Atmospheric Environment, Journal Year: 2024, Volume and Issue: 333, P. 120636 - 120636
Published: June 6, 2024
Language: Английский
Atmosphere, Journal Year: 2025, Volume and Issue: 16(3), P. 238 - 238
Published: Feb. 20, 2025
As carbon dioxide (CO2) concentrations continue to rise, climate change, characterized by global warming, presents a significant challenge sustainable development. Currently, most shortwave infrared CO2 retrievals rely on fully physical retrieval algorithms, for which complex calculations are necessary. This paper proposes method predict the concentration of column-averaged (XCO2) from hyperspectral satellite data, using machine learning avoid iterative computations method. The training dataset is constructed Orbiting Carbon Observatory-2 (OCO-2) spectral XCO2 OCO-2, surface albedo and aerosol optical depth (AOD) measurements 2019. study employed variety including Random Forest, XGBoost, LightGBM, analysis. results showed that Forest outperforms other models, achieving correlation 0.933 with products, mean absolute error (MAE) 0.713 ppm, root square (RMSE) 1.147 ppm. model was then applied retrieve column 2020. 0.760 Total Column Observing Network (TCCON) measurements, higher than 0.739 product verifying effectiveness
Language: Английский
Citations
0
Atmospheric measurement techniques, Journal Year: 2025, Volume and Issue: 18(4), P. 929 - 952
Published: Feb. 25, 2025
Abstract. Satellite-based observations of carbon dioxide (CO2) are sensitive to all processes that affect the propagation radiation in atmosphere, including scattering and absorption by atmospheric aerosols. Therefore, accurate retrievals column-averaged CO2 (XCO2) benefit from detailed information on aerosol conditions. This is particularly relevant for future missions focusing observing anthropogenic emissions, such as Copernicus Anthropogenic Monitoring mission (CO2M). To fully prepare CO2M observations, it informative investigate existing addition other approaches. Our focus here NASA Orbiting Carbon Observatory-2 (OCO-2) mission. In operational full-physics XCO2 retrieval used generate OCO-2 level 2 products, properties known have high uncertainty, but their main objective facilitate retrievals. We evaluate product point view aerosols comparing OCO-2-retrieved collocated Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua Dark Target products. find there a systematic difference between optical depth (AOD, τ) values retrieved two instruments τOCO-2∼0.4τMODIS. A similar found when with Aerosol Robotic Network (AERONET). results 16.5 % cases being misclassified low AOD (good quality) quality filtering. also dependence difference, indicating an aerosol-induced effect retrieval. Furthermore, Total Column Observing (TCCON), we small AOD-dependent bias XCO2. addition, weak statistically significant correlation MODIS XCO2, which can be partly explained natural covariance co-emission CO2. Due co-emission, using threshold filtering leads sampling bias, where more often removed. mitigate this emission monitoring, fraction acceptable data. relaxing 0.2 0.5, goal CO2M, increases data 14 percentage points globally 31 urban areas.
Language: Английский
Citations
0
Geoscientific model development, Journal Year: 2025, Volume and Issue: 18(5), P. 1505 - 1544
Published: March 10, 2025
Abstract. The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. While the analytical and variational optimization implemented in CIF are operational have proved to be accurate efficient, initial ensemble method was found incomplete could hardly compared other employed inversion community, mainly owing strong performance limitations absence of localization methods. In this paper, we present evaluate a new implementation mode, building upon developments. As first step, chose implement serial batch versions square root filter (EnSRF) algorithm because it is widely community. We provide comprehensive description technical useful features can users. Finally, demonstrate capabilities CIF-EnSRF system using large number synthetic experiments over Europe with flexible scalable high-performance transport model ICON-ART, exploring system’s sensitivity multiple parameters that tuned by expected, results sensitive size parameters. Other tested parameters, such as lags, propagation factors, or function, also substantial influence on results. introduce way interpreting set metrics automatically computed help assess success inversions compare them. This work complements previous efforts focused within CIF. ICON-ART has been used testing work, integration these algorithms enables any perform inversions, fully leveraging CIF's robust capabilities.
Language: Английский
Citations
0
Atmospheric Research, Journal Year: 2025, Volume and Issue: unknown, P. 108057 - 108057
Published: March 1, 2025
Language: Английский
Citations
0
Remote Sensing, Journal Year: 2025, Volume and Issue: 17(9), P. 1617 - 1617
Published: May 2, 2025
Climate change poses a global threat, affecting both biodiversity and human populations. To implement efficient mitigating strategies, the consistency accuracy of our monitoring greenhouse gases at local level must be improved. We can achieve this with more advanced instruments or an enhancement processing techniques, which will in turn improve data attributes such as spatial temporal resolutions accuracy. This paper presents daily high resolution XCO2 dataset aiming to help monitor atmospheric CO2 concentration on scale greater detail compared existing datasets. Using super deep learning model, we increase OCO-2-derived from 0.5° × 0.625° 0.03° 0.04° show that product maintains quality original while consistently improving pollution field. conduct benchmark highlights how outperforms similar products present use case regional level. In conclusion, work provides complementary approach area continuous reconstruction focuses adjacent problem specific features
Language: Английский
Citations
0
Earth and Space Science, Journal Year: 2025, Volume and Issue: 12(5)
Published: May 1, 2025
Abstract NASA's Orbiting Carbon Observatory‐2 (OCO‐2) has the goal of accurately estimating column‐averaged dry‐air mole fractions carbon dioxide (). In order to fit measured radiances, many parameters besides are included in optimal estimation state vector, including atmospheric water vapor and temperature. The current operational retrieval algorithm (v11) solves for a multiplicative scaling factor on an priori profile additive offset temperature profile. However, simulations have indicated that each 1.5–3 degrees freedom vertical column. This means is limited its ability true profiles vapor. Here, we use singular value decomposition determine three most explanatory “shapes” error, then retrieve single applied shape. We assess errors by comparing Total Column Observing Network (TCCON) multiple inverse models. find after applying quality filtering using Data Ordering Genetic Optimization custom bias correction, scatter error versus TCCON reduced from 1.02 1.01 ppm (2.3% reduction variance) land glint observations, 1.04 0.96 (14.5% nadir 0.68 0.66 (4.7% ocean observations. also see small improvement agreement between OCO‐2 models over oceans Amazon.
Language: Английский
Citations
0
Environmental Research Letters, Journal Year: 2023, Volume and Issue: 18(12), P. 124030 - 124030
Published: Oct. 20, 2023
Abstract Monitoring national and global greenhouse gas (GHG) emissions is a critical component of the Paris Agreement, necessary to verify collective activities reduce GHG emissions. Top-down approaches infer emission estimates from atmospheric data are widely recognized as useful tool independently inventories reported by individual countries under United Nation Framework Convention on Climate Change. Conventional top-down inversion methods often prescribe fossil fuel CO 2 (FFCO2) fit resulting model values observations adjusting natural terrestrial ocean flux estimates. This approach implicitly assumes that we have perfect knowledge FFCO2 any gap in our understanding can be explained fluxes; consequently, it also limits ability quantify non-FFCO2 Using two independent inventories, show differences sub-annual distributions aliased corresponding posterior Over China, for example, where significantly different seasonal variations FFCO2, national-scale small but significant subnational scale. We compare inferred in-situ satellite observations. find sparsely distributed best suited quantifying fluxes large-scale carbon budgets less suitable errors. Satellite provide us with opportunity errors; similar result achievable using dense, regional measurement networks. Enhancing estimation capability inventory verification requires coordinated activity (a) improve inventories; (b) extend take full advantage measurements trace gases co-emitted during combustion; (c) transport models.
Language: Английский
Citations
9
International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 129, P. 103859 - 103859
Published: April 23, 2024
Satellite measurements of the column-averaged dry air mole fraction atmospheric carbon dioxide (XCO2) play a crucial role in monitoring CO2 emissions and sinks. However, current limitations satellite observations, including sparse sampling, narrow swath coverage, data gaps caused by factors like clouds, significantly hinder their ability to accurately capture local-scale sources This study introduces an innovative data-driven approach based on deep learning, which takes into consideration both spatial temporal variations, map XCO2 using observations from multiple satellites. By leveraging advanced learning techniques conventional neural network (CNN), long short-term memory (LSTM), channel-spatial attention, artificial (ANN) model training, this not only incorporates spatiotemporal variations but also integrates information related terrestrial, anthropogenic, meteorological variables. The results demonstrate notable improvement predictive capability approach. An important advancement over previous studies is that breaks away practice model-generated simulations for training validation. Monthly deep-learning (DL-XCO2) China 2014 2022 was generated with resolution 0.25° retrievals GOSAT OCO-2/3. Cross-validation show average prediction bias −0.16 ppm. Additionally, DL-XCO2 exhibits high precision when compared two TCCON stations, errors 0.93 1.29 ppm Hefei Xianghe, respectively. Ultimately, effectively urban showcasing potential characterizing fine-scale
Language: Английский
Citations
3
Advances in Space Research, Journal Year: 2024, Volume and Issue: 74(8), P. 3804 - 3825
Published: July 6, 2024
Language: Английский
Citations
3
Atmospheric chemistry and physics, Journal Year: 2025, Volume and Issue: 25(3), P. 1949 - 1963
Published: Feb. 13, 2025
Abstract. Simultaneous monitoring of greenhouse gases and air pollutant emissions is crucial for combating global warming pollution. We previously established an air-pollution-satellite-based carbon dioxide (CO2) emission inversion system, successfully capturing CO2 nitrogen oxide (NOx) fluctuations amid socioeconomic changes. However, the system's robustness weaknesses have not yet been fully evaluated. Here, we conduct a comprehensive sensitivity analysis with 31 tests on various factors including prior emissions, model resolution, satellite constraint, system configuration to assess vulnerability estimates across temporal, sectoral, spatial dimensions. The relative change (RC) between these base reflects different configurations' impact inferred 1 standard deviation (1σ) RC indicating consistency. Although show increased tested at finer scales, demonstrates notable robustness, especially annual national total NOx most (RC < 4.0 %). Spatiotemporally diverse changes in parameters tend yield inconsistent impacts (1σ ≥ 4 %) vice versa emerge as major influential factors, underscoring their priority further optimization. Taking daily example, RC‾ ± 1σ they incur can reach −1.2 6.0 %, 1.3 3.9 10.7 0.7 respectively. This study reveals areas improvement our offering opportunities enhance reliability future.
Language: Английский
Citations
0