This study presents an error budget assessment for the ozone profiles retrieved at the University of Bremen through limb observations of the Ozone Mapper and Profiler Suite – Limb Profiler Suomi ...National Polar-orbiting Partnership (OMPS-LP SNPP) satellite instrument. The error characteristics are presented in a form that aims at being compliant with the recommendations and the standardizing effort of the Towards Unified Error Reporting (TUNER) project.
Besides the retrieval noise, contributions from retrieval parameters are extensively discussed and quantified by using synthetic retrievals performed with the SCIATRAN radiative transfer model. For this investigation, a representative set of OMPS-LP measurements is selected to provide a reliable estimation of the uncertainties as a function of latitude and season. Errors originating from model approximations and spectroscopic data are also taken into account and found to be non-negligible. The choice of the ozone cross section is found to be relevant, as expected. Overall, we classify the estimated errors as random or systematic and investigate correlations between errors from different sources.
After summing up the relevant error components, we present an estimate of the total random uncertainty on the retrieved ozone profiles, which is found to be in the 5 %–30 % range in the lower stratosphere, 3 %–5 % in the middle stratosphere, and 5 %–7 % at upper altitudes. The systematic uncertainty is mainly due to cloud contamination and model errors in the lower stratosphere and due to the retrieval bias at higher altitudes. The corresponding total bias exceeds 5 % only above 50 km and below 20 km.
After computing the estimate of the overall random and systematic error components, we also provide an ex-post assessment of the uncertainties using self-collocated OMPS-LP observations and collocated Microwave Limb Sounder (MLS) data in a χ2 fashion.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Satellite retrievals of the atmospheric dry-air column-average mole fraction of CO 2 (XCO 2 ) based on hyperspectral measurements in appropriate near (NIR) and short wave infrared (SWIR) O 2 and CO 2 ...absorption bands can help to answer important questions about the carbon cycle but the precision and accuracy requirements for XCO 2 data products are demanding. Multiple scattering of light at aerosols and clouds can be a significant error source for XCO 2 retrievals. Therefore, so called full physics retrieval algorithms were developed aiming to minimize scattering related errors by explicitly fitting scattering related properties such as cloud water/ice content, aerosol optical thickness, cloud height, etc. However, the computational costs for multiple scattering radiative transfer (RT) calculations can be immense. Processing all data of the Orbiting Carbon Observatory-2 (OCO-2) can require up to thousands of CPU cores and the next generation of CO 2 monitoring satellites will produce at least an order of magnitude more data. Here we introduce the Fast atmOspheric traCe gAs retrievaL FOCAL including a scalar RT model which approximates multiple scattering effects with an analytic solution of the RT problem of an isotropic scattering layer and a Lambertian surface. The computational performance is similar to an absorption only model and currently determined by the convolution of the simulated spectra with the instrumental line shape function (ILS). We assess FOCAL’s quality by confronting it with accurate multiple scattering vector RT simulations using SCIATRAN. The simulated scenarios do not cover all possible geophysical conditions but represent, among others, some typical cloud and aerosol scattering scenarios with optical thicknesses of up to 0.7 which have the potential to survive the pre-processing of a XCO 2 algorithm for real OCO-2 measurements. Systematic errors of XCO 2 range from −2.5 ppm (−6.3‰) to 3.0 ppm (7.6‰) and are usually smaller than ±0.3 ppm (0.8‰). The stochastic uncertainty of XCO 2 is typically about 1.0 ppm (2.5‰). FOCAL simultaneously retrieves the dry-air column-average mole fraction of H 2 O (XH 2 O) and the solar induced chlorophyll fluorescence at 760 nm (SIF). Systematic and stochastic errors of XH 2 O are most times smaller than ±6 ppm and 9 ppm, respectively. The systematic SIF errors are always below 0.02 mW/m 2 /sr/nm, i.e., it can be expected that instrumental or forward model effects causing an in-filling of the used Fraunhofer lines will dominate the systematic errors when analyzing actually measured data. The stochastic uncertainty of SIF is usually below 0.3 mW/m 2 /sr/nm. Without understating the importance of analyzing synthetic measurements as presented here, the actual retrieval performance can only be assessed by analyzing measured data which is subject to part 2 of this publication.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
This article examines the conceptual and empirical underpinnings of data on the cost of fraud—collected for a study conducted for the Association of Chief Police Officers and summarized in the ...article—and reviews the strengths and weaknesses of the data collection processes. It also raises broader questions about the relationship between crime control ideologies, institutional responsibilities, and what data on crime are kept and not kept; and concludes by reviewing the implications of recent strategies for enhancing fraud data collection.
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BFBNIB, NMLJ, NUK, PNG, PRFLJ, UL, UM, UPUK
Methane (CH4) is the second most important anthropogenic greenhouse gas, whose atmospheric concentration is modulated by human-induced activities, and it has a larger global warming potential than ...carbon dioxide (CO2). Because of its short atmospheric lifetime relative to that of CO2, the reduction of the atmospheric abundance of CH4 is an attractive target for short-term climate mitigation strategies. However, reducing the atmospheric CH4 concentration requires a reduction of its emissions and, therefore, knowledge of its sources.For this reason, the CO2 and Methane (CoMet) campaign in May and June 2018 assessed emissions of one of the largest CH4 emission hot spots in Europe, the Upper Silesian Coal Basin (USCB) in southern Poland, using top-down approaches and inventory data. In this study, we will focus on CH4 column anomalies retrieved from spectral radiance observations, which were acquired by the 1D nadir-looking passive remote sensing Methane Airborne MAPper (MAMAP) instrument, using the weighting-function-modified differential optical absorption spectroscopy (WFM-DOAS) method. The column anomalies, combined with wind lidar measurements, are inverted to cross-sectional fluxes using a mass balance approach. With the help of these fluxes, reported emissions of small clusters of coal mine ventilation shafts are then assessed.The MAMAP CH4 column observations enable an accurate assignment of observed fluxes to small clusters of ventilation shafts. CH4 fluxes are estimated for four clusters with a total of 23 ventilation shafts, which are responsible for about 40 % of the total CH4 mining emissions in the target area. The observations were made during several overflights on different days. The final average CH4 fluxes for the single clusters (or sub-clusters) range from about 1 to 9 tCH4h-1 at the time of the campaign. The fluxes observed at one cluster during different overflights vary by as much as 50 % of the average value. Associated errors (1σ) are usually between 15 % and 59 % of the average flux, depending mainly on the prevailing wind conditions, the number of flight tracks, and the magnitude of the flux itself. Comparison to known hourly emissions, where available, shows good agreement within the uncertainties. If only emissions reported annually are available for comparison with the observations, caution is advised due to possible fluctuations in emissions during a year or even within hours. To measure emissions even more precisely and to break them down further for allocation to individual shafts in a complex source region such as the USCB, imaging remote sensing instruments are recommended.
Nitrogen dioxide (NO2), produced as a result of fossil fuel combustion, biomass burning, lightning, and soil emissions, is a key urban and rural tropospheric pollutant. In this case study, ...ground-based remote sensing has been coupled with the in situ network in Vienna, Austria, to investigate NO2 distributions in the planetary boundary layer. Near-surface and path-averaged NO2 mixing ratios within the metropolitan area of Vienna are estimated from car DOAS (differential optical absorption spectroscopy) zenith-sky and tower DOAS horizon observations. The latter configuration is innovative in the sense that it obtains horizontal measurements at more than a hundred different azimuthal angles – within a 360∘ rotation taking less than half an hour. Spectral measurements were made with a DOAS instrument on nine days in April, September, October, and November 2015 in the zenith-sky mode and on five days in April and May 2016 in the off-axis mode. The analysis of tropospheric NO2 columns from the car measurements and O4 normalized NO2 path averages from the tower observations provide interesting insights into the spatial and temporal NO2 distribution over Vienna. Integrated column amounts of NO2 from both DOAS-type measurements are converted into mixing ratios by different methods. The estimation of near-surface NO2 mixing ratios from car DOAS troposphericNO2 vertical columns is based on a linear regression analysis including mixing height and other meteorological parameters that affect the dilution and reactivity in the planetary boundary layer – a new approach for such conversion. Path-averaged NO2 mixing ratios are calculated from tower DOAS NO2 slant column densities by taking into account topography and geometry. Overall, lap averages of near-surface NO2 mixing ratios obtained from car DOAS zenith-sky measurements, around a circuit in Vienna, are in the range of 3.8 to 26.1 ppb and in good agreement with values obtained from in situ NO2 measurements for days with wind from the southeast. Path-averaged NO2 mixing ratios at 160 m above the ground as derived from the tower DOAS measurements are between 2.5 and 9 ppb on two selected days with different wind conditions and pollution levels and show similar spatial distribution as seen in the car DOAS zenith-sky observations. We conclude that the application of the two methods to obtain near-surface and path-averaged NO2 mixing ratios is promising for this case study.
Multi-axis differential optical absorption spectroscopy (MAX-DOAS) tropospheric NO2 column retrievals from four European measurement stations are compared to simulations from five regional air ...quality models which contribute to the European regional ensemble forecasts and reanalyses of the operational Copernicus Atmosphere Monitoring Service (CAMS). Compared to other observational data usually applied for regional model evaluation, MAX-DOAS data are closer to the regional model data in terms of horizontal and vertical resolution, and multiple measurements are available during daylight, so that, for example, diurnal cycles of trace gases can be investigated. In general, there is good agreement between simulated and retrieved NO2 column values for individual MAX-DOAS measurements with correlations between 35 % and 70 % for individual models and 45 % to 75 % for the ensemble median for tropospheric NO2 vertical column densities (VCDs), indicating that emissions, transport and tropospheric chemistry of NOx are on average well simulated. However, large differences are found for individual pollution plumes observed by MAX-DOAS. Most of the models overestimate seasonal cycles for the majority of MAX-DOAS sites investigated. At the urban stations, weekly cycles are reproduced well, but the decrease towards the weekend is underestimated and diurnal cycles are overall not well represented. In particular, simulated morning rush hour peaks are not confirmed by MAX-DOAS retrievals, and models fail to reproduce observed changes in diurnal cycles for weekdays versus weekends. The results of this study show that future model development needs to concentrate on improving representation of diurnal cycles and associated temporal scalings.
Recent comparisons between satellite observed and global model simulated glyoxal (CHOCHO) have consistently revealed a large unknown source of CHOCHO over China. We examine this missing CHOCHO source ...by analyzing SCIAMACHY observed CHOCHO vertical column densities (VCDs) using a Regional chEmical trAnsport Model (REAM). This missing source is first quantified by the difference between SCIAMACHY observed and REAM simulated CHOCHO VCDs (ΔCCHOCHO), which have little overlap with high biogenic isoprene emissions but are collocated with dense population and high anthropogenic NOxand VOC emissions. We then apply inverse modeling to constrain CHOCHO precursor emissions based on SCIAMACHY CHOCHO and find that this missing source is most likely caused by substantially underestimated aromatics emissions (by a factor of 4–10, varying spatially) in the VOC emission inventories over China used in current regional and global models. Comparison with in situ observations in Beijing, Shanghai, and a site in the Pearl River Delta shows that the large model biases in aromatics concentrations are greatly reduced after the inversion. The top‐down estimated aromatics emission is 13.4 Tg yr−1in total, about 6 times the bottom‐up estimate (2.4 Tg yr−1). The resulting impact on regional oxidant levels is large (e.g., ∼100% increase of PAN in the afternoon). Furthermore, since aromatics are important precursors of secondary organic aerosol (SOA), such an increase of aromatics could lead to ∼50% increase of global aromatic SOA production and thereby help to reduce the low bias of simulated organic aerosols over the region in previous modeling studies.
Key Points
The missing source of glyoxal over China is explained
The missing source of organic aerosols over China is partly explained
The VOC emissions over China are highly uncertain and merit further inspection
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
The growth rate of atmospheric carbon dioxide (CO2) reflects the net
effect of emissions and uptake resulting from anthropogenic and natural
carbon sources and sinks. Annual mean CO2 growth rates ...have been
determined from satellite retrievals of column-averaged dry-air mole fractions
of CO2, i.e. XCO2, for the years 2003 to 2016. The XCO2
growth rates agree with National Oceanic and Atmospheric Administration
(NOAA) growth rates from CO2 surface observations within the uncertainty
of the satellite-derived growth rates (mean difference ± standard
deviation: 0.0±0.3 ppm year−1; R: 0.82). This new and independent data
set confirms record-large growth rates of around 3 ppm year−1
in 2015 and 2016, which are attributed to the 2015–2016 El Niño. Based on a comparison of
the satellite-derived growth rates with human CO2 emissions from fossil
fuel combustion and with El Niño Southern Oscillation (ENSO) indices, we
estimate by how much the impact of ENSO dominates the impact of fossil-fuel-burning-related emissions in explaining the variance of the atmospheric
CO2 growth rate. Our analysis shows that the ENSO impact on CO2
growth rate variations dominates that of human emissions throughout the
period 2003–2016 but in particular during the period 2010–2016 due to strong
La Niña and El Niño events. Using the derived growth rates and their
uncertainties, we estimate the probability that the impact of ENSO on the
variability is larger than the impact of human emissions to be 63 % for the
time period 2003–2016. If the time period is restricted to 2010–2016, this
probability increases to 94 %.
Carbon dioxide (CO2) is the most important anthropogenic greenhouse gas. Its atmospheric concentration has increased by almost 50 % since the beginning of the industrial era, causing climate change. ...Fossil fuel combustion is responsible for most of the atmospheric CO2 increase, which originates to a large extent from localized sources such as power stations. Independent estimates of the emissions from these sources are key to tracking the effectiveness of implemented climate policies to mitigate climate change. We developed an automatic procedure to quantify CO2 emissions from localized sources based on a cross-sectional mass-balance approach and applied it to infer CO2 emissions from the Bełchatów Power Station (Poland) using atmospheric observations from the Orbiting Carbon Observatory 3 (OCO-3) in its snapshot area map (SAM) mode. As a result of the challenge of identifying CO2 emission plumes from satellite data with adequate accuracy, we located and constrained the shape of emission plumes using TROPOspheric Monitoring Instrument (TROPOMI) NO2 column densities. We automatically analysed all available OCO-3 overpasses over the Bełchatów Power Station from July 2019 to November 2022 and found a total of nine that were suitable for the estimation of CO2 emissions using our method. The mean uncertainty in the obtained estimates was 5.8 Mt CO2 yr-1 (22.0 %), mainly driven by the dispersion of the cross-sectional fluxes downwind of the source, e.g. due to turbulence. This dispersion uncertainty was characterized using a semivariogram, made possible by the OCO-3 imaging capability over a target region in SAM mode, which provides observations containing plume information up to several tens of kilometres downwind of the source. A bottom-up emission estimate was computed based on the hourly power-plant-generated power and emission factors to validate the satellite-based estimates. We found that the two independent estimates agree within their 1σ uncertainty in eight out of nine analysed overpasses and have a high Pearson's correlation coefficient of 0.92. Our results confirm the potential to monitor large localized CO2 emission sources from space-based observations and the usefulness of NO2 estimates for plume detection. They also illustrate the potential to improve CO2 monitoring capabilities with the planned Copernicus Anthropogenic CO2 Monitoring (CO2M) satellite constellation, which will provide simultaneously retrieved XCO2 and NO2 maps.
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Underwater light field characterization is of importance for understanding biogeochemical processes and heat budget of the global oceans, which are impacting and reacting to climate change. ...Vibrational Raman Scattering (VRS) was retrieved from backscattered radiances measured by three different hyperspectral satellite sensors, SCIAMACHY, GOME-2, and OMI, using Differential Optical Absorption Spectroscopy (DOAS). Diffuse attenuation coefficient (Kd) in the blue spectral range (390 to 426 nm) was derived from the VRS signal via a look-up-table established through ocean-atmosphere coupled radiative transfer modeling. We processed one year of data, representative of the overlapping period of optimal operation for all three sensors. Resulting data sets were evaluated by comparison with Kd at 490 nm from Ocean Colour Climate Change Initiative (OC-CCI) which was first converted to Kd at 390 to 426 nm. Good agreement with the OC-CCI Kd product was achieved for all three sensors when Kd was limited to below 0.15 m
. Differences among the hyperspectral sensors and to OC-CCI were attributed to particular instrumental effects on the DOAS retrieval leading to temporal and spatial biases. This is in addition to the fact that the spatial and temporal resolution of the hyperspectral sensors data differ among themselves and are much lower then for the OC-CCI Kd-product. Further corrections (e.g., empirical) are necessary before these data sets can be merged in order to obtain a long-term Kd product for the blue spectral range.
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