Displays

The TROPOMI and OMI satellite sensors provide an exciting perspective on the sources, dispersion, and fate of air pollution, and in particular on nitrogen dioxide (NO₂). Yet it is still difficult to relate satellite observations of tropospheric NO₂ columns to the underlying NO_x emissions and their trends. Robust interpretation of satellite data relies on a good understanding of the accuracy and representativeness of the satellite data itself, but also on the relationship between NO_x emissions and the observable NO₂ amount. This relationship is influences by local chemistry, mixing and dispersion, and by the NO₂ amount in the free troposphere. We address these issues via two examples:

(1) Direct estimation of NO_x emissions from the satellite-observed build-up of pollution over the city of Paris. After validating NO₂ measurements from TROPOMI over the heart of Paris, we analyse the observed build-up of NO₂ pollution over the city along with the wind. Over the city, recently emitted NO_x has been oxidized to limited degree, facilitating the use of TROPOMI data to directly determine the strength and distribution of emissions from the city. From the observed build-up of NO₂ pollution, we find highest NO_x emissions on cold weekdays in February 2018, and lowest emissions on warm weekend days in spring 2018.

(2) Trends in NO₂ over The Netherlands. We use the QA4ECV OMI NO₂ record to investigate trends in tropospheric NO₂ columns over Europe, and in particular over The Netherlands between 2005 and 2018. In spite of the differences in metrics and sampling techniques, the NO₂ measured in the Dutch atmosphere from space and from the ground follows a trend that is consistent with predictions by emission inventories. Surface NO₂ is reduced by 32% in 2018 relative to 2005, OMI NO₂ by 35%, and NO_x emissions by 32%-38% depending on the inventory. Interestingly, the Dutch surface concentrations reveal an upward trend in the NO₂:NO ratio in line with O₃ increases. This suggests that the NO₂ makes up an increasing share of the NO_x in the lower atmosphere as NO_x emissions decline. This needs to be accounted for when interpreting NO₂ trends as proxy for NO_x trends.

How to cite: Boersma, F., Zara, M., Lorente, A., Eskes, H., and Krol, M.: Quantifying NOx emissions from a city and a small country with the TROPOMI and OMI satellite instruments, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11042, https://doi.org/10.5194/egusphere-egu2020-11042, 2020.

Nitric oxide (NO) is produced in lightning channels and quickly comes into equilibrium with nitrogen dioxide (NO₂) in the atmosphere.  The production of NO_x (NO + NO₂) leads to subsequent increases in the concentrations of ozone (O₃) and the hydroxyl radical (OH) and decreases in the concentration of methane (CH₄), thus impacting the climate system.  Global production of NO_x from lightning is uncertain by a factor of four.  NO_x production by lightning will be examined using NO₂ columns from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Copernicus Sentinel-5 Precursor Satellite with an overpass time of approximately 1330 LT and flash rates from the Geostationary Lightning Mapper (GLM) on board the NOAA GOES-16 (75.2° W) and GOES-17 (137.2° W) satellites.  Where there is overlap in coverage of the two GLM instruments, the greater of the two flash counts is used.  Two approaches have been undertaken for this analysis:  a series of case studies of storm systems over the United States, and a gridded analysis over the entire contiguous United States, Central America, northern South America, and surrounding oceans.  A modified Copernicus Sentinel 5P TROPOMI NO₂ data set is used here for the case-study analysis to improve data coverage over deep convective clouds.  In both approaches, only TROPOMI pixels with cloud fraction > 0.95 and cloud pressure < 500 hPa are used.  The stratospheric column is removed from the total slant column, and the result is divided by air mass factors appropriate for deep convective clouds containing lightning NO_x (LNO_x).  Case studies have been selected from deep convective systems over and near the United States during the warm seasons of 2018 and 2019.  For each of these systems, NO_x production per flash is determined by multiplying a TROPOMI-based estimate of the mean tropospheric column of LNO_x over each system by the storm area and then dividing by a GLM-based estimate of the flashes that contribute to the column.  In the large temporal and spatial scale analysis, the TROPOMI data are aggregated on a 0.5 x 0.5 degree grid and converted to moles LNO_x*.  GLM flash counts during the one-hour period before TROPOMI overpass are similarly binned. A tropospheric background of LNO_x* is estimated from grid cells without lightning and subtracted from LNO_x* in cells with lightning to yield an estimate of freshly produced lightning NO_x, designated LNO_x.  Results of the two approaches are compared and discussed with respect to previous LNO_x per flash estimates.

How to cite: Pickering, K., Allen, D., Bucsela, E., van Geffen, J., Eskes, H., Veefkind, P., Koshak, W., and Krotkov, N.: Estimating Lightning NOx Production Using NO2 Columns from the TROPOMI Instrument and Flashes from the Geostationary Lightning Mappers, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3974, https://doi.org/10.5194/egusphere-egu2020-3974, 2020.

New TROPOMI (Sentinel 5P) high quality satellite measurements of nitrogen dioxide (NO₂) over snow-covered regions of Siberia reveal previously undocumented but significant nitrogen oxides (NO_x = NO + NO₂) emissions associated with the natural gas industry in Western Siberia. Besides gas drilling and natural gas power plants, also gas compressor stations for the transport of natural gas are sources of high amounts of NO_x emissions, which are emitted in otherwise pristine regions. The emissions from these remote gas compressor stations are at least an order of magnitude larger than those reported for North American gas compressor stations, possibly related to less stringent environmental regulations in Siberia compared to the United States. This discovery was made possible thanks to a newly developed technique for discriminating snow covered surfaces from clouds, which for the first time allows for satellite measurements of tropospheric NO₂ columns over large boreal snow-covered areas. This results in 23% more TROPOMI observations on an annual basis. Furthermore, these observations have a precision four times better than nearly any TROPOMI observation over other areas and surfaces around the world. These new results highlight the potential of TROPOMI on Sentinel 5P as well as future satellite missions for monitoring small-scale emissions

How to cite: van der A, R., de Laat, J., Eskes, H., and Ding, J.: Connecting the dots: NOx emissions along a West Siberian natural gas pipeline., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7633, https://doi.org/10.5194/egusphere-egu2020-7633, 2020.

Prolonged exposure to high concentrations of nitrogen dioxide (NO₂) and ozone (O₃) at ground level could be harmful to human health. In-situ air pollutant concentration data observed from ground monitoring stations are limited in providing spatially continuous information. Since there are only a few stations installed above the sea, it is difficult to monitor the concentrations of air pollutants over the sea. In this study, machine learning-based models were developed to estimate ground-level NO₂ and O₃ concentrations using satellite-based remote sensing data and model-based meteorological and emission data over East Asia during 2015-2017, to overcome such limitations. NO₂ and O₃ vertical column density products from the Aura Ozone Monitoring Instrument (OMI) were used as essential predictors to estimate NO₂ and O₃ concentrations. Missing pixels of OMI products due to row anomalies were filled using a temporal convolution approach to generate the spatiotemporally continuous distribution of NO₂ and O₃ concentrations. In order to estimate the air pollutant concentrations in both land and ocean, specific values were assigned to the ocean for land-only variables. Random forest (RF) was used to develop the estimation models for NO₂ and O₃ concentrations. The RF-based models showed the results with R² values of 0.72 and 0.75, and RMSEs of 6.24 ppb and 10.56 ppb for NO₂ and O₃, respectively. The estimated results over the ocean were validated using coastal stations that are located within a 1 km distance from the coast. Compared to the model without land-only variables, the models using all variables had slightly better results. The satellite-based NO₂ and O₃ vertical column density were identified as significant variables in both models. Besides, urban land cover ratio, wind-related variables such as wind vectors, and stacked maximum wind speed had relatively high variable importance. The spatial variation of NO₂ and seasonal variation of O₃ were well shown in the estimated spatiotemporal distribution.

How to cite: Shin, M. and Im, J.: Estimating surface nitrogen dioxide and ozone concentrations using satellite-based and numerical model-based data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13635, https://doi.org/10.5194/egusphere-egu2020-13635, 2020.

IASI is a versatile mission, allowing the measurement of both meteorological parameters such as temperature and atmospheric composition for infrared absorbing species. With its long observation record and frequent overpasses, IASI is able to follow changes at different spatial scales. We studied IASI’s capability to track the anthropogenic signature associated with large cities, both in terms of temperature fingerprint (urban heat islands) and carbon monoxide (CO) content, a good tracer of human activity (transport, heating, and industrial activities). For this study we averaged the IASI data available since the launch of the first IASI, in order to increase the signal to noise, and allow discriminating the city from its surroundings. For skin temperatures we show that some cities experience much warmer temperatures than nearby rural areas, with day and night differences, whereas other urban areas appear as cold urban islands when surrounded by deserts Examples will be shown and compare with MODIS observations. For CO emitted by human activities, we identified some cities that stand out from their background, and were able to compare their CO associated signatures with measurements provided by other available spaceborne instruments such as Mopitt and TROPOMI.

How to cite: Safieddine, S., George, M., Clerbaux, C., Paracho, A., Boynard, A., Hadji-Lazaro, J., Clarisse, L., Whitburn, S., and Coheur, P.-F.: IASI observations at the city scale, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18569, https://doi.org/10.5194/egusphere-egu2020-18569, 2020.

IASI satellite ammonia (NH₃) measurements are used to identify, categorise and quantify world's NH₃ emission hotspots. In particular, applying spatial oversampling and supersampling techniques on more than 10 years of IASI measurements, we are able to track-down more than 500 localized point sources of agricultural, industrial (fertilizer, coking, soda ash, geothermal and explosives industries), urban and natural origin. We present an on-line global NH₃ point sources catalogue, consisting of an interactive global map, visualizing the distribution, type and time evolution of the different point sources (http://www.ulb.ac.be/cpm/NH3-IASI.html). Calculated satellite-based emissions of NH₃ suggest a drastic underestimation of point sources in bottom-up inventories, especially those of industrial emitters. Temporal analysis revealed rapid shifts in anthropogenic activities, such as the opening or closure of industrial plants. These results demonstrate that using NH₃ satellite data will be hugely beneficial for improving bottom-up emission inventories.

A recently obtained homogeneous data record of NH₃ total columns from IASI (ANNI-NH₃-v3R) is also used to derive trends over the last decade. We apply a bootstrap resampling method to determine the trends and to assess whether the calculated values are significant or not. We obtain the first global distribution (0.5°×0.5°) of atmospheric NH₃ trends based on 11 years (2008-2018) of IASI/Metop-A observations. Distinct temporal patterns are extracted and are analysed in light of anthropogenic activities and biomass burning events. National absolute and relative trends are also calculated and discussed.

How to cite: Clarisse, L., Van Damme, M., Franco, B., Whitburn, S., Hadji-Lazaro, J., Hurtmans, D., Clerbaux, C., and Coheur, P.-F.: Satellite monitoring of ammonia: from point sources to long-term trends, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10509, https://doi.org/10.5194/egusphere-egu2020-10509, 2020.

Air pollution is one of the leading global causes of premature mortality, necessitating routine monitoring of air quality in cities where most (55%) people now reside. Surface monitors are sparse and costly to operate, whereas satellites provide global coverage of a multitude of pollutants spanning more than 2 decades. Here we make use of the dynamic range of satellite products to understand long-term changes in air quality in target cities in the UK (London and Birmingham) and India (Kanpur and Delhi). These include nitrogen dioxide (NO₂) from OMI for 2005-2018, formaldehyde (HCHO) from OMI for 2005-2016 to monitor non-methane volatile organic compounds (NMVOCs), ammonia (NH₃) from IASI for 2008-2017 and aerosol optical depth (AOD) from MODIS for 2005-2018 to monitor PM_2.5. Where surface observations are available (almost exclusively the UK), we first evaluate the ability of the satellite observations to reproduce variability in surface air pollution. We find temporal consistency for most pollutants (R >= 0.5), with the exception of MODIS AOD and surface PM_2.5 (R = 0.3), but the decline in AOD (3.0% a^-1) and surface PM_2.5 (2.8% a^-1), so far only evaluated for London, is similar. Inconsistencies result from seasonal variability in the planetary boundary layer, differences in sampling footprint between the satellite and surface monitors, and interferences in the surface measurements (as is the case for NO₂). We find a decrease in all pollutants in Birmingham and London and an increase in all pollutants in Delhi and Kanpur, over the analysis period, but not all trends are significant. Birmingham and London NO₂ both declined by 2.5% a^-1, whereas Delhi NO₂ increased by 2.0% a^-1, so that by the end of 2018 Delhi and London have the same tropospheric column concentrations of NO₂. Only Delhi exhibits a significant NMVOCs trend (increase) of 1.8% a^-1. NH₃ trends are not significant in any of the four cities, consistent with bottom-up inventories and lack of direct controls on emissions of this pollutant, mostly from agriculture. These data show no evidence of air quality improvements in Delhi, despite rollout of strict controls on industry and vehicles.

How to cite: Vohra, K., Marais, E., Kramer, L., Bloss, W., Porter, P., Van Damme, M., Clarisse, L., and Coheur, P.-F.: A space-based perspective of trends in air quality in major cities in the UK and India, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11445, https://doi.org/10.5194/egusphere-egu2020-11445, 2020.

Formaldehyde (HCHO) observations from satellites have been widely used to constrain volatile organic compound (VOC) emission estimates. The oxidation of anthropogenic organic compounds accounts for only a small fraction(~7%) of the total HCHO column on global average (Stavrakou et al., 2009). Therefore, the use of satellite observations to infer information about anthropogenic VOC emissions is generally very challenging . However, the relative contribution of anthropogenic VOCs in and around metropolitan centers is expected to be significant. In this study, we use HCHO column data retrieved from the OMI sensor between 2005 and 2018, and calculate monthly averages for every city of more than 500,000 inhabitants based on data within 20 km of the city centers. Because of the dependence of the background and especially of the biogenic VOC source on temperature and solar radiation, and because these contributions might be significant even around large cities, it is not possible to directly infer the anthropogenic contribution to the long-term observed HCHO trends based on HCHO data. To remove these non-anthropogenic contributions, we first regress the monthly averaged columns either onto the monthly maximum surface temperature, obtained by ECMWF reanalysis data, or onto the monthly isoprene flux, calculated with the MEGAN-MOHYCAN model (Guenther et al., 2012, Stavrakou et al. 2018). Only cities for which anthropogenic emissions are estimated to exceed biogenic emission by more than a factor of 3 are considered. In this way, positive trends of up to 3% yr^-1 are found over many Asian cities, especially in China and in the Indo-gangetic Plain, whereas over European cities, South Africa and South America negative trends up to -2% yr^-1 are derived. The deduced trends are compared to the corresponding trends of global bottom-up anthropogenic VOC emission inventories and are found to be in good overall agreement. Model simulations are further needed to quantify the relationship between anthropogenic emission trends and HCHO columns, accounting for the effect of non-anthropogenic emissions and potential changes in the oxidizing capacity.

How to cite: Bauwens, M., Stavrakou, J., Müller, J.-F., De Smedt, I., and Elguindi, N.: 14 years of OMI HCHO observations reveal VOC emission trends over large cities worldwide, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11011, https://doi.org/10.5194/egusphere-egu2020-11011, 2020.

Oxygenated volatile organic compounds (OVOCs) are released to the atmosphere from biogenic, anthropogenic, and pyrogenic sources. The role and importance of OVOCs in ambient atmospheric composition and their role in climate change was established many years ago. Another topical issue is the formation of secondary organic aerosols (SOA), which potentially are relevant for cloud formation, heterogeneous chemistry, and can also contribute to the long-term transport of volatile organic compounds. OVOCs can be detected from space-borne observations using the Differential Optical Absorption Spectroscopy (DOAS) method. Here, measurements from the TROPOMI instrument, which was launched on the Sentinel-5 Precursor (S5P) platform in October 2017, are used. 

During the year 2019, large wildfires occurred in North America, Amazonia, Siberia, and Australia. These fires created elevated amounts of many different gases, e.g. CO, NOx, OVOC, O₃, SO₂, CO, HONO, CH₃CO.O₂.NO₂ (PAN) and other toxic species as well as aerosols affecting air quality. During the transport of plumes from fires, photochemical transformation of emitted species occurs. Overall, polluted air is transported to regions where the plumes are dispersed. For many of the fires, unexpectedly high amounts of OVOCs are detected in plumes as consequence of continued emission and conversion of some OVOCs. The amounts of OVOCs emitted were found to depended on the type of biomass burned and the location of the fires. 

Here, a characterization of OVOC emissions from fires is performed by using OVOC S5P observations, in combination with forward trajectories simulated with the FLEXPART model and proxies of vegetation types, leading to new insights in the emissions of OVOCs from fires.

How to cite: Alvarado, L. M. A., Richter, A., Vrekoussis, M., Hilboll, A., Kalisz Hedegaard, A. B., and Burrows, J. P.: Characterization of OVOC emission from wildfires using observations from Sentinel-5 Precursor, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19214, https://doi.org/10.5194/egusphere-egu2020-19214, 2020.

During the time period December 2019 – January 2020, a lot of biomass burning has been ongoing in the Southern Hemisphere. This led to a large amount of fine dust being emitted and transported into the atmosphere of the Southern Hemisphere.

With the dispersion model FLEXPART, we will simulate these fires, using the CAMS Global Fire Assimilation System (GFAS) which assimilates fire radiative power (FRP) observations from satellite-based sensors in order to reproduce daily estimates of biomass burning emissions. Also information on the injection height is available.

Aerosol fluxes and sources in Antarctica and its closely associated Southern Ocean are poorly constrained, in particular the particle chemistry. A detailed understanding of present-day atmospheric transport pathways of particles and of volatile organic compounds (VOC) from source to deposition in Antarctica remains essential to document biogeochemical cycles and the relative importance of natural and anthropogenic compounds. Within the CHASE project (chase.meteo.be), the Royal Meteorological Institute of Belgium, Ghent University and the Université Libre de Bruxelles are doing research at the Belgian research station Princess Elisabeth (71.9°S, 23.3°E; East Antarctica, Dronning Maud Land) on the physical-chemical composition of both atmospheric particles, particles in surface snow particles as well as of VOCs. Since 2018, samples are taken both near the Belgian research station Princess Elisabeth (active sampling with pumps) and on a transect to the coast (passive samplers and surface snow samples).

In this contribution we will thoroughly investigate the atmospheric transport pathways of the recent biomass burning plumes, and in particular to what extent parts of these plumes have reached Antarctica. The measured chemical signatures of atmospheric particles and VOCs will help to constrain the simulations of the dispersion model.

How to cite: De Causmaecker, K., Mangold, A., Walgraeve, C., Van Overmeiren, P., Mattielli, N., Gili, S., and Delcloo, A. W.: Identifying source regions at the Princes Elisabeth station in Antarctica, using dispersion modelling tools: a case study, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9127, https://doi.org/10.5194/egusphere-egu2020-9127, 2020.

Particulate matter and other air pollutants have become an increasing burden on the environment and human health. Especially in metropolitan and high-traffic areas, air quality is often remarkably reduced. For a better understanding of the air quality in specific areas, which is of great environment-political interest, data with high resolution in space and time is required. The combination of satellite observations and chemistry-transport-modelling has proven to give a good database for assessments and analyses of air pollution. In contrast to sample in-situ measurements, satellite observations provide area-wide coverage ​​of measurements and thus the possibility for an almost gapless mapping of actual air pollutants. For a high temporal resolution, chemistry-transport-models are needed, which calculate concentrations of specific pollutants in continuous time steps. Satellite observations can thus be used to improve model performances.

There are no direct satellite-measurements of fine particulate matter (PM2.5) but ground-level concentrations of PM2.5 can be derived from optical parameters such as aerosol optical depth (AOD). A wide range of methods for the determination of PM2.5 concentrations from AOD measurements has been developed so far, but it is still a big challenge. In this study a semi-empirical approach based on the physical relationships between meteorological and optical parameters was applied to determine a first-guess of ground-level PM2.5 concentrations for the year 2018 and the larger Germany region. Therefor AOD observations of MODIS (Moderate Resolution Imaging Spectroradiometer) aboard the NASA Aqua satellite were used in a spatial resolution of 3km. First results showed an overestimation of ground-level aerosols and quiet low correlations with in-situ station measurements from the European Environmental Agency (EEA). To improve the results, correction factors were calculated using the coefficients of linear regression between satellite-based and in-situ measured particulate matter concentrations. Spatial and seasonal dependencies were taken into account with it. Correlations between satellite and in-situ measurements could be improved applying this method.

The MODIS 3km AOD product was found to be a good base for area-wide calculations of ground-level PM2.5 concentrations. First comparisons to the calculated PM2.5 concentrations from chemistry-transport-model POLYPHEMUS/DLR showed significant differences though. Satellite observations will now be used to improve the general model performance, first by helping to find and understand regional and temporal dependencies in the differences. As part of the German project S-VELD funded by the Federal Ministry of Transport and Digital Infrastructure BMVI, it will help for example to adjust the derivation of particle emissions within the model.

How to cite: Handschuh, J., Baier, F., Erbertseder, T., and Schaap, M.: Satellite-based observations of ground-level fine particulate matter and comparison to a regional air quality model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10342, https://doi.org/10.5194/egusphere-egu2020-10342, 2020.

NOAA is developing the Unified Forecast System (UFS) (https://ufscommunity.org/) as the source system for operational numerical weather prediction applications.  The UFS will be a coupled, comprehensive Earth modeling system with community contributions. The UFS is designed to streamline and simplify NOAA/National Weather Service operational modeling suite.  Integration of air quality predictions into the UFS began with testing of the Community Multiscale Air Quality modeling system (CMAQ) predictions driven by the operational version of the Global Forecast System (GFS), which includes the Finite-Volume Cubed-Sphere (FV3) dynamical core since June 2019.  In addition to system integration, this testing allows us to extend ozone and PM2.5 predictions to 72 hours (from 48 hours that operational predictions currently cover).  Integration of global aerosol prediction based on the Goddard Chemistry Aerosol Radiation and Transport (GOCART) scheme into the UFS begun by including it into one member of the Global Ensemble Forecast System (GEFS-Aerosol). GEFS-Aerosol predictions demonstrate a substantial improvement for both composition and variability of aerosol distributions over those from the currently operational standalone global aerosol prediction system.

The use of satellite observations in atmospheric composition and air quality predictions is increasing at NOAA.  Real-time estimates of biomass burning emissions for predictions are based on satellite data.  Challenges for these emissions involve detection of fires, the strength and composition of the emissions, altitude of the plume rise, temporal distribution of the emissions and the uncertainty in persistence or change of emissions during the forecast period. Representation of changing fire emissions in the model becomes more important with increasing prediction length.  Assimilation of Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) observations is under development to constrain aerosol distribution in the global system. Initial testing shows promise for improvement of predictions as well as limitations indicating a need for refinements in quality control, data assimilation impacts on aerosol composition and vertical distribution, as well as a need for bias correction of satellite observations.   Plans for the next-generation regional system include assimilation of satellite retrievals of VIIRS AOD and Sentinel-5 Precursor Tropospheric Ozone Monitoring Instrument (S5P TROPOMI) NO2. Satellite data also play an important role in verification of aerosol predictions. Additional uses of satellite data include verification and evaluation of model predictions such as aerosol vertical profile with TROPOMI aerosol layer height product as well as efforts to constrain and update anthropogenic emissions.

This presentation will overview advances and challenges in model development and the use of satellite data for operational atmospheric composition and air quality predictions at NOAA.

How to cite: Stajner, I. and the Global Aerosol and Regional Air Quality Prediction Team: Advances in operational air quality and aerosol prediction at NOAA/National Weather Service, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12059, https://doi.org/10.5194/egusphere-egu2020-12059, 2020.

In contrast to most aerosol species, black carbon and dust absorb visual light and may heat the atmosphere. However, their overall effect is highly uncertain. In this study we explore the use of novel satellite AAOD (Absorptive Aerosol Optical Depth) measurements in evaluating global (AEROCOM) models.  

Two POLDER retrieval products, and one product each from OMI and a CALIOP/MODIS combination are intercompared and evaluated with AERONET ("truth") data. While all products have skill in measuring AAOD, there are substantial biases amongst the products. In particular, we note a bias between the two POLDER products of 0.04 in SSA (Single Scattering Albedo), independent of AOD (Aerosol Optical Depth). Identification of the cause of this bias would allow a substantial improvement in AAOD observations. However, we show that even with such biases, consistent evaluation of global models with satellite products is possible.

In particular we show that there can be substantial under- and over-estimates of AAOD, depending on model. Furthermore, in recent years, models have diverged amongst themselves. This can be traced to different emission inventories, and we show that satellite AAOD may be used to provide constraints on these emissions. At the same time, models still differ in their particle properties, and we show that this can, to some extent, be evaluated with observations as well.

In addition, we will introduce a similar study for an ensemble of 14 satellite products of AOD. This larger ensemble allows us to study AOD diversity between the products in detail. In particular, we show that this diversity is a pretty good predictor of AOD uncertainty (versus "truth" data) in multi-year averages. This provides us with uncertainty estimates even in the absence of truth data, which allows many exciting applications (to be discussed).

These studies are the fruit of collaboration between the AEROCOM (AEROsol Comparisons between Observations and Models, https:// aerocom.met.no) and AEROSAT (International Satellite Aerosol Science Network, https://aero-sat.org) communities.

How to cite: Schutgens, N. and the AEROCOM & AEROSAT teams: AEROCOM/AEROSAT: use of satellite observations in evaluating global aerosol models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6025, https://doi.org/10.5194/egusphere-egu2020-6025, 2020.

Atmospheric carbon monoxide (CO) has been decreasing since 2000 as observed by both satellite- and ground-based instruments, but global bottom-up emission inventories surprisingly estimate increasing anthropogenic CO emissions concurrently. In this study, we use a multi-species atmospheric Bayesian inversion approach to attribute satellite-observed atmospheric CO variations to its sources and sinks in order to achieve full closure of the global CO budget during 2000–2017. Our observation constraints include satellite retrievals of the total column mole fraction of CO from MOPITT, formaldehyde (HCHO) from OMI, and methane (CH₄) from GOSAT, which are all major components of the atmospheric CO cycle. Three inversions are performed to use the observation data to the maximum extent possible as they become available and assess the consistency of inversion results to the assimilation of more trace gas species. We identify a declining trend in the global CO budget since 2000 (three inversions are broadly consistent), driven by reduced anthropogenic emissions in the U.S. and Europe (both likely from the transport sector), and in China (likely from industry and residential sectors), as well as by reduced biomass burning emissions globally, especially in Equatorial Africa (associated with reduced burned areas). We show that the trends and drivers of the inversion-based CO budget are not affected by the inter-annual variation assumed for prior CO fluxes. All three inversions estimate that surface CO emissions contradict the global bottom-up inventories in the world’s top two emitters for the sign of anthropogenic emission trends in China (e.g., here −0.8 ± 0.5% yr⁻¹ since 2000 while the prior gives 1.3 ± 0.4% yr⁻¹) and for the rate of anthropogenic emission increase in South Asia (e.g., here 1.0 ± 0.6% yr⁻¹ since 2000 smaller than 3.5 ± 0.4% yr⁻¹ in the prior inventory). The comparison of the three inversions with different observation constraints further suggests that the most complete constrained inversion that assimilates MOPITT CO, OMI HCHO, and GOSAT CH₄ has a good representation of the global CO budget, therefore matches best with independent observations, while the inversion only assimilating MOPITT CO tends to underestimate both the decrease in anthropogenic CO emissions and the increase in the CO chemical production.

How to cite: Zheng, B., Chevallier, F., Yin, Y., Ciais, P., Fortems-Cheiney, A., Deeter, M., Parker, R., Wang, Y., Worden, H., and Zhao, Y.: Global atmospheric carbon monoxide budget 2000–2017 inferred from multi-species atmospheric inversions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6002, https://doi.org/10.5194/egusphere-egu2020-6002, 2020.

The 3 IASI instruments on-board the Metop satellites have been sounding the atmospheric composition since 2006. Up to ~30 atmospheric gases can be measured from IASI spectra, allowing monitoring of weather, atmospheric chemistry, and climate.

Extreme events such as fires, high pollution episodes, volcanic eruptions, industrial accidents, etc., that impact on the population and the environment have become a major political issue. With IASI providing global observations twice a day in near real time, a new way for the systematic and continuous detection of exceptional atmospheric events to support operational decisions is possible.

In this work, we explore and improve an automatic system for the detection and characterization of extreme events, which relies on the principal component analysis (PCA) method. We assess this PCA-based system by analysing IASI raw and compressed spectra along with their differences (residuals) for various past and documented extreme events. The benefits and limitations of this method will be discussed. A new method based on the refined analysis of residuals for the whole year 2019 is proposed, that could be used as an automatic detection method for unexpected events. Finally, we investigate the potential of deep learning methods as a way to compare residuals with a database of extreme event in order to better characterize detected events.

How to cite: Vu Van, A., Boynard, A., Prunet, P., Jolivet, D., Lezeaux, O., Henry, P., Camy-Peyret, C., and Clerbaux, C.: Detection of extreme events with IASI observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2720, https://doi.org/10.5194/egusphere-egu2020-2720, 2020.

How to cite: Yamanouchi, S., Viatte, C., Strong, K., Jones, D. B. A., Clerbaux, C., Van Damme, M., Clarisse, L., and Coheur, P. F.: Multiscale observations of NH3 around Toronto, Canada, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10486, https://doi.org/10.5194/egusphere-egu2020-10486, 2020.

Ammonia (NH₃) is a key precursor to fine particulate matter (PM_2.5) and has remarkable impacts on air quality, climate, and ecosystem diversity. Satellite NH₃ observations from the Infrared Atmospheric Sounding Interferometer (IASI) provide long-term measurements of ammonia globally since 2007 and have been validated by both ground based and airborne measurements. In this study, IASI Level 2 NH₃ columns were oversampled at high-resolution (0.02°×0.02°) from 2008 to 2017 to yield monthly NH₃ maps covering the two top agricultural exporting regions in the world, the contiguous U.S. (CONUS) and Europe. K-means clustering was applied to identify NH₃ seasonality observations. The U.S. and Europe showed large temporal variabilities that differed by region and agricultural activities. For example, in the U.S., areas dominated by livestock waste emissions had peak NH₃ column abundances in the summer, while cropland-dominated regions tended to have spring peak and sometimes a fall shoulder. We also compared IASI NH₃ column amounts to NH₃ surface concentrations provided by the Ammonia Monitoring Network (AMoN) in the CONUS. Since IASI provides column NH₃ at ~ 9:30 LST while AMoN provides biweekly averaged surface NH₃, different factors were examined to find out the most important factors for the comparison between the two datasets (spatial window, temporal coverage, data averaging). We found that IASI data temporal coverage of the 2-week AMoN sampling period was the key factor in improving correlations. The r value increased from 0.38 to 0.73 when at least 80% of the two-week AMoN period had concurrent satellite measurements within a 25 km radius of the site. Neglecting interannual variability, the r value of multiyear monthly averaged AMoN and IASI NH₃ is 0.68, indicating the importance of temporal averaging. The good agreement between AMoN and IASI NH₃ concentrations demonstrates the feasibility of utilizing satellite NH₃ retrievals to better understand NH₃ variability in these agricultural intensive regions. With the global coverage and long data record, satellite measurements are likely to be a cost-effective approach as a supplemental source of information for understanding NH₃ variability, as well as guiding the locations of future sites within ground monitoring network. Finally, IASI NH₃ spatiotemporal variabilities will be compared to AM3 model output with bottom-up emission inventory (Magnitude And Seasonality of Agricultural Emissions model for NH3, MASAGE_NH3).

How to cite: Wang, R., Guo, X., Pan, D., Sun, K., Paulot, F., Clarisse, L., Van Damme, M., Whitburn, S., Coheur, P.-F., and Zondlo, M.: Utilizing satellite ammonia observations to better understand ammonia variability, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17924, https://doi.org/10.5194/egusphere-egu2020-17924, 2020.

Satellite data are helping to fill monitoring gaps in order to better inform decision makers and assess the impact of ammonia-related policies.  Presented is an overview demonstrating the current capabilities of the ammonia (NH₃) data product derived from the CrIS satellite instrument for monitoring, air quality forecast model evaluation, dry deposition estimates, and emissions estimates.  This includes examples of daily, seasonal, and annual observations of CrIS ammonia that demonstrate the spatiotemporal variability of ammonia globally. These results further demonstrate the ability of CrIS to observe regional changes in ammonia concentrations, such as spring maximum values over agricultural regions from the fertilizing of crops.  Also shown is the importance contribution of wildfires, especially in regions where there is little or no agriculture sources, such as the northern latitudes in North America during summer.  Initial comparisons of CrIS NH₃ satellite observations with air quality model simulations show that while there is general agreement on the spatial distribution of the anthropogenic hotspots, some areas are markedly different.  Some key findings are that dry deposition estimates of NH₃ and NO₂ from CrIS and the Ozone Monitoring Instrament (OMI), respectively, indicate that the NH₃ dominates over most regions across North America. Their 2013 annual ratio shows NH₃ accounting for ~82% and ~55 % of the combined reactive nitrogen dry deposition from these two species over Canada and the U.S.  Furthermore, we show the use of CrIS satellite observations to estimate annual and seasonal emissions over Concentrated Animal Feeding Operations (CAFOs).  These results are used to evaluate the seasonal and temporal emissions profiles used in bottom-up inventories over an agriculture hotspot, which are often underreported

How to cite: Shephard, M., McLinden, C., Dammers, E., Kharol, S., Cady-Pereira, K., Zhang, J., Bittman, S., Chow, E., and White, E.: Ammonia measurements from space with the Cross-track Infrared Sounder (CrIS): characteristics and applications, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11346, https://doi.org/10.5194/egusphere-egu2020-11346, 2020.

In this study, we used long-term (2003–2018) aerosol datasets acquired from Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2),  along with two products of MODerate resolution Imaging Spectroradiometer (MODIS)/Terra and Aqua, Collection 6.1(C6.1) and Level 2 (L2), to conduct a comprehensive validation of Aerosol Optical Depth (AOD) using 9 AERONET sites with continuous observations for at least 1 year over the Eastern Mediterranean (EM) region. In order to reduce the number of missing cells from the satellite instruments and produce a near-daily dataset, the “Dark _Target _Deep_Blue_AOD _550_Combined” (DTDB) for both Tera and Aqua at 550 nm were merged, and named as MODIS AOD. The MODIS and MERRA-2 AODs at high spatial resolution (10 km × 10 km and 50 km× 62.5 km, respectively) were resampled to 100 km spatial grid, using the nearest neighbor interpolation technique to overlap the pixels of each other, and to match the full field view of AERONET.Using the 9 collected AERONET observations, the overall performance of the daily MODIS and MERRA-2 AODs over the EM region was validated first. The results showed a significant spatial agreement between these AODs and ground-based AOD, with an acceptable bias (For MODIS: R=0.71, RMSE=0.12, MAE= 0.09, MFE= 40.3% and IOA= 0.81), (For MERRA-2: R=0.75, RMSE=0.10, MAE= 0.06, MFE= 32.1% and IOA=0.83) Moreover, the monthly mean of these AODs were also validated using the monthly mean of the AERONET observations.  The results also showed significant spatial agreement, with an acceptable bias (For MODIS: R=0.78, RMSE=0.06, MAE= 0.05, MFE= 23.7% and IOA =0.79) and (For MERRA-2: R=0.83, RMSE=0.04, MAE= 0.03, MFE= 19.5% and IOA=0.86).The obvious over estimation of daily and monthly mean in the performance of MODIS AOD [i.e., (for daily; RMB=1.2, FGE=24.4%) and (for monthly; RMB=1.21, FGE=20.1%)], and the under estimation of MERRA-2 AOD [ i.e., (for daily; RMB=0.86, FGE=-9.34% ) and (for monthly RMB=0.87, FGE=-13.2%)], should not be overlooked. Due to these systematic under-over estimation error, a new merged AOD product of MERRA-2 and MODIS named as MERRA-2 MODIS Merged AOD (MMM) was generated, on daily time series data during the years 2003-2018 using the combination method. The MMM performance results not only indicated an agreement between the MMM AOD and AERNONT AOD higher than MODIS and MERRA-2 [ i.e., (for daily; R=0.77, RMSE=0.09, MAE= 0.05, MFE= 31.3% and IOA=0.86) and (for monthly; ( R=0.86, RMSE=0.04, MAE= 0.02, MFE= 14.03% and IOA=0.92)] , but also it showed an efficient estimation closer to line1:1 [ i.e., (for daily; RMB=0.99 and FGE=6.6%) and (for daily; RMB=1.03 and FGE=5.5%)]. 

How to cite: Shaheen, A.: A New MODIS C6.1 and MERRA-2 Merged Aerosol Products: Validation over The Eastern Mediterranean Region, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-639, https://doi.org/10.5194/egusphere-egu2020-639, 2020.

Based on CALIPSO level 2 aerosol profile data and surface meteorological observation data，aerosol extinction feature in haze was statistically analysed along Huaihe River. Using backward trajectory and cluster analysis method, pollution sources were investigated. Then vertical feature mask data(VFM) and ERA Interim data were used to analyse aerosol type, vertical distribution characteristics and typical weather patterns. The results showed that aerosol extinction coefficient along Huaihe River was largest near the ground with extinction coefficient 0.53 and decreased obviously along with high. Local pollution was primary source with contribution ratio of 46%. Furth more, pollution transmission from Yangtze River delta pollution zone and Beijing-tianjin-hebei was very important for the pollution event in Huai River basin. During stagnant synoptic situation, thermal inversion layer caused by warm advection at 850 hPa resulted in local air pollution, which was composed of continental aerosol. Weak upward motion in the surface layer transported pollutants to 0.4~0.8 km, where aerosol concentration was higher than that on the ground. When subtropical anticyclone 5880 isopiestic line location moves northward and westward, Yangtze River delta was controlled by high pressure through whole layer of the atmosphere, which lead to polluted dust aerosol accumulation. Due to downdraft, extinction effect was strongest near surface and decreased with height. In the early stage of cold air south down, cold north-west airstream caused by cold advection at 850 hPa brought Beijing-tianjin-hebei pollution to Huai River basin. Polluted continental aerosol and dust aerosol was main type of pollutants. The transport height of aerosols may be higher than 2 km with maximum transport being 1~2 km.

How to cite: yu, C.: The analysis of aerosol type and vertical distribution characteristics along Huaihe River based on CALIOP satellite measuring, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6903, https://doi.org/10.5194/egusphere-egu2020-6903, 2020.

Satellite imagery over the last several decades have provided spectacular views of dust storms, biomass burning smoke, and pollution aerosols near and far downwind of source regions. However, the effect on tropospheric aerosols near the surface is perhaps the most pressing issue, especially PM2.5 which is particulate matter with aerodynamic diameters less than 2.5 µm. In fact, the Global Burden of Disease (GBD) project of the Institute for Health Metrics and Evaluation (IHME) ranks ambient PM2.5 as the 6th-highest risk factor for early death. Also, the World Health Organization assessments indicate that more than 2 million deaths occur each year due to outdoor and indoor air pollution and more than half of this population lives in developing nations. Traditionally, PM_2.5 is measured from ground-based instruments such as the Tapered Element Oscillating Microbalance (TEOM). Even though some nations have a good network of ground monitors they still cannot provide adequate coverage especially in regions that are not well populated. In most countries, monitoring is probably not a priority and measurements could vary from non-existent to very few monitors although recently there is a proliferation of low-cost sensors. However, it is indeed promising that the number of ground monitors have increased over the last decade and there are nearly 4000 monitors across the globe for which data are publicly available. The research community has long recognized that ground monitors alone are inadequate for providing a global picture of PM_2.5 especially since a vast of population centers have no ground-monitoring networks. Therefore, other data sets are used to fill the gaps and complement the ground monitors. Satellite data by far offers the best solution for monitoring global air quality at spatial and temporal scales that are not possible by other means. Converting the column AOD to surface PM_2.5 has been a subject of numerous studies and methods range from simple liner regression to complex statistical methods and machine learning approaches. We will use low earth orbiting and geostationary data sets coupled with meteorological data sets and ancillary information to demonstrate the progress and potential of satellite data for estimating PM2.5.

How to cite: Christopher, S., Xue, Z., and Gupta, P.: Remote Sensing of Particulate Matter Air Quality using Geostationary and low earth orbiting satellite data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3067, https://doi.org/10.5194/egusphere-egu2020-3067, 2020.

Delhi is the world’s most polluted capital city, with annual mean concentrations of PM_2.5, O₃ and NO₂ well above the safe legal limits for Europe. Exposure to these pollutants over short and long-time scales is associated with increases in diseases such as heart disease, stroke and lower respiratory tract infections. Local NO₂ concentrations vary by month and season and are controlled by both emissions and meteorology. Locally, vehicle pollution contributes to 67% of the total air pollution load and 48% of NO_x. The vehicle population has increased substantially in recent years due to an increase in the number of vehicles travelling into Delhi each day from surrounding areas. High pollution episodes, especially in winter, also contribute to the high annual mean observed. This may be due to the trapping of pollutants in a shallow, stable boundary layer or through the long-range transport of pollutants from surrounding regions to Delhi under favourable wind directions. However, the relative contribution of local vs regional emissions has not been quantified previously. This inhibits the introduction of targeted policies to reduce concentrations in the city.

We use observational datasets to quantify the relative contribution of local and regional emissions to local NO₂ air quality in Delhi rather than running a computationally expensive atmospheric chemistry transport model (Stirling et al., 2020). We combine satellite data from the TROPOMI instrument on the Sentinel 5 – Precursor (S5P) platform with back-trajectories, from the Reading Offline Trajectory Model (ROTRAJ). This allows us to investigate how different wind directions affect the relative contributions of local and regional NO₂ pollution to Delhi NO₂. We will then quantify the contribution of different regions and sectors to NO₂ in Delhi by combining the back-trajectories with a high resolution emission inventory for India and Delhi. This method also allows us to consider future emission control scenarios and quantify their impacts on air quality in Delhi.

How to cite: Graham, A., Pope, R., Chipperfield, M., and Stirling, E.: Impact of emissions and long-range transport on Air Quality in Delhi, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8354, https://doi.org/10.5194/egusphere-egu2020-8354, 2020.

How to cite: Pimlott, M., Chipperfield, M., Pope, R., Kerridge, B., and Siddans, R.: The potential of satellite data to calculate the global OH distribution using simplified steady state approximations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9908, https://doi.org/10.5194/egusphere-egu2020-9908, 2020.

The spatial and temporal changes of SO₂ regimes over China during 2005 to 2016 and their associated driving mechanism are investigated based on a state-of-the-art retrieval dataset. Climatological SO₂exhibits pronounced seasonal and regional variations, with higher loadings in wintertime and two prominent maxima centered in the North China Plain and the Cheng-Yu District. In the last decade, overall SO₂ decreasing trends have been reported nationwide, with spatially varying downward rates according to a general rule—the higher the SO₂ loading, the more significant the decrease. However, such decline is in fact not monotonic, but instead four distinct temporal regimes can be identified by empirical orthogonal function analysis. After an initial rise at the beginning, SO₂ in China undergoes two sharp drops in the periods 2007-2008 and 2014-2016, amid which 5-year moderate rebounding is sustained. Despite spatial coherent behaviors, different mechanisms are tied to North China and South China. In North China, the same four regimes are detected in the time series of emission that is expected to drive the regime of atmospheric SO₂, with a percentage of explained variance amounting to 81%. In contrast to North China, SO₂ emissions in South China exhibit a continuous descending tendency, due to the coordinated cuts of industrial and household emissions. As a result, the role of emissions only makes up about 45% of the SO₂ variation, primarily owing to the decoupled pathways of emission and atmospheric content during 2009 to 2013 when the emissions continue to decline but atmospheric content witnesses a rebound. Unfavorable meteorological conditions, including deficient precipitation, weaker wind speed and increased static stability, outweigh the effect of decreasing emissions and thus give rise to the rebound of SO₂ during 2009 to 2013.

How to cite: Wang, T., Wang, P., Theys, N., Tong, D., Hendrick, F., Zhang, Q., and Van Roozendael, M.: spatial and temporal changes in SO2 over China in the recent decade and the Impacts of emissions and meteorology, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4459, https://doi.org/10.5194/egusphere-egu2020-4459, 2020.

With the fast industrialization and urbanization in China, environmental pollution has become more serious and complex. Precise and real-time monitoring is the prerequisite for knowing the distribution characteristics and the evolution mechanism of the atmospheric pollutants. Over the last few years, we have successfully monitored atmospheric composition by using remote sensing from different platform, including satellite, ground and mobile vehicle, which have been validated to have good performance.

Remote sensing by satellite can describe the global distribution of various pollutants, which can also locate the emission point sources, such as factories etc. Ground-based MAX-DOAS monitor the vertical evolutions of these trace gases and aerosol at a fixed position, the column density of pollutants was divided into different layers, so we could detect transport plum in all altitude. Up to now, we have established a notional monitoring network with more than 30 MAX-DOAS, which could provide sufficient validation for satellite products and conduct scientific researches. Combining these two methods, which could provide precise horizontal and vertical distribution of pollutant, we could get a 3-D distribution of pollutants and the transport flux. Here, we analyzed the spatial distribution and temporal trends of satellite-observed air pollutants over eastern China during 2005–2017. We found significant decreasing trends in NO₂ and SO₂ since 2011 over most regions. Furthermore, we used the generalized additive models to clarify the relative contribution of local emissions and meteorological conditions. Our results show that meteorological determines daily changes in pollutants, while long-term, inter annual changes are determined by emissions. Emission reduction has played a decisive role in the recent reduction of the pollution!

How to cite: Liu, C., Hu, Q., Liu, H., Zhang, C., Tan, W., Xing, C., Su, W., Ji, X., and Lin, H.: Remote sensing of air pollution from satellite and MAX-DOAS network in China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1757, https://doi.org/10.5194/egusphere-egu2020-1757, 2020.

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.

How to cite: Pinardi, G., Van Roozendael, M., Hendrick, F., Theys, N., Compernolle, S., Lambert, J.-C., Valks, P., Liu, S., Boersma, F., and Eskes, H. and the NIDFORVAL NO2 team: 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, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9325, https://doi.org/10.5194/egusphere-egu2020-9325, 2020.

Sentinel-5P, with the TROPOMI instrument, was launched in October 2017 and is providing unique high-quality and high-resolution (5 km) observations of trace gas pollutants with a daily global coverage. In our contribution we will discuss the retrieval of nitrogen dioxide (NO2). A major contributions to the total uncertainty of these measurements are the TROPOMI retrievals of cloud fraction and effective cloud pressure (or altitude). Several cloud retrieval algorithms have been implemented, deriving cloud height information from the near-infrared O2-A band, O2-B band or the O2-O2 absorption feature near 477nm. In our presentation we will show the importance of a consistent treatment of clouds and albedo as input for the retrieval radiative transfer calculations. The impact of the different cloud products on the retrieved NO2 is demonstrated for a new implementation of the FRESCO O2-A band cloud retrieval algorithm and an implementation of the O2-O2 retrievals for TROPOMI. A recipe to make optimal use of the available cloud information is presented.

How to cite: Eskes, H., Sneep, M., van Geffen, J., Boersma, F., Wang, P., and Veefkind, P.: Impact of albedo and cloud retrievals on the NO2 tropospheric column derived from Sentinel-5P TROPOMI observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8150, https://doi.org/10.5194/egusphere-egu2020-8150, 2020.

The TROPOspheric Monitoring Instrument (TROPOMI) on-board the Sentinel-5 Precursor satellite (launched on 13 October 2017) is a nadir-viewing spectrometer measuring reflected sunlight in the ultraviolet, visible, near-infrared, and shortwave infrared spectral ranges. The measured spectra are used to retrieve total columns of trace gases, including nitrogen dioxide (NO₂). In this study, Pandora NO₂ measurements made at three sites located in or north of the Greater Toronto Area (GTA) are used to evaluate the TROPOMI NO₂ data products, including the standard Royal Netherlands Meteorological Institute (KNMI) NO₂ data product and a research data product developed by Environment and Climate Change Canada (ECCC) using a high-resolution regional air quality forecast model (used in the airmass factor calculation).

TROPOMI pixels located upwind and downwind from the Pandora sites were analyzed using a new wind-based validation method that increases the number of coincident measurements by about a factor of five compared to standard techniques. Using this larger number of coincident measurements, this work shows that both TROPOMI and Pandora instruments can reveal detailed spatial patterns (i.e., horizontal distributions) of local and transported NO₂ emissions, which can be used to evaluate regional air quality changes. The TROPOMI ECCC NO₂ research data product shows improved agreement with Pandora measurements compared to the TROPOMI standard tropospheric NO₂ data product, demonstrating the benefit of using the high-resolution regional air quality forecast model to derive NO₂ airmass factors.

How to cite: Zhao, X., Griffin, D., Fioletov, V., McLinden, C., Cede, A., Tiefengraber, M., Müeller, M., Bognar, K., Strong, K., Boersma, F., Eskes, H., Davies, J., Ogyu, A., and Lee, S. C.: Assessment of the quality of TROPOMI high-spatial-resolution NO2 data products, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10604, https://doi.org/10.5194/egusphere-egu2020-10604, 2020.

Satellite observations of the high-resolution instrument TROPOMI on Sentinel-5P make it possible to measure nitrogen dioxide (NO₂) at city level and even to quantify the variability of NO_x emissions and lifetimes on a seasonal and daily basis.
NO₂ is an air pollutant and especially in cities of particular importance due to the large number and strength of emission sources in combination with people living nearby exposing their health to the polluted air. To quantify nitrogen oxide emissions and lifetimes with their variability in space and time, satellite data is especially suited as it provides daily global coverage and large number of measurements. The TROPOspheric Monitoring Instrument (TROPOMI) on Sentinel-5P, launched in October 2017, provides, thanks to its higher spatial resolution when compared to previous satellite instruments, the possibility of detailed investigations on lifetimes and emissions of air pollutants.
Two years of TROPOMI NO₂ data with a spatial resolution of up to 3.5 km x 5.5 km together with ECMWF ERA5 wind data are analyzed. The NO₂ data around a source is linked to the ERA5 wind data and rotated to a uniform wind direction to get clear emission patterns. Out of these two-dimensional maps of the mean NO₂ distribution, one dimensional line densities are calculated by integration across wind direction. Lifetimes and emission fluxes are calculated for different NO_x sources such as cities and power plants distributed over the world. They are compared among each other and to bottom-up emission inventories. Seasonal variability and weekday versus weekend effects in lifetimes and emissions are discussed.

How to cite: Lange, K., Richter, A., and Burrows, J. P.: Variability of nitrogen oxide lifetimes and emission fluxes estimated by Sentinel-5P observations , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10121, https://doi.org/10.5194/egusphere-egu2020-10121, 2020.

Most of the anthropogenic emissions of nitrogen oxides (NOx = NO₂ + NO) are linked to burning of fossil fuels for energy production, transportation or industrial processes. However, biomass burning and in particular large wild fires in tropical and sub-tropical regions can also be important sources of nitrogen oxides, at least locally. Depending on the size of the fires, particles and gases are lifted into the free troposphere and even higher, increasing the atmospheric lifetime of NOx in these plumes and enabling long range transport.

The TROPOMI instrument on board of Sentinel 5 precursor (S5p) is a nadir viewing UV/vis imaging spectrometer launched in October 2017 and operationally providing data since July 2018. One of the main products that can be retrieved from TROPOMI spectra is tropospheric and total column NO₂. Compared to previous UV/vis satellite instruments such as GOME, SCIAMACHY, GOME2 and OMI, TROPOMI has a higher spatial resolution of 3.5 x 5.5 km². This reduced foot print size enables detection and evaluation of more localised sources such as individual fires and their plumes, and better separation of different contributions to the overall NO₂ loading.

In this presentation, IUP-Bremen TROPOMI NO₂ retrievals are evaluated for biomass burning signatures during 2018 and 2019, two years with very different burning seasons. The amounts and spatial distributions of NO₂ from fires are compared between the two years and between different fire regions, and their impact on regions downwind of the sources is investigated.

How to cite: Richter, A., Lange, K., Latsch, M., and Burrows, J. P.: What can we learn from TROPOMI observations of biomass burning NO2?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13877, https://doi.org/10.5194/egusphere-egu2020-13877, 2020.

Measurements of nitrogen dioxide (NO₂) are essential for understanding air pollution and evaluating its impacts, and satellite remote sensing is an essential approach for obtaining tropospheric NO₂ columns over wide temporal and spatial ranges. However, Ozone Monitoring Instrument (OMI) onboard Aura is affected by a loss of spatial coverage (around one-third of 60 viewing positions) commonly referred to as row anomaly since June 25^th, 2007, and especially after June 5^th, 2011. Global Ozone Monitoring Experiment-2 (GOME-2) onboard MetOp-A/B provides data with a maximum swath width of 1920 km, and it needs one and a half days to cover the globe. Therefore, it is challenging to obtain diurnal spatially continuous vertical column densities (VCDs) of tropospheric NO₂, which is limited by the performance of the instruments. Besides, the presence of clouds generates numerous missing and abnormal values that affect the application of VCDs data. To fill data gaps due to the above two reasons, this study proposes a framework for reconstructing OMI (afternoon overpass) tropospheric NO VCDs over China by combining GOME-2 (morning overpass) products. First, we investigated the ground-based hourly NO₂ concentration to characterize the diurnal variations, thus deriving the underlying factors that cause the difference between morning VCDs and afternoon ones. Then, the eXtreme Gradient Boosting (XGBoost) method was applied to estimate the missing values of OMI QA4ECV tropospheric NO₂ VCDs from GOME-2 GDP offline products and other ancillary variables. The spatial coverage of OMI grids (binned to 0.25°) over China from 2015 to 2018 increased from 22% to 63% averagely. Furthermore, for those grids that are null in both products, we utilized an adaptive weighted temporal fitting method to fill missing data that the previous step produced. The reconstructed data set shows spatial and temporal patterns that are coherent with the adjacent areas. Our approach has great potential for reconstructing spatially continuous tropospheric NO₂ columns, which are critical for daily air quality monitoring.

How to cite: He, Q., Qin, K., Loyola, D., Li, D., Shi, J., and Xue, Y.: Reconstruction of spatially continuous OMI tropospheric NO2 columns over China by combining GOME-2 products, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11790, https://doi.org/10.5194/egusphere-egu2020-11790, 2020.

We compare tropospheric nitrogen dioxide (NO₂) in the United Kingdom Chemistry and Aerosol (UKCA) model v11.0 with satellite measurements from NASA Earth Observing System (EOS) Aura satellite Ozone Monitoring Instrument (OMI) troposphere NO₂ data over South and East Asia (S/A). UKCA is the atmospheric composition component of the UK Earth System Model (UKESM). UKCA has been run on ARCHER (UK National Supercomputing Service) with (a) nudged hourly outputs over S/A as well as monthly outputs globally and (b) free run monthly globally output for 2005-2015. OMI satellite Averaging Kernels (AK) has been applied on the model hourly outputs for accurate model satellite comparison for 2005 - 2015. OMI and UKCA data has been analysed spatially and temporally. Background UKCA and OMI tropospheric column NO₂ typically ranges between 0-3 x 10¹⁵ molecules/cm². Model is overestimating tropospheric NO₂ over the S/A predominantly during winter by a factor of ~2.5. 

How to cite: Pandey, A., Stevenson, D., Zhao, A., Pope, R., and Kumar, K.: Evaluating Tropospheric Nitrogen Dioxide over South and East Asia in UKCA using OMI Satellite data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19775, https://doi.org/10.5194/egusphere-egu2020-19775, 2020.

Sentinel 5 Precursor (S5P) satellite was launched into a polar orbit in October 2017, carrying the TROPOMI instrument. S5P has sun synchronous orbit with an equator crossing time of 13:30 LT; TROPOMI achieves an almost daily coverage, due to the wide swath width of 2600 km. Based on the observed spectra in the UV-Vis range the trace gases ozone, formaldehyde and nitrogen dioxide are retrieved. We developed a research tropospheric ozone product based on the operational total column from S5P and the stratospheric column obtained from Aura MLS assimilated ozone profiles using the BASCOE system. The enhanced tropospheric ozone columns are observed at several places and often collocate with locale enhancements of NO₂ and HCHO. Both these trace gases are known to be involved in the tropospheric ozone formation.

The tropospheric ozone product will be briefly presented; the focus will be on large scale enhancements of tropospheric ozone and collocated HCHO and NO₂ observations. In addition examples of enhancemnets of HONO or Glyoxal may be stduied. The large fires in the Amazonian forest and Australia cause an enhancement compared to previous years. Over the southern US both HCHO and tropospheric ozone are enhanced in summer time. Some transport of ozone and its’ precursors can be found in East Asia.

How to cite: Heue, K.-P., Loyola, D., Romahn, F., Zimmer, W., Lerot, C., van Roozendael, M., de Smedt, I., Theys, N., Chabrillat, S., Errera, Q., Christophe, Y., Eskes, H., and van Geffen, J.: TROPOMI/S5P MLS/BASCOE tropospheric ozone product and TROPOMI observation of ozone precursors, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16968, https://doi.org/10.5194/egusphere-egu2020-16968, 2020.

Launch of the Geostationary Environmental Monitoring Spectrometer (GEMS) is scheduled in early 2020 to support public service and science related to air quality and climate by providing diurnal variation of concentrations of trace gases and aerosols with high spatial/temporal resolution over Asian region. We will introduce GEMS validation methodology in parallel with a strategy for integration of existed independent measurements like as low-orbit satellite, ground-based remote sensing, and ambient surface observation data. As collections of nearly real-time and quality-assured data from existing ground-based networks are still in great needs for GEMS validation, efforts to expand observational infra-structure have been going on. Currently, two PANDORA instruments started to be in operation at Seoul and Ulsan in Korea, and PANDORA Asian Network initiated by NIER, Korea will be expanded into South East Asian region beyond Korea, China and Japan in addition. In this study, we especially try to validate the initial L2 product of GEMS gathered during IOT period by utilizing PANDORA data and other ground remote sensing data as well so that availability and feasibility of those ground observations could be assessed for GEMS validation.

Keywords: GEMS validation, ground-based remote sensing data, PANDORA

How to cite: Bae, K., Song, C.-K., Park, S.-S., Kim, S.-W., Kim, J., Lee, C.-S., Yoon, J.-M., and Chang, L.: Validation of GEMS L2 products using ground-based remote sensing data including PANDORA measurements, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20978, https://doi.org/10.5194/egusphere-egu2020-20978, 2020.

The Geostationary Environment Monitoring Spectrometer (GEMS) will be launched in February 2020 and will provide hourly observations of atmospheric compositions in the daytime. Prior to the GEMS launch, we explore an application of GEMS data as constraints for estimating anthropogenic volatile organic compound (AVOC) emissions in South Korea using formaldehyde (HCHO) vertical column densities observations from the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) onboard the B200 aircraft during the KORUS-AQ campaign. Our top-down estimates of total AVOC emissions are higher by a factor of four over the petrochemical industries compared to the bottom-up emissions. However, the national AVOC emissions from the top-down estimates are by 37% lower than those of the bottom-up emission inventory in South Korea. We also show that hourly column observations of HCHO can improve not only the total magnitude of AVOC emissions but also their diurnal variation, which is poorly constrained and used in air quality models. Our hourly estimates of AVOC emissions may, thus, improve air quality model simulations in which the simulated ozone sensitivity to AVOC emission changes are also investigated.

How to cite: Park, R., Kwon, H.-A., and Oak, Y.: Preview of the future application of the environmental geo-satellites data using aircraft platform: Estimation of anthropogenic VOC emissions from formaldehyde columns, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12587, https://doi.org/10.5194/egusphere-egu2020-12587, 2020.

Massive wildfires erupted in Amazonia and through the subarctic region in summer 2019, and in Australia in winter 2019-2020. During such biomass burning events, sizeable amounts of volatile organic compounds (VOCs) can be emitted directly by the fires as well as rapidly produced in plumes via the degradation of short-lived gas precursors. The VOCs have a significant impact on tropospheric chemistry by, e.g., affecting the oxidative capacity of the atmosphere. Nadir-viewing infrared sensors onboard meteorological satellites provide global and spatially dense observations that are very useful to track biomass burning events throughout the globe and to provide trace gas quantification in fire plumes.

We apply a general retrieval framework, based on an artificial neural network, to derive the integrated abundance (total column) of several major VOCs from the infrared radiance spectra recorded by IASI (Infrared Atmospheric Sounding Interferometer) embarked on the Metop platforms. Quasi-global distributions of methanol (CH₃OH), formic (HCOOH) and acetic (CH₃COOH) acids, PAN, acetone (CH₃COCH₃), acetylene (C₂H₂) and hydrogen cyanide (HCN) column abundance are produced twice-daily from the a.m. and p.m. overpasses of the satellite instrument. In particular, we use the IASI data to produce daily regional snapshots over biomass burning areas of interest and to quantify the VOC enhancements in the plumes from the recent Amazonian, Australian and subarctic wildfires. Finally, the abundance ratios of these VOCs to IASI carbon monoxide (CO) are presented and discussed.

How to cite: Franco, B., Clarisse, L., Hadji-Lazaro, J., Hurtmans, D., Lecomte, G., Turquety, S., Clerbaux, C., and Coheur, P.-F.: Large VOC enhancements in recent massive wildfires observed from space, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20243, https://doi.org/10.5194/egusphere-egu2020-20243, 2020.

An emission inverse modeling system to estimate wildfire smoke source strength, vertical distribution, and temporal variations by assimilating satellite observations with the HYSPLIT dispersion model for smoke forecasting has been built. In this so-called HEIMS-fire system, a cost function is defined to quantify the differences between the satellite smoke products and their model counterparts, weighted by the model and observation uncertainties. Smoke sources that minimize this cost function provide the optimal smoke emission estimates. It has been successfully applied to hindcast smoke distribution during a Southeast US wildfire event in 2016 using GOES GASP products. A new Advanced Baseline Imager (ABI) sensor onboard GOES-16 has become fully operational since December 2017. The ABI smoke products have better spatial and temporal resolutions than those from its predecessors. In this study, the ABI observations during the 2018 Camp Fire event in California USA are tested in the HEIMS-fire system. Hindcasts using the emission estimates by the HEIMS-fire system will be performed. Comparison between this new emission estimation system and other emission estimates will be conducted. In addition, the impact of additional observations including the tailored ones will be investigated.

How to cite: Chai, T., Kim, H., Stein, A., Tong, D., Li, Y., and Kondragunta, S.: A case study of the 2018 Camp Fire event using HYSPLIT-based emission inverse modeling system with GOES Advanced Baseline Imager (ABI) observations and other measurements for wildfire smoke forecasts, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12525, https://doi.org/10.5194/egusphere-egu2020-12525, 2020.

Bromine monoxide (BrO) is a halogen radical altering the atmospheric ozone chemistry, e. g. in polar regions, the stratosphere as well as volcanic plumes. In particular, the molar bromine to sulphur ratio in volcanic gas emissions is characteristic to the magmatic composition of a volcano.

The high spatial resolution of S5-P/TROPOMI (up to 3.5x5.5km²) and daily coverage offer the potential to detect BrO even during minor eruptions and determine BrO/SO₂ ratios during continuous passive degassing.

Here, we present a global overview of BrO/SO₂ molar ratios in volcanic plumes derived from a systematic investigation of two years (2018 and 2019) of TROPOMI data.

We retrieved BrO column densities as well as SO₂ column densities using Differential Optical Absorption Spectroscopy (DOAS) and calculated mean BrO/SO₂ molar ratios for each volcano. The calculated BrO/SO₂ molar ratios differ strongly between different volcanoes ranging between several 10^-5 and 10^-4. The data are classified and discussed with regard to several volcanic parameters –  more specific the volcanic region, volcano type (i. e. subduction zone, hotspot etc.) as well as activity level.

How to cite: Warnach, S., Sihler, H., Borger, C., Bobrowski, N., Schmitt, S., Schöne, M., Beirle, S., Platt, U., and Wagner, T.: A global perspective on Bromine monoxide composition in volcanic plumes derived from S5-P/TROPOMI, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21303, https://doi.org/10.5194/egusphere-egu2020-21303, 2020.

Halogen radicals can drastically alter the atmospheric chemistry. In the polar regions, this is made evident, among others, by the almost complete destruction of boundary layer ozone during polar springs. These recurrent episodes of catalytic ozone depletion, referred to as Ozone Depletion Events (ODE), are caused by enhanced concentrations of reactive bromine compounds. The proposed mechanism by which these are released into the atmosphere is called bromine explosions -  reactive bromine is formed autocatalytically from the condensed phase. Enhanced bromine oxide concentrations have been observed by ground-based measurements as well as from aircraft and satellite, where the large spatial coverage allows to study the spatial extent of the phenomenon and its correlation with meteorological data as well as climate change.

The spatial resolution of S-5P/TROPOMI of 3,5 km x 7 km allows improved localization of these events and to resolve finer structures compared to previous satellite measurements. Together with the better than daily coverage over the polar regions, this allows investigations of the spatio-temporal variability of enhanced BrO levels and their relation to different possible bromine sources and release mechanisms.

We present tropospheric BrO column densities retrieved from TROPOMI data using Differential Optical Absorption Spectroscopy (DOAS). Building on methods from statistical data analysis and machine learning, we separate the tropospheric partial column from the total column using solely data (BrO, O3 and NO2) measured by satellite. The observations are discussed with regards to sea ice coverage and meteorological influences.

How to cite: Schöne, M., Sihler, H., Warnach, S., Borger, C., Beirle, S., Wagner, T., and Platt, U.: Separating tropospheric and stratospheric BrO columns over the Arctic using TROPOMI data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20775, https://doi.org/10.5194/egusphere-egu2020-20775, 2020.

Iodine compounds are mainly emitted from the oceans through organic and inorganic pathways followed by photolysis and reaction with ozone to create iodine monoxide (IO) molecules. Emission sources of iodine species include the sea surface, phytoplankton and macroalgae as well as volcanic eruptions. IO is an indicator of active iodine chemistry, which may be relevant for tropospheric composition due to its impact on ozone levels, the NO/NO₂ ratio and potential particle formation. Rapid changes in Polar sea ice coverage and conditions may have an impact on iodine levels in Polar Regions with respective consequences for tropospheric composition in the Arctic and Antarctic.

Differential Optical Absorption Spectroscopy is used to retrieve IO column densities from various satellite sensors, including SCIAMACHY (2002 to 2012), GOME-2 (since 2006) and TROPOMI (since 2017). Case studies are presented with a focus on the intercomparison of the retrieval quality and IO column densities from the applied instruments. Previous satellite studies have shown slightly enhanced IO column densities mainly above the Antarctic Region and within one occasion of a strong volcanic plume, while IO column densities in the Arctic remain mostly below the detection limit of the applied instruments.

Reported column densities of tropospheric IO, as previously measured from ground and from space, are fairly small and close to the detection limits of current and former satellite sensors. Optical depth values of IO absorption are on the order of a few times 10^-4. Individual satellite spectra allow trace gas retrievals with residual RMS values which lie around and often above the expected IO absorption optical depth. This is a challenge for the identification of optimal retrieval settings, especially the choice of an adequate wavelength window. Several aspects for quality control are discussed. In addition to the immediate retrieval RMS, the IO standard deviation in areas with expected low IO absorption, consistency checks with other retrieval parameters as well as plausibility of IO column density results are considered.

How to cite: Schoenhardt, A., Richter, A., Blechschmidt, A.-M., Bracher, A., and Burrows, J. P.: Retrieval quality and column densities of iodine monoxide from multiple satellite sensors – from SCIAMACHY to TROPOMI, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17183, https://doi.org/10.5194/egusphere-egu2020-17183, 2020.

Global monitoring of isotopic composition of water vapor in the atmosphere provides information regarding atmospheric hydgrological cycle in the Earth’s climate system. The observed HDO to H₂O ratio (δD) in atmospheric water vapor gives information on origin and history of air masses in the atmosphere.

In this study we present a method, software tool and some results on δD ratio retrieval from spectra, measured simultaneously in the thermal (TIR) and short wave infrared (SWIR) spectral ranges. The TANSO-FTS high spectral resolution spectrometer on board GOSAT-2 satellite is unique to perform simultaneous measurements in TIR and SWIR spectral bands. A method of simultaneous using of these both bands can improve vertical resolution of δD retrieved profile.

We applied conventional optimal estimation method to solve inverse problem. The output data of atmospheric general circulation model ECHAM5-wiso were used as a statistical ensemble of HDO and H2O a priori profiles. The averaging kernels, a posteriori covariance matrices and degrees of freedom are calculated. The retrieval algorithm is implemented using original FIRE-ARMS software and VLIDORT radiation transfer model.

This study is supported by the Russian Science Foundation grant No. 18-11-00024.

How to cite: Ilya, Z., Konstantin, G., Vyacheslav, Z., Nina, D., Ryoichi, I., and Martin, W.: A new approach to HDO/H2O ratio profile retrieval in the atmosphere from TANSO-FTS/GOSAT-2 spectrum data by using TIR and SWIR spectral ranges simultaneously: method and software, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12230, https://doi.org/10.5194/egusphere-egu2020-12230, 2020.