Understanding the Simulated Ammonia Increasing Trend from 2008 to 2015 over Europe with CHIMERE and Comparison with IASI Observations

Fortems-Cheiney, Audrey; Dufour, G.; Forêt, Gilles; Siour, Guillaume; Damme, Martin Van; Coheur, Pierre‐François; Clarisse, Lieven; Clerbaux, Cathy; Beekmann, Matthias

doi:10.3390/atmos13071101

Cited by 3 publications

(2 citation statements)

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“…CHIMERE is used as a tool to forecast and analyze the daily air quality in terms of particulate and gaseous pollution [28]. It is widely used for aerosol, and aerosol precursor research, e.g., [29][30][31][32][33].…”

Section: Chimere Simulationsmentioning

confidence: 99%

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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Section: Chimere Simulationsmentioning

confidence: 99%

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

et al. 2023

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“…CHIMERE is used as a tool to forecast and analyze the daily air quality in terms of particulate and gaseous pollution [31]. It is widely used for aerosols, and aerosol precursors research e.g., [32][33][34][35][36].…”

Section: Chimere Simulationsmentioning

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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An extraction method for nitrogen isotope measurement of ammonium in a low-concentration environment

Lamothe,

Savarino,

Ginot

et al. 2023

Atmos. Meas. Tech.

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Abstract. Ammonia (NH3) participates in the nucleation and growth of aerosols and thus plays a major role in atmospheric transparency, pollution, health, and climate-related issues. Understanding its emission sources through nitrogen stable isotopes is therefore a major focus of current work to mitigate the adverse effects of aerosol formation. Since ice cores can preserve the past chemical composition of the atmosphere for centuries, they are a top tool of choice for understanding past NH3 emissions through ammonium (NH4+), the form of NH3 archived in ice. However, the remote or high-altitude sites where glaciers and ice sheets are typically localized have relatively low fluxes of atmospheric NH4+ deposition, which makes ice core samples very sensitive to laboratory NH3 contamination. As a result, accurate techniques for identifying and tracking NH3 emissions through concentration and isotopic measurements are highly sought to constrain uncertainties in NH3 emission inventories and atmospheric reactivity unknowns. Here, we describe a solid-phase extraction method for NH4+ samples of low concentration that limits external contamination and produces precise isotopic results. By limiting NH3atm exposure with a scavenging fume hood and concentrating the targeted NH4+ through ion exchange resin, we successfully achieve isotopic analysis of 50 nmol NH4+ samples with a 0.6 ‰ standard deviation. This extraction method is applied to an alpine glacier ice core from Col du Dôme, Mont Blanc, where we successfully demonstrate the analytical approach through the analysis of two replicate 8 m water equivalent ice cores representing 4 years of accumulation with a reproducibility of ±2.1 ‰. Applying this methodology to other ice cores in alpine and polar environments will open new opportunities for understanding past changes in NH3 emissions and atmospheric chemistry.

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Understanding the Simulated Ammonia Increasing Trend from 2008 to 2015 over Europe with CHIMERE and Comparison with IASI Observations

Cited by 3 publications

References 46 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

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

An extraction method for nitrogen isotope measurement of ammonium in a low-concentration environment

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