An improved tropospheric NO<sub>2</sub> column retrieval algorithm for TROPOMI over Europe

Abstract

Abstract. Launched in October 2017, the TROPOspheric Monitoring Instrument (TROPOMI) aboard Sentinel-5 Precursor provides the potential to monitor air quality over point sources across the globe with a spatial resolution as high as 5.5 km × 3.5 km (7 km × 3.5 km before 6 August 2019). The nitrogen dioxide (NO2) retrieval algorithm for the TROPOMI instrument consists of three steps: the spectral fitting of the slant column, the separation of stratospheric and tropospheric contributions, and the conversion of the slant column to a vertical column using an air mass factor (AMF) calculation. In this work, an improved tropospheric NO2 retrieval algorithm from TROPOMI measurements over Europe is presented. The stratospheric estimation is implemented using the STRatospheric Estimation Algorithm from Mainz (STREAM), which was developed as a verification algorithm for TROPOMI and does not require chemistry transport model data as input. A directionally dependent STREAM (DSTREAM) is developed to correct for the dependency of the stratospheric NO2 on the viewing geometry by up to 2 × 1014 molec/cm2. Applied to synthetic TROPOMI data, the uncertainty in the stratospheric column is 3.5 × 1014 molec/cm2 for polluted conditions. Applied to actual measurements, the smooth variation of stratospheric NO2 at low latitudes is conserved, and stronger stratospheric variation at higher latitudes are captured. For AMF calculation, the climatological surface albedo data is replaced by geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) obtained directly from TROPOMI measurements with a high spatial resolution. Mesoscale-resolution a priori NO2 profiles are obtained from the regional POLYPHEMUS/DLR chemistry transport model with the TNO-MACC emission inventory. Based on the latest TROPOMI operational cloud parameters, a more realistic cloud treatment is provided by a clouds-as-layers (CAL) model, which treats the clouds as uniform layers of water droplets, instead of the clouds-as-reflecting-boundaries (CRB) model, in which clouds are simplified as Lambertian reflectors. For the error analysis, the tropospheric AMF uncertainty, which is the largest source of NO2 uncertainty for polluted scenarios, ranges between 20 % and 50 %, leading to a total uncertainty in the tropospheric NO2 column in the 30–60 % range. From a validation performed with ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements, the improved tropospheric NO2 data shows good correlations for nine European urban/suburban stations with an average correlation coefficient of 0.78. The implementation of the algorithm improvements leads to a decrease of the relative difference from −55.3 % to −34.7 % on average.

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

“…STREAM is a modified reference sector method that does not require additional model input, and can be considered as a complement to the operational stratospheric correction based on data assimilation. A more detailed description of the tropospheric NO 2 retrieval algorithm can be found in Liu et al [40]. The retrieved tropospheric NO 2 columns show good agreement with ground-based Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements with mean biases of −39% over Munich and −26 to −46% over polluted regions over the globe [40].…”

“…To obtain a balance between having sufficient measurements while minimizing the influences of cloud contaminated data, only satellite data with cloud radiance fraction (CRF) of less than 50% are considered in the analysis. Measurement uncertainties related to cloud contamination are typically within 15% for observations with CRF smaller than 0.5 over polluted regions [40]. Therefore, the threshold of CRF of 0.5 is selected in this study.…”

Comparing Sentinel-5P TROPOMI NO2 column observations with the CAMS-regional air quality ensemble

“…This latter approach removes the TROPOMI a priori dependence from the relative comparison and it has been shown in the recent quarterly validation report for the TROPOMI products (Lambert et al, 2021) that the (negative) "bias estimate is reduced by up to 20% 495 when the MAX-DOAS profile data are smoothed vertically using S5P averaging kernels", indicating a significant impact of the profile shape due to the different vertical sensitivity profiles of MAX-DOAS versus the satellite retrievals. It was also shown in Liu et al (2021) that using the S5P averaging kernel reduced the bias of about 16% in Munich for their regional TROPOMI product. It is therefore likely that application of the satellite averaging kernel would remove part of the residual bias of S5P-RG.…”

“…All these aspects need to be accounted for. In particular, the spatial resolution of the a priori is recognised as an important factor to improve the tropospheric column, and several groups have developed regional satellite data products for China, Europe and the USA based on high-resolution regional air-quality modelling systems (Lin et al, 2014;Liu et al, 2021Liu et al, , 2020Zhou et al, 2009;McLinden et al, 2014;Griffin et al, 2019;Laughner et al, 2019).…”

Lightning‐Produced Nitrogen Oxides Per Flash Length Obtained by Using TROPOMI Observations and the Ebro Lightning Mapping Array

“…In this work, we investigate the relationship between LNO x , lightning flash length and lightning flash rate. We used the Deutsches Zentrum für Luft-und Raumfahrt (DLR) TROPOMI operational cloud and TROPOMI research NO 2 products, also known as TROP-DLR (Liu et al, 2021;Loyola et al, 2018;Pérez-Invernón et al, 2022), combined with lightning measurements from the Ebro LMA (ELMA) (López et al, 2017;Pineda et al, 2016;van der Velde & Montanyà, 2013) and from the ENGLN (Lapierre et al, 2020;Zhu et al, 2017) for 16 cases. The ELMA system covers the Ebro Valley and the eastern coast of the Iberian Peninsula, an area with a high occurrence of thunderstorms during the end of the summer.…”