Three-Dimensional Distribution of Biomass Burning Aerosols from Australian Wildfires Observed by TROPOMI Satellite Observations

Lemmouchi, Farouk; Cuesta, Juan; Eremenko, Maxim; Derognat, Claude; Siour, Guillaume; Dufour, G.; Sellitto, Pasquale; Turquety, Solène; Tran, Dung Viet; Liu, Xueliang; Zoogman, P.; Lutz, Ronny; Loyola, Diego

doi:10.20944/preprints202204.0211.v1

Cited by 3 publications

(1 citation statement)

References 65 publications

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“…We think that the results of our method could potentially be used as a gap filling system for the global MODIS AOD maps. Additionally, further development will be conducted for correcting the CHIMERE vertical profile of the aerosols concentration using state of the art retrievals of the 3D distribution of aerosols, such as AEROIASI [81] which uses the Infrared Atmospheric Sounding Interferometer (IASI) thermal infrared measurements for coarse particles; AEROS5P [82] Author Contributions: Conceptualization, formal analysis, methodology, data curation, software, visualization, validation, writing-original draft, FL; conceptualization, funding acquisition, project administration, supervision, writing-review and editing, JC; data curation, writing-review and editing, ML; methodology, writing-review and editing, JB; methodology, writing-review, AC; writing-review and editing, MB; funding acquisition, writing -review, CD; All authors participated in the analysis and have read and agreed to the published version of the manuscript. Please turn to the CRediT taxonomy for the term explanation.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Improvement of Aerosol Optical Depth from CHIMERE Simulations using MODIS Satellite Observations

Lemmouchi¹,

Cuesta²,

Lachâtre³

et al. 2022

Preprint

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We develop a machine learning (ML) approach for improving the accuracy of the horizontal dis-tribution of the aerosol optical depth (AOD) simulated by the CHIMERE chemistry-transport model over Northern Africa using Moderate-Resolution Imaging Spectroradiometer (MODIS) AOD satellite observations. These observations are used during the training phase of the ML method for deriving a regional bias correction of AODs simulated by CHIMERE. The results are daily maps of regional bias corrected AODs with full horizontal coverage over Northern Africa. We test four types of ML models: multiple linear regression (MLR), random forests (RF), gradi-ent boosting (XGB), and multiple layer perceptron networks (NN). We perform comparisons with satellite and independent ground-based observations of AOD that are not used in the training phase. They suggest that all models have overall comparable performances with a slight advantage of the RF model which expresses less spatial artifacts. While the method slightly underestimates the very high AODs, it significantly reduces biases and absolute errors, and clearly enhances linear correlations with respect to independent observations. This im-provement for deriving the AOD is particularly relevant for high dust pollution regions like the Sahara Desert, which dramatically lack ground-based measurements for validations of chemis-try-transport modeling which currently remains challenging and imprecise.

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Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Improvement of Aerosol Optical Depth from CHIMERE Simulations using MODIS Satellite Observations

Lemmouchi¹,

Cuesta²,

Lachâtre³

et al. 2022

Preprint

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Seasonal Comparison of the Wildfire Emissions in Southern African Region during the Strong ENSO Events of 2010/11 and 2015/16 Using Trend Analysis and Anomaly Detection

Shikwambana

Kganyago

2023

Remote Sensing

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This study investigates the wildfire biomass-burning emission levels during strong El Niño–southern oscillation (ENSO) events of 2010–2011 (characterized by a strong La Niña event) and 2015–2016 (characterized by a strong El Niño event) over the southern African region. Specifically, the biomass-burning parameters of black carbon (BC), carbon monoxide (CO) and sulfur dioxide (SO2) were investigated. Of interest in the current study was the strong El Niño (2015–2016) and La Niña (2010–2011) events during the main fire seasons in southern Africa, i.e., June–July–August (JJA) and September–October–November (SON). Furthermore, the study looks at how meteorological parameters (temperature and precipitation) are influenced by the two strong ENSO events. The sequential Mann–Kendall (SQMK) test is used to study the long-term trends of the emission and meteorological parameters. Anomaly detection on the long-term emission trends and meteorological parameters are performed using the seasonal and trend decomposition loess (STL) and generalized extreme studentized deviate (GESD). Overall, the results show higher emission levels of SO2, CO, and BC during the JJA season compared to the SON season. The SQMK results show an increasing trend of SO2, CO, and BC over time, indicating an increase in the amount of biomass burning. The GESD showed significant anomalies for BC, SO2, and CO emanating from the two strong El Niño and La Niña events. On the other hand, no significant anomalies were detected for temperature and precipitation. The results in this study highlight the significant effect of strong ENSO events on wildfire emissions, thus retrospectively showing the potential effect of future events, especially in the context of climate change.

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Machine Learning-Based Improvement of Aerosol Optical Depth from CHIMERE Simulations Using MODIS Satellite Observations

et al. 2023

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We present a supervised machine learning (ML) approach to improve the accuracy of the regional horizontal distribution of the aerosol optical depth (AOD) simulated by the CHIMERE chemistry transport model over North Africa and the Arabian Peninsula using Moderate Resolution Imaging Spectroradiometer (MODIS) AOD satellite observations. Our method produces daily AOD maps with enhanced precision and full spatial domain coverage, which is particularly relevant for regions with a high aerosol abundance, such as the Sahara Desert, where there is a dramatic lack of ground-based measurements for validating chemistry transport simulations. We use satellite observations and some geophysical variables to train four popular regression models, namely multiple linear regression (MLR), random forests (RF), gradient boosting (XGB), and artificial neural networks (NN). We evaluate their performances against satellite and independent ground-based AOD observations. The results indicate that all models perform similarly, with RF exhibiting fewer spatial artifacts. While the regression slightly overcorrects extreme AODs, it remarkably reduces biases and absolute errors and significantly improves linear correlations with respect to the independent observations. We analyze a case study to illustrate the importance of the geophysical input variables and demonstrate the regional significance of some of them.

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Three-Dimensional Distribution of Biomass Burning Aerosols from Australian Wildfires Observed by TROPOMI Satellite Observations

Cited by 3 publications

References 65 publications

Machine Learning-Based Improvement of Aerosol Optical Depth from CHIMERE Simulations using MODIS Satellite Observations

Machine Learning-Based Improvement of Aerosol Optical Depth from CHIMERE Simulations using MODIS Satellite Observations

Seasonal Comparison of the Wildfire Emissions in Southern African Region during the Strong ENSO Events of 2010/11 and 2015/16 Using Trend Analysis and Anomaly Detection

Machine Learning-Based Improvement of Aerosol Optical Depth from CHIMERE Simulations Using MODIS Satellite Observations

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