An improved air mass factor calculation for nitrogen dioxide measurements from the Global Ozone Monitoring Experiment-2 (GOME-2)

Abstract

Abstract. An improved tropospheric nitrogen dioxide (NO2) retrieval algorithm from the Global Ozone Monitoring Experiment-2 (GOME-2) instrument based on air mass factor (AMF) calculations performed with more realistic model parameters is presented. The viewing angle dependency of surface albedo is taken into account by improving the GOME-2 Lambertian-equivalent reflectivity (LER) climatology with a directionally dependent LER (DLER) dataset over land and an ocean surface albedo parameterisation over water. A priori NO2 profiles with higher spatial and temporal resolutions are obtained from the IFS (CB05BASCOE) chemistry transport model based on recent emission inventories. A more realistic cloud treatment is provided by a clouds-as-layers (CAL) approach, which treats the clouds as uniform layers of water droplets, instead of the current clouds-as-reflecting-boundaries (CRB) model, which assumes that the clouds are Lambertian reflectors. On average, improvements in the AMF calculation affect the tropospheric NO2 columns by ±15 % in winter and ±5 % in summer over largely polluted regions. In addition, the impact of aerosols on our tropospheric NO2 retrieval is investigated by comparing the concurrent retrievals based on ground-based aerosol measurements (explicit aerosol correction) and the aerosol-induced cloud parameters (implicit aerosol correction). Compared with the implicit aerosol correction utilising the CRB cloud parameters, the use of the CAL approach reduces the AMF errors by more than 10 %. Finally, to evaluate the improved GOME-2 tropospheric NO2 columns, a validation is performed using ground-based multi-axis differential optical absorption spectroscopy (MAXDOAS) measurements at different BIRA-IASB stations. At the suburban Xianghe station, the improved tropospheric NO2 dataset shows better agreement with coincident ground-based measurements with a correlation coefficient of 0.94.

Validation of tropospheric NO<sub>2</sub> column measurements of GOME-2A and OMI using MAX-DOAS and direct sun network observations

“…Different data products have been generated for each satellite instrument, using different assumptions for each of the three aforementioned steps (see Boersma et al, 2004;Richter et al, 2011;Lin et al, 2014;Bucsela et al, 2013;Lamsal et al, 2014;van Geffen et al, 2015;Krotkov et al, 2016;Lorente et al, 2017;Liu et al, 2019a. In addition to instrument-specific differences, structural uncertainties arising from the application of different retrieval methodologies to the same satellite observations (sometimes also called forward model uncertainties) can introduce differences in the retrieved tropospheric NO 2 columns (VCD tropo ) of 10 %-50 % (e.g., van Noije et al, 2006;Lorente et al, 2017;Zara et al, 2018). SCD structural uncertainties generally do not exceed 1×10 15 molecules cm −2 , while the AMF calculation leads to more significant uncertainties (Boersma et al, 2004), which can be separated into implementation differences (when dif-6144 G. Pinardi et al: GOME-2A and OMI tropospheric NO 2 validation ferent groups use identical ancillary data for the calculation of tropospheric NO 2 AMFs) of about 6 % and structural differences, due to ancillary data selection, which can reach 31 %-42 % (Lorente et al, 2017).…”

“…In addition to instrument-specific differences, structural uncertainties arising from the application of different retrieval methodologies to the same satellite observations (sometimes also called forward model uncertainties) can introduce differences in the retrieved tropospheric NO 2 columns (VCD tropo ) of 10 %-50 % (e.g., van Noije et al, 2006;Lorente et al, 2017;Zara et al, 2018). SCD structural uncertainties generally do not exceed 1×10 15 molecules cm −2 , while the AMF calculation leads to more significant uncertainties (Boersma et al, 2004), which can be separated into implementation differences (when dif-6144 G. Pinardi et al: GOME-2A and OMI tropospheric NO 2 validation ferent groups use identical ancillary data for the calculation of tropospheric NO 2 AMFs) of about 6 % and structural differences, due to ancillary data selection, which can reach 31 %-42 % (Lorente et al, 2017). The uncertainty in separating the stratospheric and tropospheric columns is about 0.5 × 10 15 molecules cm −2 (Dirksen et al, 2011;Lorente et al, 2017).…”

“…SCD structural uncertainties generally do not exceed 1×10 15 molecules cm −2 , while the AMF calculation leads to more significant uncertainties (Boersma et al, 2004), which can be separated into implementation differences (when dif-6144 G. Pinardi et al: GOME-2A and OMI tropospheric NO 2 validation ferent groups use identical ancillary data for the calculation of tropospheric NO 2 AMFs) of about 6 % and structural differences, due to ancillary data selection, which can reach 31 %-42 % (Lorente et al, 2017). The uncertainty in separating the stratospheric and tropospheric columns is about 0.5 × 10 15 molecules cm −2 (Dirksen et al, 2011;Lorente et al, 2017).…”

“…Total, tropospheric and stratospheric NO 2 columns are operationally retrieved with the GOME Data Processor (GDP, and a description of this algorithm can be found in Valks et al (2011) and Liu et al (2019b). Within the QA4ECV (Quality Assurance for Essential Climate Variables) project, a coherent offline NO 2 dataset has been created for GOME, SCIAMACHY, GOME-2A and OMI (Boersma et al, 2018;Zara et al, 2018;Lorente et al, 2017), and comparisons with this dataset are also included at the end of this study. Table 1 summarizes the main retrieval steps for the various tropospheric NO 2 products considered here.…”

“…Satellite validation also contributes to the improvement of retrieval algorithms through investigation of the accuracy of the data products and their sensitivity to retrieval parameter choices. Tropospheric satellite data products depend on various sources of ancillary data, e.g., a priori vertical distribution of the absorbing and scattering species, surface albedo and information on clouds and aerosols (Boersma et al, 2004;Lin et al, 2015;Lorente et al, 2017;Liu et al, 2019a). In the case of NO 2 , separation between stratospheric and tropospheric contributions is an additional source of complexity in the retrieval, and there is considerable debate on the importance of the role of free tropospheric (background) NO 2 in the retrieval process (Jiang et al, 2018;Silvern et al, 2019).…”