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Microscopic traffic simulations have become increasingly important for research targeting connected vehicles. They are especially appreciated for enabling investigations targeting large areas, which would be practically impossible or too expensive in the real world. However, such large-scale simulation scenarios often lack validation with real-world measurements since these data are often not available. To overcome this issue, this work integrates probe counts from floating car data as reference counts to model a large-scale microscopic traffic scenario with high-resolution detector data. To integrate the frequent probe counts, a road network matching is required. Thus, a novel road network matching method based on a decision tree classifier is proposed. The classifier automatically adjusts its cosine similarity and Hausdorff distance-based similarity metrics to match the network’s requirements. The approach performs well with an F1-score of 95.6%. However, post-processing steps are required to produce a sufficiently consistent detector dataset for the subsequent traffic simulation. The finally modeled traffic shows a good agreement of 95.1%. with upscaled probe counts and no unrealistic traffic jams, teleports, or collisions in the simulation. We conclude that probe counts can lead to consistent traffic simulations and, especially with increasing and consistent penetration rates in the future, help to accurately model large-scale microscopic traffic simulations.

Operational retrievals of tropospheric trace gases from space-borne spectrometers are based on one-dimensional radiative transfer models. To minimize cloud effects, trace gas retrievals generally implement a simple cloud model based on radiometric cloud fraction estimates and photon path length corrections. The latter relies on measurements of the oxygen collision pair (O2–O2) absorption at 477 nm or on the oxygen A-band around 760 nm to determine an effective cloud height. In reality however, the impact of clouds is much more complex, involving unresolved sub-pixel clouds, scattering of clouds in neighbouring pixels, and cloud shadow effects, such that unresolved three-dimensional effects due to clouds may introduce significant biases in trace gas retrievals. Although clouds have significant effects on trace gas retrievals, the current cloud correction schemes are based on a simple cloud model, and the retrieved cloud parameters must be interpreted as effective values. Consequently, it is difficult to assess the accuracy of the cloud correction only based on analysis of the accuracy of the cloud retrievals, and this study focuses solely on the impact of the 3D cloud structures on the trace gas retrievals. In order to quantify this impact, we study NO2 as a trace gas example and apply standard retrieval methods including approximate cloud corrections to synthetic data generated by the state-of-the-art three-dimensional Monte Carlo radiative transfer model MYSTIC. A sensitivity study is performed for simulations including a box cloud, and the dependency on various parameters is investigated. The most significant bias is found for cloud shadow effects under polluted conditions. Biases depend strongly on cloud shadow fraction, NO2 profile, cloud optical thickness, solar zenith angle, and surface albedo. Several approaches to correct NO2 retrievals under cloud shadow conditions are explored. We find that air mass factors calculated using fitted surface albedo or corrected using the O2–O2 slant column density can partly mitigate cloud shadow effects. However, these approaches are limited to cloud-free pixels affected by surrounding clouds. A parameterization approach is presented based on relationships derived from the sensitivity study. This allows measurements to be identified for which the standard NO2 retrieval produces a significant bias and therefore provides a way to improve the current data flagging approach.

Three-dimensional (3D) cloud structures may impact atmospheric trace gas products from ultraviolet–visible (UV–Vis) sounders. We used synthetic and observational data to identify and quantify possible cloud-related bias in NO2 tropospheric vertical column density (TVCD). The synthetic data were based on high-resolution large eddy simulations which were input to a 3D radiative transfer model. The simulated visible spectra for low-earth-orbiting and geostationary geometries were analysed with standard retrieval methods and cloud correction schemes that are employed in operational NO2 satellite products. For the observational data, the NO2 products from the TROPOspheric Monitoring Instrument (TROPOMI) were used, while the Visible Infrared Imaging Radiometer Suite (VIIRS) provided high-spatial-resolution cloud and radiance data. NO2 profile shape, cloud shadow fraction, cloud top height, cloud optical depth, and solar zenith and viewing angles were identified as the metrics being the most important in identifying 3D cloud impacts on NO2 TVCD retrievals. For a solar zenith angle less than about 40∘ the synthetic data show that the NO2 TVCD bias is typically below 10 %, while for larger solar zenith angles the NO2 TVCD is low-biased by tens of percent. The horizontal variability of NO2 and differences in TROPOMI and VIIRS overpass times make it challenging to identify a similar bias in the observational data. However, for optically thick clouds above 3000 m, a low bias appears to be present in the observational data.

The next and current generations of methane-retrieving satellite instruments are reliant on the Total Carbon Column Observing Network (TCCON) for validation. Understanding the biases inherent in TCCON and satellite methane retrievals is as important now as when TCCON started in 2004. In this study we highlight possible biases between different methane products by assessing the retrievals of the main methane isotopologue 12CH4. Using the TCCON GGG2014 retrieval environment, retrievals are performed using five separate spectroscopic databases from four separate TCCON sites (namely, Ascension Island, Ny-Ålesund, Darwin and Tsukuba) over the course of a year. The spectroscopic databases include those native to TCCON, GGG2014 and GGG2020; the high-resolution transmission molecular absorption database 2016 (HITRAN2016); the Gestion et Etude des Informations Spectroscopiques Atmosphériques 2020 (GEISA2020) database; and the ESA Scientific Exploitation of Operational Missions – Improved Atmospheric Spectroscopy Databases (SEOM-IAS). We assess the biases in retrieving methane using the standard TCCON windows and the methane window used by the Sentinel-5 Precursor (S5P) TROPOspheric Ozone Monitoring Instrument (TROPOMI) for each of the different spectroscopic databases. By assessing the retrieved 12CH4 values from individual windows against the standard TCCON retrievals, we find bias values of between 0.05 and 2.5 times the retrieval noise limit. These values vary depending on the window and TCCON site, with Ascension Island showing the lowest biases (typically ) and Ny-Ålesund or Tsukuba showing the largest. For the spectroscopic databases, GEISA2020 shows the largest biases, often greater than 1.5 across the TCCON sites and considered windows. The TROPOMI spectral window (4190–4340 cm−1) shows the largest biases of all the spectral windows, typically >1, for all spectroscopic databases, suggesting that further improvements in spectroscopic parameters are necessary. We further assess the sensitivity of these biases to locally changing atmospheric conditions such as the solar zenith angle (SZA), water vapour and temperature. We find evidence of significant non-linear relationships between the variation in local conditions and the retrieval biases based on regression analysis. In general, each site, database and window combination indicates different degrees of sensitivity, with GEISA2020 often showing the most sensitivity for all TCCON sites. Ny-Ålesund and Tsukuba show the most sensitivity to variations in local condition, while Ascension Island indicates limited sensitivity. Finally, we investigate the biases associated with retrieving 13CH4 from each TCCON site and spectroscopic database, through the calculation of δ13C values. We find high levels of inconsistency, in some cases >100 ‰ between databases, suggesting more work is required to refine the spectroscopic parameters of 13CH4.

Retrievals of trace gas concentrations from satellite observations are mostly performed for clear regions or regions with low cloud coverage. However, even fully clear pixels can be affected by clouds in the vicinity, either by shadowing or by scattering of radiation from clouds in the clear region. Quantifying the error of retrieved trace gas concentrations due to cloud scattering is a difficult task. One possibility is to generate synthetic data by three-dimensional (3D) radiative transfer simulations using realistic 3D atmospheric input data, including 3D cloud structures. Retrieval algorithms may be applied on the synthetic data, and comparison to the known input trace gas concentrations yields the retrieval error due to cloud scattering. In this paper we present a comprehensive synthetic dataset which has been generated using the Monte Carlo radiative transfer model MYSTIC (Monte Carlo code for the phYSically correct Tracing of photons In Cloudy atmospheres). The dataset includes simulated spectra in two spectral ranges (400–500 nm and the O2A-band from 755–775 nm). Moreover it includes layer air mass factors (layer-AMFs) calculated at 460 nm. All simulations are performed for a fixed background atmosphere for various sun positions, viewing directions and surface albedos. Two cloud setups are considered: the first includes simple box clouds with various geometrical and optical thicknesses. This can be used to systematically investigate the sensitivity of the retrieval error on solar zenith angle, surface albedo and cloud parameters. Corresponding 1D simulations are also provided. The second includes realistic three-dimensional clouds from an ICON large eddy simulation (LES) for a region covering Germany and parts of surrounding countries. The scene includes cloud types typical of central Europe such as shallow cumulus, convective cloud cells, cirrus and stratocumulus. This large dataset can be used to quantify the trace gas concentration retrieval error statistically. Along with the dataset, the impact of horizontal photon transport on reflectance spectra and layer-AMFs is analysed for the box-cloud scenarios. Moreover, the impact of 3D cloud scattering on the NO2 vertical column density (VCD) retrieval is presented for a specific LES case. We find that the retrieval error is largest in cloud shadow regions, where the NO2 VCD is underestimated by more than 20 %. The dataset is available for the scientific community to assess the behaviour of trace gas retrieval algorithms and cloud correction schemes in cloud conditions with 3D structure.

TEMPO was selected in 2012 by NASA as the first Earth Venture Instrument, for launch between 2018 and 2021. It will measure atmospheric pollution for greater North America from space using ultraviolet and visible spectroscopy. TEMPO observes from Mexico City, Cuba, and the Bahamas to the Canadian oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution (~2.1 km N/S×4.4 km E/W at 36.5°N, 100°W). TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry, as well as contributing to carbon cycle knowledge. Measurements are made hourly from geostationary (GEO) orbit, to capture the high variability present in the diurnal cycle of emissions and chemistry that are unobservable from current low-Earth orbit (LEO) satellites that measure once per day. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a commercial GEO host spacecraft to provide a modest cost mission that measures the spectra required to retrieve ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), formaldehyde (H2CO), glyoxal (C2H2O2), bromine monoxide (BrO), IO (iodine monoxide),water vapor, aerosols, cloud parameters, ultraviolet radiation, and foliage properties. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O3 chemistry cycle. Multi-spectral observations provide sensitivity to O3 in the lowermost troposphere, substantially reducing uncertainty in air quality predictions. TEMPO quantifies and tracks the evolution of aerosol loading. It provides these near-real-time air quality products that will be made publicly available. TEMPO will launch at a prime time to be the North American component of the global geostationary constellation of pollution monitoring together with the European Sentinel-4 (S4) and Korean Geostationary Environment Monitoring Spectrometer (GEMS) instruments.

It is estimated that a significant number of spinal surgeries are performed, but many patients do not often benefit. Our aim was to determine how effective minimally invasive pain procedures (MIP) are in chronic low back pain (CLBP) patients with proven degenerative causes (specific low back pain). 386 eligible patients with CLBP/sciatica resistant to conservative therapy and scheduled for open surgery were screened, and 167 could be enrolled in this study. Indications for MIP in the remaining 150 individuals were made by one experienced spinal surgeon. Before and 6 months after the intervention, the numeric rating scale (NRS) and Oswestry Disability Index (ODI) were recorded. MIP was performed, such as radiofrequency of the facet and SI-joint, intradiscal electrothermal therapy in case of discogenic pain, as well as epidural neuroplasty in patients with disc herniation/epidural fibrosis. There was a statistically significant decrease in NRS (p< 0.05), as well as a significant increase in ODI (p< 0.001) 6 months after the procedures. This was also true for the results of all different pain generators and subsequent performed procedures alone. The indication of MIP should be routinely reviewed in patients with CLBP to avoid potentially open surgery and a burden on healthcare costs. [ABSTRACT FROM AUTHOR]

Microscopic traffic simulations have become increasingly important for research targeting connected vehicles. They are especially appreciated for enabling investigations targeting large areas, which would be practically impossible or too expensive in the real world. However, such large-scale simulation scenarios often lack validation with real-world measurements since these data are often not available. To overcome this issue, this work integrates probe counts from floating car data as reference counts to model a large-scale microscopic traffic scenario with high-resolution detector data. To integrate the frequent probe counts, a road network matching is required. Thus, a novel road network matching method based on a decision tree classifier is proposed. The classifier automatically adjusts its cosine similarity and Hausdorff distance-based similarity metrics to match the network's requirements. The approach performs well with an F1-score of 95.6%. However, post-processing steps are required to produce a sufficiently consistent detector dataset for the subsequent traffic simulation. The finally modeled traffic shows a good agreement of 95.1%. with upscaled probe counts and no unrealistic traffic jams, teleports, or collisions in the simulation. We conclude that probe counts can lead to consistent traffic simulations and, especially with increasing and consistent penetration rates in the future, help to accurately model large-scale microscopic traffic simulations. [ABSTRACT FROM AUTHOR]

Retrievals of methane isotopologues have the potential to differentiate between natural and anthropogenic methane sources types, which can provide much needed information about the current global methane budget. We investigate the feasibility of retrieving the second most abundant isotopologue of atmospheric methane (13CH4, roughly 1.1 % of total atmospheric methane) from the shortwave infrared (SWIR) channels of the future Sentinel-5/ultra-violet, visible, near-infrared, shortwave infrared (UVNS) and current Copernicus Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) instruments. With the intended goal of calculating the δ13C value, we assume that a δ13C uncertainty of better than 1 ‰ is sufficient to differentiate between source types, which corresponds to a 13CH4 uncertainty of  ppb. Using the well-established information content analysis techniques and assuming clear-sky, non-scattering conditions, we find that the SWIR3 (2305–2385 nm) channel on the TROPOMI instrument can achieve a mean uncertainty of  ppb, while the SWIR1 channel (1590–1675 nm) on the Sentinel-5 UVNS instrument can achieve  ppb or  ppb in high signal-to-noise ratio (SNR) cases. These uncertainties combined with significant spatial and/or temporal averaging techniques can reduce δ13C uncertainty to the target magnitude or better. However, we find that 13CH4 retrievals are highly sensitive to errors in a priori knowledge of temperature and pressure, and accurate knowledge of these profiles is required before 13CH4 retrievals can be performed on TROPOMI and future Sentinel-5/UVNS data. In addition, we assess the assumption that scattering-induced light path errors are cancelled out by comparing the δ13C values calculated for non-scattering and scattering scenarios. We find that there is a minor bias in δ13C values from scattering and non-scattering retrievals, but this is unrelated to scattering-induced errors.

This work provides a comparison of satellite retrievals of Saharan desert dust aerosol optical depth (AOD) during a strong dust event through March 2006. In this event, a large dust plume was transported over desert, vegetated, and ocean surfaces. The aim is to identify the differences between current datasets. The satellite instruments considered are AATSR, AIRS, MERIS, MISR, MODIS, OMI, POLDER, and SEVIRI. An interesting aspect is that the different algorithms make use of different instrument characteristics to obtain retrievals over bright surfaces. These include multi-angle approaches (MISR, AATSR), polarisation measurements (POLDER), single-view approaches using solar wavelengths (OMI, MODIS), and the thermal infrared spectral region (SEVIRI, AIRS). Differences between instruments, together with the comparison of different retrieval algorithms applied to measurements from the same instrument, provide a unique insight into the performance and characteristics of the various techniques employed. As well as the intercomparison between different satellite products, the AODs have also been compared to co-located AERONET data. Despite the fact that the agreement between satellite and AERONET AODs is reasonably good for all of the datasets, there are significant differences between them when compared to each other, especially over land. These differences are partially due to differences in the algorithms, such as assumptions about aerosol model and surface properties. However, in this comparison of spatially and temporally averaged data, it is important to note that differences in sampling, related to the actual footprint of each instrument on the heterogeneous aerosol field, cloud identification and the quality control flags of each dataset can be an important issue.

The quality of trace gas products derived from measurements of a space-borne imaging spectrometer is affected by the inhomogeneity of the illumination of the instrument slit and thus by the heterogeneity of the observed scene. This paper aims to quantify this effect and summarise findings on how to mitigate the impact of inhomogeneous slit illumination on tropospheric O3, NO2, SO2 and HCHO columns derived from measurements of the Sentinel-4 UVN imaging spectrometer. For this purpose, spectra for inhomogeneous ground scenes have been simulated based on a combination of a radiative transfer model and spatially high resolved MODIS (Moderate Resolution Imaging Spectroradiometer) data. The resulting errors on tropospheric O3, NO2, SO2 and HCHO columns derived from these spectra have been determined via an optimal estimation approach. We conclude that inhomogeneous illumination results in significant errors in the data products if the natural inhomogeneity of the observed scenes are not accounted for. O3 columns are less affected than the other data products; largest errors occur for NO2 (mean absolute errors about 5%, maximum error exceeding 50%, standard deviation of the errors about 8%). These errors may be significantly reduced (by factors up to about 10) by an appropriate wavelength calibration applied individually to each Earthshine radiance spectrum. With wavelength calibration the estimated mean absolute errors due to inhomogeneity are for all gases well below 1%; standard deviations of the errors are 1.5% or lower; maximum errors are about 10% for NO2 and around 5% for the other gases.

Airborne sunphotometer measurements are used to evaluate retrievals of extinction aerosol optical depth (AOD) from spatially coincident and temporally near-coincident measurements by the Ozone Monitoring Instrument (OMI) aboard the Aura satellite during the March 2006 Megacity Initiative-Local And Global Research Observations/Phase B of the Intercontinental Chemical Transport Experiment (MILAGRO/INTEX-B). The 14-channel NASA Ames Airborne Tracking Sunphotometer (AATS) flew on nine missions over the Gulf of Mexico and four in or near the Mexico City area. Retrievals of AOD from near-coincident AATS and OMI measurements are compared for three flights over the Gulf of Mexico for flight segments when the aircraft flew at altitudes 60–70 m above sea level, and for one flight over the Mexico City area where the aircraft was restricted to altitudes ~320–800 m above ground level over the rural area and ~550–750 m over the city. OMI-measured top of atmosphere (TOA) reflectances are routinely inverted to yield aerosol products such as AOD and aerosol absorption optical depth (AAOD) using two different retrieval algorithms: a near-UV (OMAERUV) and a multiwavelength (OMAERO) technique. This study uses the archived Collection 3 data products from both algorithms. In particular, AATS and OMI AOD comparisons are presented for AATS data acquired in 20 OMAERUV retrieval pixels (15 over water) and 19 OMAERO pixels (also 15 over water). At least four pixels for one of the over-water coincidences and all pixels for the over-land case were cloud-free. Coincident AOD retrievals from 17 pixels of the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Aqua are available for two of the over-water flights and are shown to agree with AATS AODs to within root mean square (RMS) differences of 0.00–0.06, depending on wavelength. Near-coincident ground-based AOD measurements from ground-based sun/sky radiometers operated as part of the Aerosol Robotic Network (AERONET) at three sites in and near Mexico City are also shown and are generally consistent with the AATS AODs (which exclude any AOD below the aircraft) both in magnitude and spectral dependence. The OMAERUV algorithm retrieves AODs corresponding to a non-absorbing aerosol model for all three over-water comparisons whereas the OMAERO algorithm retrieves best-fit AODs corresponding to an absorbing biomass-burning aerosol model for two of the three over-water cases. For the four cloud-free pixels in one over-water coincidence (10 March), the OMAERUV retrievals underestimate the AATS AODs by ~0.20, which exceeds the expected retrieval uncertainty, but retrieved AODs agree with AATS values within uncertainties for the other two over-water events. When OMAERO retrieves AODs corresponding to a biomass-burning aerosol over water, the values significantly overestimate the AATS AODs (by up to 0.55). For the Mexico City coincidence, comparisons are presented for a non-urban region ~50–70 km northeast of the city and for a site near the center of the city. OMAERUV retrievals are consistent with AERONET AOD magnitudes for the non-urban site, but are nearly double the AATS and AERONET AODs (with differences of up to 0.29) in the center of the city. Corresponding OMAERO retrievals exceed the AATS and/or AERONET AODs by factors of 3 to 10.

The Ozone Monitoring Instrument (OMI) is an imaging UV-VIS solar backscatter spectrometer and is designed and used primarily to retrieve trace gases like O3 and NO2 from the measured Earth reflectance spectrum in the UV-visible (270–500 nm). However, also aerosols are an important science target of OMI. The multi-wavelength algorithm is used to retrieve aerosol parameters from OMI spectral reflectance measurements in up to 20 wavelength bands. A Principal Component Analysis (PCA) is performed to quantify the information content of OMI reflectance measurements on aerosols and to assess the capability of the multi-wavelength algorithm to discern various aerosol types. This analysis is applied to synthetic reflectance measurements for desert dust, biomass burning aerosols, and weakly absorbing anthropogenic aerosol with a variety of aerosol optical thicknesses, aerosol layer altitudes, refractive indices and size distributions. The range of aerosol parameters considered covers the natural variability of tropospheric aerosols. This theoretical analysis is performed for a large number of scenarios with various geometries and surface albedo spectra for ocean, soil and vegetation. When the surface albedo spectrum is accurately known and clouds are absent, OMI reflectance measurements have 2 to 4 degrees of freedom that can be attributed to aerosol parameters. This information content depends on the observation geometry and the surface albedo spectrum. An additional wavelength band is evaluated, that comprises the O2-O2 absorption band at a wavelength of 477 nm. It is found that this wavelength band adds significantly more information than any other individual band.