Deep learning bias correction of GEMS tropospheric NO2: A comparative validation of NO2 from GEMS and TROPOMI using Pandora observations

Environment International, Journal Year: 2024, Volume and Issue: 190, P. 108818 - 108818

Published: June 14, 2024

Despite advancements in satellite instruments, such as those geostationary orbit, biases continue to affect the accuracy of data. This research pioneers use a deep convolutional neural network correct bias tropospheric column density NO2 (TCDNO2) from Geostationary Environment Monitoring Spectrometer (GEMS) during 2021–2023. Initially, we validate GEMS TCDNO2 against Pandora observations and compare its with measurements TROPOspheric Instrument (TROPOMI). displays acceptable measurements, correlation coefficient (R) 0.68, an index agreement (IOA) 0.79, mean absolute (MAB) 5.73321 × 1015 molecules/cm2, though it is not highly accurate. The evaluation showcases moderate high across all stations, R values spanning 0.46 0.80. Comparing TROPOMI at overpass time shows satisfactory performance achieving R, IOA, MAB 0.71, 0.78, 6.82182 respectively. However, these figures are overshadowed by TROPOMI's superior accuracy, which reports 0.81, 0.89, 3.26769 While overestimates 52 % time, underestimates 9 %. learning corrected (GEMS-DL) demonstrates marked enhancement original measurements. GEMS-DL product improves 0.68 0.88, IOA 0.79 0.93, 2.67659 reduces percentage (MABP) 64 30 represents significant reduction bias, exceeding 50 Although 28 %, remarkably minimizes this error, underestimating mere 1 Spatial cross-validation stations MABP, range 45 %-105.6 data 24 %-59 GEMS-DL.

Language: Английский

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Quantifying the diurnal variation in atmospheric NO₂ from Geostationary Environment Monitoring Spectrometer (GEMS) observations

Atmospheric chemistry and physics, Journal Year: 2024, Volume and Issue: 24(15), P. 8943 - 8961

Published: Aug. 15, 2024

Abstract. The Geostationary Environment Monitoring Spectrometer (GEMS) over Asia is the first geostationary Earth orbit instrument in virtual constellation of sensors for atmospheric chemistry and composition air quality research applications. For time, hourly observations enable studies diurnal variation several important trace gas aerosol pollutants including nitrogen dioxide (NO2), which focus this work. NO2 a regulated pollutant an indicator anthropogenic emissions addition to being involved tropospheric ozone particulate matter formation. We present new quantitative measures column can be greater than 50 % amount, especially polluted environments. distribution seen change quite different from what would by once-a-day low-Earth-orbit satellite observation. use GEMS data combination with TROPOspheric Instrument (TROPOMI) Pandora ground-based remote sensing measurements Multi-Scale Infrastructure Chemistry Aerosols (Version 0, MUSICAv0) 3D chemical transport model analysis examine January June 2023 Northeast Seoul, South Korea, study regions distinguish emissions, chemistry, meteorological processes that drive variation. Understanding relative importance these will key models aimed at determining true exposure levels studies. work presented here also provides path investigating similar cycles Venture Instrument-1 Tropospheric Emissions: Pollution (TEMPO) North America, later Europe Sentinel-4.

Language: Английский

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Validation of GEMS tropospheric NO₂ columns and their diurnal variation with ground-based DOAS measurements

Atmospheric measurement techniques, Journal Year: 2024, Volume and Issue: 17(21), P. 6315 - 6344

Published: Oct. 30, 2024

Abstract. Instruments for air quality observations on geostationary satellites provide multiple per day and allow the analysis of diurnal variation in important pollutants such as nitrogen dioxide (NO2). The South Korean instrument GEMS (Geostationary Environmental Monitoring Spectrometer), launched February 2020, is first that able to observe NO2. measurements have a spatial resolution 3.5 km × 8 cover large part Asia. This study compares 1 year tropospheric NO2 vertical column density (VCD) from operational L2 product, scientific IUP-UB (Institute Physics at University Bremen) TROPOspheric Instrument (TROPOMI) ground-based differential optical absorption spectroscopy (DOAS) Korea. VCDs overestimate with median relative difference +61 % correlation coefficient 0.76. −2 product −16 TROPOMI coefficients 0.83 0.89, respectively. scatter products can be reduced when are limited overpass time. Diurnal variations differ by pollution level analyzed site but good agreement between observations. Low-pollution sites show weak or almost no variation. In summer, polluted minimum around noon, indicating influence photochemical loss. Most seen spring autumn, increasing morning, maximum close decrease towards afternoon. Winter rather flat slightly decreasing throughout day. under low-wind-speed conditions high-pollution enhancements indicates calm conditions, dilution less effective chemical loss winter do not balance accumulating emissions. observed low-pollution follows seasonal wind patterns. A weekday–weekend effect shows different products. However, while agreeing other data sets weekdays, significantly reduction weekends. stratospheric contribution surface reflectivity satellite VCD investigated. While TM5 model's VCDs, used too high, resulting low even negative, retrieval, low. Surface comparisons indicate makes overestimation scatter.

Language: Английский

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A bias-corrected GEMS geostationary satellite product for nitrogen dioxide using machine learning to enforce consistency with the TROPOMI satellite instrument

Atmospheric measurement techniques, Journal Year: 2024, Volume and Issue: 17(17), P. 5147 - 5159

Published: Sept. 5, 2024

Abstract. The Geostationary Environment Monitoring Spectrometer (GEMS) launched in February 2020 is now providing continuous daytime hourly observations of nitrogen dioxide (NO2) columns over eastern Asia (5° S–45° N, 75–145° E) with 3.5 × 7.7 km2 pixel resolution. These data provide unique information to improve understanding the sources, chemistry, and transport oxides (NOx) implications for atmospheric chemistry air quality, but opportunities direct validation are very limited. Here we correct operational level-2 (L2) NO2 vertical column densities (VCDs) from GEMS a machine learning (ML) model match much sparser more mature low Earth orbit TROPOspheric Instrument (TROPOMI), preserving density making them consistent TROPOMI. We first reprocess TROPOMI L2 products use common prior profiles (shape factors) GEOS-Chem chemical model. This removes major inconsistency between two satellite greatly improves their agreement ground-based Pandora VCD source regions. then apply ML remaining differences, Δ(GEMS–TROPOMI), using VCDs retrieval parameters as predictor variables. train colocated VCDs, taking advantage off-track viewing cover wide range effective zenith angles (EZAs) observed by GEMS. most important variables Δ(GEMS–TROPOMI) EZA. corrected product unbiased relative shows diurnal variation regions than product.

Language: Английский

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Impact of COVID-19 policy changes on tropospheric NO₂ in the Yangtze River Basin: insights from GF-5 02 EMI-II observations

Remote Sensing Letters, Journal Year: 2025, Volume and Issue: 16(5), P. 472 - 482

Published: March 6, 2025

Language: Английский

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Global retrieval of TROPOMI tropospheric HCHO and NO₂ columns with improved consistency based on the updated Peking University OMI NO₂ algorithm

Atmospheric measurement techniques, Journal Year: 2025, Volume and Issue: 18(7), P. 1561 - 1589

Published: April 3, 2025

Abstract. The TROPOspheric Monitoring Instrument (TROPOMI), aboard the Sentinel-5 Precursor (S5P) satellite launched in October 2017, is dedicated to monitoring atmospheric composition associated with air quality and climate change. This paper presents global retrieval of TROPOMI tropospheric formaldehyde (HCHO) nitrogen dioxide (NO2) vertical columns using an updated version Peking University OMI NO2 (POMINO) algorithm, which focuses on improving calculation mass factors (AMFs). algorithm features explicit corrections for surface reflectance anisotropy aerosol optical effects, it uses daily high-resolution (0.25°×0.25°) a priori HCHO profiles from Global Earth Observing System Composition Forecast (GEOS-CF) dataset. For cloud correction, consistent approach used both retrievals, where (1) fraction recalculated at 440 nm same ancillary parameters as those AMF calculation, (2) cloud-top pressure taken operational FRESCO-S product. comparison between POMINO reprocessed (RPRO) products April, July 2021 well January 2022 exhibits high spatial agreement, but RPRO are lower by 10 % 20 over polluted regions. Sensitivity tests show that differences mainly caused different correction methods (implicit versus explicit), prior information profile shapes background corrections, while discrepancies result reflectances their nonlinear interactions. With structural uncertainty due within ±20 %, height differences. Validation against ground-based measurements Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) observations Pandonia Network (PGN) shows retrievals present comparable day-to-day correlation reduced bias (normalized mean bias, NMB) compared (HCHO: R=0.62, NMB=-30.8% R=0.68, NMB=-35.0%; NO2: R=0.84, NMB=-9.5% R=0.85, NMB=-19.4%). An improved agreement HCHO/NO2 ratio (FNR, ratio) MAX-DOAS PGN based also found (NMB: −14.8 −21.1 %). Our provides useful source information, particularly studies combining NO2.

Language: Английский

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Tropospheric NO2 Column over Tibet Plateau According to Geostationary Environment Monitoring Spectrometer: Spatial, Seasonal, and Diurnal Variations

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(10), P. 1690 - 1690

Published: May 12, 2025

Nitrogen oxides (NOx) are key precursors of tropospheric ozone and particulate matter. The sparse local observations make it challenging to understand NOx cycling across the Tibetan Plateau (TP), which plays a crucial role in regional global atmospheric processes. Here, we utilized Geostationary Environment Monitoring Spectrometer (GEMS) data examine NO2 vertical column density (ΩNO2) spatiotemporal variability over TP, pristine environment marked with natural sources. GEMS revealed that ΩNO2 TP is generally low compared surrounding regions significant surface emissions, such as India Sichuan basin. A spatial decreasing trend observed from south center north Tibet. Unlike regions, exhibits opposing seasonal patterns negative correlation between ΩNO2. In Lhasa Nam Co areas within Xizang, highest spring contrasts lowest concentration. Diurnally, midday increase warm season reflects some external sources affecting remote area. Trajectory analysis suggests strong convection lifted air mass Southeast Asia into upper troposphere TP. These findings highlight mixing interplay nonlocal shaping high-altitude environment. Future research should explore these transport mechanisms their implications for chemistry climate dynamics

Language: Английский

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Deep Learning Bias Correction of Gems Tropospheric No2: A Comparative Validation of No2 from Gems and Tropomi Using Pandora Observations

Published: Jan. 1, 2024

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Language: Английский

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Estimation of ground-level NO₂ and its spatiotemporal variations in China using GEMS measurements and a nested machine learning model

Atmospheric chemistry and physics, Journal Year: 2024, Volume and Issue: 24(16), P. 9645 - 9665

Published: Aug. 30, 2024

Abstract. The major link between satellite-derived vertical column densities (VCDs) of nitrogen dioxide (NO2) and ground-level concentrations is theoretically the NO2 mixing height (NMH). Various meteorological parameters have been used as a proxy for NMH in existing studies. This study developed nested XGBoost machine learning model to convert VCDs into across China using Geostationary Environmental Monitoring Spectrometer (GEMS) measurements. was designed directly incorporate methodological framework estimate concentrations. inner predicted from parameters, which were then input main predict its VCDs. inclusion significantly enhanced accuracy concentration estimates; i.e., R2 values improved 0.73 0.93 10-fold cross-validation 0.88 0.99 fully trained model. Furthermore, identified second most important predictor variable, following NO2. Subsequently, data analyzed subregions with varying geographic locations urbanization levels. Highly populated areas typically experienced peak during early morning rush hour, whereas categorized lightly observed slight increase levels 1 or 2 h later, likely due regional pollutant dispersion urban sources. underscores importance incorporating estimating satellite measurements highlights significant advantages geostationary satellites providing detailed air pollution information at an hourly resolution.

Language: Английский

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Reply to: NO2 satellite retrievals biased by absorption in water

Nature Geoscience, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 20, 2024

Language: Английский

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