75 results on '"Steven Compernolle"'
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2. Removing Prior Information from Remotely Sensed Atmospheric Profiles by Wiener Deconvolution Based on the Complete Data Fusion Framework
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Arno Keppens, Steven Compernolle, Daan Hubert, Tijl Verhoelst, José Granville, and Jean-Christopher Lambert
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atmospheric retrieval ,prior information ,Wiener deconvolution ,complete data fusion ,Science - Abstract
A method is developed that removes a priori information from remotely sensed atmospheric state profiles. This consists of a Wiener deconvolution, whereby the required cost function is obtained from the complete data fusion framework. Asserting that the deconvoluted averaging kernel matrix has to equal the unit matrix, results in an iterative process for determining a profile-specific deconvolution matrix. In contrast with previous deconvolution approaches, only the dimensions of this matrix have to be fixed beforehand, while the iteration process optimizes the vertical grid. This method is applied to ozone profile retrievals from simulated and real measurements co-located with the Izaña ground station. Individual profile deconvolutions yield strong outliers, including negative ozone concentration values, but their spatiotemporal averaging results in prior-free atmospheric state representations that correspond to the initial retrievals within their uncertainty. Averaging deconvoluted profiles thus looks like a viable alternative in the creation of harmonized Level-3 data, avoiding vertical smoothing difference errors and the difficulties that arise with averaged averaging kernels.
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- 2022
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3. Quality Assurance Framework Development Based on Six New ECV Data Products to Enhance User Confidence for Climate Applications
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Joanne Nightingale, Klaas Folkert Boersma, Jan-Peter Muller, Steven Compernolle, Jean-Christopher Lambert, Simon Blessing, Ralf Giering, Nadine Gobron, Isabelle De Smedt, Pierre Coheur, Maya George, Jörg Schulz, and Alexander Wood
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essential climate variables ,climate data records ,earth observation satellites ,quality assurance ,traceability ,user requirements ,climate applications ,surface albedo ,LAI ,FAPAR ,NO2 ,HCHO ,CO ,Science - Abstract
Data from Earth observation (EO) satellites are increasingly used to monitor the environment, understand variability and change, inform evaluations of climate model forecasts, and manage natural resources. Policymakers are progressively relying on the information derived from these datasets to make decisions on mitigating and adapting to climate change. These decisions should be evidence based, which requires confidence in derived products, as well as the reference measurements used to calibrate, validate, or inform product development. In support of the European Union’s Earth Observation Programmes Copernicus Climate Change Service (C3S), the Quality Assurance for Essential Climate Variables (QA4ECV) project fulfilled a gap in the delivery of climate quality satellite-derived datasets, by prototyping a generic system for the implementation and evaluation of quality assurance (QA) measures for satellite-derived ECV climate data record products. The project demonstrated the QA system on six new long-term, climate quality ECV data records for surface albedo, leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), nitrogen dioxide (NO2), formaldehyde (HCHO), and carbon monoxide (CO). The provision of standardised QA information provides data users with evidence-based confidence in the products and enables judgement on the fitness-for-purpose of various ECV data products and their specific applications.
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- 2018
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4. Monitoring Belgian air quality with LEO and GEO atmospheric composition data
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Tijl Verhoelst, Steven Compernolle, Jean-Christopher Lambert, Frans Fierens, and Charlotte Vanpoucke
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Air Quality (AQ) monitoring in Belgium has hitherto been relying mostly on in-situ measurements of surface concentration, with geographical gaps between observations filled in with numerical modelling ingesting (proxies for) bottom-up emission estimates. However, a new generation of satellite sounders on sun-synchronous Low Earth Orbits (LEO) – like the Copernicus Sentinel-5(p) series – performs now daily global mapping of atmospheric composition down to the 3-km scale. Soon this daily global mapping will be complemented with geostationary instruments (GEO, e.g. Sentinel-4) observing the diurnal cycle in trace gas concentrations, although over the limited geographical area accessible from a geostationary viewpoint. This new constellation of satellite sounders is built to support detailed monitoring of AQ on the different relevant scales: from point-like emissions to intercontinental transport, and from city-level to international regulations established by public authorities to manage AQ in their area of responsibility. Nevertheless, uptake of these new satellite AQ data by the various Belgian stakeholders is not guaranteed. Indeed, to realize the full complementary impact of this constellation of LEO and GEO satellites, i.e. to make their observations fit-for-purpose for air quality applications at the different scales, several challenges need to be addressed. These include (1) the need to enhance to sub-city scales the resolution of satellite data to make them better suited for the monitoring of e.g. the impact of the Low Emission Zones enforced in several European cities, (2) to characterize the non-trivial relation between the column amount of the pollutant measured by a satellite and the near-surface concentrations measured by in-situ networks, and (3) to determine how the different LEO and GEO vantage points lead to a different perception of atmospheric and surface details and how we can benefit from - or correct for - these differences.Work on these challenges is taking place in the dedicated Belgian federal research project LEGO-BEL-AQ (2020-2023, https://lego-bel-aq.aeronomie.be/index.php) funded by BELSPO, with a particular focus on AQ in Belgium. In this contribution, we demonstrate that a combination of temporal aggregation, careful data selection, and horizontal oversampling can produce a meaningful increase in horizontal resolution in S5P tropospheric NO2 column maps, revealing policy-relevant features in the NO2 distribution over Belgium’s major cities. Comparisons between our high-resolution S5P NO2 maps and the near-surface in-situ observations as procured by the Belgian authorities, reveal high correlation when considering longer time scales (seasonal and annual), allowing a pragmatic conversion from tropospheric columns to near-surface concentrations over the complete Belgian domain, and consequently also a confrontation to WHO annual thresholds at the level of individual Belgian municipalities.Acknowledgements This work has been supported by the BELSPO BRAIN-be 2.0 project LEGO-BEL-AQ (https://lego-bel-aq.aeronomie.be)
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- 2023
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5. Harmonisation of Free Tropospheric Ozone Satellite Data Records in Support of TOAR Phase II
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Daan Hubert, Arno Keppens, Tijl Verhoelst, Steven Compernolle, and Jean-Christopher Lambert
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The first TOAR assessment encountered several observational challenges that limited the confidence in estimates of the burden, short-term variability and long-term changes of ozone in the free troposphere. One of these challenges is the difficulty to interpret tropospheric observations from space, especially when combining data records from multiple satellites with differences in vertical sensitivity, prior information, resolution and spatial domain. Additional confounding factors are time-varying biases and the lack of harmonisation of geophysical quantities, units and definition of the tropospheric top level. All together, these increased the uncertainty on the distribution and trends of tropospheric ozone, impeding firm assessments relevant for policy and science. These challenges motivated the Committee on Earth Observation Satellites (CEOS) to initiate a coordinated activity on improving assessments of tropospheric ozone measured from space. Here, we report on work that contributes to this CEOS activity and to various Working Groups (SOWG, OPT, HEGIFTOM, TOP, ROSTEES, ...) of the ongoing second TOAR assessment. Our primary objective is to harmonise the vertical perspective of different satellite data records. A first class of tropospheric ozone products is obtained through an inversion of spectral measurements by nadir-viewing sounders into a vertical profile. We describe two complementary approaches (Prior Replacement and Complete Data Fusion) to harmonise the differing profile retrieval set-up for GOME-2, IASI and other UV-visible and infrared nadir sensors, using information conveyed in the prior and the averaging kernels. A second class of products is obtained through subtraction of the stratospheric component from total column retrievals. The stratospheric column is derived with various methods, resulting in differing spatial coverage, tropospheric top level, sampling frequency, etc… We present how all tropospheric ozone products, from both classes, are harmonised to a common tropospheric top level. We then intercompare all harmonised satellite records, and report on the differences and how these reduce upon harmonisation. Finally, we reflect on the importance of the vertical harmonisation process to improve constraints of the spatial distribution and trends in tropospheric ozone. Acknowledgements : We are grateful to the sustained effort and committment of the teams, institutes and agencies that collect and provide satellite and ozonesonde data records of high quality.
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- 2023
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6. Assessing the quality of the Sentinel-5p TROPOMI cloud products and their reprocessing using ground-based Cloudnet data
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Steven Compernolle, Athina Argyrouli, Ronny Lutz, Maarten Sneep, Jean-Christopher Lambert, Ann Mari Fjaeraa, José Granville, Daan Hubert, Arno Keppens, Diego Loyola, Ewan O'Connor, Gaia Pinardi, Olivier Rasson, Fabian Romahn, Piet Stammes, Tijl Verhoelst, and Ping Wang
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The retrieval of atmospheric composition from space-based measurements, by e.g., Sentinel-5p TROPOMI, is strongly affected by radiative interferences with clouds. Dedicated cloud data products, typically retrieved from measurements by the same sounder, are therefore essential. Cloud information is used to filter data and as input to the modelling of atmospheric radiative transfer and the conversion of slant column densities into vertical column densities. The three main TROPOMI cloud retrieval algorithms are: (i) L2_CLOUD OCRA/ROCINN CAL (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks; Clouds-As-Layers), (ii) L2_CLOUD OCRA/ROCINN CRB (Clouds-as Reflecting Boundaries), and (iii) the S5P support product FRESCO-S (Fast Retrieval Scheme for Clouds from Oxygen absorption bands for Sentinel). The cloud variables provided by these products (radiometric cloud fraction, cloud (top) height, and cloud albedo/cloud optical thickness) are subsequently used in the retrieval of the TROPOMI trace gas products. The quality of cloud products and trace gas products is routinely assessed by the ESA/Copernicus Atmospheric Mission Performance Cluster (ATM-MPC) validation service, with ad hoc support from Sentinel-5p Validation Team (S5PVT) AO projects. Version upgrades have had a significant impact on the characteristics of S5P cloud data. The change of the wavelength window in the FRESCO product since version 1.4 (‘FRESCO-wide’) leads to a clear increase in the height of low clouds with a large impact on the tropospheric NO2 retrieval (van Geffen, 2022), and improving the validation results regarding the tropospheric and total NO2 column. The first upgrades of the ROCINN products (from v1 to v2.1-v2.3) led to an increase in correlation with CLOUDNET cloud height, but to a more negative bias for the low clouds, with ROCINN CRB cloud height even dropping below the CLOUDNET cloud base height on average. However, this effect seems alleviated with the latest upgrade to v2.4. The impact on the HCHO validation results is investigated but is less clear compared to the NO2 case. To resolve the discontinuities due to the processor version jumps, a full mission reprocessing is currently ongoing and largely carried out for the L2_CLOUD and FRESCO-S products. The reprocessed ROCINN data have a lower dispersion and higher correlation with respect to the CLOUDNET cloud heights. The bias of the L2_CLOUD OCRA/ROCINN CAL CTH becomes more negative, but that of L2_CLOUD OCRA/ROCINN CRB CH bias improves. Finally, we also discuss the impact of the FRESCO-S reprocessing on the validation results.
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- 2023
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7. Development of a merged HCHO climate data record from the EUMETSAT AC SAF GOME-2 Level-2 products
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Isabelle De Smedt, Gaia Pinardi, Pieter Valks, Klaus-Peter Heue, Steven Compernolle, Jeroen Van Gent, Jonas Vlietinck, Huan Yu, Diego Loyola, Nicolas Theys, and Michel Van Roozendael
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Within the framework of the EUMETSAT AC SAF project, the production of climate data records (CDRs) of short lived O3 precursors (HCHO and NO2) is under development, based on the three GOME-2 instruments and the future S5 and S4 Copernicus platforms.This work presents the development of the formaldehyde CDR, combining the current GOME-2 A, B and C operational AC SAF Level-2 orbital products to create one single L3 averaged product using advanced gridding tools. To this aim, the consistency between the three GOME-2 instrument needs to be improved by means of suitable correction schemes accounting for inter-sensor biases due to inconsistent auxiliary data or instrumental issues. The use of the CAMS model reanalysis as a source of prior HCHO profiles is explored with the aim to produce one consistent dataset from 2007 to now. Estimation of the random and systematic uncertainty is included for each grid cell. Furthermore, we plan to include meteorological re-analysis data (surface temperature and winds) in the output files to further support the interpretation of the data and of their observed variations.Validation results of this new monthly averaged CDR dataset will be presented, with a special focus on bias, precision and stability in time. To this aim, long-term ground-based HCHO measurements are collected and assessed.
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- 2023
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8. Potential of space-based TROPOMI observations for understanding the spatial and temporal variability of surface NO2 and its dependencies upon land use over south-western Europe
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Hervé Petetin, Marc Guevara, Steven Compernolle, Dene Bowdalo, Pierre-Antoine Bretonnière, Santiago Enciso, Oriol Jorba, Franco Lopez, Albert Soret, and Carlos Pérez García-Pando
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This study presents a comprehensive analysis of the spatio-temporal variability of TROPOMI NO2 tropospheric columns (TrC-NO2) over the Iberian Peninsula over the period 2018-2021 (using the recently released PAL product to ensure consistency).A first exploration of the impact of cloud cover on the availability of TROPOMI TrC-NO2 observations indicates that data gaps range between 20-30% in summer to 55-70% in April and November, with substantial spatial differences between northern and southern and/or arid areas. The spatial distribution of TrC-NO2 highlights strong hotspots over urban areas (especially Madrid and Barcelona), with additional enhancements along international maritime routes and major highways. A reasonable correlation with surface NO2 mixing ratios is found, around 0.7-0.8 depending on the averaging time.The weekly and monthly variability of TrC-NO2 over the peninsula is then analyzed at the light of the urban cover fraction (taken from the Copernicus Land Monitoring Service). From least to most urbanized areas, the weekend reduction is found to range from -10 to -40%. A detailed analysis at the intra-agglomeration scale highlights that strongest weekend effects do not always peak in the center but sometimes in surrounding cities, which suggests a larger contribution of commuting to total NOx anthropogenic emissions. Similarly, the monthly profiles strongly change depending on the level of urbanization, from -40%/+26% in summer/winter in most urbanized areas, to -10%/+20% in least urbanized ones. Interestingly, the same analysis applied to cropland fraction highlight an enhancement in June-July that could be due to natural soil NO emissions that are known to peak during the warm season. Beyond some specific discrepancies, a generally good consistency is found between the variability of NO2 seen from space with TROPOMI and the one observed at the surface.Our study thus illustrates the potential of TROPOMI TrC-NO2 to provide a valuable complement to surface monitoring network, especially in agricultural and maritime areas where surface NO2 observations are missing but yet crucial for better understanding the impact of local NOx emissions, especially for the production of tropospheric ozone. Petetin, H., Guevara, M., Compernolle, S., Bowdalo, D., Bretonnière, P.-A., Enciso, S., Jorba, O., Lopez, F., Soret, A., and Pérez García-Pando, C.: Potential of TROPOMI for understanding spatio-temporal variations in surface NO2 and their dependencies upon land use over the Iberian Peninsula, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1056, 2022.
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- 2023
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9. Potential of TROPOMI for understanding spatio-temporal variations in surface NO2 and their dependencies upon land use over the Iberian Peninsula
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Hervé Petetin, Marc Guevara, Steven Compernolle, Dene Bowdalo, Pierre-Antoine Bretonnière, Santiago Enciso, Oriol Jorba, Franco Lopez, Albert Soret, and Carlos Pérez García-Pando
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Atmospheric Science - Abstract
In orbit since late 2017, the Tropospheric Monitoring Instrument (TROPOMI) is offering new outstanding opportunities for better understanding the emission and fate of nitrogen dioxide (NO2) pollution in the troposphere. In this study, we provide a comprehensive analysis of the spatio-temporal variability of TROPOMI NO2 tropospheric columns (TrC-NO2) over the Iberian Peninsula during 2018–2021, considering the recently developed Product Algorithm Laboratory (PAL) product. We complement our analysis with estimates of NOx anthropogenic and natural soil emissions. Closely related to cloud cover, the data availability of TROPOMI observations ranges from 30 %–45 % during April and November to 70 %–80 % during summertime, with strong variations between northern and southern Spain. Strongest TrC-NO2 hotspots are located over Madrid and Barcelona, while TrC-NO2 enhancements are also observed along international maritime routes close the strait of Gibraltar, and to a lesser extent along specific major highways. TROPOMI TrC-NO2 appear reasonably well correlated with collocated surface NO2 mixing ratios, with correlations around 0.7–0.8 depending on the averaging time. We investigate the changes of weekly and monthly variability of TROPOMI TrC-NO2 depending on the urban cover fraction. Weekly profiles show a reduction of TrC-NO2 during the weekend ranging from −10 % to −40 % from least to most urbanized areas, in reasonable agreement with surface NO2. In the largest agglomerations like Madrid or Barcelona, this weekend effect peaks not in the city center but in specific suburban areas/cities, suggesting a larger relative contribution of commuting to total NOx anthropogenic emissions. The TROPOMI TrC-NO2 monthly variability also strongly varies with the level of urbanization, with monthly differences relative to annual mean ranging from −40 % in summer to +60 % in winter in the most urbanized areas, and from −10 % to +20 % in the least urbanized areas. When focusing on agricultural areas, TROPOMI observations depict an enhancement in June–July that could come from natural soil NO emissions. Some specific analysis of surface NO2 observations in Madrid show that the relatively sharp NO2 minimum used to occur in August (drop of road transport during holidays) has now evolved into a much broader minimum partly de-coupled from the observed local road traffic counting; this change started in 2018, thus before the COVID-19 outbreak. Over 2019–2021, a reasonable consistency of the inter-annual variability of NO2 is also found between both datasets. Our study illustrates the strong potential of TROPOMI TrC-NO2 observations for complementing the existing surface NO2 monitoring stations, especially in the poorly covered rural and maritime areas where NOx can play a key role, notably for the production of tropospheric O3.
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- 2023
10. Sentinel-5P TROPOMI NO2 retrieval: impact of version v2.2 improvements and comparisons with OMI and ground-based data
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Jos van Geffen, Henk Eskes, Steven Compernolle, Gaia Pinardi, Tijl Verhoelst, Jean-Christopher Lambert, Maarten Sneep, Mark ter Linden, Antje Ludewig, K. Folkert Boersma, and J. Pepijn Veefkind
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Atmospheric Science - Abstract
Nitrogen dioxide (NO2) is one of the main data products measured by the Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite, which combines a high signal-to-noise ratio with daily global coverage and high spatial resolution. TROPOMI provides a valuable source of information to monitor emissions from local sources such as power plants, industry, cities, traffic and ships, and variability of these sources in time. Validation exercises of NO2 v1.2–v1.3 data, however, have revealed that TROPOMI's tropospheric vertical column densities (VCDs) are too low by up to 50 % over highly polluted areas. These findings are mainly attributed to biases in the cloud pressure retrieval, the surface albedo climatology and the low resolution of the a priori profiles derived from global simulations of the TM5-MP chemistry model. This study describes improvements in the TROPOMI NO2 retrieval leading to version v2.2, operational since 1 July 2021. Compared to v1.x, the main changes are the following. (1) The NO2-v2.2 data are based on version-2 level-1b (ir)radiance spectra with improved calibration, which results in a small and fairly homogeneous increase in the NO2 slant columns of 3 % to 4 %, most of which ends up as a small increase in the stratospheric columns. (2) The cloud pressures are derived with a new version of the FRESCO cloud retrieval already introduced in NO2-v1.4, which led to a lowering of the cloud pressure, resulting in larger tropospheric NO2 columns over polluted scenes with a small but non-zero cloud coverage. (3) For cloud-free scenes a surface albedo correction is introduced based on the observed reflectance, which also leads to a general increase in the tropospheric NO2 columns over polluted scenes of order 15 %. (4) An outlier removal was implemented in the spectral fit, which increases the number of good-quality retrievals over the South Atlantic Anomaly region and over bright clouds where saturation may occur. (5) Snow/ice information is now obtained from ECMWF weather data, increasing the number of valid retrievals at high latitudes. On average the NO2-v2.2 data have tropospheric VCDs that are between 10 % and 40 % larger than the v1.x data, depending on the level of pollution and season; the largest impact is found at mid and high latitudes in wintertime. This has brought these tropospheric NO2 closer to Ozone Monitoring Instrument (OMI) observations. Ground-based validation shows on average an improvement of the negative bias of the stratospheric (from −6 % to −3 %), tropospheric (from −32 % to −23 %) and total (from −12 % to −5 %) columns. For individual measurement stations, however, the picture is more complex, in particular for the tropospheric and total columns.
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- 2022
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11. Characterization of errors in satellite-based HCHO ∕ NO2 tropospheric column ratios with respect to chemistry, column-to-PBL translation, spatial representation, and retrieval uncertainties
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Amir H. Souri, Matthew S. Johnson, Glenn M. Wolfe, James H. Crawford, Alan Fried, Armin Wisthaler, William H. Brune, Donald R. Blake, Andrew J. Weinheimer, Tijl Verhoelst, Steven Compernolle, Gaia Pinardi, Corinne Vigouroux, Bavo Langerock, Sungyeon Choi, Lok Lamsal, Lei Zhu, Shuai Sun, Ronald C. Cohen, Kyung-Eun Min, Changmin Cho, Sajeev Philip, Xiong Liu, and Kelly Chance
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Atmospheric Science - Abstract
The availability of formaldehyde (HCHO) (a proxy for volatile organic compound reactivity) and nitrogen dioxide (NO2) (a proxy for nitrogen oxides) tropospheric columns from ultraviolet–visible (UV–Vis) satellites has motivated many to use their ratios to gain some insights into the near-surface ozone sensitivity. Strong emphasis has been placed on the challenges that come with transforming what is being observed in the tropospheric column to what is actually in the planetary boundary layer (PBL) and near the surface; however, little attention has been paid to other sources of error such as chemistry, spatial representation, and retrieval uncertainties. Here we leverage a wide spectrum of tools and data to quantify those errors carefully. Concerning the chemistry error, a well-characterized box model constrained by more than 500 h of aircraft data from NASA's air quality campaigns is used to simulate the ratio of the chemical loss of HO2 + RO2 (LROx) to the chemical loss of NOx (LNOx). Subsequently, we challenge the predictive power of HCHO/NO2 ratios (FNRs), which are commonly applied in current research, in detecting the underlying ozone regimes by comparing them to LROx/LNOx. FNRs show a strongly linear (R2=0.94) relationship with LROx/LNOx, but only on the logarithmic scale. Following the baseline (i.e., ln(LROx/LNOx) = −1.0 ± 0.2) with the model and mechanism (CB06, r2) used for segregating NOx-sensitive from VOC-sensitive regimes, we observe a broad range of FNR thresholds ranging from 1 to 4. The transitioning ratios strictly follow a Gaussian distribution with a mean and standard deviation of 1.8 and 0.4, respectively. This implies that the FNR has an inherent 20 % standard error (1σ) resulting from not accurately describing the ROx–HOx cycle. We calculate high ozone production rates (PO3) dominated by large HCHO × NO2 concentration levels, a new proxy for the abundance of ozone precursors. The relationship between PO3 and HCHO × NO2 becomes more pronounced when moving towards NOx-sensitive regions due to nonlinear chemistry; our results indicate that there is fruitful information in the HCHO × NO2 metric that has not been utilized in ozone studies. The vast amount of vertical information on HCHO and NO2 concentrations from the air quality campaigns enables us to parameterize the vertical shapes of FNRs using a second-order rational function permitting an analytical solution for an altitude adjustment factor to partition the tropospheric columns into the PBL region. We propose a mathematical solution to the spatial representation error based on modeling isotropic semivariograms. Based on summertime-averaged data, the Ozone Monitoring Instrument (OMI) loses 12 % of its spatial information at its native resolution with respect to a high-resolution sensor like the TROPOspheric Monitoring Instrument (TROPOMI) (> 5.5 × 3.5 km2). A pixel with a grid size of 216 km2 fails at capturing ∼ 65 % of the spatial information in FNRs at a 50 km length scale comparable to the size of a large urban center (e.g., Los Angeles). We ultimately leverage a large suite of in situ and ground-based remote sensing measurements to draw the error distributions of daily TROPOMI and OMI tropospheric NO2 and HCHO columns. At a 68 % confidence interval (1σ), errors pertaining to daily TROPOMI observations, either HCHO or tropospheric NO2 columns, should be above 1.2–1.5 × 1016 molec. cm−2 to attain a 20 %–30 % standard error in the ratio. This level of error is almost non-achievable with the OMI given its large error in HCHO. The satellite column retrieval error is the largest contributor to the total error (40 %–90 %) in the FNRs. Due to a stronger signal in cities, the total relative error (< 50 %) tends to be mild, whereas areas with low vegetation and anthropogenic sources (e.g., the Rocky Mountains) are markedly uncertain (> 100 %). Our study suggests that continuing development in the retrieval algorithm and sensor design and calibration is essential to be able to advance the application of FNRs beyond a qualitative metric.
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- 2023
12. Comparing Sentinel-5P TROPOMI NO2 column observations with the CAMS-regional air quality ensemble
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John Douros, Henk Eskes, Jos van Geffen, K. Folkert Boersma, Steven Compernolle, Gaia Pinardi, Anne-Marlene Blechschmidt, Vincent-Henri Peuch, Augustin Colette, and Pepijn Veefkind
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The Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) instrument, launched in October 2017, provides unique observations of atmospheric trace gases at a high resolution of about 5 km, with near-daily global coverage, resolving individual sources like thermal powerplants, industrial complexes, fires, medium-scale towns, roads, and shipping routes. Even though Sentinel-5P (S5P) is a global mission, these datasets are especially well suited to test high-resolution regional-scale air quality (AQ) models and provide valuable input for emission inversion systems. In Europe, the Copernicus Atmosphere Monitoring Service (CAMS) has implemented an operational regional AQ forecasting capability based on an ensemble of several European models, available at a resolution of 0.1∘ × 0.1∘. In this paper, we present comparisons between TROPOMI observations of nitrogen dioxide (NO2) and the CAMS AQ forecasts and analyses of NO2. We discuss the different ways of making these comparisons and present quantitative results in the form of maps for individual days, summer and winter months, and a time series for European subregions and cities between May 2018 and March 2021. The CAMS regional products generally capture the fine-scale daily and averaged features observed by TROPOMI in much detail. In summer, the comparison shows a close agreement between TROPOMI and the CAMS ensemble NO2 tropospheric columns with a relative difference of up to 15 % for most European cities. In winter, however, we find a significant discrepancy in the column amounts over much of Europe, with relative differences up to 50 %. The possible causes for these differences are discussed, focusing on the possible impact of retrieval and modeling errors. Apart from comparisons with the CAMS ensemble, we also present results for comparisons with the individual CAMS models for selected months. Furthermore, we demonstrate the importance of the free tropospheric contribution to the estimation of the tropospheric column and thus include profile information from the CAMS configuration of the ECMWF's (European Centre for Medium-Range Weather Forecasts) global integrated model above 3 km altitude in the comparisons. We also show that replacing the global 1∘ × 1∘ a priori information in the retrieval by the regional 0.1∘ × 0.1∘ resolution profiles of CAMS leads to significant changes in the TROPOMI-retrieved tropospheric column, with typical increases at the emission hotspots up to 30 % and smaller increases or decreases elsewhere. As a spinoff, we present a new TROPOMI NO2 level 2 (L2) data product for Europe, based on the replacement of the original TM5-MP generated global a priori profile by the regional CAMS ensemble profile. This European NO2 product is compared with ground-based remote sensing measurements of six Pandora instruments of the Pandonia Global Network and nine Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) instruments. As compared to the standard S5P tropospheric NO2 column data, the overall bias of the new product for all except two stations is 5 % to 12 % smaller, owing to a reduction in the multiplicative bias. Compared to the CAMS tropospheric NO2 columns, dispersion and correlation parameters with respect to the standard data are, however, superior.
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- 2023
13. Supplementary material to 'Characterization of Errors in Satellite-based HCHO / NO2 Tropospheric Column Ratios with Respect to Chemistry, Column to PBL Translation, Spatial Representation, and Retrieval Uncertainties'
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Amir H. Souri, Matthew S. Johnson, Glenn M. Wolfe, James H. Crawford, Alan Fried, Armin Wisthaler, William H. Brune, Donald R. Blake, Andrew J. Weinheimer, Tijl Verhoelst, Steven Compernolle, Gaia Pinardi, Corinne Vigouroux, Bavo Langerock, Sungyeon Choi, Lok Lamsal, Lei Zhu, Shuai Sun, Ronald C. Cohen, Kyung-Eun Min, Changmin Cho, Sajeev Philip, Xiong Liu, and Kelly Chance
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- 2022
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14. Characterization of Errors in Satellite-based HCHO / NO2 Tropospheric Column Ratios with Respect to Chemistry, Column to PBL Translation, Spatial Representation, and Retrieval Uncertainties
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Amir H. Souri, Matthew S. Johnson, Glenn M. Wolfe, James H. Crawford, Alan Fried, Armin Wisthaler, William H. Brune, Donald R. Blake, Andrew J. Weinheimer, Tijl Verhoelst, Steven Compernolle, Gaia Pinardi, Corinne Vigouroux, Bavo Langerock, Sungyeon Choi, Lok Lamsal, Lei Zhu, Shuai Sun, Ronald C. Cohen, Kyung-Eun Min, Changmin Cho, Sajeev Philip, Xiong Liu, and Kelly Chance
- Abstract
The availability of formaldehyde (HCHO) (a proxy for volatile organic compound reactivity) and nitrogen dioxide (NO2) (a proxy for nitrogen oxides) tropospheric columns from Ultraviolet-Visible (UV-Vis) satellites has motivated many to use their ratios to gain some insights into the near-surface ozone sensitivity. Strong emphasis has been placed on the challenges that come with transforming what is being observed in the tropospheric column to what is actually in the planetary boundary layer (PBL) and near to the surface; however, little attention has been paid to other sources of error such as chemistry, spatial representation, and retrieval uncertainties. Here we leverage a wide spectrum of tools and data to carefully quantify those errors. Concerning the chemistry error, a well-characterized box model constrained by more than 500 hours of aircraft data from NASA’s air quality campaigns is used to simulate the ratio of the chemical loss of HO2+RO2 (LROx) to the chemical loss of NOx (LNOx). Subsequently, we challenge the predictive power of HCHO / NO2 ratios (FNRs), which are commonly applied in current research, at detecting the underlying ozone regimes by comparing them to LROx / LNOx. FNRs show a strongly linear (R2=0.94) relationship to LROx / LNOx in the log-log scale. Following the baseline (i.e., ln(LROx / LNOx) = -1.0±0.2) with the model and mechanism (CB06, r2) used for segregating NOx-sensitive from VOC-sensitive regimes, we observe a broad range of FNR thresholds ranging from 1 to 4. The transitioning ratios strictly follow a Gaussian distribution with a mean and standard deviation of 1.8 and 0.4, respectively. This implies that FNR has an inherent 20 % standard error (1-sigma) resulting from not being able to fully describe the ROx-HOx cycle. We calculate high ozone production rates (PO3) dominated by large HCHO×NO2 concentration levels, a new proxy for the abundance of ozone precursors. The relationship between PO3 and HCHO×NO2 becomes more pronounced when moving towards NOx-sensitive regions due to non-linear chemistry; our results indicate that there is fruitful information in the HCHO×NO2 metric that has not been utilized in ozone studies. The vast amount of vertical information on HCHO and NO2 concentration from the air quality campaigns enables us to parameterize the vertical shapes of FNRs using a second-order rational function permitting an analytical solution for an altitude adjustment factor to partition the tropospheric columns to the PBL region. We propose a mathematical solution to the spatial representation error based on modeling isotropic semivariograms. With respect to a high-resolution sensor like TROPOspheric Monitoring Instrument (TROPOMI) (>5.5×3.5 km2), Ozone Monitoring Instrument (OMI) loses 12 % of spatial information at its native resolution. A pixel with a grid size of 216 km2 fails at capturing ~65 % of the spatial information in FNRs at a 50 km length scale comparable to the size of a large urban center (e.g., Los Angeles). We ultimately leverage a large suite of in-situ and ground-based remote sensing measurements to draw the error distributions of daily TROPOMI and OMI tropospheric NO2 and HCHO columns. At 68 % confidence interval (1 sigma) errors pertaining to daily TROPOMI observations, either HCHO or tropospheric NO2 columns should be above 1.2–1.5×1016 molec.cm-2 to attain 20–30 % standard error in the ratio. This level of error is almost non-achievable with OMI given its large error in HCHO. The satellite column retrieval error is the largest contributor to the total error (40–90 %) in the FNRs. Due to a stronger signal in cities, the total relative error (100 %). Our study suggests that continuing development in the retrieval algorithm and sensor design and calibration is essential to be able to advance the application of FNRs beyond a qualitative metric.
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- 2022
15. Validation of the Sentinel-5 Precursor TROPOMI cloud data with Cloudnet, Aura OMI O2–O2, MODIS, and Suomi-NPP VIIRS
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Ann Mari Fjæraa, Jean-Christopher Lambert, Arno Keppens, Maarten Sneep, Ronny Lutz, Piet Stammes, Tijl Verhoelst, Fabian Romahn, Daan Hubert, Steven Compernolle, Ewan O'Connor, Ping Wang, Diego Loyola, and Athina Argyrouli
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Atmospheric Science ,010504 meteorology & atmospheric sciences ,business.industry ,Cloud fraction ,0211 other engineering and technologies ,Cloud computing ,02 engineering and technology ,01 natural sciences ,ddc ,Atmospheric composition ,Cloud optical thickness ,Cloud data ,Cloud height ,Oxygen absorption ,Environmental science ,Satellite ,business ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Remote sensing - Abstract
Accurate knowledge of cloud properties is essential to the measurement of atmospheric composition from space. In this work we assess the quality of the cloud data from three Copernicus Sentinel-5 Precursor (S5P) TROPOMI cloud products: (i) S5P OCRA/ROCINN_CAL (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks;Clouds-As-Layers), (ii) S5P OCRA/ROCINN_CRB (Clouds-as-Reflecting Boundaries), and (iii) S5P FRESCO-S (Fast Retrieval Scheme for Clouds from Oxygen absorption bands – Sentinel). Target properties of this work are cloud-top height and cloud optical thickness (OCRA/ROCINN_CAL), cloud height (OCRA/ROCINN_CRB and FRESCO-S), and radiometric cloud fraction (all three algorithms). The analysis combines (i) the examination of cloud maps for artificial geographical patterns, (ii) the comparison to other satellite cloud data (MODIS, NPP-VIIRS, and OMI O2–O2), and (iii) ground-based validation with respect to correlative observations (30 April 2018 to 27 February 2020) from the Cloudnet network of ceilometers, lidars, and radars. Zonal mean latitudinal variation of S5P cloud properties is similar to that of other satellite data. S5P OCRA/ROCINN_CAL agrees well with NPP VIIRS cloud-top height and cloud optical thickness and with Cloudnet cloud-top height, especially for the low (mostly liquid) clouds. For the high clouds, S5P OCRA/ROCINN_CAL cloud-top height is below the cloud-top height of VIIRS and of Cloudnet, while its cloud optical thickness is higher than that of VIIRS. S5P OCRA/ROCINN_CRB and S5P FRESCO cloud height are well below the Cloudnet cloud mean height for the low clouds but match on average better with the Cloudnet cloud mean height for the higher clouds. As opposed to S5P OCRA/ROCINN_CRB and S5P FRESCO, S5P OCRA/ROCINN_CAL is well able to match the lowest CTH mode of the Cloudnet observations. Peculiar geographical patterns are identified in the cloud products and will be mitigated in future releases of the cloud data products.
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- 2021
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16. Ground-based validation of the Copernicus Sentinel-5P TROPOMI NO2 measurements with the NDACC ZSL-DOAS, MAX-DOAS and Pandonia global networks
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Ann Mari Fjæraa, Thomas Wagner, Kimberly Strong, Claus Zehner, Aleksandr N. Gruzdev, Richard Querel, Moritz Müller, Julia Remmers, Michel Van Roozendael, Manuel Gebetsberger, Karin Kreher, Nis Jepsen, Tijl Verhoelst, Michel Grutter de la Mora, Andreas Richter, Aleksandr Elokhov, J. Pepijn Veefkind, Dimitris Karagkiozidis, Jean-Christopher Lambert, Andrea Pazmino, Ariane Bazureau, Rigel Kivi, K. Folkert Boersma, Wolfgang Stremme, Kristof Bognar, Gaia Pinardi, Florence Goutail, Martin Tiefengraber, Georg Hansen, Lidia Saavedra de Miguel, Margarita Yela Gonzalez, Sebastian Donner, Henk Eskes, Angelika Dehn, Ankie Piters, Olga Puentedura, Valery P. Sinyakov, Steven Compernolle, Yugo Kanaya, Kai Uwe Eichmann, C. Prados-Roman, Claudia Rivera Cárdenas, Myrto Gratsea, Hitoshi Irie, Alexander Cede, Folkard Wittrock, Sander Niemeijer, Thierry Portafaix, José Granville, J. S. Rimmer, Cheng Liu, Alkiviadis F. Bais, François Hendrick, Monica Navarro Comas, Jean-Pierre Pommereau, and Pieternel F. Levelt
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Atmospheric Science ,010504 meteorology & atmospheric sciences ,Sampling (statistics) ,010501 environmental sciences ,Atmospheric sciences ,01 natural sciences ,Column (database) ,Troposphere ,Amplitude ,13. Climate action ,Diurnal cycle ,Satellite ,Positive bias ,Smoothing ,0105 earth and related environmental sciences - Abstract
This paper reports on consolidated ground-based validation results of the atmospheric NO2 data produced operationally since April 2018 by the TROPOspheric Monitoring Instrument (TROPOMI) on board of the ESA/EU Copernicus Sentinel-5 Precursor (S5P) satellite. Tropospheric, stratospheric, and total NO2 column data from S5P are compared to correlative measurements collected from, respectively, 19 Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS), 26 Network for the Detection of Atmospheric Composition Change (NDACC) Zenith-Scattered-Light DOAS (ZSL-DOAS), and 25 Pandonia Global Network (PGN)/Pandora instruments distributed globally. The validation methodology gives special care to minimizing mismatch errors due to imperfect spatio-temporal co-location of the satellite and correlative data, e.g. by using tailored observation operators to account for differences in smoothing and in sampling of atmospheric structures and variability and photochemical modelling to reduce diurnal cycle effects. Compared to the ground-based measurements, S5P data show, on average, (i) a negative bias for the tropospheric column data, of typically −23 % to −37 % in clean to slightly polluted conditions but reaching values as high as −51 % over highly polluted areas; (ii) a slight negative median difference for the stratospheric column data, of about −0.2 Pmolec cm−2, i.e. approx. −2 % in summer to −15 % in winter; and (iii) a bias ranging from zero to −50 % for the total column data, found to depend on the amplitude of the total NO2 column, with small to slightly positive bias values for columns below 6 Pmolec cm−2 and negative values above. The dispersion between S5P and correlative measurements contains mostly random components, which remain within mission requirements for the stratospheric column data (0.5 Pmolec cm−2) but exceed those for the tropospheric column data (0.7 Pmolec cm−2). While a part of the biases and dispersion may be due to representativeness differences such as different area averaging and measurement times, it is known that errors in the S5P tropospheric columns exist due to shortcomings in the (horizontally coarse) a priori profile representation in the TM5-MP chemical transport model used in the S5P retrieval and, to a lesser extent, to the treatment of cloud effects and aerosols. Although considerable differences (up to 2 Pmolec cm−2 and more) are observed at single ground-pixel level, the near-real-time (NRTI) and offline (OFFL) versions of the S5P NO2 operational data processor provide similar NO2 column values and validation results when globally averaged, with the NRTI values being on average 0.79 % larger than the OFFL values.
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- 2021
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17. Validation of Aura-OMI QA4ECV NO2 climate data records with ground-based DOAS networks: the role of measurement and comparison uncertainties
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John P. Burrows, Alkis Bais, Andreas Richter, Sander Niemeijer, José Granville, Daan Hubert, François Hendrick, Alba Lorente, Enno Peters, Steven Compernolle, Jos van Geffen, Andrea Pazmino, Ankie Piters, Bruno Rino, Arno Keppens, Thomas Wagner, Folkert Boersma, Florence Goutail, J. C. Lambert, Tijl Verhoelst, Julia Remmers, Isabelle De Smedt, Henk Eskes, Gaia Pinardi, Michel Van Roozendael, Steffen Beirle, and Jean-Pierre Pommereau
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Data records ,Atmospheric Science ,010504 meteorology & atmospheric sciences ,Negative bias ,010502 geochemistry & geophysics ,01 natural sciences ,Troposphere ,13. Climate action ,Environmental science ,Satellite ,Scattered light ,Zenith ,Smoothing ,0105 earth and related environmental sciences ,Remote sensing ,Sampling bias - Abstract
The QA4ECV (Quality Assurance for Essential Climate Variables) version 1.1 stratospheric and tropospheric NO2 vertical column density (VCD) climate data records (CDRs) from the OMI (Ozone Monitoring Instrument) satellite sensor are validated using NDACC (Network for the Detection of Atmospheric Composition Change) zenith-scattered light differential optical absorption spectroscopy (ZSL-DOAS) and multi-axis DOAS (MAX-DOAS) data as a reference. The QA4ECV OMI stratospheric VCDs have a small bias of ∼0.2 Pmolec.cm-2 (5 %–10 %) and a dispersion of 0.2 to 1 Pmolec.cm-2 with respect to the ZSL-DOAS measurements. QA4ECV tropospheric VCD observations from OMI are restricted to near-cloud-free scenes, leading to a negative sampling bias (with respect to the unrestricted scene ensemble) of a few peta molecules per square centimetre (Pmolec.cm-2) up to −10 Pmolec.cm-2 (−40 %) in one extreme high-pollution case. The QA4ECV OMI tropospheric VCD has a negative bias with respect to the MAX-DOAS data (−1 to −4 Pmolec.cm-2), which is a feature also found for the OMI OMNO2 standard data product. The tropospheric VCD discrepancies between satellite measurements and ground-based data greatly exceed the combined measurement uncertainties. Depending on the site, part of the discrepancy can be attributed to a combination of comparison errors (notably horizontal smoothing difference error), measurement/retrieval errors related to clouds and aerosols, and the difference in vertical smoothing and a priori profile assumptions.
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- 2020
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18. Comparing Sentinel-5P TROPOMI NO2 column observations with the CAMS-regional air quality ensemble
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Henk Eskes, John Douros, Jos van Geffen, Folkert Boersma, Steven Compernolle, Gaia Pinardi, Anne Blechschmidt, Vincent-Henri Peuch, Augustin Colette, and Pepijn Veefkind
- Abstract
The Sentinel-5P TROPOMI instrument provides unique observations of atmospheric trace gases at a high resolution of about 5 km with near-daily global coverage, resolving individual sources like thermal power plants, industrial complexes, fires, medium-scale towns, roads and shipping routes. These datasets are especially well suited to test high-resolution regional-scale air quality (AQ) models and provide valuable input for regional emission inversion systems. In Europe, the Copernicus Atmosphere Monitoring Service (CAMS) has implemented an operational regional AQ forecasting capability for Europe based on an ensemble of 7 up to 11 European models. In the presentation we show comparisons between TROPOMI observations of nitrogen dioxide (NO2) and the CAMS AQ forecasts and analyses of NO2. We discuss the different ways of making these comparisons, and present the quantitative results for time series for regions and cities between May 2018 to March 2021, for summer and winter months and individual days. We demonstrate the importance of the free tropospheric contribution to the tropospheric column, and include profiles from the CAMS configuration of the ECMWF’s global integrated model above 3 km altitude in the comparison. The models generally capture the fine-scale daily and averaged features observed by TROPOMI in much detail. In summer, the quantitative comparison of the NO2 tropospheric column shows a close agreement, but in winter we find a significant discrepancy in the average column amount over Europe. Recently a new TROPOMI NO2 reprocessing with processor version 2.3.1 has become available, and impact of this new version on the comparisons is discussed. As spin-off, we present a new TROPOMI NO2 level-2 data product for Europe, based on the replacement of the original TM5-MP generated global a priori profile (1x1 degree resolution) by the regional CAMS ensemble profile at 0.1x0.1 degree resolution. This a-priori replacement leads to significant changes in the TROPOMI retrieved tropospheric column, with typical increases at the emission hotspots in the order of 20%. The European NO2 product is compared with ground-based remote sensing measurements of 6 PANDORA instruments of the Pandonia global network and 8 MAX-DOAS instruments. As compared to the standard S5P tropospheric NO2 column data, the overall bias of the new product is smaller owing to a reduction of the multiplicative bias linked to the profile shape.
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- 2022
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19. Sentinel-5P TROPOMI NO2 retrieval: impact of version v2.2 improvements and comparisons with OMI and ground-based data
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Henk Eskes, Mark ter Linden, Steven Compernolle, Tijl Verhoelst, Maarten Sneep, Antje Ludewig, J. Pepijn Veefkind, K. Folkert Boersma, Jos van Geffen, Gaia Pinardi, and Jean-Christopher Lambert
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Troposphere ,chemistry.chemical_compound ,Meteorology ,chemistry ,Anomaly (natural sciences) ,Radiance ,Calibration ,Satellite ,Nitrogen dioxide ,Albedo ,Latitude - Abstract
Nitrogen dioxide (NO2) is one of the main data products measured by the Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite, which combines a high signal-to-noise ratio with daily global coverage and high spatial resolution. TROPOMI provides a valuable source of information to monitor emissions from local sources such as power plants, industry, cities, traffic and ships, and variability of these sources in time. Validation exercises of NO2 version v1.2-v1.3 data, however, have revealed that TROPOMI's tropospheric vertical columns (VCDs) are too low by up to 50 % over highly polluted areas. These findings are mainly attributed to biases in the cloud pressure retrieval, the surface albedo climatology and the low resolution of the a-priori profiles derived from global simulations of the TM5-MP chemistry model. This study describes improvements in the TROPOMI NO2 retrieval leading to version v2.2, operational since 1 July 2021. Compared to v1.x, the main changes are: (1) The NO2-v2.2 data is based on version 2 level-1B (ir)radiance spectra with improved calibration, which results in a small and fairly homogeneous increase of the NO2 slant columns of 3 to 4 %, most of which ends up as a small increase of the stratospheric columns; (2) The cloud pressures are derived with a new version of the FRESCO cloud retrieval already introduced in NO2-v1.4, which lead to a lowering of the cloud pressure, resulting in larger tropospheric NO2 columns over polluted scenes with a small but non-zero cloud coverage; (3) For cloud-free scenes a surface albedo correction is introduced based on the observed reflectance, which also leads to a general increase of the tropospheric NO2 columns over polluted scenes of order 15 %; (4) An outlier removal was implemented in the spectral fit, which increases the number of good quality retrievals over the South-Atlantic Anomaly region and over bright clouds where saturation may occur; (5) Snow-Ice information is now obtained from ECMWF weather data, increasing the number of valid retrievals at high latitudes. On average the NO2-v2.2 data have tropospheric VCDs that are between 10 and 40 % larger than the v1.x data, depending on the level of pollution and season; the largest impact is found at mid- and high-latitudes in wintertime. This has brought these tropospheric NO2 closer to OMI observations. Ground-based validation shows on average an improvement of the negative bias of the stratospheric (from −6 % to −3 %), tropospheric (from −32 % to −23 %) and total (from −12 % to −5 %) columns. For individual measurement stations, however, the picture is more complicated, in particular for the tropospheric and total columns.
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- 2021
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20. Supplementary material to 'Comparative assessment of TROPOMI and OMI formaldehyde observations against MAX-DOAS network column measurements'
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Isabelle De Smedt, Gaia Pinardi, Corinne Vigouroux, Steven Compernolle, Alkis Bais, Nuria Benavent, Folkert Boersma, Ka-Lok Chan, Sebastian Donner, Kai-Uwe Eichmann, Pascal Hedelt, François Hendrick, Hitoshi Irie, Vinod Kumar, Jean-Christopher Lambert, Bavo Langerock, Christophe Lerot, Cheng Liu, Diego Loyola, Ankie Piters, Andreas Richter, Claudia Inés Rivera Cárdenas, Fabian Romahn, Robert George Ryan, Vinayak Sinha, Nicolas Theys, Jonas Vlietinck, Thomas Wagner, Ting Wang, Huan Yu, and Michel Van Roozendael
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- 2021
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21. Comparative assessment of TROPOMI and OMI formaldehyde observations against MAX-DOAS network column measurements
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Fabian Romahn, Bavo Langerock, Pascal Hedelt, Robert G. Ryan, Christophe Lerot, Gaia Pinardi, Jean-Christopher Lambert, Steven Compernolle, Ting Wang, Ka Lok Chan, Alkis Bais, Kai-Uwe Eichmann, Nicolas Theys, Thomas Wagner, Sebastian Donner, Claudia Rivera Cárdenas, Nuria Benavent, Vinayak Sinha, Cheng Liu, Jonas Vlietinck, Corinne Vigouroux, François Hendrick, Hitoshi Irie, Andreas Richter, Vinod Kumar, Diego Loyola, Folkert Boersma, Ankie Piters, Isabelle De Smedt, Huan Yu, and Michel Van Roozendael
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Troposphere ,Horizontal resolution ,On board ,Atmosphere ,chemistry.chemical_compound ,chemistry ,Formaldehyde ,Environmental science ,Satellite ,Positive bias ,Column (database) ,Remote sensing - Abstract
The TROPOspheric Monitoring Instrument (TROPOMI), launched in October 2017 on board the Sentinel-5 Precursor (S5P) satellite, monitors the composition of the Earth's atmosphere at an unprecedented horizontal resolution as fine as 3.5 × 5.5 km2. This paper assess the performances of the TROPOMI formaldehyde (HCHO) operational product compared to its predecessor, the OMI HCHO QA4ECV product, at different spatial and temporal scales. The parallel development of the two algorithms favored the consistency of the products, which facilitates the production of long-term combined time series. The main difference between the two satellite products is related to the use of different cloud algorithms, leading to a positive bias of OMI compared to TROPOMI of up to 30 % in Tropical regions. We show that after switching off the explicit correction for cloud effects, the two datasets come into an excellent agreement. For medium to large HCHO vertical columns (larger than 5 × 1015 molec.cm−2) the median bias between OMI and TROPOMI HCHO columns is not larger than 10 % (
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- 2021
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22. TROPOMI NO2 retrieval: December 2020 (v1.4) and April 2021 (v2.2) upgrades, and comparisons with OMI and ground-based remote sensing
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Gaia Pinardi, Folkert Boersma, Steven Compernolle, Pepijn Veefkind, Henk Eskes, Jos van Geffen, Mark ter Linden, Maarten Sneep, and Tijl Verhoelst
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Remote sensing (archaeology) ,Environmental science ,Remote sensing - Abstract
The Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite is a unique instrument, combining daily global coverage, very high signal-to-noise, a broad spectral range and very small pixels up to 3.5 x 5.5 km2. Retrievals are available for a large number of species, including NO2. Due to the very small pixels and daily revisit, TROPOMI provides detailed information on individual sources and source sectors like individual power plants, industrial complexes, cities and suburbs, highways, and even individual ships. The TROPOMI Level-2 NO2 product is available from 30 April 2018 onwards.Validation exercises of TROPOMI v1.2 & v1.3 data (2018-2020) with OMI and ground-based remote sensing observations have shown that TROPOMI's tropospheric NO2 column are low by up to 50% over highly polluted areas compared to independent data. In contrast, the underlying slant columns of TROPOMI agree well with OMI and independent SAOZ observations. Differences between OMI and TROPOMI have been mainly attributed to the different cloud height retrieval, using the O2-O2 versus O2-A bands respectively.In our presentation we discuss recent improvements in the TROPOMI NO2 retrieval and the impact these have on the tropospheric columns and on the comparisons with OMI and ground-based remote-sensing data.Version v1.4, which became operational on 2 December 2020, entails a major improvement in the cloud height retrieval, based on a modification of the FRESCO-S cloud retrieval using the O2-A band observations. In particular the cloud height over scenes with a small cloud coverage have increased, resulting in larger tropospheric columns in the retrievals over polluted areas.Version v2.2, to become operational in April/May 2021, includes similar cloud retrieval modifications. Furthermore, it provides a better treatment of saturation issues and transients, is using improved (ir)radiance measurements (level-1b v2 spectra) including degradation corrections, and includes a new albedo treatment.The TROPOMI NO2 retrievals are compared with OMI retrievals (from the QA4ECV product) and to ground-based observations with MAXDOAS and PANDORA instruments.
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- 2021
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23. Quality assessment of three years of Sentinel-5p TROPOMI NO2 data
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Ariane Bazureau, Florence Goutail, Sander Niemeijer, Kai-Uwe Eichmann, Tijl Verhoelst, Andrea Pazmino, Steven Compernolle, Henk Eskes, José Granville, Gaia Pinardi, Ann Mari Fjæraa, Martin Tiefengraber, Alexander Cede, Jean-Christopher Lambert, and Jean-Pierre Pommereau
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business.industry ,Cloud cover ,Reference data (financial markets) ,Radiance ,Environmental science ,Context (language use) ,Statistical dispersion ,Satellite ,Data center ,business ,Column (database) ,Remote sensing - Abstract
For more than three years now, the first atmospheric satellite of the Copernicus EO programme, Sentinel-5p (S5P) TROPOMI, has acquired spectral measurements of the Earth radiance in the visible range, from which near-real-time (NRTI) and offline (OFFL) processors retrieve the total, tropospheric and stratospheric column abundance of NO2. The S5P Mission Performance Centre performs continuous QA/QC of these data products enabling users to verify the fitness-for-purpose of the S5P data. Quality Indicators are derived from comparisons to ground-based reference data, both station-by-station in the S5P Automated Validation Server (AVS), and globally in more in-depth analyses. Complementary quality information is obtained from product intercomparisons (NRTI vs. OFFL) and from satellite-to-satellite comparisons. After three years of successful operation we present here a consolidated overview of the quality of the S5P TROPOMI NO2 data products, with particular attention paid to the impact of the various processor improvements, especially in the latest version (v1.4), activated on 2 December 2020, which introduces an updated cloud retrieval resulting in higher NO2 columns in polluted regions. Also the upcoming v2, due in April 2021 but already used to produce a Diagnostic Data Set, is discussed. S5P NO2 data are compared to ground-based measurements collected through either the ESA Validation Data Centre (EVDC) or network data archives (NDACC, PGN). Measurements from the Pandonia Global Network (PGN) serve as a reference for total NO2 validation, Multi-Axis DOAS data for tropospheric NO2 validation, and NDACC zenith-scattered-light DOAS data for stratospheric NO2 validation. Comparison methods are optimized to limit spatial and temporal mismatch errors (co-location strategy, photochemical adjustment to account for local time difference). Comparison results are analyzed to derive Quality Indicators and to conclude on the compliance w.r.t. the mission requirements. This include estimates of: (1) the bias, as proxy for systematic errors, (2) the dispersion of the differences, which combines random errors with seasonal and mismatch errors, and (3) the dependence of these on key influence quantities (surface albedo, cloud cover…)Overall, the MPC quality assessment of S5P NO2 data concludes to an excellent performance for the stratospheric data (bias2 vs. ground-based data. This dispersion is larger than the mission requirement on data precision, but it can partly be attributed to comparison errors such as those due to differences in resolution. Total column data are found to be biased low by 20%, with a 30% station-to-station scatter. After gridding to monthly means on a 0.8°x0.4° grid, comparisons to OMI data yield a much smaller dispersion (within the requirement of 0.7Pmolec/cm2), and a minor relative bias. NRTI and OFFL perform similarly, even if they occasionally differ over specific scenes. Besides the impact of the processor upgrade to v1.4 on the bias in polluted scenes, we discuss the implications of the reported negative biases in S5P tropospheric (and total) columns on NO2 reduction estimates, e.g. in the context of SARS-CoV-2 lockdown measures. Feedback from this work on the ground-based reference data is also briefly reported.
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- 2021
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24. Geophysical patterns in tropical tropospheric ozone by TROPOMI, OMI, GOME-2B and ozonesonde
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Jean-Christopher Lambert, Diego G. Loyola R., Tijl Verhoelst, Steven Compernolle, and Daan Hubert
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Ecosystems and human health are severely harmed by elevated concentrations of tropospheric ozone, in the short and the long term. Monitoring ozone at all relevant spatial and temporal scales simultaneously is a challenge for a global observing system due to the large variability of ozone levels in the troposphere. Space-based sensors provide near-global coverage at the synoptic scale, but their accuracy is limited since the large stratospheric O3 column shields the view on the relatively small tropospheric ozone concentrations. In contrast, in-situ soundings by balloons are sparse, but these are more accurate and at a high vertical resolution. As a result, the geophysical information that can be inferred from tropospheric ozone data records differs.We present a comprehensive comparison of the spatial and temporal patterns in tropical tropospheric ozone column observations by nadir-viewing satellite sensors (Sentinel-5 Precursor/TROPOMI, EOS-Aura/OMI and Metop-B/GOME-2) and ozonesondes for the period 2018-2020. We discuss how each data record perceives well-known structures and cycles such as the zonal wave-one, the seasonal cycle and biomass burning periods. Imprints of (sensor-dependent) sampling characteristics are generally less relevant on large scales. However, these can dominate the uncertainty budget when satellite data are used at their finest sampling resolution. Nonetheless, we recognise the signature of the Madden-Julian Oscillation and hints of Kelvin wave activity.
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- 2021
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25. Validation of TROPOMI nadir ozone profile retrievals: Methodology and first results
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Daan Hubert, Ann Mari Fjæraa, Pepijn Veefkind, Maarten Sneep, Steven Compernolle, Sander Niemeijer, Tijl Verhoelst, Jean-Christopher Lambert, Arno Keppens, Johan de Haan, and Mark ter Linden
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chemistry.chemical_compound ,Ozone ,chemistry ,Environmental science ,Nadir (topography) ,Remote sensing - Abstract
Part of the space segment of EU’s Copernicus Earth Observation programme, the Sentinel-5 Precursor (S5P) mission is dedicated to global and European atmospheric composition measurements of air quality, climate and the stratospheric ozone layer. On board of the S5P early afternoon polar satellite, the imaging spectrometer TROPOMI (TROPOspheric Monitoring Instrument) performs nadir measurements of the Earth radiance within the UV-visible and near-infrared spectral ranges, from which atmospheric ozone profile data are retrieved. Developed at the Royal Netherlands Meteorological Institute (KNMI) and based on the optimal estimation method, TROPOMI’s operational ozone profile retrieval algorithm has recently been upgraded. With respect to early retrieval attempts, accuracy is expected to have improved significantly, also thanks to recent updates of the TROPOMI Level-1b data product. This work reports on the initial validation of the improved TROPOMI height-resolved ozone data in the troposphere and stratosphere, as collected both from the operational S5P Mission Performance Centre/Validation Data Analysis Facility (MPC/VDAF) and from the S5PVT scientific project CHEOPS-5p. Based on the same validation best practices as developed for and applied to heritage sensors like GOME-2, OMI and IASI (Keppens et al., 2015, 2018), the validation methodology relies on the analysis of data retrieval diagnostics – like the averaging kernels’ information content – and on comparisons of TROPOMI data with reference ozone profile measurements. The latter are acquired by ozonesonde, stratospheric lidar, and tropospheric lidar stations performing network operation in the context of WMO's Global Atmosphere Watch and its contributing networks NDACC and SHADOZ. The dependence of TROPOMI’s ozone profile uncertainty on several influence quantities like cloud fraction and measurement parameters like sun and scan angles is examined and discussed. This work concludes with a set of quality indicators, enabling users to verify the fitness-for-purpose of the S5P data.
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- 2021
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26. Validation of Sentinel-5p TROPOMI cloud data with ground-based Cloudnet and other satellite data products
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Ping Wang, Jean-Christopher Lambert, Ewan O'Connor, Ronny Lutz, José Granville, Piet Stammes, Athina Argyrouli, Ann Mari Fjæraa, Olivier Rasson, Arno Keppens, Daan Hubert, Steven Compernolle, Maarten Sneep, Diego Loyola, Tijl Verhoelst, and Fabian Romahn
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Cloud data ,Satellite data ,Environmental science ,Remote sensing - Abstract
Space-born atmospheric composition measurements, like those from Sentinel-5p TROPOMI, are strongly affected by the presence of clouds. Dedicated cloud data products, typically retrieved with the same sensor, are therefore an important tool for the provider of atmospheric trace gas retrievals. Cloud products are used for filtering and modification of the modelled radiative transfer.In this work, we assess the quality of the cloud data derived from Copernicus Sentinel-5 Precursor TROPOMI radiance measurements. Three cloud products are considered: (i) L2_CLOUD OCRA/ROCINN CAL (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks; Clouds-As-Layers), (ii) L2_CLOUD OCRA/ROCINN CRB (same; Clouds-as Reflecting Boundaries), and (iii) the S5p support product FRESCO-S (Fast Retrieval Scheme for Clouds from Oxygen absorption bands for Sentinel). These cloud products are used in the retrieval of several S5p trace gas products (e.g., ozone columns and profile, total and tropospheric nitrogen dioxide, sulfur dioxide, formaldehyde). The quality assessment of these cloud products is carried out within the framework of ESA’s Sentinel-5p Mission Performance Centre (MPC) with support from AO validation projects focusing on the respective atmospheric gases.Cloud height data from the three S5p cloud products is compared to radar/lidar based cloud profile information from the ground-based networks CLOUDNET and ARM. The cloud height from S5p CLOUD CRB and S5p FRESCO are on average 0.6 km below the cloud mid-height of CLOUDNET measurements, and the cloud top height from S5p CLOUD CAL is on average 1 km below CLOUDNET’s cloud top height. However, the comparison is different for low and high clouds, with S5p CLOUD CAL cloud top height being only 0.3 km below CLOUDNET’s for low clouds. The radiometric cloud fraction and cloud (top) height are compared to those of other satellite cloud products like Aura OMI O2-O2. While the latitudinal variation is often similar, offsets are encountered.Recently, major S5p cloud product upgrades were released for S5p OCRA/ROCINN (July 2020) and for S5p FRESCO (December 2020), leading to a decrease of the ROCINN CRB cloud height and an increase of the FRESCO cloud height on average. Moreover, a major change in the ROCINN surface albedo treatment leads to a clear improvement of the comparison with CLOUDNET at the complicated sea/land/ice/snow site Ny-Alesund.
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- 2021
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27. Reply to all 3 reviewers
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Steven Compernolle
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- 2020
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28. Supplementary material to 'TROPOMI tropospheric ozone column data: Geophysical assessment and comparison to ozonesondes, GOME-2B and OMI'
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Daan Hubert, Klaus-Peter Heue, Jean-Christopher Lambert, Tijl Verhoelst, Marc Allaart, Steven Compernolle, Patrick D. Cullis, Angelika Dehn, Christian Félix, Bryan J. Johnson, Arno Keppens, Debra E. Kollonige, Christophe Lerot, Diego Loyola, Matakite Maata, Sukarni Mitro, Maznorizan Mohamad, Ankie Piters, Fabian Romahn, Henry B. Selkirk, Francisco R. da Silva, Ryan M. Stauffer, Anne M. Thompson, J. Pepijn Veefkind, Holger Vömel, Jacquelyn C. Witte, and Claus Zehner
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- 2020
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29. Supplementary material to 'Validation of the Sentinel-5 Precursor TROPOMI cloud data with Cloudnet, Aura OMI O2-O2, MODIS and Suomi-NPP VIIRS'
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Steven Compernolle, Athina Argyrouli, Ronny Lutz, Maarten Sneep, Jean-Christopher Lambert, Ann Mari Fjæraa, Daan Hubert, Arno Keppens, Diego Loyola, Ewan O'Connor, Fabian Romahn, Piet Stammes, Tijl Verhoelst, and Ping Wang
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- 2020
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30. Validation of the Sentinel-5 Precursor TROPOMI cloud data with Cloudnet, Aura OMI O2-O2, MODIS and Suomi-NPP VIIRS
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Steven Compernolle, Athina Argyrouli, Ronny Lutz, Maarten Sneep, Jean-Christopher Lambert, Ann Mari Fjæraa, Daan Hubert, Arno Keppens, Diego Loyola, Ewan O'Connor, Fabian Romahn, Piet Stammes, Tijl Verhoelst, and Ping Wang
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Accurate knowledge of cloud properties is essential to the measurement of atmospheric composition from space. In this work we assess the quality of the cloud data derived from Copernicus Sentinel-5 Precursor (S5P) TROPOMI radiance measurements: cloud top height and cloud optical thickness (retrieved with the S5P OCRA/ROCINN_CAL algorithm), cloud height (S5P OCRA/ROCINN_CRB and S5P FRESCO) and radiometric cloud fraction (all three algorithms). The analysis combines: (i) the examination of cloud maps for artificial geographical patterns, (ii) the comparison to other satellite cloud data (MODIS, NPP-VIIRS and OMI O2-O2), and (iii) ground-based validation with respect to correlative observations (2018-04-30 to 2020-02-27) from the CLOUDNET network of ceilometers, lidars and radars. Peculiar geographical patterns were identified, and will be mitigated in future releases of the cloud data products. Zonal mean latitudinal variation of S5P cloud properties are similar to that of other satellite data. S5P OCRA/ROCINN_CAL agrees well with NPP VIIRS cloud top height and cloud optical thickness, and with CLOUDNET cloud top height, especially for the low (mostly liquid) clouds. For the high clouds, S5P OCRA/ROCINN_CAL cloud top height is below the cloud top height of VIIRS and of CLOUDNET, while its cloud optical thickness is higher than that of VIIRS. S5P OCRA/ROCINN_CRB and S5P FRESCO cloud height are well below the CLOUDNET cloud mean height for the low clouds, but match on an average better with the CLOUDNET cloud mean height for the higher clouds. As opposed to S5P OCRA/ROCINN_CRB and S5P FRESCO, S5P OCRA/ROCINN_CAL is well able to match the lowest CTH mode of the CLOUDNET observations.
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- 2020
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31. Impact of Coronavirus Outbreak on NO 2 Pollution Assessed Using TROPOMI and OMI Observations
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J.-F. Müller, J. van Gent, Steven Compernolle, Pieternel F. Levelt, Maite Bauwens, T. Stavrakou, Jonas Vlietinck, Huan Yu, Claus Zehner, Henk Eskes, and J. P. Veefkind
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Pollution ,Atmospheric Science ,010504 meteorology & atmospheric sciences ,Coronavirus disease 2019 (COVID-19) ,media_common.quotation_subject ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Atmospheric Composition and Structure ,010502 geochemistry & geophysics ,medicine.disease_cause ,01 natural sciences ,lockdown ,Evolution of the Earth ,Research Letter ,coronavirus outbreak ,medicine ,Global Change ,China ,Air quality index ,0105 earth and related environmental sciences ,media_common ,Coronavirus ,Evolution of the Atmosphere ,satellite NO2 ,satellite NO ,Atmosphere ,emissions ,Outbreak ,air quality ,Research Letters ,Tectonophysics ,Geophysics ,Geography ,Climatology ,Western europe ,General Earth and Planetary Sciences ,Troposphere: Composition and Chemistry ,The COVID‐19 pandemic: linking health, society and environment - Abstract
Spaceborne NO2 column observations from two high‐resolution instruments, TROPOMI onboard Sentinel‐5 Precursor and OMI on Aura, reveal unprecedented NO2 decreases over China, South Korea, Western Europe and the U.S. as a result of public health measures enforced to contain the coronavirus disease outbreak (Covid‐19) in January‐April 2020. The average NO2 column drop over all Chinese cities amounts to ‐40% relative to the same period in 2019, and reaches up to a factor of ~2 at heavily hit cities, e.g. Wuhan, Jinan, while the decreases in Western Europe and the U.S. are also significant (‐20 to ‐38%). In contrast with this, although Iran is also strongly affected by the disease, the observations do not show evidence of lower emissions, reflecting more limited health measures., Key Points Satellite NO2 data show substantial decreases by 40% on average over Chinese cities due to lockdown measures against the Covid‐19 outbreakWestern Europe and U.S. display robust NO2 decreases in 2020, 20‐38% relative to the same period in 2019Satellite NO2 observations above Iran, a region strongly affected by coronavirus, do not show clear evidence of lower emissions
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- 2020
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32. Ground-based validation of the Copernicus Sentinel-5p TROPOMI NO2 measurements with the NDACC ZSL-DOAS, MAX-DOAS and Pandonia global networks
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Tijl Verhoelst, Steven Compernolle, Gaia Pinardi, Jean-Christopher Lambert, Henk J. Eskes, Kai-Uwe Eichmann, Ann Mari Fjæraa, José Granville, Sander Niemeijer, Alexander Cede, Martin Tiefengraber, François Hendrick, Andrea Pazmiño, Alkiviadis Bais, Ariane Bazureau, K. Folkert Boersma, Kristof Bognar, Angelika Dehn, Sebastian Donner, Aleksandr Elokhov, Manuel Gebetsberger, Florence Goutail, Michel Grutter de la Mora, Aleksandr Gruzdev, Myrto Gratsea, Georg H. Hansen, Hitoshi Irie, Nis Jepsen, Yugo Kanaya, Dimitris Karagkiozidis, Rigel Kivi, Karin Kreher, Pieternel F. Levelt, Cheng Liu, Moritz Müller, Monica Navarro Comas, Ankie J. M. Piters, Jean-Pierre Pommereau, Thierry Portafaix, Olga Puentedura, Richard Querel, Julia Remmers, Andreas Richter, John Rimmer, Claudia Rivera Cárdenas, Lidia Saavedra de Miguel, Valery P. Sinyakov, Kimberley Strong, Michel Van Roozendael, J. Pepijn Veefkind, Thomas Wagner, Folkard Wittrock, Margarita Yela González, and Claus Zehner
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010504 meteorology & atmospheric sciences ,13. Climate action ,010501 environmental sciences ,01 natural sciences ,0105 earth and related environmental sciences - Abstract
This paper reports on consolidated ground-based validation results of the atmospheric NO2 data produced operationally since April 2018 by the TROPOMI instrument on board of the ESA/EU Copernicus Sentinel-5 Precursor (S5p) satellite. Tropospheric, stratospheric, and total NO2 column data from S5p are compared to correlative measurements collected from, respectively, 19 Multi-Axis DOAS (MAX-DOAS), 26 NDACC Zenith-Scattered-Light DOAS (ZSL-DOAS), and 25 PGN/Pandora instruments distributed globally. The validation methodology gives special care to minimizing mismatch errors due to imperfect spatio-temporal co-location of the satellite and correlative data, e.g., by using tailored observation operators to account for differences in smoothing and in sampling of atmospheric structures and variability, and photochemical modelling to reduce diurnal cycle effects. Compared to the ground-based measurements, S5p data show, on an average: (i) a negative bias for the tropospheric column data, of typically −23 to −37 % in clean to slightly polluted conditions, but reaching values as high as −51 % over highly polluted areas; (ii) a slight negative bias for the stratospheric column data, of about −0.2 Pmolec/cm2, i.e. approx. −2 % in summer to −15 % in winter; and (iii) a bias ranging from zero to −50 % for the total column data, found to depend on the amplitude of the total NO2 column, with small to slightly positive bias values for columns below 6 Pmolec/cm2 and negative values above. The dispersion between S5p and correlative measurements contains mostly random components, which remain within mission requirements for the stratospheric column data (0.5 Pmolec/cm2), but exceed those for the tropospheric column data (0.7 Pmolec/cm2). While a part of the biases and dispersion may be due to representativeness differences, it is known that errors in the S5p tropospheric columns exist due to shortcomings in the (horizontally coarse) a-priori profile representation in the TM5-MP chemistry transport model used in the S5p retrieval, and to a lesser extent, to the treatment of cloud effects. Although considerable differences (up to 2 Pmolec/cm2 and more) are observed at single ground-pixel level, the near-real-time (NRTI) and off-line (OFFL) versions of the S5p NO2 operational data processor provide similar NO2 column values and validation results when globally averaged, with the NRTI values being on average 0.79 % larger than the OFFL values.
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- 2020
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33. Author reply
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Steven Compernolle
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- 2020
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34. Geophysical validation of two years of Sentinel-5p tropical tropospheric ozone columns
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Ankie Piters, Mark Weber, Maznorizan Mohamad, Diego Loyola, José Granville, Francisco Raimundo da Silva, Tijl Verhoelst, Daan Hubert, Christian Félix, Arno Keppens, Steven Compernolle, Holger Vömel, René Stübi, Klaus-Peter Heue, Anne M. Thompson, Jean-Christopher Lambert, Henry B. Selkirk, Bryan J. Johnson, Marc Allaart, and Kai-Uwe Eichmann
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chemistry.chemical_compound ,chemistry ,Environmental science ,Tropospheric ozone ,Atmospheric sciences - Abstract
Tropospheric ozone damages ecosystems and causes human health problems. The high spatial and temporal variability of ozone concentrations in the troposphere challenges global observing systems to monitor ozone at all relevant scales. TROPOMI is a nadir-viewing UV-Vis-NIR-SWIR sensor that combines a high spatial resolution, a large swath width and the spectral measurement characteristics required to deliver trace gas data records at unprecedented detail. The first tropospheric data product was publicly released in Fall 2018, a year after launch on the Sentinel-5p platform (S5p). It is based on the convective-cloud differential technique (CCD) to infer 0.5°x1° resolved daily maps of 3-day moving mean values of the tropospheric ozone column (surface to 270 hPa) between 20°S and 20°N in clear-sky conditions. This makes it the highest resolved tropospheric ozone data set currently available for the tropical belt. About two years of data have been collected since the end of the commissioning phase in April 2018.We present an assessment of the quality of the Sentinel-5p TROPOMI convective-cloud differential tropospheric ozone column data products (O3_TCL OFFL v01.01.05-01.01.07), carried out within the context of ESA’s Sentinel-5p Mission Performance Center (MPC) and the S5PVT AO project CHEOPS-5p. Our assessment of the first two years of TROPOMI data is based on comparisons with (a) quality-assured co-located in-situ measurements by the SHADOZ ozonesonde network, and, (b) satellite data by the GOME-2 and OMI sensors. These well-characterized observational data records serve as references to evaluate the bias and the dispersion of S5p data, and their dependence on influence quantities. Additional visual inspections of the S5p tropospheric ozone maps unveiled non-geophysical structures introduced by the sampling pattern of sensor and clouds. We conclude by assessing the compliance of S5p tropospheric ozone data with respect to mission and user requirements for key data applications.
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- 2020
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35. Validation of the S5P Formaldehyde L2 product using MAX-DOAS network observations
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Christophe Lerot, Nicolas Theys, Huan Yu, Fabian Romahn, Kai Uwe Eichman, Isabelle De Smedt, Jean-Christopher Lambert, Zhibin Cheng, Jonas Vlietinck, Michel Van Roozendael, Corinne Vigouroux, Gaia Pinardi, Diego Loyola, Bavo Langerock, Pascal Hedelt, and Steven Compernolle
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S5P ,chemistry.chemical_compound ,chemistry ,business.industry ,Formaldehyde ,Product (mathematics) ,TROPOMI ,MAX-DOAS ,Process engineering ,business ,Mathematics - Abstract
The Sentinel-5 Precursor (S5P) was launched on the 13th of October 2017, with on board the TROPOspheric Monitoring Instrument (TROPOMI). The formaldehyde (HCHO) L2 product is operational since the end of 2018. The prototype of the tropospheric HCHO retrieval algorithm is developed at BIRA-IASB and implemented at the German Aerospace Center (DLR) in the S5P operational processor (De Smedt et al., 2018).In this work, we investigate the quality of the HCHO tropospheric column product and its validation within the MPC framework (Mission Performance Center) and the S5PVT NIDFORVAL project (S5P NItrogen Dioxide and FORmaldehyde VALidation). Within NIDFORVAL, the S5P HCHO product has been validated using the full FTIR and MAXDOAS dataset. Validation results have been assessed against reported product uncertainties taking into account the full comparison error budget, showing that the product quality reaches its requirements.Here, we focus on satellite-satellite comparison based on the OMI QA4ECV HCHO product and on ground-based validation using MAX-DOAS and Pandora network observations. About 15 HCHO measuring stations are involved, providing data corresponding to a wide range of observation conditions at mid and low latitudes, and covering remote, sub-urban, and urban polluted sites. Comparison results show usually negative biases for large HCHO columns, while a positive offset is observed for the lowest columns. For the MAX-DOAS stations providing vertical profile retrievals, the impact of a priori profiles on the comparison is assessed. The dataset allows to discuss validation results as a function of emission source. Seasonal and diurnal variations are compared. Long term variation are also monitored using the OMI and MAX-DOAS QA4ECV dataset.
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- 2020
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36. Quality assessment of two years of Sentinel-5p TROPOMI NO2 data
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Tijl Verhoelst, Steven Compernolle, José Granville, Arno Keppens, Gaia Pinardi, Jean-Christopher Lambert, Kai-Uwe Eichmann, Henk Eskes, Sander Niemeijer, Ann Mari Fjæraa, Andrea Pazmoni, Florence Goutail, Jean-Pierre Pommereau, Alexander Cede, and Martin Tiefengraber
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For more than two years now the first atmospheric satellite of the Copernicus EO programme, Sentinel-5p (S5P) TROPOMI, has acquired spectral measurements of the Earth radiance in the visible range, from which near-real-time (NRTI) and offline (OFFL) processors retrieve operationally the total, tropospheric and stratospheric column abundance of atmospheric NO2. In support of these routine operations, the S5P Mission Performance Centre (MPC) performs continuous QA/QC of these data products and produces key Quality Indicators enabling users to verify the fitness-for-purpose of the S5P data. Quality Indicators are derived from comparisons to ground-based reference data, both station-by-station in monitoring mode in the S5P Automated Validation Server (AVS) and globally in more complex in-depth analyses. Complementary quality information is obtained from product intercomparisons (NRTI vs. OFFL) and from satellite-to-satellite comparisons. After two years of successful operation we present here a consolidated overview of the quality of the S5P TROPOMI NO2 data products delivered publicly.S5P NO2 data are compared routinely to ground-based network measurements collected through either the ESA Validation Data Centre (EVDC) or network data archives (NDACC, PGN). Direct-sun measurements from the Pandonia Global Network (PGN) serve as a reference for total NO2 validation, Multi-Axis DOAS network data for tropospheric NO2 validation, and NDACC zenith-scattered-light DOAS network data for stratospheric NO2 validation. Comparison methods are optimized to limit spatial and temporal mismatch to a minimum (information-based spatial co-location strategy, photochemical adjustment to account for local time measurement difference). Comparison results are analyzed to derive Quality Indicators and to conclude on the compliance w.r.t. the mission requirements. This include estimates of: (1) the bias, as proxy for systematic errors, (2) the dispersion of the differences, which combines random errors with seasonal and irreducible mismatch errors, and (3) the dependence of bias and dispersion on key influence quantities (surface albedo, cloud cover…)Intercomparison of S5P products (NRTI vs. OFFL) and comparison to other satellite data, including a similar processing of OMI measurements, complement the ground-based validation with relative biases and spatio-temporal patterns/artefacts related to instrumental issues (e.g. striping) and to the sensitivity to geophysical features (e.g. clouds and sea/ice albedo contrast). Overall, the MPC quality assessment of S5P NO2 data concludes to an excellent performance for the stratospheric column data (bias2 vs. ground-based data. This dispersion larger than the mission requirement on data precision can partly be attributed to comparisons errors such as those due to differences in horizontal resolution. Total column data are found to be biased low by 20%, with a 30% station-to-station scatter. After gridding to monthly means on a 0.8°x0.4° grid, comparisons to OMI data yield a much smaller dispersion (within the requirement of 0.7Pmolec/cm2), and a minor relative bias. NRTI and OFFL perform similarly, even if they occasionally differ in specific cases of direct comparisons.
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- 2020
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37. Validation of tropospheric NO2 columns measurements from GOME-2, OMI and TROPOMI using MAX-DOAS and direct-sun network observations with focus on dilution effects
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Henk Eskes, Steven Compernolle, Gaia Pinardi, and Klaas Boersma
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Ground-based remote sensing MAX-DOAS and Pandora direct-sun instruments measuring in the UV-Vis spectral region are nowadays widely used to monitor atmospheric NO2 columns. Owing to the multiple geometries used, these techniques can differentiate total, tropospheric and stratospheric NO2 content and therefore provide an appropriate source of correlative data for the validation of satellite instruments such as GOME-2, OMI and TROPOMI.In this study we combine ground-based remote sensing correlative measurements available from over 40 sites distributed worldwide to address the validation of GOME-2, OMI and TROPOMI data products. For GOME-2, we concentrate on the GDP operational product generated within the EUMETSAT AC SAF project and on the climate data record generated within the EU QA4ECV project, while for OMI we address both the TEMIS and QA4ECV data products. Regarding TROPOMI, the operational OFFL product is considered. To derive tropospheric NO2 columns from direct-sun total NO2 data, we use estimates of the stratospheric contribution available from each satellite data product.A negative bias is generally found between the different satellite data products and the ground-based tropospheric NO2 measurements, which is mostly prominent in urban sites characterized by strong localized emission sources (up to about -32% to -45%, e.g. for OMI TEMIS and GOME-2 GDP vs MAX-DOAS ensemble). In an attempt to quantify and correct for the horizontal dilution happening around urban stations (due to diffusion and transport and to the spatial averaging of high resolution structures), we use high-resolution gridded NO2 maps obtained from one year of QA4ECV data. Results from applying this dilution correction show a clear improvement of the agreement between GOME-2 and OMI data at polluted urban locations. Further, the impact of the satellite ground pixel size (GOME-2 40x80km², OMI 13x24km²) and site location is investigated.
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- 2020
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38. Validation of TROPOMI nadir ozone profile retrievals: Methodology and first results
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Daan Hubert, Tijl Verhoelst, Steven Compernolle, and Maarten Sneep
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Part of the space segment of EU’s Copernicus Earth Observation programme, the Sentinel-5 Precursor (S5P) mission is dedicated to global and European atmospheric composition measurements of air quality, climate and the stratospheric ozone layer. On board of the S5P early afternoon polar satellite, the imaging spectrometer TROPOMI (TROPOspheric Monitoring Instrument) performs nadir measurements of the Earth radiance within the UV-visible and near-infrared spectral ranges, from which atmospheric ozone profile data are retrieved. Developed at the Royal Netherlands Meteorological Institute (KNMI) and based on the optimal estimation method, TROPOMI’s operational ozone profile retrieval algorithm has recently been upgraded. With respect to early retrieval attempts, accuracy is expected to have improved significantly, also thanks to recent updates of the TROPOMI Level-1b data product. This work reports on the initial validation of the improved TROPOMI height-resolved ozone data in the troposphere and stratosphere, as collected both from the operational S5P Mission Performance Centre/Validation Data Analysis Facility (MPC/VDAF) and from the S5PVT scientific project CHEOPS-5p. Based on the same validation best practices as developed for and applied to heritage sensors like GOME-2, OMI and IASI (Keppens et al., 2015, 2018), the validation methodology relies on the analysis of data retrieval diagnostics – like the averaging kernels’ information content – and on comparisons of TROPOMI data with reference ozone profile measurements. The latter are acquired by ozonesonde, stratospheric lidar, and tropospheric lidar stations performing network operation in the context of WMO's Global Atmosphere Watch and its contributing networks NDACC and SHADOZ. The dependence of TROPOMI’s ozone profile uncertainty on several influence quantities like cloud fraction and measurement parameters like sun and scan angles is examined and discussed. This work concludes with a set of quality indicators enabling users to verify the fitness-for-purpose of the S5P data.
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- 2020
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39. Validation of Sentinel-5p retrieved cloud height data using ground-based radar/lidar measurements from the CLOUDNET network
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Steven Compernolle, Athina Argyrouli, Ronny Lutz, Maarten Sneep, José Granville, Daan Hubert, Arno Keppens, Tijl Verhoelst, Ann Mari Fjaeraa, Diego Loyola, Ewan O'Connor, and Jean-Christopher Lambert
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Satellite measurements of tropospheric or total column trace gas species, including those from Sentinel-5p TROPOMI, are affected by the presence of clouds. Therefore, cloud data products retrieved with the same sensor play an essential role, as they allow the data provider to take an estimated cloud impact on the trace gas retrieval into account. Examples are the modification of the radiative transfer and associated quantities such as the air mass factor, and the partial masking of the measurement scene. Evidently, the accuracy of these corrections relies on the accuracy of the retrieved cloud properties, like radiometric cloud fraction (CF), cloud top height (CTH) or cloud height (CH), and cloud optical thickness (COT) or cloud albedo (CA).We consider here three S5p TROPOMI-based cloud products: (i) L2_CLOUD OCRA/ROCINN CAL (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks; Clouds-As-Layers), (ii) L2_CLOUD OCRA/ROCINN CRB (Clouds-as Reflecting Boundaries), and (iii) the S5p support product FRESCO-S (Fast Retrieval Scheme for Clouds from Oxygen absorption bands). These are input to the S5p operational processors for several trace gas products, including ozone columns and profile, total and tropospheric NO2, formaldehyde, sulfur dioxide. The quality assessment of these cloud products is carried out within the framework of ESA’s Sentinel-5p Mission Performance Centre (MPC) with support from AO validation projects focusing on the respective trace gases.In this work, cloud height (from S5p CLOUD CRB and S5p FRESCO algorithms) and cloud top height (from S5p CLOUD CAL) S5p data is validated with radar/lidar-based cloud profile information from the ground-based networks CLOUDNET and ARM at 17 sites. For some sites the comparison is difficult due to e.g., orography or snow/ice cover. S5P and CLOUDNET report similar cloud height variations at several sites, and the correlation between the S5p cloud products and CLOUDNET can be high (Pearson R up to 0.9). However, there is a site-dependent negative bias of the S5p cloud (top) height with respect to the CLOUDNET data: up to -2.5 km for S5p CLOUD CAL cloud top height and -1.5 km for S5p CLOUD CRB and S5p FRESCO cloud height. The dependence on other parameters measured by S5p and CLOUDNET (e.g., radiometric cloud fraction, cloud phase,…) is investigated.
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40. Validation of Aura-OMI QA4ECV NO2 Climate Data Records with ground-based DOAS networks: role of measurement and comparison uncertainties
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Steven Compernolle, Tijl Verhoelst, Gaia Pinardi, José Granville, Daan Hubert, Arno Keppens, Sander Niemeijer, Bruno Rino, Alkis Bais, Steffen Beirle, Folkert Boersma, John P. Burrows, Isabelle De Smedt, Henk Eskes, Florence Goutail, François Hendrick, Alba Lorente, Andrea Pazmino, Ankie Piters, Enno Peters, Jean-Pierre Pommereau, Julia Remmers, Andreas Richter, Jos van Geffen, Michel Van Roozendael, Thomas Wagner, and Jean-Christopher Lambert
- Abstract
The QA4ECV version 1.1 stratospheric and tropospheric NO2 vertical column density (VCD) climate data records (CDR) from the satellite sensor OMI are validated, using NDACC zenith scattered light DOAS (ZSL-DOAS) and Multi Axis-DOAS (MAX-DOAS) data as a reference. The QA4ECV OMI stratospheric VCD have a small bias of ~ 0.2 Pmolec cm-2 (5–10 %) and a dispersion of 0.2 to 1 Pmolec cm-2 with respect to the ZSL-DOAS measurements. QA4ECV tropospheric VCD observations from OMI are restricted to near-cloud-free scenes, leading to a negative sampling bias (with respect to the unrestricted scene ensemble) of a few Pmolec cm-2 up to −10 Pmolec cm-2 (−40 %) in one extreme high-pollution case. QA4ECV OMI tropospheric VCD has a negative bias with respect to the MAX-DOAS data (−1 to −4 Pmolec cm-2), a feature also found for the OMI OMNO2 standard data product. The tropospheric VCD discrepancies between satellite and ground-based data exceed by far the combined measurement uncertainties. Depending on the site, part of the discrepancy can be attributed to a combination of comparison errors (notably horizontal smoothing difference error), measurement/retrieval errors related to clouds and aerosols, and to the difference in vertical smoothing and a priori profile assumptions.
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- 2020
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41. Supplementary material to 'Validation of Aura-OMI QA4ECV NO2 Climate Data Records with ground-based DOAS networks: role of measurement and comparison uncertainties'
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Steven Compernolle, Tijl Verhoelst, Gaia Pinardi, José Granville, Daan Hubert, Arno Keppens, Sander Niemeijer, Bruno Rino, Alkis Bais, Steffen Beirle, Folkert Boersma, John P. Burrows, Isabelle De Smedt, Henk Eskes, Florence Goutail, François Hendrick, Alba Lorente, Andrea Pazmino, Ankie Piters, Enno Peters, Jean-Pierre Pommereau, Julia Remmers, Andreas Richter, Jos van Geffen, Michel Van Roozendael, Thomas Wagner, and Jean-Christopher Lambert
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- 2020
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42. Estimating and Reporting Uncertainties in Remotely Sensed Atmospheric Composition and Temperature
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Thomas von Clarmann, Douglas A. Degenstein, Nathaniel J. Livesey, Stefan Bender, Amy Braverman, André Butz, Steven Compernolle, Robert Damadeo, Seth Dueck, Patrick Eriksson, Bernd Funke, Margaret C. Johnson, Yasuko Kasai, Arno Keppens, Anne Kleinert, Natalya A. Kramarova, Alexandra Laeng, Vivienne H. Payne, Alexei Rozanov, Tomohiro O. Sato, Matthias Schneider, Patrick Sheese, Viktoria Sofieva, Gabriele P. Stiller, Christian von Savigny, and Daniel Zawada
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Remote sensing of atmospheric state variables typically relies on the inverse solution of the radiative transfer equation. An adequately characterized retrieval provides information on the uncertainties of the estimated state variables as well as on how any constraint or a priori assumption affects the estimate. Reported characterization data should be intercomparable between different instruments, empirically validatable, grid-independent, usable without detailed knowledge of the instrument or retrieval technique, traceable and still have reasonable data volume. The latter may force one to work with representative rather than individual characterization data. Many errors derive from approximations and simplifications used in real-world retrieval schemes, which are reviewed in this paper, along with related error estimation schemes. The main sources of uncertainty are measurement noise, calibration errors, simplifications and idealizations in the radiative transfer model and retrieval scheme, auxiliary data errors, and uncertainties in atmospheric or instrumental parameters. Some of these errors affect the result in a random way, while others chiefly cause a bias or are of mixed character. Beyond this, it is of utmost importance to know the influence of any constraint and prior information on the solution. While different instruments or retrieval schemes may require different error estimation schemes, we provide a list of recommendations which should help to unify retrieval error reporting. © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License
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- 2019
43. Review report of Ialongo et al
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Steven Compernolle
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- 2019
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44. Harmonization and comparison of vertically resolved atmospheric state observations: Methods, effects, and uncertainty budget
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Daan Hubert, Steven Compernolle, Jean-Christopher Lambert, Arno Keppens, and Tijl Verhoelst
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Atmospheric Science ,State variable ,Matching (statistics) ,010504 meteorology & atmospheric sciences ,lcsh:TA715-787 ,lcsh:Earthwork. Foundations ,0211 other engineering and technologies ,Process (computing) ,Sampling (statistics) ,Harmonization ,02 engineering and technology ,01 natural sciences ,lcsh:Environmental engineering ,13. Climate action ,Kernel (statistics) ,State (computer science) ,lcsh:TA170-171 ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Remote sensing ,Physical quantity - Abstract
Many applications of atmospheric composition and climate data involve the comparison or combination of vertically resolved atmospheric state variables. Calculating differences and combining data require harmonization of data representations in terms of physical quantities and vertical sampling at least. If one or both datasets result from a retrieval process, knowledge of prior information and averaging kernel matrices in principle allows accounting for retrieval differences as well. Spatiotemporal mismatch of the sensed air masses and its contribution to the data discrepancies can be estimated with chemistry-transport modelling support. In this work an overview of harmonization or matching operations for atmospheric profile observations is provided. The effect of these manipulations on the information content of the original data and on the uncertainty budget of data comparisons is examined and discussed.
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- 2019
45. Chemistry and deposition in the Model of Atmospheric composition at Global and Regional scales using Inversion Techniques for Trace gas Emissions (MAGRITTE v1.0). Part B. Dry deposition
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Jean-François Müller, Trissevgeni Stavrakou, Maite Bauwens, Steven Compernolle, and Jozef Peeters
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A new module for calculating the dry deposition of trace gases is presented and implemented in the Model of Atmospheric composition at Global and Regional scales using Inversion Techniques for Trace gas Emissions (MAGRITTE v1.0). The dry deposition velocities are calculated using Wesely's classical resistance-in-series approach. While relying on analyses of the European Centre for Medium-range Weather Forecasts (ECMWF) for meteorological fields, the aerodynamic resistance calculation module is based on the ECMWF model equations for turbulent transfer within the surface layer. The stomatal resistance for water vapour is calculated using a Jarvis-type parameterization in a multi-layer canopy environment model accounting for the leaf area index (LAI). The gas-phase diffusion coefficients needed to relate the stomatal resistances of different species are calculated from molecular structure. The cuticular, mesophyll and soil resistances depend on the species reactivity and Henry's Law constant (HLC). The HLCs of organic species for which no experimental data is available are estimated using a newly-developed prediction method based on existing methods for vapour pressures (EVAPORATION, Estimation of VApour Pressure of Organics) and infinite dilution activity coefficients (AIOMFAC, Aerosol Inorganic Organic Mixtures Functional groups Activity Coefficients). Acknowledging the dominance of stomatal uptake for ozone dry deposition, the stomatal resistance model parameters for 6 of the 7 major plant functional types (PFT) are adjusted based on extensive model comparisons with field measurements of ozone deposition velocity at 24 sites worldwide. The modelled OVOC deposition velocities for 25 different OVOCs are evaluated against field data from a total of 20 studies. The comparison shows the need for a species-dependent adjustment of the canopy resistances in order to match the observed variability among different species. This is realized by multiplying the HLC of each OVOC by a species-dependent parameter f1 adjusted based on the comparisons. The values of f1 span a wide range, from values of the order of unity or less for formaldehyde and several trifunctional compounds, to > 104 for compounds seen to deposit rapidly despite their low water-solubility, like MVK, MACR, CH3CHO and PAN. Despite the acknowledged caveats of the approach, the resulting modelled deposition velocities are consistent with the existing experimental data. The results of global-scale MAGRITTE model simulations demonstrate the importance of OVOC dry deposition on their global abundance. It is found to remove from the atmosphere the equivalent of 27 % of the global NMVOC emissions on a carbon basis, as well as about 8 % of NOx emissions in the form of organic nitrates and PAN-like compounds.
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- 2018
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46. Supplementary material to 'Chemistry and deposition in the Model of Atmospheric composition at Global and Regional scales using Inversion Techniques for Trace gas Emissions (MAGRITTE v1.0). Part B. Dry deposition'
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Jean-François Müller, Trissevgeni Stavrakou, Maite Bauwens, Steven Compernolle, and Jozef Peeters
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- 2018
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47. Improving algorithms and uncertainty estimates for satellite NO2 retrievals: Results from the Quality Assurance for Essential Climate Variables (QA4ECV) project
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K. Folkert Boersma, Henk J. Eskes, Andreas Richter, Isabelle De Smedt, Alba Lorente, Steffen Beirle, Jos H. G. M. van Geffen, Marina Zara, Enno Peters, Michel Van Roozendael, Thomas Wagner, Joannes D. Maasakkers, Ronald J. van der A, Joanne Nightingale, Anne De Rudder, Hitoshi Irie, Gaia Pinardi, Jean-Christopher Lambert, and Steven Compernolle
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Global observations of tropospheric nitrogen dioxide (NO2) columns have been shown to be feasible from space, but consistent multi-sensor records do not yet exist, nor are they covered by planned activities on the international level. Harmonised, multi-decadal records of NO2 columns and their associated uncertainties can provide crucial information how the emissions and concentrations of nitrogen oxides evolve over time. Here we describe the development of a new, community best practice NO2 retrieval algorithm based on a synthesis of existing approaches. Detailed comparisons of these approaches led us to implement an enhanced spectral fitting method for NO2, a 1° × 1° TM5-MP data assimilation scheme to estimate the stratospheric background, and improve air mass factor calculations. Guided by the needs expressed by data users, producers, and WMO GCOS guidelines, we incorporated detailed per-pixel uncertainty information in the data product, along with easily traceable information on the relevant quality aspects of the retrieval. We applied the improved QA4ECV NO2 algorithm on the most actual level-1 data sets to produce a complete 22-year data record that includes GOME (1995-2003), SCIAMACHY (2002–2012), GOME-2(A) (2007 onwards) and OMI (2004 onwards). The QA4ECV NO2 spectral fitting recommendations and TM5-MP stratospheric column and air mass factor approach are currently also applied to S5P-TROPOMI. The uncertainties in the QA4ECV tropospheric NO2 columns amount to typically 40 % over polluted scenes. First validation results of the QA4ECV OMI NO2 columns and their uncertainties over Tai’an, China in June 2006 suggests little bias (−27thinsp;%) and better precision than suggested by uncertainty propagation. We conclude that our improved QA4ECV NO2 long-term data record is providing valuable information to quantitatively constrain emissions, deposition, and trends in nitrogen oxides on a global scale.
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- 2018
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48. Supplementary material to 'Improving algorithms and uncertainty estimates for satellite NO2 retrievals: Results from the Quality Assurance for Essential Climate Variables (QA4ECV) project'
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K. Folkert Boersma, Henk J. Eskes, Andreas Richter, Isabelle De Smedt, Alba Lorente, Steffen Beirle, Jos H. G. M. van Geffen, Marina Zara, Enno Peters, Michel Van Roozendael, Thomas Wagner, Joannes D. Maasakkers, Ronald J. van der A, Joanne Nightingale, Anne De Rudder, Hitoshi Irie, Gaia Pinardi, Jean-Christopher Lambert, and Steven Compernolle
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- 2018
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49. Quality Assurance Framework Development Based on Six New ECV Data Products to Enhance User Confidence for Climate Applications
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Jan-Peter Muller, Nadine Gobron, Steven Compernolle, Jörg Bernhard Schulz, Pierre Coheur, Ralf Giering, Simon Blessing, Alexander M. Wood, Isabelle De Smedt, Jean-Christopher Lambert, Joanne Nightingale, Folkert Boersma, Maya George, National Physical Laboratory [Teddington] (NPL), Royal Netherlands Meteorological Institute (KNMI), Wageningen University and Research [Wageningen] (WUR), Mullard Space Science Laboratory (MSSL), University College of London [London] (UCL), Belgian Institute for Space Aeronomy / Institut d'Aéronomie Spatiale de Belgique (BIRA-IASB), Fastopt GmbH, D-20357 Hamburg, Germany, European Commission - Joint Research Centre [Ispra] (JRC), TROPO - LATMOS, Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS), and European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)
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Meteorologie en Luchtkwaliteit ,Earth observation ,Process management ,010504 meteorology & atmospheric sciences ,Computer science ,essential climate variables ,climate data records ,earth observation satellites ,quality assurance ,traceability ,user requirements ,climate applications ,surface albedo ,LAI ,FAPAR ,NO2 ,HCHO ,CO ,space_science ,0211 other engineering and technologies ,02 engineering and technology ,01 natural sciences ,7. Clean energy ,User confidence ,Climate data records ,Climate applications ,lcsh:Science ,media_common ,Natural resource ,Quality assurance ,[SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology ,Data products ,Traceability ,Meteorology and Air Quality ,media_common.quotation_subject ,Physique de l'état solide ,Climate change ,User requirements document ,Earth observation satellite ,Surface albedo ,media_common.cataloged_instance ,Quality (business) ,Earth observation satellites ,European union ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,WIMEK ,business.industry ,Essential climate variables ,Métallurgie ,Environmental economics ,Albedo ,13. Climate action ,New product development ,General Earth and Planetary Sciences ,User requirements ,Climate model ,lcsh:Q ,business - Abstract
Data from Earth observation (EO) satellites are increasingly used to monitor the environment, understand variability and change, inform evaluations of climate model forecasts, and manage natural resources. Policymakers are progressively relying on the information derived from these datasets to make decisions on mitigating and adapting to climate change. These decisions should be evidence based, which requires confidence in derived products, as well as the reference measurements used to calibrate, validate, or inform product development. In support of the European Union's Earth Observation Programmes Copernicus Climate Change Service (C3S), the Quality Assurance for Essential Climate Variables (QA4ECV) project fulfilled a gap in the delivery of climate quality satellite-derived datasets, by prototyping a generic system for the implementation and evaluation of quality assurance (QA) measures for satellite-derived ECV climate data record products. The project demonstrated the QA system on six new long-term, climate quality ECV data records for surface albedo, leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), nitrogen dioxide (NO2), formaldehyde (HCHO), and carbon monoxide (CO). The provision of standardised QA information provides data users with evidence-based confidence in the products and enables judgement on the fitness-for-purpose of various ECV data products and their specific applications., SCOPUS: ar.j, info:eu-repo/semantics/published
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- 2018
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50. Quality assessment of the Ozone_cci Climate Research Data Package (release 2017): 2. Ground-based validation of nadir ozone profile data products
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Arno Keppens, Jean-Christopher Lambert, José Granville, Daan Hubert, Tijl Verhoelst, Steven Compernolle, Barry Latter, Brian Kerridge, Richard Siddans, Anne Boynard, Juliette Hadji-Lazaro, Cathy Clerbaux, Catherine Wespes, Daniel R. Hurtmans, Pierre-François Coheur, Jacob C. A. van Peet, Ronald J. van der A, Katerina Garane, Maria Elissavet Koukouli, Dimitris S. Balis, Andy Delcloo, Rigel Kivi, Réné Stübi, Sophie Godin-Beekmann, Michel Van Roozendael, and Claus Zehner
- Abstract
Atmospheric ozone plays a key role in air quality and the radiation budget of the Earth, both directly and through its chemical influence on other trace gases. Assessments of the atmospheric ozone distribution and associated climate change therefore demand accurate vertically-resolved ozone observations with both stratospheric and tropospheric sensitivity, both on the global and regional scales, and both in the long term and at shorter timescales. Such observations have been acquired by two series of European nadir-viewing ozone profilers, namely the scattered-light UV-visible spectrometers of the GOME family, launched regularly since 1995 (GOME, SCIAMACHY, OMI, GOME-2A/B, TROPOMI, and the upcoming Sentinel-5 series), and the thermal infrared emission sounders of the IASI type, launched regularly since 2006 (IASI on Metop platforms and the upcoming IASI-NG on Metop-SG). In particular, several Level-2 retrieved, Level-3 monthly gridded, and Level-4 assimilated nadir ozone profile data products have been improved and harmonised in the context of the ozone project of the European Space Agency’s Climate Change Initiative (ESA Ozone_cci). To verify their fitness-for-purpose, these ozone datasets must undergo a comprehensive quality assessment (QA), including (a) detailed identification of their geographical, vertical and temporal domains of validity, (b) quantification of their potential bias, noise and drift and their dependences on major influence quantities, and (c) assessment of the mutual consistency of data from different sounders. For this purpose we have applied to the Ozone_cci Climate Research Data Package (CRDP) released in 2017 the versatile QA/validation system Multi-TASTE which has been developed in the context of several heritage projects (ESA’s Multi-TASTE, EUMETSAT’s O3M-SAF, and the European Commission’s FP6 GEOmon and FP7 QA4ECV). This work, as the second in a series of four Ozone_cci validation papers, reports for the first time on data content studies, information content studies and ground-based validation for both the GOME- and IASI-type climate data records combined. The ground-based reference measurements have been provided by the Network for the Detection of Atmospheric Composition Change (NDACC), NASA’s Southern Hemisphere Additional Ozonesonde programme (SHADOZ), and other ozonesonde and lidar stations contributing to the World Meteorological Organisation’s Global Atmosphere Watch (WMO GAW). Dependence of the Ozone_cci data quality on major influence quantities – resulting in data screening suggestions to users – and perspectives for the Copernicus Sentinel missions are discussed.
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- 2018
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