GOME Instrument Research Articles

Comparison of Satellite and Ground-Based Measurements of Tropospheric Ozone Columns in the Vicinity of St. Petersburg

The monitoring of tropospheric ozone in world science has recently received considerable attention since ozone in the troposphere is both a greenhouse and a pollutant gas. It also plays an important role in various chemical and photochemical processes. Ground-based measurements can be used to assess the quality and to validate satellite measurements of the global ozone distribution. The time series of ozone tropospheric columns in the 0-8 km layer derived from spectral measurements of the IASI satellite instrument using two different algorithms (IASI_LATMOS and IASI_LISA), as well as from joint measurements by the IASI and GOME-2 instruments (IASI-GOME2) were compared to ground-based measurements using the Bruker IFS 125HR Fourier spectrometer at the NDACC St. Petersburg site for 2009–2021. IASI_LISA and IASI-GOME2 on average overestimate ground-based ozone measurements by 9.8 and 5.1%, respectively, while there is no bias between the IASI_LATMOS and FTIR data. The standard deviations of the differences between ground measurements and the IASI_LISA and IASI_LATMOS data do not exceed 12–13%; for the IASI-GOME2 data they are 24.5%. Ground-based and satellite measurements agree better in spring and summer. Ground-based and IASI_LATMOS demonstrate a statistically significant negative trend in the ozone columns in the 0–8 km layer in the vicinity of St. Petersburg for the period 2012–2021, amounting to –0.71 ± 0.35% per year and –0.60 ± 0.21% per year, respectively.

Ground-based validation of the MetOp-A and MetOp-B GOME-2 OClO measurements

Abstract. This paper reports on ground-based validation of the atmospheric OClO data record produced within the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC SAF) using the Global Ozone Monitoring Experiment (GOME)-2A and GOME-2B instrument measurements, covering the 2007–2016 and 2013–2016 periods, respectively. OClO slant column densities are compared to correlative measurements collected from nine Zenith-Scattered-Light Differential Optical Absorption Spectroscopy (ZSL-DOAS) instruments from the Network for the Detection of Atmospheric Composition Change (NDACC) distributed in both the Arctic and Antarctic. Sensitivity tests are performed on the ground-based data to estimate the impact of the different OClO DOAS analysis settings. On this basis, we infer systematic uncertainties of about 25 % (i.e., about 3.75×1013 molec. cm−2) between the different ground-based data analyses, reaching total uncertainties ranging from about 26 % to 33 % for the different stations (i.e., around 4 to 5×1013 molec. cm−2). Time series at the different sites show good agreement between satellite and ground-based data for both the inter-annual variability and the overall OClO seasonal behavior. GOME-2A results are found to be noisier than those of GOME-2B, especially after 2011, probably due to instrumental degradation effects. Daily linear regression analysis for OClO-activated periods yield correlation coefficients of 0.8 for GOME-2A and 0.87 for GOME-2B, with slopes with respect to the ground-based data ensemble of 0.64 and 0.72, respectively. Satellite minus ground-based offsets are within 8×1013 molec. cm−2, with some differences between GOME-2A and GOME-2B depending on the station. Overall, considering all the stations, a median offset of about -2.2×1013 molec. cm−2 is found for both GOME-2 instruments.

Evaluating the assimilation of S5P/TROPOMI near real-time SO<sub>2</sub> columns and layer height data into the CAMS integrated forecasting system (CY47R1), based on a case study of the 2019 Raikoke eruption

Abstract. The Copernicus Atmosphere Monitoring Service (CAMS), operated by the European Centre for Medium-Range Weather Forecasts on behalf of the European Commission, provides daily analyses and 5 d forecasts of atmospheric composition, including forecasts of volcanic sulfur dioxide (SO2) in near real time. CAMS currently assimilates total column SO2 products from the GOME-2 instruments on MetOp-B and MetOp-C and the TROPOMI instrument on Sentinel-5P, which give information about the location and strength of volcanic plumes. However, the operational TROPOMI and GOME-2 data do not provide any information about the height of the volcanic plumes, and therefore some prior assumptions need to be made in the CAMS data assimilation system about where to place the resulting SO2 increments in the vertical. In the current operational CAMS configuration, the SO2 increments are placed in the mid-troposphere, around 550 hPa or 5 km. While this gives good results for the majority of volcanic emissions, it will clearly be wrong for eruptions that inject SO2 at very different altitudes, in particular exceptional events where part of the SO2 plume reaches the stratosphere. A new algorithm, developed by the German Aerospace Centre (DLR) for GOME-2 and TROPOMI, optimized in the frame of the ESA-funded Sentinel-5P Innovation–SO2 Layer Height Project, and known as the Full-Physics Inverse Learning Machine (FP_ILM) algorithm, retrieves SO2 layer height from TROPOMI in near real time (NRT) in addition to the SO2 column. CAMS is testing the assimilation of these products, making use of the NRT layer height information to place the SO2 increments at a retrieved altitude. Assimilation tests with the TROPOMI SO2 layer height data for the Raikoke eruption in June 2019 show that the resulting CAMS SO2 plume heights agree better with IASI plume height data than operational CAMS runs without the TROPOMI SO2 layer height information and show that making use of the additional layer height information leads to improved SO2 forecasts. Including the layer height information leads to higher modelled total column SO2 values in better agreement with the satellite observations. However, the plume area and SO2 burden are generally also overestimated in the CAMS analysis when layer height data are used. The main reason for this overestimation is the coarse horizontal resolution used in the minimizations. By assimilating the SO2 layer height data, the CAMS system can predict the overall location of the Raikoke SO2 plume up to 5 d in advance for about 20 d after the initial eruption, which is better than with the operational CAMS configuration (without prior knowledge of the plume height) where the forecast skill is much more reduced for longer forecast lead times.

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

Abstract. Multi-axis differential optical absorption spectroscopy (MAX-DOAS) and direct sun NO2 vertical column network data are used to investigate the accuracy of tropospheric NO2 column measurements of the GOME-2 instrument on the MetOp-A satellite platform and the OMI instrument on Aura. The study is based on 23 MAX-DOAS and 16 direct sun instruments at stations distributed worldwide. A method to quantify and correct for horizontal dilution effects in heterogeneous NO2 field conditions is proposed. After systematic application of this correction to urban sites, satellite measurements are found to present smaller biases compared to ground-based reference data in almost all cases. We investigate the seasonal dependence of the validation results as well as the impact of using different approaches to select satellite ground pixels in coincidence with ground-based data. In optimal comparison conditions (satellite pixels containing the station) the median bias between satellite tropospheric NO2 column measurements and the ensemble of MAX-DOAS and direct sun measurements is found to be significant and equal to −34 % for GOME-2A and −24 % for OMI. These biases are further reduced to −24 % and −18 % respectively, after application of the dilution correction. Comparisons with the QA4ECV satellite product for both GOME-2A and OMI are also performed, showing less scatter but also a slightly larger median tropospheric NO2 column bias with respect to the ensemble of MAX-DOAS and direct sun measurements.

Updated tropospheric chemistry reanalysis and emission estimates, TCR-2, for 2005–2018

Abstract. This study presents the results from the Tropospheric Chemistry Reanalysis version 2 (TCR-2) for the period 2005–2018 at 1.1∘ horizontal resolution obtained from the assimilation of multiple updated satellite measurements of ozone, CO, NO2, HNO3, and SO2 from the OMI, SCIAMACHY, GOME-2, TES, MLS, and MOPITT satellite instruments. The reanalysis calculation was conducted using a global chemical transport model MIROC-CHASER and an ensemble Kalman filter technique that optimizes both chemical concentrations of various species and emissions of several precursors, which was efficient for the correction of the entire tropospheric profile of various species and its year-to-year variations. Comparisons against independent aircraft, satellite, and ozonesonde observations demonstrate the quality of the reanalysis fields for numerous key species on regional and global scales, as well as for seasonal, yearly, and decadal scales, from the surface to the lower stratosphere. The multi-constituent data assimilation brought the model vertical profiles and interhemispheric gradient of OH closer to observational estimates, which was important in improving the description of the oxidation capacity of the atmosphere and thus vertical profiles of various species. The evaluation results demonstrate the capability of the chemical reanalysis to improve understanding of the processes controlling variations in atmospheric composition, including long-term changes in near-surface air quality and emissions. The estimated emissions can be employed for the elucidation of detailed distributions of the anthropogenic and biomass burning emissions of co-emitted species (NOx, CO, SO2) in all major regions, as well as their seasonal and decadal variabilities. The data sets are available at https://doi.org/10.25966/9qgv-fe81 (Miyazaki et al., 2019a).

Evaluating the impact of blowing-snow sea salt aerosol on springtime BrO and O<sub>3</sub> in the Arctic

Abstract. We use the GEOS-Chem chemical transport model to examine the influence of bromine release from blowing-snow sea salt aerosol (SSA) on springtime bromine activation and O3 depletion events (ODEs) in the Arctic lower troposphere. We evaluate our simulation against observations of tropospheric BrO vertical column densities (VCDtropo) from the GOME-2 (second Global Ozone Monitoring Experiment) and Ozone Monitoring Instrument (OMI) spaceborne instruments for 3 years (2007–2009), as well as against surface observations of O3. We conduct a simulation with blowing-snow SSA emissions from first-year sea ice (FYI; with a surface snow salinity of 0.1 psu) and multi-year sea ice (MYI; with a surface snow salinity of 0.05 psu), assuming a factor of 5 bromide enrichment of surface snow relative to seawater. This simulation captures the magnitude of observed March–April GOME-2 and OMI VCDtropo to within 17 %, as well as their spatiotemporal variability (r=0.76–0.85). Many of the large-scale bromine explosions are successfully reproduced, with the exception of events in May, which are absent or systematically underpredicted in the model. If we assume a lower salinity on MYI (0.01 psu), some of the bromine explosions events observed over MYI are not captured, suggesting that blowing snow over MYI is an important source of bromine activation. We find that the modeled atmospheric deposition onto snow-covered sea ice becomes highly enriched in bromide, increasing from enrichment factors of ∼5 in September–February to 10–60 in May, consistent with composition observations of freshly fallen snow. We propose that this progressive enrichment in deposition could enable blowing-snow-induced halogen activation to propagate into May and might explain our late-spring underestimate in VCDtropo. We estimate that the atmospheric deposition of SSA could increase snow salinity by up to 0.04 psu between February and April, which could be an important source of salinity for surface snow on MYI as well as FYI covered by deep snowpack. Inclusion of halogen release from blowing-snow SSA in our simulations decreases monthly mean Arctic surface O3 by 4–8 ppbv (15 %–30 %) in March and 8–14 ppbv (30 %–40 %) in April. We reproduce a transport event of depleted O3 Arctic air down to 40∘ N observed at many sub-Arctic surface sites in early April 2007. While our simulation captures 25 %–40 % of the ODEs observed at coastal Arctic surface sites, it underestimates the magnitude of many of these events and entirely misses 60 %–75 % of ODEs. This difficulty in reproducing observed surface ODEs could be related to the coarse horizontal resolution of the model, the known biases in simulating Arctic boundary layer exchange processes, the lack of detailed chlorine chemistry, and/or the fact that we did not include direct halogen activation by snowpack chemistry.

Analysis of tropospheric NO2 over Iraq using OMI satellite measurements

Tropospheric nitrogen dioxide (NO2) is a trace gas with important impact on atmospheric chemistry, human health and a key pollutant in particular cities, measured from space since the mid-1990s by the GOME, SCIAMACHY, OMI, and GOME-2 instruments. This study present ten years (monthly and yearly averaged) dataset from Ozone Monitoring Instrument (OMI) used to investigate tropospheric NO2 characteristics and variations over Iraq during 2005–2014. Annual NO2 shows an elevation from the northern to the southern and highest values was at central parts of Iraq. Monthly distributions revels higher values NO2 in winter and summer than spring and autumn seasons, and rising NO2 throughout study period over industrial and crowded urban zones. The trend analysis over Baghdad shows a linear growth rate 9.8% per year with an annual average (5.6·1015 molecules per 1 cm2). The air mass trajectory analysis as hotspot regions shows seasonal fluctuations between winter and summer seasons depend on weather conditions and topography. The increased NO2 values in winter are due to anthropogenic emissions and subsequent plumes from Europe. In addition, in summer because of hot weather and large paddy fields emissions. The lowest NO2 value was at monsoon period mostly linked to the rains. The OMI data and satellite information are able to observe the troposphere NO2 elevation at different regions.

Analysis of tropospheric NO2 over Iraq using OMI satellite measurements

Tropospheric nitrogen dioxide (NO2) is a trace gas with important impact on atmospheric chemistry, human health and a key pollutant in particular cities, measured from space since the mid-1990s by the GOME, SCIAMACHY, OMI, and GOME-2 instruments. This study present ten years (monthly and yearly averaged) dataset from Ozone Monitoring Instrument (OMI) used to investigate tropospheric NO2 characteristics and variations over Iraq during 2005–2014. Annual NO2 shows an elevation from the northern to the southern and highest values was at central parts of Iraq. Monthly distributions revels higher values NO2 in winter and summer than spring and autumn seasons, and rising NO2 throughout study period over industrial and crowded urban zones. The trend analysis over Baghdad shows a linear growth rate 9.8% per year with an annual average (5.6·1015 molecules per 1 cm2). The air mass trajectory analysis as hotspot regions shows seasonal fluctuations between winter and summer seasons depend on weather conditions and topography. The increased NO2 values in winter are due to anthropogenic emissions and subsequent plumes from Europe. In addition, in summer because of hot weather and large paddy fields emissions. The lowest NO2 value was at monsoon period mostly linked to the rains. The OMI data and satellite information are able to observe the troposphere NO2 elevation at different regions.

Validation of the GOME-2 Absorbing Aerosol Height Product Using Elevated Layer Top Height Obtained from Thessaloniki EARLINET Station

The purpose of this research is to investigate the ability of GOME-2 instrument on board on the MetOpA and MetOpB platforms, to deliver accurate geometrical features of lofted aerosol layers over the area of Thessaloniki. For this purpose, we use ground-based lidar data from the Thessaloniki lidar station that belongs to EARLINET network. Results of the validation between absorbing aerosol height (ΑΑΗ) fromΑΑΗ) from) from GOME-2 sensor and THELISYS lidar dataset products will be presented.

Satellite-observed solar-induced chlorophyll fluorescence reveals higher sensitivity of alpine ecosystems to snow cover on the Tibetan Plateau

Snow cover, which has been declining over recent decades because of rising temperatures, impacts alpine vegetation in various ways on the Tibetan Plateau (TP). However, it is still unclear how the alpine ecosystem responses to the rapid changes of snow cover on the TP. Here, we use the novel spaceborne measurements of solar-induced fluorescence (SIF) from the GOME-2 instrument, a direct proxy of photosynthetic activity, along with traditional vegetation indices (VIs) to study the responses of an alpine ecosystem to changes in snow cover over the TP. We find that seasonal dynamics of SIF are more consistent with alpine vegetation gross primary productivity (GPP) than those of enhanced vegetation index (EVI), suggesting the decoupling of photosynthesis and changes in greenness-based VIs. The snow cover explains most of the interannual variability of SIF, with secondary effects due to summer air temperature, while summer precipitation has little impact on alpine vegetation photosynthesis, indicating the critical role of snow cover in regulating alpine vegetation growth on the TP. This study suggests that the alpine vegetation photosynthesis on the TP is highly dependent on the snow cover at the start of the growing season and air temperature at the end of the growing season. Our results also demonstrate that the on-going satellite measurements of SIF shed new light on the understanding of future vegetation growth and vegetation–atmosphere interactions under a changing climate on the TP.

Stratosphere–troposphere separation of nitrogen dioxide columns from the TEMPO geostationary satellite instrument

Abstract. Separating the stratospheric and tropospheric contributions in satellite retrievals of atmospheric NO2 column abundance is a crucial step in the interpretation and application of the satellite observations. A variety of stratosphere–troposphere separation algorithms have been developed for sun-synchronous instruments in low Earth orbit (LEO) that benefit from global coverage, including broad clean regions with negligible tropospheric NO2 compared to stratospheric NO2. These global sun-synchronous algorithms need to be evaluated and refined for forthcoming geostationary instruments focused on continental regions, which lack this global context and require hourly estimates of the stratospheric column. Here we develop and assess a spatial filtering algorithm for the upcoming TEMPO geostationary instrument that will target North America. Developments include using independent satellite observations to identify likely locations of tropospheric enhancements, using independent LEO observations for spatial context, consideration of diurnally varying partial fields of regard, and a filter based on stratospheric to tropospheric air mass factor ratios. We test the algorithm with LEO observations from the OMI instrument with an afternoon overpass, and from the GOME-2 instrument with a morning overpass. We compare our TEMPO field of regard algorithm against an identical global algorithm to investigate the penalty resulting from the limited spatial coverage in geostationary orbit, and find excellent agreement in the estimated mean daily tropospheric NO2 column densities (R2=0.999, slope=1.009 for July and R2=0.998, slope=0.999 for January). The algorithm performs well even when only small parts of the continent are observed by TEMPO. The algorithm is challenged the most by east coast morning retrievals in the wintertime (e.g., R2=0.995, slope=1.038 at 14:00 UTC). We find independent global LEO observations (corrected for time of day) provide important context near the field-of-regard edges. We also test the performance of the TEMPO algorithm without these supporting global observations. Most of the continent is unaffected (R2=0.924 and slope=0.973 for July and R2=0.996 and slope=1.008 for January), with 90 % of the pixels having differences of less than ±0.2×1015 molecules cm−2 between the TEMPO tropospheric NO2 column density and the global algorithm. For near-real-time retrieval, even a climatological estimate of the stratospheric NO2 surrounding the field of regard would improve this agreement. In general, the additional penalty of a limited field of regard from TEMPO introduces no more error than normally expected in most global stratosphere–troposphere separation algorithms. Overall, we conclude that hourly near-real-time stratosphere–troposphere separation for the retrieval of NO2 tropospheric column densities by the TEMPO geostationary instrument is both feasible and robust, regardless of the diurnally varying limited field of regard.

IMPROVEMENTS TO THE RETRIEVAL OF NO2 COLUMN FOR THE GOME-2 INSTRUMENT

Abstract. NO2 is an important trace gas in both the stratosphere and troposphere. Since January 2007, the nadir-looking GOME-2 satellite instrument has been providing NO2 data on a global scale which is of great interest for climate research and atmospheric chemistry studies. In this contribution, we report on an improved algorithm for the retrieval of total and tropospheric NO2 columns from the GOME-2 instrument.

The ESA GOME-Evolution “Climate” water vapor product: a homogenized time series of H<sub>2</sub>O columns from GOME, SCIAMACHY, and GOME-2

Abstract. We present time series of the global distribution of water vapor columns over more than 2 decades based on measurements from the satellite instruments GOME, SCIAMACHY, and GOME-2 in the red spectral range. A particular focus is the consistency amongst the different sensors to avoid jumps from one instrument to another. This is reached by applying robust and simple retrieval settings consistently. Potentially systematic effects due to differences in ground pixel size are avoided by merging SCIAMACHY and GOME-2 observations to GOME spatial resolution, which also allows for a consistent treatment of cloud effects. In addition, the GOME-2 swath is reduced to that of GOME and SCIAMACHY to have consistent viewing geometries.Remaining systematic differences between the different sensors are investigated during overlap periods and are corrected for in the homogenized time series. The resulting Climate product v2.2 (https://doi.org/10.1594/WDCC/GOME-EVL_water_vapor_clim_v2.2) allows the study of the temporal evolution of water vapor over the last 20 years on a global scale.

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

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

Multi-satellite sensor study on precipitation-induced emission pulses of NO<sub><i>x</i></sub> from soils in semi-arid ecosystems

Abstract. We present a top-down approach to infer and quantify rain-induced emission pulses of NOx ( ≡ NO + NO2), stemming from biotic emissions of NO from soils, from satellite-borne measurements of NO2. This is achieved by synchronizing time series at single grid pixels according to the first day of rain after a dry spell of prescribed duration. The full track of the temporal evolution several weeks before and after a rain pulse is retained with daily resolution. These are needed for a sophisticated background correction, which accounts for seasonal variations in the time series and allows for improved quantification of rain-induced soil emissions. The method is applied globally and provides constraints on pulsed soil emissions of NOx in regions where the NOx budget is seasonally dominated by soil emissions. We find strong peaks of enhanced NO2 vertical column densities (VCDs) induced by the first intense precipitation after prolonged droughts in many semi-arid regions of the world, in particular in the Sahel. Detailed investigations show that the rain-induced NO2 pulse detected by the OMI (Ozone Monitoring Instrument), GOME-2 and SCIAMACHY satellite instruments could not be explained by other sources, such as biomass burning or lightning, or by retrieval artefacts (e.g. due to clouds). For the Sahel region, absolute enhancements of the NO2 VCDs on the first day of rain based on OMI measurements 2007–2010 are on average 4 × 1014 molec cm−2 and exceed 1 × 1015 molec cm−2 for individual grid cells. Assuming a NOx lifetime of 4 h, this corresponds to soil NOx emissions in the range of 6 up to 65 ng N m−2 s−1, which is in good agreement with literature values. Apart from the clear first-day peak, NO2 VCDs are moderately enhanced (2 × 1014 molec cm−2) compared to the background over the following 2 weeks, suggesting potential further emissions during that period of about 3.3 ng N m−2 s−1. The pulsed emissions contribute about 21–44 % to total soil NOx emissions over the Sahel.

Influence of cloudiness on erythemal solar irradiance in Marsaxlokk, Malta: Two case studies

The effects of cloudiness on solar ultraviolet irradiance recorded between May and October 2012 at Marsaxlokk, Malta, are analysed. A statistical analysis of cloudiness is carried out, with clear skies (0–2oktas) proving to be the most frequent conditions under total and low cloud cover. Hence, the dependence of erythemal UV solar irradiance (UVER) on total and low cloud cover is evaluated. In all cases, low clouds show higher attenuation. The effects of total cloud cover and low clouds on UVER are evaluated by means of the cloud modification factor (CMF) that is defined as the ratio between the measured irradiance and the estimated irradiance under cloud-free conditions. The results show that the effect on UVER is less than on global solar irradiance and that UVER transmissivity under overcast conditions can reach 50%. The effect of cloud optical thickness (COT) on UVER under all conditions shows a clear exponential dependence. The analysis shows that for fairly thick clouds with a COT of 10, the UVER reaching the Earth’s surface is about 50% of its cloud-free value. The CMFUVER is found to decrease from 0.7 for COT=0.5 to 0.25 for COT=30. 10-min estimated COT from global solar irradiance are then compared with the GOME2 instrument. The correlation is found to be low. The influence of COT is studied by observing the evolution of irradiance on two cloudy days. When comparing the 10-min CMF under high COT for UVER and global irradiance, the first variable is found to be slightly higher than the second. As a consequence, changes in the ultraviolet index (UVI) due to COT effects are then described. Finally, the results obtained are compared with those from previous studies performed by different authors.

The STRatospheric Estimation Algorithm from Mainz (STREAM): estimating stratospheric NO<sub>2</sub> from nadir-viewing satellites by weighted convolution

Abstract. The STRatospheric Estimation Algorithm from Mainz (STREAM) determines stratospheric columns of NO2 which are needed for the retrieval of tropospheric columns from satellite observations. It is based on the total column measurements over clean, remote regions as well as over clouded scenes where the tropospheric column is effectively shielded. The contribution of individual satellite measurements to the stratospheric estimate is controlled by various weighting factors. STREAM is a flexible and robust algorithm and does not require input from chemical transport models. It was developed as a verification algorithm for the upcoming satellite instrument TROPOMI, as a complement to the operational stratospheric correction based on data assimilation. STREAM was successfully applied to the UV/vis satellite instruments GOME 1/2, SCIAMACHY, and OMI. It overcomes some of the artifacts of previous algorithms, as it is capable of reproducing gradients of stratospheric NO2, e.g., related to the polar vortex, and reduces interpolation errors over continents. Based on synthetic input data, the uncertainty of STREAM was quantified as about 0.1–0.2 × 1015 molecules cm−2, in accordance with the typical deviations between stratospheric estimates from different algorithms compared in this study.

Satellite chlorophyll fluorescence measurements reveal large-scale decoupling of photosynthesis and greenness dynamics in boreal evergreen forests.

Mid-to-high latitude forests play an important role in the terrestrial carbon cycle, but the representation of photosynthesis in boreal forests by current modelling and observational methods is still challenging. In particular, the applicability of existing satellite-based proxies of greenness to indicate photosynthetic activity is hindered by small annual changes in green biomass of the often evergreen tree population and by the confounding effects of background materials such as snow. As an alternative, satellite measurements of sun-induced chlorophyll fluorescence (SIF) can be used as a direct proxy of photosynthetic activity. In this study, the start and end of the photosynthetically active season of the main boreal forests are analysed using spaceborne SIF measurements retrieved from the GOME-2 instrument and compared to that of green biomass, proxied by vegetation indices including the Enhanced Vegetation Index (EVI) derived from MODIS data. We find that photosynthesis and greenness show a similar seasonality in deciduous forests. In high-latitude evergreen needleleaf forests, however, the length of the photosynthetically active period indicated by SIF is up to 6weeks longer than the green biomass changing period proxied by EVI, with SIF showing a start-of-season of approximately 1month earlier than EVI. On average, the photosynthetic spring recovery as signalled by SIF occurs as soon as air temperatures exceed the freezing point (2-3°C) and when the snow on the ground has not yet completely melted. These findings are supported by model data of gross primary production and a number of other studies which evaluated insitu observations of CO2 fluxes, meteorology and the physiological state of the needles. Our results demonstrate the sensitivity of space-based SIF measurements to light-use efficiency of boreal forests and their potential for an unbiased detection of photosynthetic activity even under the challenging conditions interposed by evergreen boreal ecosystems.

Validation of ash optical depth and layer height retrieved from passive satellite sensors using EARLINET and airborne lidar data: the case of the Eyjafjallajökull eruption

Abstract. The vulnerability of the European airspace to volcanic eruptions was brought to the attention of the public and the scientific community by the 2010 eruptions of the Icelandic volcano Eyjafjallajökull. As a consequence of this event, ash concentration thresholds replaced the “zero tolerance to ash” rule, drastically changing the requirements on satellite ash retrievals. In response to that, the ESA funded several projects aiming at creating an optimal end-to-end system for volcanic ash plume monitoring and prediction. Two of them, namely the SACS-2 and SMASH projects, developed and improved dedicated satellite-derived ash plume and sulfur dioxide level assessments. The validation of volcanic ash levels and height extracted from the GOME-2 and IASI instruments on board the MetOp-A satellite is presented in this work. EARLINET lidar measurements are compared to different satellite retrievals for two eruptive episodes in April and May 2010. Comparisons were also made between satellite retrievals and aircraft lidar data obtained with the UK's BAe-146-301 Atmospheric Research Aircraft (managed by the Facility for Airborne Atmospheric Measurements, FAAM) over the United Kingdom and the surrounding regions. The validation results are promising for most satellite products and are within the estimated uncertainties of each of the comparative data sets, but more collocation scenes would be desirable to perform a comprehensive statistical analysis. The satellite estimates and the validation data sets are better correlated for high ash optical depth values, with correlation coefficients greater than 0.8. The IASI retrievals show a better agreement concerning the ash optical depth and ash layer height when compared with the ground-based and airborne lidar data.

Development and characterisation of a state-of-the-art GOME-2 formaldehyde air-mass factor algorithm

Abstract. Space-borne observations of formaldehyde (HCHO) are frequently used to derive surface emissions of isoprene, an important biogenic volatile organic compound. The conversion of retrieved HCHO slant column concentrations from satellite line-of-sight measurements to vertical columns is determined through application of an air mass factor (AMF), accounting for instrument viewing geometry, radiative transfer, and vertical profile of the absorber in the atmosphere. This step in the trace gas retrieval is subject to large errors. This work presents the AMF algorithm in use at the University of Leicester (UoL), which introduces scene-specific variables into a per-observation full radiative transfer AMF calculation, including increasing spatial resolution of key environmental parameter databases, input variable area weighting, instrument-specific scattering weight calculation, and inclusion of an ozone vertical profile climatology. Application of these updates to HCHO slant columns from the GOME-2 instrument is shown to typically adjust the AMF by ±20 %, compared to a reference algorithm without these advanced parameterisations. On average the GOME-2 AMFs increase by 4 %, with over 70 % of locations having an AMF of 0–20 % larger than originally, largely resulting from the use of the latest GOME-2 reflectance product. Furthermore, the new UoL algorithm also incorporates a full radiative transfer error calculation for each scene to help characterise AMF uncertainties. Global median AMF errors are typically 50–60 %, and are driven by uncertainties in the HCHO profile shape and its vertical distribution relative to clouds and aerosols. If uncertainty on the a priori HCHO profile is relatively small (< 10 %) then the median AMF total error decreases to about 30–40 %.