A tropospheric ozone column (TrOC) dataset from the Ozone Mapping and Profiler Suite (OMPS) observations was generated by combining the retrieved total ozone column from OMPS – Nadir Mapper (OMPS-NM) and limb profiles from OMPS – Limb Profiler (OMPS-LP) data. All datasets were generated at the University of Bremen, and the TrOC product was obtained by applying the limb–nadir matching technique (LNM). The retrieval algorithm and a comprehensive analysis of the uncertainty budget are presented here. The OMPS-LNM-TrOC dataset (2012–2018) is analysed and validated through comparison with ozonesondes, tropospheric ozone residual (TOR) data from the combined Ozone Monitoring Instrument/Microwave Limb Sounder (OMI/MLS) observations, and the TROPOspheric Monitoring Instrument (TROPOMI) Convective Cloud Differential technique (CCD) dataset. The OMPS-LNM TrOC is generally lower than the other datasets. The average bias with respect to ozonesondes is −1.7 DU with no significant latitudinal dependence identified. The mean difference with respect to OMI/MLS TOR and TROPOMI CCD is −3.4−1.8 DU, respectively. The seasonality and inter-annual variability are in good agreement with all comparison datasets.
Oxidation of volatile organic compounds (VOCs) can lead to the formation of secondary organic aerosol (SOA), a significant component of atmospheric fine particles, which can affect air quality, human health, and climate change. However, the current understanding of the formation mechanism of SOA is still incomplete, which is not only due to the complexity of the chemistry but also relates to analytical challenges in SOA precursor detection and quantification. Recent instrumental advances, especially the development of high-resolution time-of-flight chemical ionization mass spectrometry (CIMS), greatly improved both the detection and quantification of low- and extremely low-volatility organic molecules (LVOCs/ELVOCs), which largely facilitated the investigation of SOA formation pathways. However, analyzing and interpreting complex mass spectrometric data remain a challenging task. This necessitates the use of dimension reduction techniques to simplify mass spectrometric data with the purpose of extracting chemical and kinetic information of the investigated system. Here we present an approach to apply fuzzy c-means clustering (FCM) to analyze CIMS data from a chamber experiment, aiming to investigate the gas phase chemistry of the nitrate-radical-initiated oxidation of isoprene.
The performance of FCM was evaluated and validated. By applying FCM to measurements, various oxidation products were classified into different groups, based on their chemical and kinetic properties, and the common patterns of their time series were identified, which provided insight into the chemistry of the investigated system. The chemical properties of the clusters are described by elemental ratios and the average carbon oxidation state, and the kinetic behaviors are parameterized with a generation number and effective rate coefficient (describing the average reactivity of a species) using the gamma kinetic parameterization model. In addition, the fuzziness of FCM algorithm provides a possibility for the separation of isomers or different chemical processes that species are involved in, which could be useful for mechanism development. Overall, FCM is a technique that can be applied well to simplify complex mass spectrometric data, and the chemical and kinetic properties derived from clustering can be utilized to understand the reaction system of interest.The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on Envisat provided infrared limb emission spectra, which were used to infer global distributions of CFC-11, CFC-12, and HCFC-22. Spectra were analysed using constrained non-linear least-squares fitting. Changes with respect to earlier data versions refer to the use of version 8 spectra, the altitude range where the background continuum is considered, details of the regularization and microwindow selection, and the occasional joint fitting of interfering species, the use of new spectroscopic data, the joint fit of a tangent-height-dependent spectral offset, and the use of 2D temperature fields. In the lower stratosphere the error budget is dominated by uncertainties in spectroscopic data, while above this measurement noise is the leading error source. The vertical resolution of CFC-11 and CFC-12 is 2–3 kmkmkmkmkm. The vertical resolution of HCFC-22 is somewhat coarser, 3–4 kmkmkmkm, which can become an issue only for comparisons with model simulations with high horizontal resolution or localized in situ observations. Along with the regular data product, an alternative representation of the data on a coarser vertical grid is offered. These data can be used without consideration of the averaging kernels. The new data version provides improvement with respect to reduction of biases and improved consistency between the full- and reduced-resolution mission period of MIPAS.
The effective radiative forcing (ERF) due to aerosol–cloud interactions (ACIs) and rapid adjustments (ERFaci) still causes the largest uncertainty in the assessment of climate change. It is understood only with medium confidence and is studied primarily for warm clouds. Here, we present a novel cloud-by-cloud (C×C) approach for studying ACI in satellite observations that combines the concentration of cloud condensation nuclei (nCCN) and ice nucleating particles (nINP) from polar-orbiting lidar measurements with the development of the properties of individual clouds by tracking them in geostationary observations. We present a step-by-step description for obtaining matched aerosol–cloud cases. The application to satellite observations over central Europe and northern Africa during 2014, together with rigorous quality assurance, leads to 399 liquid-only clouds and 95 ice-containing clouds that can be matched to surrounding nCCNnINPNd) and effective radius (reff) of liquid clouds and the phase of clouds in the regime of heterogeneous ice formation. We find a
Long time series of observations of atmospheric dynamics and composition are collected at the French Pyrenean Platform for Observation of the Atmosphere (P2OA). Planetary boundary layer depth is a key variable of the climate system, but it remains difficult to estimate and analyse statistically. In order to obtain reliable estimates of the convective boundary layer height (Zi) and to allow long-term series analyses, a new restitution algorithm, named CALOTRITON, has been developed. It is based on the observations of an ultra-high-frequency (UHF) radar wind profiler (RWP) from P2OA with the help of other instruments for evaluation. Estimates of Zix. We then search for the most appropriate local maxima of this parameter for ZiZixx=3ZiZiZiZi
In this study, we present a new approach for the determination of polarization parameters of the Nicosia Cimel CE376 lidar system, using the PollyXTXT, a widely used depolarization lidar, as our reference to evaluate the CE376 system's gain ratio and channel cross-talk. We use observations of transported dust from desert regions for this approach, with layers in the free troposphere. Above the boundary layer and the highest terrain elevation of the region, we can expect that, for long-range transport of aerosols, local effects should not affect the aerosol mixture enough for us to expect similar depolarization properties at the two stations (separated by ∼ 60 km). Algebraic equations are used to derive polarization parameters from the comparison of the volume depolarization ratio measured by the two systems. The applied methodology offers a promising opportunity to evaluate the polarization parameters of a lidar system, in cases where a priori knowledge of the cross-talk parameters is not available, or to transfer the polarization parameters from one system to the other.
Cloud radiative properties play a significant role in radiation and energy budgets and are influenced by both the cloud top height and the particle size distribution. Both cloud top heights and particle size distributions can be derived from 2-D intensity and polarization measurements by the airborne spectrometer of the Munich Aerosol Cloud Scanner (specMACS). The cloud top heights are determined using a stereographic method (Kölling et al., 2019), and the particle size distributions are derived in terms of the cloud effective radius and the effective variance from multidirectional polarized measurements of the cloudbow (Pörtge et al., 2023). In this study, the accuracy of the two methods is evaluated using realistic 3-D radiative transfer simulations of specMACS measurements of a synthetic field of shallow cumulus clouds, and possible error sources are determined. The simulations are performed with the 3-D Monte Carlo radiative transport model MYSTIC (Mayer, 2009)70 m with a standard deviation of about 130 m compared to the expected heights from the model is found. The elimination of the cloud development as a possible error source results in mean differences of (46±140) m. For the effective radius, an absolute average difference of about
We present the first measurements of simultaneous horizontal and vertical winds using a new lidar system developed at the Leibniz Institute of Atmospheric Physics in Kühlungsborn, Germany (54.12° N, 11.77° E), for the concept of Vertical And Horizontal COverage by LIdars (VAHCOLI). We describe the technical details of a multi-field-of-view (MFOV) upgrade, which allows the measurement of wind dynamics in the transition region from microscale to mesoscale (103–104 m). The method was applied at the edge of a developing high-pressure region, covering altitudes between 3 and 25 km. Comparisons between the lidar measurements and data from the European Centre for Medium-Range Weather Forecasts (ECMWF) show excellent agreement for the meridional wind component along the north beam of the lidar, which is better than 0.30±0.33 m s−1, while along the south beam, a higher deviation with
Methane emissions from natural gas systems are increasingly scrutinized, and accurate reporting requires quantification of site- and source-level measurement. We evaluate the performance of 10 available state-of-the-art CH4−1. Measurement platforms included aircraft, drones, trucks, vans, ground-based stations, and handheld systems. Herewith, we compare their respective strengths, weaknesses, and potential complementarity depending on the emission rates and atmospheric conditions. Most systems were able to quantify the releases within an order of magnitude. The level of errors from the different systems was not significantly influenced by release rates larger than 0.1 kg h−1, with much poorer results for the 0.01 kg h−1−1), the experiments did not reveal a significant dependence on wind speed. The ability to quantify individual sources degraded during multiple-source releases. Compliance with the Oil and Gas Methane Partnership's (OGMP 2.0) highest level of reporting may require a combination of the specific advantages of each measurement technique and will depend on reconciliation approaches. Self-reported uncertainties were either not available or were based on the standard deviation in a series of independent realizations or fixed values from expert judgment or theoretical considerations. For most systems, the overall relative errors estimated in this study are higher than self-reported uncertainties.
Ground-based high resolution observations of downward longwave irradiance (DLI), surface air temperature, water vapor surface partial pressure and column amount, zenith sky infrared (IR) radiance in the atmospheric window, and all-sky camera images are regularly obtained at the Thule High Arctic Atmospheric Observatory (THAAO, 76.5° N, 68.8° W), northwestern Greenland. The datasets for the years 2017 and 2018 have been used to assess the performance of different empirical formulas used to infer clear sky DLI. An algorithm to identify clear sky observations has been developed, based on value, variability, and persistence of zenith sky IR radiance. Seventeen different formulas to estimate DLI have been tested against the THAAO dataset, using the originally determined coefficients. The formulas that combine information on total column water vapor and surface air temperature appear to perform better than others, with a mean bias with respect to the measured DLI smaller than 1 W m−2−2. Unexpectedly, some formulas specifically developed for the Arctic are found to produce poor statistical results. This is attributed partly to limitations in the originally used dataset, which does not cover a whole year or is relative to very specific condition (i.e., the presence of an ice sheet). As expected, the bias displays a significant improvement when the coefficients of the different formulas are calculated using the THAAO dataset. The presence of 2 full years of data allows the determination and the applicability of the coefficients for singular years and the evaluation of results. The smallest values of the bias and RMSE reach 0.1 and 5 W m−2, respectively. Overall, the best results are found for formulas that use both surface parameters and total water vapor column content, and have been developed from global datasets. Conversely, formulas that express the atmospheric emissivity as a linear function of the logarithm of the column integrated water vapor appear to reproduce poorly the observations at THAAO.
The significance of air quality monitoring for analyzing impact on public health is growing worldwide. A crucial part of smart city development includes deployment of suitable air pollution sensors at critical locations. Note that there are various air quality measurement instruments, ranging from expensive reference stations that provide accurate data to low-cost sensors that provide less accurate air quality measurements. In this research, we use a combination of sensors and monitors, which we call hybrid instruments, and focus on optimal placement of such instruments across a region. The objective of the problem is to maximize a satisfaction function that quantifies the weighted closeness of different regions to the places where such hybrid instruments are placed (here weights for different regions are quantified in terms of the relative population density and relative PM2.52.5
Atmospheric methane (CH4) is the second-most-important anthropogenic greenhouse gas and has a 20-year global warming potential 82 times greater than carbon dioxide (CO2). Anthropogenic sources account for ∼ 60 % of global CH4444 h−1, whereas the mobile surface measurements are 634–846 kg CH4 h−1. The large variability is likely down to variations in flow through the pipe and engineering works across the 11-week period. Modelled flux estimates in NAME are 181–1243 kg CH4 h−1, which are lower than the satellite- and mobile-survey-derived fluxes but are within the uncertainty. After detecting the leak in March 2023, the local utility company was contacted, and the leak was fixed by mid-June 2023. Our results demonstrate that GHGSat's observations can produce flux estimates that broadly agree with surface-based mobile measurements. Validating the accuracy of the information provided by targeted, high-resolution satellite monitoring shows how it can play an important role in identifying emission sources, including unplanned fugitive releases that are inherently challenging to identify, track, and estimate their impact and duration. Rapid, widespread access to such data to inform local action to address fugitive emission sources across the oil and gas supply chain could play a significant role in reducing anthropogenic contributions to climate change.
Stratiform rain and the overlying ice play crucial roles in Earth's climate system. From a microphysics standpoint, water mass flux primarily depends on two variables: particles' concentration and their mass. The Dual-frequency Precipitation Radar (DPR) on the Global Precipitation Measurement mission core satellite is a spaceborne instrument capable of estimating these two quantities through dual-wavelength measurements. In this study, we evaluate bulk statistics on the ice particle properties derived from dual-wavelength radar data in relation to the properties of rain underneath. Specifically, we focus on DPR observations over stratiform precipitation, characterized by columns exhibiting a prominent bright band, where the melting layer can be easily detected.
Our analysis reveals a large increase in the retrieved mass flux as we transition from the ice to the rain phase in the official DPR product. This observation is in disagreement with our expectation that mass flux should remain relatively stable across the bright band in cold-rain conditions. To address these discrepancies, we propose an alternative retrieval algorithm that ensures a gradual transition of DmThe paper describes technical characteristics and presents the first scientific results of a novel infrared imaging system (imager) for studies of nightglow emissions coming from the hydroxyl (OH) and molecular oxygen (O2) layers in the mesopause region (80–100 km) above northern Scandinavia. The OH imager was put into operation in November 2022 at the Swedish Institute of Space Physics in Kiruna (67.86° N, 20.42° E; 400 m altitude). The OH imager records selected emission lines in the OH(3-1) band near 1500 nm to obtain intensity and temperature maps at around 87 km altitude. In addition, the OH imager registers infrared emissions coming from the O22