reflectometry (GNSS-R) and broadening it to incorporate recent data from the Fengyun-3E (FY-3E) satellite. The study presents a unique empirical method with the goal of lessening dependencies on supplementary data, thereby enabling independent SIT extraction purely via GNSS-R data. This effort highlights the feasibility of constructing a functional semiempirical model and parameterization scheme for the estimation of SIT through GNSS-R data. © 2024 IEEE.

关键词： Sea ice

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Reconstructing NDVI for Lakes: Early Insights Leveraging CYGNSS and ERA-5 Data

Reconstructing NDVI for Lakes: Early Insights Leveraging CYG...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Zhen, Yinqing Yan, Qingyun Nanjing University of Information Science and Technology School of Remote Sensing and Geomatics Engineering Nanjing210044 China

ISBN: (纸本)9798350375909

The increasingly serious water pollution in numerous lakes has resulted in the more frequent occurrences of algal bloom disaster in recent decades. Assessing the severity of these algal bloom events can be accomplished through the utilization of remote sensing methodologies, with measurements facilitated by the Normalized Difference Vegetation Index (NDVI). However, the effectiveness of NDVI observations via optical sensors is often impeded by the presence of cloud and fog cover in regions encompassing various water bodies, such as Taihu Lake. Sensors operating in the microwave band, particularly the emerging Global Satellite Navigation System-Reflectometry (GNSS-R) technique can avoid this problem effectively, and it also offers notable advantages in terms of temporal resolution and cost-efficiency. In this study, we propose a novel approach for the retrieval of lake-surface NDVI values on overcast days based on GNSS-R and meteorological data, with the aid of a machine learning regression algorithm, specifically the Bagging Tree method. The resultant NDVI regression model demonstrates an impressive coefficient of determination (R2) of 0.95, a Root Mean Square Error (RMSE) of 0.021, and a mean Absolute Error (MAE) of 0.010. The recovered NDVI images exhibit remarkable clarity and closely approximate the reference NDVI imagery. © 2024 IEEE.

关键词： Lakes

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Vortex Electromagnetic Wave Three-Dimensional Imaging Based on OMP

Vortex Electromagnetic Wave Three-Dimensional Imaging Based ...

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2024 IEEE International Conference on Computational Electromagnetics, ICCEM 2024

作者： Wen, Peng He, Zi Ding, Dazhi Jiang, Xinrui Zhou, Ying Nanjing University of Science and Technology Nanjing China Beijing Institute of Remote Sensing Equipment Beijing China

ISBN: (纸本)9798350383317

The phase wavefront of vortex electromagnetic wave contains abundant information, which can be applied to realize the distance and azimuth imaging of targets. However, two-dimensional imaging can only provide the projected information of the target on a plane and cannot accurately reflect the vertical position and shape of the targets. Besides, due to the electromagnetic vortex radar does not have the resolution of elevation, so it is necessary to explore the three-dimensional imaging algorithm. In this paper, a 3D-object imaging algorithm based on orthogonal matching pursuit (OMP) is proposed. Firstly, the staring imaging scenario and the 3-D echo model are established. Second, the three-dimensional targets imaging is obtained based on the OMP algorithm. Finally, the practicability of the theoretical analysis is verified by the simulation result. © 2024 IEEE.

关键词： Vortex flow

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Multi-sensor InSAR time series fusion for long-term land subsidence monitoring

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地球空间信息科学学报（英文版） 2024年第5期27卷中插2,1424-1440页

作者： Haonan Jiang Timo Balz Francesca Cigna Deodato Tapete Jianan Li Yakun Han State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing(LIESMARS)Wuhan UniversityWuhanChina Helmholtz Centre Potsdam GFZ German Research Centre for GeosciencesPotsdamGermany State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing(LIESMARS)Wuhan UniversityWuhanChina Institute of Atmospheric Sciences and Climate(ISAC) National Research Council(CNR)RomaItaly Italian Space Agency(ASI) RomaItaly Institute for Remote Sensing Science and Application School of GeomaticsLiaoning Technical UniversityFuxinChina College of Earth Science Chengdu University of TechnologyChengduChina

Satellite Interferometric Synthetic Aperture Radar(InSAR)is widely used for topographic,geological and natural resource ***,most of the existing InSAR studies of ground deformation are based on relatively short periods and single *** paper introduces a new multi-sensor InSAR time series data fusion method for time-overlapping and time-interval datasets,to address cases when partial overlaps and/or temporal gaps exist.A new Power Exponential Knothe Model(PEKM)fits and fuses overlaps in the deformation curves,while a Long Short-Term Memory(LSTM)neural network predicts and fuses any temporal gaps in the *** the city of Wuhan(China)as experiment area,COSMO-SkyMed(2011-2015),TerraSAR-X(2015-2019)and Sentinel-1(2019-2021)SAR datasets were fused to map long-term surface deformation over the last *** independent 2011-2020 InSAR time series analysis based on 230 COSMO-SkyMed scenes was also used as reference for *** correlation coefficient between the results of the fusion algorithm and the reference data is 0.87 in the time overlapping region and 0.97 in the time-interval *** correlation coefficient of the overall results is 0.78,which fully demonstrates that the algorithm proposed in our paper achieves a similar trend as the reference deformation *** experimental results are consistent with existing studies of surface deformation at Wuhan,demonstrating the accuracy of the proposed new fusion method to provide robust time series for the analysis of long-term land subsidence mechanisms.

关键词： Interferometric Synthetic Aperture Radar(InSAR) Power Exponential Knothe Model(PEKM) Long Short-Term Memory Network(LSTM) data fusion

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Investigations for Soil Contamination with Hydrocarbon Compounds near Oil Stations 18

Investigations for Soil Contamination with Hydrocarbon Compo...

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18th International Conference on Technologies and Materials for Renewable Energy, Environment and Sustainability, TMREES 2023

作者： Khaleel, Zahraa A. Shaban, Auday H. Al Maliki, Ali A. Department of Remote Sensing & GIS College of Science University of Baghdad Baghdad Iraq Ministry of Science and Technology Environment and Water Directorate Baghdad Iraq

Field companies are subject to many standards to preserve the environment. Oil companies are considered among the companies that cause the most environmental pollution for two reasons: the first is the extraction of large quantities of crude oil, and the second is their violation of the controls imposed on them. In this research the investigations on the nearby and around a Rumaila oil station area were abducted scientifically through a real tested sample. In this research, investigations in an area near and around the Rumaila Oil Station were conducted scientifically through real laboratory samples. An analysis was made to determine the total petroleum hydrocarbons (TPH), where 22 samples were collected, as well as 5 laboratory samples of soil from an unpolluted area, and crude oil was added to it in known proportions to determine the spectral signature. The ASD Field spec 3 device was used to record the spectral signatures of each sample. Areas sensitive to hydrocarbons 17050 and 1350 appeared. The results show a variation in the concentrations of hydrocarbon in the samples from (2 ppm) at the minimum and reach (197 ppm) at other locations around the station. © Published under licence by IOP Publishing Ltd.

关键词： remote sensing

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Oil-source correlation and Paleozoic source rock analysis in the Siwa Basin,Western Desert:Insights from well-logs,Rock-Eval pyrolysis,and biomarker data

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Energy Geoscience 2024年第3期5卷 313-327页

作者： Mohamed I.Abdel-Fattah Mohamed Reda Mohamed Fathy Diaa A.Saadawi Fahad Alshehri Mohamed S.Ahmed Petroleum Geosciences and Remote Sensing Program Department of Applied Physics and AstronomyCollege of SciencesUniversity of SharjahSharjah27272United Arab Emirates Geology Department Faculty of ScienceSuez Canal UniversityIsmailiaEgypt Faculty of Science Geology DepartmentAl-Azhar UniversityP.O.Box 11884Nasr CityCairoEgypt Abdullah Alrushaid Chair for Earth Science Remote Sensing Research Geology and Geophysics DepartmentCollege of ScienceKing Saud UniversityRiyadh11451Saudi Arabia

Understanding the origins of potential source rocks and unraveling the intricate connections between reservoir oils and their source formations in the Siwa Basin(Western Desert,Egypt)necessitate a thorough oil-source correlation *** objective is achieved through a meticulous analysis of well-log responses,Rock-Eval pyrolysis,and biomarker *** analysis of Total Organic Carbon across 31 samples representing Paleozoic formations in the Siwa A-1X well reveals a spectrum of organic richness ranging from 0.17 wt%to 2.04 wt%,thereby highlighting diverse levels of organic content and the presence of both Type II and Type III *** of the fingerprint characteristics of eight samples from the well suggests that the Dhiffah Formation comprises a blend of terrestrial and marine organic ***,a significant contribution from more oxidized residual organic matter and gas-prone Type III kerogen is ***,the Desouky and Zeitoun formations exhibit mixed organic matter indicative of a transitional environment,and thus featuring a pronounced marine influence within a more reducing setting,which is associated with Type II *** analysis of five oil samples from different wells—SIWA L-1X,SIWA R-3X,SIWA D-1X,PTAH 5X,and PTAH 6X,it is evident that terrestrial organic matter,augmented by considerable marine input,was deposited in an oxidizing environment,and contains Type III *** scrutiny confirms the coexistence of mixed terrestrial organic matter within varying redox *** is the uniformity of identified kerogen Types II and III across all samples,known to have potential for hydrocarbon *** discovery presented in this paper unveils captivating prospects concerning the genesis of oil in the Jurassic Safa reservoir,suggesting potential links to Paleozoic sources or even originating from the Safa Member *** revelations mark a substantial advancement in understanding source

关键词： Biomarker data Oil-source correlation Rock-Eval pyrolysis Source rocks Siwa Basin

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