Nitrogen dioxide decline and rebound observed by GOME-2 and TROPOMI during COVID-19 pandemic

Since its first confirmed case in December 2019, coronavirus disease 2019 (COVID-19) has become a worldwide pandemic with more than 90 million confirmed cases by January 2021. Countries around the world have enforced lockdown measures to prevent the spread of the virus, introducing a temporal change of air pollutants such as nitrogen dioxide (NO2) that are strongly related to transportation, industry, and energy. In this study, NO2 variations over regions with strong responses to COVID-19 are analysed using datasets from the Global Ozone Monitoring Experiment-2 (GOME-2) sensor aboard the EUMETSAT Metop satellites and TROPOspheric Monitoring Instrument (TROPOMI) aboard the EU/ESA Sentinel-5 Precursor satellite. The global GOME-2 and TROPOMI NO2 datasets are generated at the German Aerospace Center (DLR) using harmonized retrieval algorithms; potential influences of the long-term trend and seasonal cycle, as well as the short-term meteorological variation, are taken into account statistically. We present the application of the GOME-2 data to analyze the lockdown-related NO2 variations for morning conditions. Consistent NO2 variations are observed for the GOME-2 measurements and the early afternoon TROPOMI data: regions with strong social responses to COVID-19 in Asia, Europe, North America, and South America show strong NO2 reductions of 30–50% on average due to restriction of social and economic activities, followed by a gradual rebound with lifted restriction measures

Trends of tropical tropospheric ozone from 20 years of European satellite measurements and perspectives for the Sentinel-5 Precursor

In preparation of the TROPOMI/S5P launch in early 2017, a tropospheric ozone retrieval based on the convective cloud differential method was developed. For intensive tests we applied the algorithm to the total ozone columns and cloud data of the satellite instruments GOME, SCIAMACHY, OMI, GOME-2A and GOME-2B. Thereby a time series of 20 years (1995–2015) of tropospheric column ozone was generated. To have a consistent total ozone data set for all sensors, one common retrieval algorithm, namely GODFITv3, was applied and the L1 reflectances were also soft calibrated. The total ozone columns and the cloud data were input into the tropospheric ozone retrieval. However, the tropical tropospheric column ozone (TCO) for the individual instruments still showed small differences and, therefore, we harmonised the data set. For this purpose, a multilinear function was fitted to the averaged difference between SCIAMACHY's TCO and those from the other sensors. The original TCO was corrected by the fitted offset. GOME-2B data were corrected relative to the harmonised data from OMI and GOME-2A. The harmonisation leads to a better agreement between the different instruments. Also, a direct comparison of the TCO in the overlapping periods proves that GOME-2A agrees much better with SCIAMACHY after the harmonisation. The improvements for OMI were small. Based on the harmonised observations, we created a merged data product, containing the TCO from July 1995 to December 2015. A first application of this 20-year record is a trend analysis. The tropical trend is 0.7 ± 0.12 DU decade−1. Regionally the trends reach up to 1.8 DU decade−1 like on the African Atlantic coast, while over the western Pacific the tropospheric ozone declined over the last 20 years with up to 0.8 DU decade−1. The tropical tropospheric data record will be extended in the future with the TROPOMI/S5P data, where the TCO is part of the operational products

Two decades of homogenized satellite ozone measurements for climate services

Since the launch of GOME onboard ERS-2 in 1995 total and tropospheric ozone have been derived from European satellite instruments. In the framework of the ESA CCI and the EU ECMWF C3S projects, BIRA generates total ozone products from the satellite sensors GOME, SCIAMACHY, OMI, and GOME-2 using the GODFIT algorithm and DLR is responsible for harmonizing the total column data from all these sensors and generating a merged product, which encompasses more than two decades of global total ozone observations. Additionally, tropospheric ozone columns form the European sensors are generated by DLR using the convective cloud differential algorithm. Total and tropospheric ozone from GOME-2 onboard MetOp-A and -B are operational products from the EUMETSAT AC-SAF and within the ESA CCI project the tropical tropospheric ozone products from GOME, SCIAMACHY, OMI, and GOME-2 were harmonized and a merged data product was delivered and has been updated regularly. On a global scale a slight increase in total ozone columns is observed over the years since 1995 until today indicating that the total ozone starts to emerge into the expected recovery phase. Tropospheric data from the last 22 years show a slightly increasing trend with strong regional variations especially in the tropical eastern Pacific and Atlantic Ocean. These unique ozone datasets will be extended during the next two decades with measurements from the EU Copernicus missions Sentinel-5 Precursors (successfully launched in October 2017) and the future Sentinel-4 and Sentinel-5 missions

Nonlinear operators on graphs via stacks

International audienceWe consider a framework for nonlinear operators on functions evaluated on graphs via stacks of level sets. We investigate a family of transformations on functions evaluated on graph which includes adaptive flat and non-flat erosions and dilations in the sense of mathematical morphology. Additionally, the connection to mean motion curvature on graphs is noted. Proposed operators are illustrated in the cases of functions on graphs, textured meshes and graphs of images

Comparison of Cloud Parameters from GOME-2 and Assessment of Cloud Impact on Tropospheric NO2 and HCHO Retrievals

In recent decades, there has been an increasing interest in making use of satellite measurements for identifying trends in atmospheric composition and climate. Instruments like GOME-2 and TROPOMI are dedicated to air-quality and global trace gas monitoring. For the accurate retrieval of columnar information of the trace gases, cloud correction is necessary. This work is meant to examine the quality of the GOME-2 operational cloud product from AC SAF and to propose enhancements of the current dataset to improve the retrieval of the NO2 and HCHO tropospheric gases

A tropospheric NO2 research product from TROPOMI for air quality applications in Europe

This study focuses on a tropospheric NO₂ research product from TROPOMI measurements over Europe based on an improved retrieval algorithm. We present an overview of the DLR NO₂ algorithm and validation with ground-based measurements. In addition, the use of TROPOMI tropospheric NO₂ columns for air quality purposes in Europe will be discussed. The DLR NO₂ retrieval algorithm for TROPOMI consists of mainly three steps: (1) the spectral fitting of the slant column based on the differential optical absorption spectroscopy (DOAS) method, (2) the separation of stratospheric and tropospheric contributions, and (3) the conversion of the slant column to a vertical column using an air mass factor (AMF) calculation. To calculate the NO₂ slant columns, a 405-465 nm fitting window is applied in the DOAS fit for consistency with other NO₂ retrievals from OMI and TROPOMI. Absorption cross-sections of interfering species and a linear intensity offset correction are applied. The stratospheric NO₂ columns are estimated using a directionally dependent STRatospheric Estimation Algorithm from Mainz (DSTREAM) method to correct for the dependency of the stratospheric NO₂ on the viewing geometry. For AMF computation, the climatological OMI surface albedo database is replaced by the geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) and directionally dependent (DLER) data obtained from TROPOMI measurements with higher spatial resolution. As surface albedo is an important parameter for accurate retrieval of trace gas columns, the effect of surface albedo in TROPOMI NO₂ retrieval was investigated by comparing results applying different surface albedo datasets. Mesoscale-resolution a priori NO₂ profiles obtained from the regional chemistry transport model POLYPHEMUS/DLR and LOTOS-EUROS are used. The cloud correction in this TROPOMI NO₂ retrieval is improved using the Clouds-As-Layers (CAL) model from the ROCINN cloud algorithm which is more representative of the real situation than the Clouds-As-Reflecting-Boundaries (CRB) model. Validation of the TROPOMI tropospheric NO₂ columns is performed by comparisons with ground-based MAX-DOAS measurements at nine European stations with urban/suburban conditions. The improved DLR tropospheric NO₂ product shows a similar seasonal variation and good agreement with MAX-DOAS measurements. In particular, the retrievals applying a priori NO₂ profiles from the regional model with a high spatial resolution and recent emission inventory improve an underestimation in TROPOMI tropospheric NO₂ columns in polluted urban areas. Finally, we present the use of the TROPOMI tropospheric NO₂ research product in the regional POLYPHEMUS and LOTOS-EUROS chemistry transport models to analyse the effect of traffic emission on air quality in Germany with the framework of the S-VELD project

TROPOMI/S5P total ozone column data: global ground-based validation and consistency with other satellite missions

In this work, the TROPOMI near real time (NRTI) and offline (OFFL) total ozone column (TOC) products are presented and compared to daily ground-based quality-assured Brewer and Dobson TOC measurements deposited in the World Ozone and Ultraviolet Radiation Data Centre (WOUDC). Additional comparisons to individual Brewer measurements from the Canadian Brewer Network and the European Brewer Network (Eubrewnet) are performed. Furthermore, twilight zenith-sky measurements obtained with ZSL-DOAS (Zenith Scattered Light Differential Optical Absorption Spectroscopy) instruments, which form part of the SAOZ network (Système d'Analyse par Observation Zénitale), are used for the validation. The quality of the TROPOMI TOC data is evaluated in terms of the influence of location, solar zenith angle, viewing angle, season, effective temperature, surface albedo and clouds. For this purpose, globally distributed ground-based measurements have been utilized as the background truth. The overall statistical analysis of the global comparison shows that the mean bias and the mean standard deviation of the percentage difference between TROPOMI and ground-based TOC is within 0 –1.5 % and 2.5 %–4.5 %, respectively. The mean bias that results from the comparisons is well within the S5P product requirements, while the mean standard deviation is very close to those limits, especially considering that the statistics shown here originate both from the satellite and the ground-based measurements.This research has been supported by the European Space Agency “Preparation and Operations of the Mission Performance Centre (MPC) for the Copernicus Sentinel-5 Precursor Satellite” (contract no. 4000117151/16/1-LG)

Intercomparison of Sentinel-5P TROPOMI cloud products for tropospheric trace gas retrievals

Clouds have a strong impact on satellite measurements of tropospheric trace gases in the ultraviolet, visible, and near-infrared spectral ranges from space. Therefore, trace gas retrievals rely on information on cloud fraction, cloud albedo, and cloud height from cloud products. In this study, the cloud parameters from different cloud retrieval algorithms for the Sentinel-5 Precursor (S5P) TROPOspheric Monitoring Instrument (TROPOMI) are compared: the Optical Cloud Recognition Algorithm (OCRA) a priori cloud fraction, the Retrieval Of Cloud Information using Neural Networks (ROCINN) CAL (Clouds-As-Layers) cloud fraction and cloud top and base height, the ROCINN CRB (Clouds-as-Reflecting-Boundaries) cloud fraction and cloud height, the Fast Retrieval Scheme for Clouds from the Oxygen A-band (FRESCO) cloud fraction, the interpolated FRESCO cloud height from the TROPOMI NO2 product, the cloud fraction from the NO2 fitting window, the O2–O2 cloud fraction and cloud height, the Mainz Iterative Cloud Retrieval Utilities (MICRU) cloud fraction, and the Visible Infrared Imaging Radiometer Suite (VIIRS) cloud fraction. Two different versions of the TROPOMI cloud products OCRA/ROCINN, FRESCO, and the TROPOMI NO2 product are included in the comparisons (processor version 1.x and 2.x). Overall, the cloud parameters retrieved by the different algorithms show qualitative consistency in version 1.x and good agreement in version 2.x with the exception of the VIIRS cloud fraction, which cannot be directly compared to the other data. Differences between the cloud retrievals are found especially for small cloud heights with a cloud fraction threshold of 0.2, i.e. clouds that are particularly relevant for tropospheric trace gas retrievals. The cloud fractions of the different version 2 cloud products primarily differ over snow- and ice-covered pixels and scenes with sun glint, for which only MICRU includes an explicit treatment. All cloud parameters show some systematic problems related to the across-track dependence, where larger values are found at the edges of the satellite view. The consistency between the cloud parameters from different algorithms depends strongly on how the data are filtered for the comparison, for example, what quality value is used or whether snow- and ice-covered pixels are excluded from the analysis. In summary, clear differences were found between the results of various algorithms, but these differences are reduced in the most recent versions of the cloud data