Quality assessment of two years of Sentinel-5p TROPOMI NO2 data

2020 ◽  
Author(s):  
Tijl Verhoelst ◽  
Steven Compernolle ◽  
José Granville ◽  
Arno Keppens ◽  
Gaia Pinardi ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Quality Indicators ◽  
Scattered Light ◽  
Global Network ◽  
Visible Range ◽  
Network Data ◽  
Random Errors ◽  
Validation Data ◽  
Comparison Results ◽  
Data Products

<p>For more than two years now the first atmospheric satellite of the Copernicus EO programme, Sentinel-5p (S5P) TROPOMI, has acquired spectral measurements of the Earth radiance in the visible range, from which near-real-time (NRTI) and offline (OFFL) processors retrieve operationally the total, tropospheric and stratospheric  column abundance of atmospheric NO<sub>2</sub>.  In support of these routine operations, the S5P Mission Performance Centre (MPC) performs continuous QA/QC of these data products and produces key Quality Indicators enabling users to verify the fitness-for-purpose of the S5P data. Quality Indicators are derived from comparisons to ground-based reference data, both station-by-station in monitoring mode in the S5P Automated Validation Server (AVS) and globally in more complex in-depth analyses.  Complementary quality information is obtained from product intercomparisons (NRTI vs. OFFL) and from satellite-to-satellite comparisons.  After two years of successful operation we present here a consolidated overview of the quality of the S5P TROPOMI NO<sub>2</sub> data products delivered publicly.</p><p>S5P NO2 data are compared routinely to ground-based network measurements collected through either the ESA Validation Data Centre (EVDC) or network data archives (NDACC, PGN). Direct-sun measurements from the Pandonia Global Network (PGN) serve as a reference for total NO<sub>2</sub> validation, Multi-Axis DOAS network data for tropospheric  NO<sub>2</sub> validation, and NDACC zenith-scattered-light DOAS network data for stratospheric NO<sub>2</sub> validation.  Comparison methods are optimized to limit spatial and temporal mismatch to a minimum (information-based spatial co-location strategy, photochemical adjustment to account for local time measurement difference). Comparison results are analyzed to derive Quality Indicators and to conclude on the compliance w.r.t. the mission requirements. This include estimates of: (1) the bias, as proxy for systematic errors, (2) the dispersion of the differences, which combines random errors with seasonal and irreducible mismatch errors, and (3) the dependence of bias and dispersion on key influence quantities (surface albedo, cloud cover…)</p><p>Intercomparison of S5P products (NRTI vs. OFFL) and comparison to other satellite data, including a similar processing of OMI measurements, complement the ground-based validation with relative biases and spatio-temporal patterns/artefacts related to instrumental issues (e.g. striping) and to the sensitivity to geophysical features (e.g. clouds and sea/ice albedo contrast).  </p><p>Overall, the MPC quality assessment of S5P NO<sub>2</sub> data concludes to an excellent performance for the stratospheric column data (bias2 vs. ground-based data. This dispersion larger than the mission requirement on data precision can partly be attributed to comparisons errors such as those due to differences in horizontal resolution. Total column data are found to be biased low by 20%, with a 30% station-to-station scatter. After gridding to monthly means on a 0.8°x0.4° grid, comparisons to OMI data yield a much smaller dispersion (within the requirement of 0.7Pmolec/cm<sup>2</sup>), and a minor relative bias. NRTI and OFFL perform similarly, even if they occasionally differ in specific cases of direct comparisons.       </p>

Quality assessment of three years of Sentinel-5p TROPOMI NO2 data 

2021 ◽  
Author(s):  
Tijl Verhoelst ◽  
Steven Compernolle ◽  
Gaia Pinardi ◽  
José Granville ◽  
Jean-Christopher Lambert ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Quality Indicators ◽  
Reference Data ◽  
Scattered Light ◽  
Global Network ◽  
Validation Data ◽  
Data Set ◽  
Comparison Results ◽  
Data Products ◽  
The Impact

<p>For more than three years now, the first atmospheric satellite of the Copernicus EO programme, Sentinel-5p (S5P) TROPOMI, has acquired spectral measurements of the Earth radiance in the visible range, from which near-real-time (NRTI) and offline (OFFL) processors retrieve the total, tropospheric and stratospheric  column abundance of  NO<sub>2</sub>.   The S5P Mission Performance Centre  performs continuous QA/QC of these data products enabling users to verify the fitness-for-purpose of the S5P data. Quality Indicators are derived from comparisons to ground-based reference data, both station-by-station in the S5P Automated Validation Server (AVS), and globally in more in-depth analyses.  Complementary quality information is obtained from product intercomparisons (NRTI vs. OFFL) and from satellite-to-satellite comparisons.  After three years of successful operation we present here a consolidated overview of the quality of the S5P TROPOMI NO<sub>2</sub> data products, with particular attention paid to the impact of the various processor improvements, especially in the latest version (v1.4), activated on 2 December 2020, which introduces an updated cloud retrieval resulting in higher NO<sub>2</sub> columns in polluted regions. Also the upcoming v2, due in April 2021 but already used to produce a Diagnostic Data Set, is discussed. </p><p>S5P NO<sub>2</sub> data are compared to ground-based measurements collected through either the ESA Validation Data Centre (EVDC) or network data archives (NDACC, PGN). Measurements from the Pandonia Global Network (PGN) serve as a reference for total NO<sub>2</sub> validation, Multi-Axis DOAS data for tropospheric  NO<sub>2</sub> validation, and NDACC zenith-scattered-light DOAS data for stratospheric NO<sub>2</sub> validation.  Comparison methods are optimized to limit spatial and temporal mismatch errors (co-location strategy, photochemical adjustment to account for local time difference). Comparison results are analyzed to derive Quality Indicators and to conclude on the compliance w.r.t. the mission requirements.  This include estimates of: (1) the bias, as proxy for systematic errors, (2) the dispersion of the differences, which combines random errors with seasonal and mismatch errors, and (3) the dependence of these on key influence quantities (surface albedo, cloud cover…)</p><p>Overall, the MPC quality assessment of S5P NO<sub>2</sub> data concludes to an excellent performance for the stratospheric data (bias<5%, dispersion<10%). The tropospheric data show a negative bias of -30% and a dispersion of 3Pmolec/cm<sup>2</sup> vs. ground-based data. This dispersion is larger than the mission requirement on data precision, but it can partly be attributed to comparison errors such as those due to differences in resolution. Total column data are found to be biased low by 20%, with a 30% station-to-station scatter. After gridding to monthly means on a 0.8°x0.4° grid, comparisons to OMI data yield a much smaller dispersion (within the requirement of 0.7Pmolec/cm<sup>2</sup>), and a minor relative bias. NRTI and OFFL perform similarly, even if they occasionally differ over specific scenes. Besides the impact of the processor upgrade to v1.4 on the bias in polluted scenes, we discuss the implications of the reported negative biases in S5P tropospheric (and total) columns on NO<sub>2</sub> reduction estimates, e.g. in the context of SARS-CoV-2 lockdown measures. Feedback from this work on the ground-based reference data is also briefly reported.         </p>

Operational satellite validation with data from the Pandonia Global Network (PGN)

2020 ◽  
Author(s):  
Alexander Cede ◽  
Martin Tiefengraber ◽  
Angelika Dehn ◽  
Barry Lefer ◽  
Jonas von Bismarck ◽  
...  
Keyword(s):  
Program Planning ◽  
Low Earth Orbit ◽  
Global Network ◽  
High Temporal Resolution ◽  
Vertical Column ◽  
Validation Data ◽  
Passive Remote Sensing ◽  
Satellite Validation ◽  
Data Products ◽  
Planning Group

<p>The Pandonia Global Network (PGN) is a worldwide operating network of passive remote sensing spectrometer systems named “Pandora”. PGN is measuring atmospheric trace gases at high temporal resolution with the purpose of air quality monitoring and satellite validation. PGN is an activity carried out jointly by NASA, through the Pandora project at Goddard Space Flight Center, and ESA, through the Austrian contractor LuftBlick, as part of their Joint Program Planning Group Subgroup on calibration and validation and field activities. Many of the more than 50 actual PGN instruments are directly owned by NASA or ESA, another part belongs to other collaborating governmental and academic institutions. A major objective of the PGN is to support the validation and verification of more than a dozen low-earth orbit and geostationary orbit based UV-visible sensors, most notably Sentinel 5P, TEMPO, GEMS and Sentinel 4. PGN instruments are homogeneously calibrated and their data are centrally processed in real-time. Starting in June 2019, the PGN team has made more and more network locations “official PGN sites”, which means all required technical and logistical steps for this purpose have been performed. At the end of 2019 there are 18 such official network sites, where quality assured data are uploaded daily to EVDC (ESA Atmospheric Validation Data Centre), where they are used for operational validation of Sentinel 5P retrievals (see e.g. http://mpc-vdaf-server.tropomi.eu/no2/no2-offl-pandora). The current official PGN data products are total vertical column amounts of NO2 and O3 from direct sun observations. Research data products include total vertical columns amounts of SO2 and HCHO from direct sun observations as well as surface concentrations, tropospheric columns amounts, and vertical profiles for NO2 and HCHO from sky observations. These named research products are planned to become official over the course of the year 2020.</p>

scholarly journals Social Networks and Loneliness During the COVID-19 Pandemic

2021 ◽  
Vol 7 ◽  
pp. 237802312098525
Author(s):  
Balazs Kovacs ◽  
Nicholas Caplan ◽  
Samuel Grob ◽  
Marissa King
Keyword(s):  
Social Network ◽  
Social Isolation ◽  
Close Relationships ◽  
Network Size ◽  
Global Network ◽  
Network Data ◽  
Network Characteristics ◽  
Face To Face ◽  
Social Network Data ◽  
Social Network Size

We utilize longitudinal social network data collected pre–COVID-19 in June 2019 and compare them with data collected in the midst of COVID in June 2020. We find significant decreases in network density and global network size following a period of profound social isolation. While there is an overall increase in loneliness during this era, certain social network characteristics of individuals are associated with smaller increases in loneliness. Specifically, we find that people with fewer than five “very close” relationships report increases in loneliness. We further find that face-to-face interactions, as well as the duration and frequency of interactions with very close ties, are associated with smaller increases in loneliness during the pandemic. We also report on factors that do not moderate the effect of social isolation on perceived loneliness, such as gender, age, or overall social network size.

Water Quality Assessment on Fujiang River of China Using Projection Pursuit Model

2011 ◽  
Vol 204-210 ◽  
pp. 1326-1329
Author(s):  
Ben Lin Dai ◽  
Yu Long He ◽  
Jin Rong ◽  
Xiao Hui Jiang
Keyword(s):  
Water Quality ◽  
Quality Assessment ◽  
Projection Pursuit ◽  
Water Quality Assessment ◽  
Comparison Results ◽  
Volatile Phenol ◽  
Status Assessment ◽  
Water Quality Status ◽  
Projection Pursuit Model ◽  
Quality Status

Due to the increasingly serious water quality degradation on river, the study on river water quality status assessment has attracted more and more attention of the researchers and decision-makers. In this paper, water quality assessment of Fujiang River from January 2005 to December 2005 was studied by projection pursuit model (PPM). The water quality status of Fujiang River was assessed by the use of 10 monitoring sections, with DO, CODMn, BOD5, NH3-N, Petroleum, and Volatile phenol indicators. Based on the PPM analysis procedures, the assessed sections are described into 1 “category 1”, 4 “category 2” and 5 “category 3” states in 2005. The relative comparison results show that water quality status spatial order of Fujiang River from bad to good is: Fj09<Fj02< Fj10<Fj07< Fj03<Fj05<Fj04< Fj06<Fj08<Fj01.

scholarly journals GLIDE: combining local methods and diffusion state embeddings to predict missing interactions in biological networks

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i464-i473
Author(s):  
Kapil Devkota ◽  
James M Murphy ◽  
Lenore J Cowen
Keyword(s):  
Network Structure ◽  
Biological Networks ◽  
Link Prediction ◽  
Prediction Method ◽  
Global Network ◽  
Local Network ◽  
Supplementary Information ◽  
Network Data ◽  
Ppi Network ◽  
Diffusion State

Abstract Motivation One of the core problems in the analysis of biological networks is the link prediction problem. In particular, existing interactions networks are noisy and incomplete snapshots of the true network, with many true links missing because those interactions have not yet been experimentally observed. Methods to predict missing links have been more extensively studied for social than for biological networks; it was recently argued that there is some special structure in protein–protein interaction (PPI) network data that might mean that alternate methods may outperform the best methods for social networks. Based on a generalization of the diffusion state distance, we design a new embedding-based link prediction method called global and local integrated diffusion embedding (GLIDE). GLIDE is designed to effectively capture global network structure, combined with alternative network type-specific customized measures that capture local network structure. We test GLIDE on a collection of three recently curated human biological networks derived from the 2016 DREAM disease module identification challenge as well as a classical version of the yeast PPI network in rigorous cross validation experiments. Results We indeed find that different local network structure is dominant in different types of biological networks. We find that the simple local network measures are dominant in the highly connected network core between hub genes, but that GLIDE’s global embedding measure adds value in the rest of the network. For example, we make GLIDE-based link predictions from genes known to be involved in Crohn’s disease, to genes that are not known to have an association, and make some new predictions, finding support in other network data and the literature. Availability and implementation GLIDE can be downloaded at https://bitbucket.org/kap_devkota/glide. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Estimation of Surface Downward Shortwave Radiation over China from Himawari-8 AHI Data Based on Random Forest

2020 ◽  
Vol 12 (1) ◽  
pp. 181 ◽  
Author(s):  
Ning Hou ◽  
Xiaotong Zhang ◽  
Weiyu Zhang ◽  
Yu Wei ◽  
Kun Jia ◽  
...  
Keyword(s):  
Random Forest ◽  
Temporal Resolution ◽  
Great Influence ◽  
Radiant Energy ◽  
Shortwave Radiation ◽  
Mean Bias Error ◽  
Bias Error ◽  
Validation Data ◽  
Downward Shortwave Radiation ◽  
Comparison Results

Downward shortwave radiation (RS) drives many processes related to atmosphere–surface interactions and has great influence on the earth’s climate system. However, ground-measured RS is still insufficient to represent the land surface, so it is still critical to generate high accuracy and spatially continuous RS data. This study tries to apply the random forest (RF) method to estimate the RS from the Himawari-8 Advanced Himawari Imager (AHI) data from February to May 2016 with a two-km spatial resolution and a one-day temporal resolution. The ground-measured RS at 86 stations of the Climate Data Center of the Chinese Meteorological Administration (CDC/CMA) are collected to evaluate the estimated RS data from the RF method. The evaluation results indicate that the RF method is capable of estimating the RS well at both the daily and monthly time scales. For the daily time scale, the evaluation results based on validation data show an overall R value of 0.92, a root mean square error (RMSE) value of 35.38 (18.40%) Wm−2, and a mean bias error (MBE) value of 0.01 (0.01%) Wm−2. For the estimated monthly RS, the overall R was 0.99, the RMSE was 7.74 (4.09%) Wm−2, and the MBE was 0.03 (0.02%) Wm−2 at the selected stations. The comparison between the estimated RS data over China and the Clouds and Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) RS dataset was also conducted in this study. The comparison results indicate that the RS estimates from the RF method have comparable accuracy with the CERES-EBAF RS data over China but provide higher spatial and temporal resolution.

scholarly journals Research on Distributed Data Sharing System based on Internet of Things and Blockchain

2021 ◽  
Vol 9 (3) ◽  
pp. 239-254
Author(s):  
Enchang Sun ◽  
Kang Meng ◽  
Ruizhe Yang ◽  
Yanhua Zhang ◽  
Meng Li
Keyword(s):  
Internet Of Things ◽  
Data Sharing ◽  
Data Storage ◽  
Network Architecture ◽  
Data Privacy ◽  
Distributed Storage ◽  
Global Network ◽  
Local Network ◽  
Network Data ◽  
Distributed Data

Abstract Aiming at the problems of the traditional centralized data sharing platform, such as poor data privacy protection ability, insufficient scalability of the system and poor interaction ability, this paper proposes a distributed data sharing system architecture based on the Internet of Things and blockchain technology. In this system, the distributed consensus mechanism of blockchain and the distributed storage technology are employed to manage the access and storage of Internet of Things data in a secure manner. Up to the physical topology of the network, a hierarchical blockchain network architecture is proposed for local network data storage and global network data sharing, which reduces networking complexity and improves the scalability of the system. In addition, smart contract and distributed machine learning are adopted to design automatic processing functions for different types of data (public or private) and supervise the data sharing process, improving both the security and interactive ability of the system.

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