Is a scaling factor required to obtain closure between measured and modelled atmospheric O&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt; absorptions? – A case study for two days during the MADCAT campaign

Wagner, Thomas; Beirle, Steffen; Benavent, Nuria; Bösch, Tim; Chan, Kai‐Ming; Donner, Sebastian; Dörner, Steffen; Fayt, C.; Frieß, Udo; García-Nieto, David; Gielen, C.; Gonzalez-Bartolomé, David; Gómez, Laura; Hendrick, F.; Henzing, Bas; Jin, Jun Li; Lampel, Johannes; Ma, Jianzhong; Mies, Kornelia; Navarro, M.; Peters, Enno; Pinardi, Gaia; Puentedura, Olga; Puķīte, Jānis; Remmers, Julia; Richter, Andreas; Saiz‐Lopez, Alfonso; Shaiganfar, Reza; Sihler, Holger; Roozendaël, Michel Van; Wang, Yang; Yela, Margarita

doi:10.5194/amt-2018-238

Cited by 4 publications

(10 citation statements)

References 26 publications

(31 reference statements)

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“…This poor performance is related to a general mismatch between modeled and measured dSCDs, as has also been found for other campaigns in the past (see Wagner et al, 2018, and references therein). We thus perform another MAPA retrieval with an O 4 SF of f = 0.8 (Fig.…”

Section: Aerosolssupporting

confidence: 61%

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The Mainz profile algorithm (MAPA)

et al. 2019

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Abstract. The Mainz profile algorithm (MAPA) derives vertical profiles of aerosol extinction and trace gas concentrations from MAX-DOAS measurements of slant column densities under multiple elevation angles. This paper presents (a) a detailed description of the MAPA (v0.98), (b) results for the CINDI-2 campaign, and (c) sensitivity studies on the impact of a priori assumptions such as flag thresholds. Like previous profile retrieval schemes developed at MPIC, MAPA is based on a profile parameterization combining box profiles, which also might be lifted, and exponential profiles. But in contrast to previous inversion schemes based on least-square fits, MAPA follows a Monte Carlo approach for deriving those profile parameters yielding best match to the MAX-DOAS observations. This is much faster and directly provides physically meaningful distributions of profile parameters. In addition, MAPA includes an elaborated flagging scheme for the identification of questionable or dubious results. The AODs derived with MAPA for the CINDI-2 campaign show good agreement with AERONET if a scaling factor of 0.8 is applied for O4, and the respective NO2 and HCHO surface mixing ratios match those derived from coincident long-path DOAS measurements. MAPA results are robust with respect to modifications of the a priori MAPA settings within plausible limits.

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Section: Aerosolssupporting

confidence: 61%

“…Ortega et al, 2016) do not see a need for a SF. An in-depth discussion of the O 4 SF is provided in Wagner et al (2018).…”

Section: Scaling Of O 4 Dscdsmentioning

confidence: 99%

The Mainz profile algorithm (MAPA)

et al. 2019

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“…In order to estimate temp , we compared the O 4 DSCDs retrieved using absorption cross sections measured at 253 K and 293 K to those retrieved with cross section measured at 273 K. The comparison shows that the O 4 DSCDs are underestimated by 5.1% at the UV band and 2.5% at the VIS band when the effective temperature is 293 K. On the other hand, the O 4 DSCDs 25 are overestimated by 6.9% at the UV band and 3.9% at the VIS band when the effective temperature is 253 K. These systematic errors are almost constant, regardless of the observation geometry. Between 253 and 293 K, the average variation rate of O 4 DSCD at UV band is 0.3% / K. This result is in general agreement with Wagner et al (2018). They found that with the fitting window of 352 -387 nm, O 4 DSCDs retrieved using the cross section at 203 K are reported to be 30% smaller than those retrieved using the cross section at 293 K, i.e.…”

Section: Error Caused By Temperaturesupporting

confidence: 84%

“…As discussed in Section 3.1, O 4 absorption cross section measured at 273 K is used in the DOAS fitting. However, the effective temperature of the MAX-DOAS measurements could be signifi-20 cantly different from 273 K. Previous studies show that O 4 absorption has a strong and systematic dependence on temperature (Thalman and Volkamer, 2013;Wagner et al, 2018).…”

Section: Error Caused By Temperaturementioning

confidence: 95%

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A MAX-DOAS aerosol profile retrieval algorithm for high altitude measurements: application to measurements at Schneefernerhaus (UFS), Germany

Wang

Chan

Heue

et al. 2019

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Abstract. We present a new aerosol profile retrieval algorithm for Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements at high altitude sites. The study is based on the long-term measurement (February 2012 to February 2016) at the Environmental Research Station Schneefernerhaus (UFS), Germany, which is located near the summit of Zugspitze, at an altitude of 2,650 m. Due to the low signal to noise ratio, commonly used MAX-DOAS retrieval algorithms based on the optimal estimation method are not suitable for the retrieval of high altitude measurements. We developed a new retrieval algorithm using an O4 differential slant column density (DSCD) look-up table. The look-up table consists of simulated O4 DSCDs corresponding to numerous possible aerosol profiles. The sensitivities of O4 absorption to several parameters were investigated for the design and parameterization of the look-up table. In the retrieval, the simulated O4 DSCDs for each possible profile are derived by interpolating the look-up table to the observation geometries. The cost functions are calculated for each aerosol profile in the look-up table based on the simulated O4 DSCDs, the O4 DSCD observations as well as the measurement uncertainties. Valid profiles are selected from all the possible profiles according to the cost function, and the optimal solution is defined as the weighted mean of all valid profiles. A comprehensive error analysis is performed to better estimate the total uncertainty. Based on the assumption that the look-up table covers all the possible profiles under clear sky conditions, we determined a set of O4 DSCD scaling factors for different elevation angles and wavelengths. The dependence of the scaling factors on elevation angle might be partly related to the specific properties of the high altitude station, e.g. the highly structured topography, horizontal gradients of the aerosol extinction and the systematic dependence of the surface albedo on altitude. The retrieved aerosol optical depths (AODs) are compared to coincident and co-located sun photometer observations. High correlation coefficients of 0.733 and 0.798 are found for measurements at 360 and 477 nm, respectively. However, especially in summer the sun photometer AODs are systematically higher than the MAX-DOAS retrievals by a factor of 2. The discrepancy might be related to the limited measurement range of the MAX-DOAS, and is probably also related to the decreased sensitivity of the MAX-DOAS measurements at higher altitudes. Our results also show maximum AOD and maximum Ångström exponent in summer which is consistent with observations from an AERONET station located ~ 43 km of the MAX-DOAS.

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MAX-DOAS measurements of tropospheric NO2 and HCHO in Nanjing and the comparison to OMI observations

Chan

Wang

Ding

et al. 2019

Preprint

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Abstract. In this paper, we present long term observations of atmospheric nitrogen dioxide (NO2) and formaldehyde (HCHO) in Nanjing using a Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) instrument. Ground based MAX-DOAS measurements were performed from April 2013 to February 2017. The MAX-DOAS measurements of NO2 and HCHO vertical column densities (VCDs) are used to validate OMI satellite observations over Nanjing. The comparison shows that the OMI observations of NO2 correlate well with the MAX-DOAS data with Pearson correlation coefficient (R) of 0.91. However, OMI observations are on average a factor of 3 lower than the MAX-DOAS measurements. Replacing the a priori NO2 profiles by the MAX-DOAS profiles in the OMI NO2 VCD retrieval would increase the OMI NO2 VCDs by ~&#8201;30&#8201;% with correlation nearly unchanged. The comparison result of MAX-DOAS and OMI observations of HCHO VCD shows a good agreement with R of 0.75 and the slope of the regression line is 0.99. We developed a new technique to assemble the source contribution map using backward trajectory analysis. The age weighted backward propagation approach is applied to the MAX-DOAS measurements of NO2 and HCHO to reconstruct the spatial distribution of NO2 and HCHO over the Yangtze River Delta during summer and winter time. The reconstructed NO2 fields show a distinct agreement with OMI satellite observations. However, due to the short atmospheric lifetime of HCHO, the backward propagated HCHO data does not show a strong spatial correlation with the OMI HCHO observations. The result shows the MAX-DOAS measurements are sensitive to the air pollution transportation in the Yangtze River Delta, indicating the air quality in Nanjing is significantly influenced by regional transportation of air pollutants. The MAX-DOAS data are also used to evaluate the effectiveness of air pollution control measures implemented during the Youth Olympic Games 2014. The MAX-DOAS data show a significant reduction of ambient aerosol, NO2 and HCHO (30&#8201;%&#8211;50&#8201;%) during the Youth Olympic Games. Our results provide a better understanding of the transportation and sources of pollutants in over the Yangtze River Delta as well as the effect of emission control measures during large international event, which are important for the future design of air pollution control policies.

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Is a scaling factor required to obtain closure between measured and modelled atmospheric O<sub>4</sub> absorptions? – A case study for two days during the MADCAT campaign

Cited by 4 publications

References 26 publications

The Mainz profile algorithm (MAPA)

The Mainz profile algorithm (MAPA)

A MAX-DOAS aerosol profile retrieval algorithm for high altitude measurements: application to measurements at Schneefernerhaus (UFS), Germany

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

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Is a scaling factor required to obtain closure between measured and modelled atmospheric O&lt;sub&gt;4&lt;/sub&gt; absorptions? – A case study for two days during the MADCAT campaign

Cited by 4 publications

References 26 publications

The Mainz profile algorithm (MAPA)

The Mainz profile algorithm (MAPA)

A MAX-DOAS aerosol profile retrieval algorithm for high altitude measurements: application to measurements at Schneefernerhaus (UFS), Germany

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

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Is a scaling factor required to obtain closure between measured and modelled atmospheric O<sub>4</sub> absorptions? – A case study for two days during the MADCAT campaign

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