An improved total and tropospheric NO<sub>2</sub> column retrieval for GOME-2

Abstract

Abstract. An improved algorithm for the retrieval of total and tropospheric nitrogen dioxide (NO2) columns from the Global Ozone Monitoring Experiment-2 (GOME-2) is presented. The refined retrieval will be implemented in a future version of the GOME Data Processor (GDP) as used by the EUMETSAT Satellite Application Facility on Atmospheric Composition and UV Radiation (AC-SAF). The first main improvement is the application of an extended 425–497 nm wavelength fitting window in the differential optical absorption spectroscopy (DOAS) retrieval of the NO2 slant column density, based on which initial total NO2 columns are computed using stratospheric air mass factors (AMFs). Updated absorption cross sections and a linear offset correction are used for the large fitting window. An improved slit function treatment is applied to compensate for both long-term and in-orbit drift of the GOME-2 slit function. Compared to the current operational (GDP 4.8) dataset, the use of these new features increases the NO2 columns by ∼1–3×1014 molec cm2 and reduces the slant column error by ∼24 %. In addition, the bias between GOME-2A and GOME-2B measurements is largely reduced by adopting a new level 1b data version in the DOAS retrieval. The retrieved NO2 slant columns show good consistency with the Quality Assurance for Essential Climate Variables (QA4ECV) retrieval with a good overall quality. Second, the STRatospheric Estimation Algorithm from Mainz (STREAM), which was originally developed for the TROPOspheric Monitoring Instrument (TROPOMI) instrument, was optimised for GOME-2 measurements to determine the stratospheric NO2 column density. Applied to synthetic GOME-2 data, the estimated stratospheric NO2 columns from STREAM shows good agreement with the a priori truth. An improved latitudinal correction is introduced in STREAM to reduce the biases over the subtropics. Applied to GOME-2 measurements, STREAM largely reduces the overestimation of stratospheric NO2 columns over polluted regions in the GDP 4.8 dataset. Third, the calculation of AMF applies an updated box-air-mass factor (box-AMF) look-up table (LUT) calculated using the latest version 2.7 of the Vector-LInearized Discrete Ordinate Radiative Transfer (VLIDORT) model with an increased number of reference points and vertical layers, a new GOME-2 surface albedo climatology, and improved a priori NO2 profiles obtained from the TM5-MP chemistry transport model. A large effect (mainly enhancement in summer and reduction in winter) on the retrieved tropospheric NO2 columns by more than 10 % is found over polluted regions. To evaluate the GOME-2 tropospheric NO2 columns, an end-to-end validation is performed using ground-based multiple-axis DOAS (MAXDOAS) measurements. The validation is illustrated for six stations covering urban, suburban, and background situations. Compared to the GDP 4.8 product, the new dataset presents improved agreement with the MAXDOAS measurements for all the stations.

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

“…Previous GOME-2 validation highlighted the effect of GOME-2 large pixels, and the aerosol shielding effect, leading e.g., to differences of 5% to 25% over China (Ma et al, 2013;Wu et al, 2013;Drosoglou et al, 2018). Liu et al (2019b) showed possible improvements of the GDP 4.8 product, leading to reduced discrepancies of the satellite-to-ground-10 based biases of the order of 10% to 25% for several MAX-DOAS stations.…”

“…JAMSTEC data from the MADRAS network have been used in Kanaya et al (2014) for the validation of the OMI DOMINO and NASA 30 tropospheric NO2 data. BIRA-IASB MAX-DOAS stations have been regularly used for the validation of GOME-2 GDP products from MetOp-A and MetOp-B (Valks et al, 2011;Pinardi et al, 2011;Liu et al, 2019b) as part of the AC SAF activities (Hassinnen et al, 2016; see also www.cdop.aeronomie.be/validation/validresults). Pandora datasets have also been used in satellite validation of total and tropospheric NO2 columns (Herman et al 2009;Tzortziou et al, 2014;Judd et al, 2019) and a recent study of Herman et al (2019) 35 presented an overview at 14 Pandora sites showing that NASA OMI NO2 overpass data consistently underestimate the Pandora derived NO2 amounts.…”

“…Operational products are retrieved from GOME-2 measurements in the framework of the Atmospheric Composition Satellite Application Facility AC SAF (www.acsaf.fmi.fi, formerly O3M SAF; see also Hassinnen et al, 2016). Total, tropospheric and stratospheric NO2 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).…”

“…Previous validation of GOME-2A GDP 4.8 data can be found in Valks et al (2011); Hassinnen et al 2016; Liu et al (2018b) for a few MAX-DOAS stations, and results of regular validation exercises can be found on 5 www.cdop.aeronomie.be/valid-results. Satellite-to-satellite comparisons of the GOME-2A QA4ECV data has been performed by Zara et al (2018), Lorente et al (2018) and Liu et al (2019b). Previous GOME-2 validation highlighted the effect of GOME-2 large pixels, and the aerosol shielding effect, leading e.g., to differences of 5% to 25% over China (Ma et al, 2013;Wu et al, 2013;Drosoglou et al, 2018).…”

An improved air mass factor calculation for NO<sub>2</sub> measurements from GOME-2

“…The presence of clouds is taken into account in the NO 2 AMF calculation using cloud parameters based on the Optical Cloud Recognition Algorithm (OCRA) and the Retrieval Of Cloud Information using Neural Networks (ROCINN) algorithms (Loyola et al, 2007(Loyola et al, , 2011. OCRA/ROCINN has been applied in the operational retrieval of trace gases from GOME (Van Roozendael et al, 2006), GOME-2 (Valks et al, 2011;Hao et al, 2014;Liu et al, 2019b), and TROPOMI (Heue et al, 2016;Theys et al, 2017;Loyola, in preparation). The latest version of OCRA/ROCINN (Lutz et al, 2016;Loyola et al, 2018) provides two sets of cloud products: one treats clouds as ideal Lambertian reflectors in a "Clouds-as-Reflecting-Boundaries" (CRB) model, and the second treats clouds as uniform layers of water droplets in a "Clouds-As-Layers" (CAL) model.…”