MAX-DOAS measurements of tropospheric NO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; and HCHO in Munich and the comparison to OMI and TROPOMI satellite observations

Chan, Ka Lok; Wiegner, Matthias; Geffen, Jos van; Smedt, Isabelle De; Alberti, Carlos; Cheng, Zhibin; Ye, Sheng; Wenig, Mark

doi:10.5194/amt-13-4499-2020

Cited by 72 publications

(65 citation statements)

References 87 publications

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“…In former publications (e.g. Irie et al, 2008;Clémer et al, 2010;Frieß et al, 2016;Bösch et al, 2018) and also during this comparison study, it was found that MAX-DOAS vertically integrated aerosol profiles systematically underestimate AOTs. It has already been pro-J.-L. Tirpitz et al: CINDI-2 profiling comparison Figure 10.…”

Section: Aerosol Optical Thicknesssupporting

confidence: 65%

“…The scaling factor is applied to the measured O 4 dSCDs prior to the retrieval and was initially motivated by previous MAX-DOAS studies which reported a significant yet debated mismatch between measured and simulated dSCDs (e.g. Wagner et al, 2009;Clémer et al, 2010;Ortega et al, 2016;Wagner et al, 2019, and references therein). Also for MAPA during CINDI-2, a scaling factor of 0.8 was found to improve the dSCD agreement, enhance the number of valid profiles and significantly improve the agreement with the Sun photometer aerosol optical thickness (Beirle et al, 2019).…”

Section: Participating Groups and Algorithmsmentioning

confidence: 99%

“…Multi-axis differential optical absorption spectroscopy (MAX-DOAS) (e.g. Hönninger and Platt, 2002;Hönninger et al, 2004;Wagner et al, 2004;Heckel et al, 2005;Frieß et al, 2006;Platt and Stutz, 2008;Irie et al, 2008;Clémer et al, 2010;Wagner et al, 2011;Vlemmix et al, 2015b) is a widely used ground-based measurement technique for the detection of aerosols and trace gases particularly in the lower troposphere: ultraviolet (UV) and visible (Vis) absorption spectra of skylight are analysed to obtain information on different atmospheric absorbers and scatterers, integrated over the light path (in fact, a superposition of a multitude of light paths). The amount of atmospheric trace gases along the light path is inferred by identifying and analysing their characteristic narrow spectral absorption features, applying DOAS (Platt and Stutz, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Intercomparison of MAX-DOAS vertical profile retrieval algorithms: studies on field data from the CINDI-2 campaign

et al. 2021

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Abstract. The second Cabauw Intercomparison of Nitrogen Dioxide measuring Instruments (CINDI-2) took place in Cabauw (the Netherlands) in September 2016 with the aim of assessing the consistency of multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements of tropospheric species (NO2, HCHO, O3, HONO, CHOCHO and O4). This was achieved through the coordinated operation of 36 spectrometers operated by 24 groups from all over the world, together with a wide range of supporting reference observations (in situ analysers, balloon sondes, lidars, long-path DOAS, direct-sun DOAS, Sun photometer and meteorological instruments). In the presented study, the retrieved CINDI-2 MAX-DOAS trace gas (NO2, HCHO) and aerosol vertical profiles of 15 participating groups using different inversion algorithms are compared and validated against the colocated supporting observations, with the focus on aerosol optical thicknesses (AOTs), trace gas vertical column densities (VCDs) and trace gas surface concentrations. The algorithms are based on three different techniques: six use the optimal estimation method, two use a parameterized approach and one algorithm relies on simplified radiative transport assumptions and analytical calculations. To assess the agreement among the inversion algorithms independent of inconsistencies in the trace gas slant column density acquisition, participants applied their inversion to a common set of slant columns. Further, important settings like the retrieval grid, profiles of O3, temperature and pressure as well as aerosol optical properties and a priori assumptions (for optimal estimation algorithms) have been prescribed to reduce possible sources of discrepancies. The profiling results were found to be in good qualitative agreement: most participants obtained the same features in the retrieved vertical trace gas and aerosol distributions; however, these are sometimes at different altitudes and of different magnitudes. Under clear-sky conditions, the root-mean-square differences (RMSDs) among the results of individual participants are in the range of 0.01–0.1 for AOTs, (1.5–15) ×1014molec.cm-2 for trace gas (NO2, HCHO) VCDs and (0.3–8)×1010molec.cm-3 for trace gas surface concentrations. These values compare to approximate average optical thicknesses of 0.3, trace gas vertical columns of 90×1014molec.cm-2 and trace gas surface concentrations of 11×1010molec.cm-3 observed over the campaign period. The discrepancies originate from differences in the applied techniques, the exact implementation of the algorithms and the user-defined settings that were not prescribed. For the comparison against supporting observations, the RMSDs increase to a range of 0.02–0.2 against AOTs from the Sun photometer, (11–55)×1014molec.cm-2 against trace gas VCDs from direct-sun DOAS observations and (0.8–9)×1010molec.cm-3 against surface concentrations from the long-path DOAS instrument. This increase in RMSDs is most likely caused by uncertainties in the supporting data, spatiotemporal mismatch among the observations and simplified assumptions particularly on aerosol optical properties made for the MAX-DOAS retrieval. As a side investigation, the comparison was repeated with the participants retrieving profiles from their own differential slant column densities (dSCDs) acquired during the campaign. In this case, the consistency among the participants degrades by about 30 % for AOTs, by 180 % (40 %) for HCHO (NO2) VCDs and by 90 % (20 %) for HCHO (NO2) surface concentrations. In former publications and also during this comparison study, it was found that MAX-DOAS vertically integrated aerosol extinction coefficient profiles systematically underestimate the AOT observed by the Sun photometer. For the first time, it is quantitatively shown that for optimal estimation algorithms this can be largely explained and compensated by considering biases arising from the reduced sensitivity of MAX-DOAS observations to higher altitudes and associated a priori assumptions.

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Section: Aerosol Optical Thicknesssupporting

confidence: 65%

Section: Participating Groups and Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Intercomparison of MAX-DOAS vertical profile retrieval algorithms: studies on field data from the CINDI-2 campaign

et al. 2021

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“…λ indicates the wavelength shift parameter. X r (λ) refers to the Raman spectrum (Chance and Spurr, 1997), and α r is the fitted coefficient to X r (λ). α j refers to the cross section of trace gas j .…”

Section: Radiance Fittingmentioning

confidence: 99%

An improved TROPOMI tropospheric HCHO retrieval over China

Liu

Chan

et al. 2020

Atmos. Meas. Tech.

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Abstract. We present an improved TROPOspheric Monitoring Instrument (TROPOMI) retrieval of formaldehyde (HCHO) over China. The new retrieval optimizes the slant column density (SCD) retrieval and air mass factor (AMF) calculation for TROPOMI observations of HCHO over China. Retrieval of HCHO differential SCDs (DSCDs) is improved using the basic optical differential spectroscopy (BOAS) technique resulting in lower noise and smaller random error, while AMFs are improved with a priori HCHO profiles from a higher resolution regional chemistry transport model. Compared to the operational product, the new TROPOMI HCHO retrieval shows better agreement with ground-based Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements in Beijing. The improvements are mainly related to the AMF calculation with more precise a priori profiles in winter. Using more precise a priori profiles in general reduces HCHO vertical column densities (VCDs) by 52.37 % (± 27.09 %) in winter. Considering the aerosol effect in AMF calculation reduces the operational product by 11.46 % (± 1.48 %) and our retrieval by 17.61 % (± 1.92 %) in winter. The improved and operational HCHO are also used to investigate the spatial–temporal characteristics of HCHO over China. The result shows that both improved and operational HCHO VCDs reach maximum in summer and minimum in winter. High HCHO VCDs mainly located over populated areas, i.e., Sichuan Basin and central and eastern China, indicate a significant contribution of anthropogenic emissions. The hotspots are more obvious on the map of the improved HCHO retrieval than the operational product. The result indicates that the improved TROPOMI HCHO retrieval is more suitable for the analysis of regional- and city-scale pollution in China.

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“…In addition, in situ measurements are only representative for a very small area surrounding the station and governed by local emissions and meteorological conditions [5,6]. Spaceborne observations provide indispensable spatial distribution information of atmospheric pollutants like NO 2 on a global scale.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method

et al. 2021

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In this paper, we present the estimation of surface NO2 concentrations over Germany using a machine learning approach. TROPOMI satellite observations of tropospheric NO2 vertical column densities (VCDs) and several meteorological parameters are used to train the neural network model for the prediction of surface NO2 concentrations. The neural network model is validated against ground-based in situ air quality monitoring network measurements and regional chemical transport model (CTM) simulations. Neural network estimation of surface NO2 concentrations show good agreement with in situ monitor data with Pearson correlation coefficient (R) of 0.80. The results also show that the machine learning approach is performing better than regional CTM simulations in predicting surface NO2 concentrations. We also performed a sensitivity analysis for each input parameter of the neural network model. The validated neural network model is then used to estimate surface NO2 concentrations over Germany from 2018 to 2020. Estimated surface NO2 concentrations are used to investigate the spatio-temporal characteristics, such as seasonal and weekly variations of NO2 in Germany. The estimated surface NO2 concentrations provide comprehensive information of NO2 spatial distribution which is very useful for exposure estimation. We estimated the annual average NO2 exposure for 2018, 2019 and 2020 is 15.53, 15.24 and 13.27 µµg/m3, respectively. While the annual average NO2 concentration of 2018, 2019 and 2020 is only 12.79, 12.60 and 11.15 µµg/m3. In addition, we used the surface NO2 data set to investigate the impacts of the coronavirus disease 2019 (COVID-19) pandemic on ambient NO2 levels in Germany. In general, 10–30% lower surface NO2 concentrations are observed in 2020 compared to 2018 and 2019, indicating the significant impacts of a series of restriction measures to reduce the spread of the virus.

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MAX-DOAS measurements of tropospheric NO<sub>2</sub> and HCHO in Munich and the comparison to OMI and TROPOMI satellite observations

Cited by 72 publications

References 87 publications

Intercomparison of MAX-DOAS vertical profile retrieval algorithms: studies on field data from the CINDI-2 campaign

Intercomparison of MAX-DOAS vertical profile retrieval algorithms: studies on field data from the CINDI-2 campaign

An improved TROPOMI tropospheric HCHO retrieval over China

Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method

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