Detecting and quantifying greenhouse gas (GHG) emissions is essential for understanding global GHG budgets, updating emission inventories, and evaluating climate change mitigation efforts. Most anthropogenic emissions occur at the scale of facilities, and emission distribution in time and space relates to facility operations. This paper presents a novel GHG monitoring technique for facility-scale, dynamic emission quantification under complex wind conditions, referred to as laser dispersion tomography (LDT), which integrates laser dispersion spectroscopy (LDS) with Bayesian inversion methods. It uses sequential multi-beam open-path LDS measurements and wind data to infer dynamic GHG concentration and source maps at facility scale. In this work, the use of LDT for monitoring methane emissions in agriculture is demonstrated by deploying it on an operational farm. For this aim, computational methods used in data analysis of LDT are also further developed. Particularly, we introduce spatial constraints to the tomographic reconstruction based on prior knowledge on potential source locations – information often available in facility-scale GHG monitoring applications. We investigate numerically whether such constraints could improve the tolerance of LDT to misrepresentations induced by complex wind fields caused by building effects, and/or presence of interfering external emission sources, both highly likely to characterize a real-world farm environment. The results of numerical studies indicate that including spatial constraints reduces the uncertainty and improves the reliability of source quantification in such conditions, with one simulation case showing an average reduction in posterior uncertainty of 36.2 %. In the experimental study, dynamic emission patterns caused by various operations in the farm, such as slurry and dry manure management, are well captured, both temporally and spatially. The results support the feasibility of LDT as a tool for robust quantification of GHG mass emission rates at farms, especially when the spatial constraining of sources is possible. Owing to the fine spatial and temporal resolution of LDT, we foresee its use in improving GHG emission inventories through fine parametrization, and also its extension to other GHGs and other sectors contributing to global emissions.
Reactive nitrogen gases (NO, NO2, HONO, NH3Nr) play important roles in atmospheric processes, and their cascading impacts throughout the Earth system have adverse effects on both the environment and human health. The fluxes of these gases at the surface-atmosphere interface have been studied in isolation or smaller subsets, but simultaneous fluxes of all NrNrNrSystem modifications to passivate surfaces reduced an initial 36 % loss of NO2∼ 10 %) for relevant atmospheric conditions. The modified 72 L chamber response times (τ) did not change for GHGs or NO (τ = 37–39 min versus a theoretical 36 min) at a flow rate of 2 L min−1. The modifications improved the transfer of NO2, HONO, and NH3NH3Proof-of-concept measurements of NrNO2, HONO, NH3, and N2ONrNO2NrNr
Year-round continuous measurements of near-surface carbon dioxide (CO2) concentrations using in-situ trace gas analyzers were conducted simultaneously with nitrogen dioxide (NO2) measurements by International Air Quality and SKY Research Remote Sensing Network (A-SKY) Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) at Chiba (35.625° N, 140.104° E, 60 m above sea level), located within the Greater Tokyo Area, Japan, during 2024. These simultaneous measurements revealed that CO2222-concentration days ([ΔCO2]N) was positively correlated with NO2ΔCO2]N22
Aircraft-based measurements of gas-phase species and aerosols provide crucial knowledge about the composition and vertical structure of the atmosphere, enhancing the study of atmospheric physics and chemistry. Unlike aircraft-based aerosol particle sampling systems, the gas loss mechanisms and transmission efficiency of aircraft-based gas sampling systems are rarely discussed. In particular, the gas transmission of condensable vapors through these sampling systems requires systematic study to clarify the key factors of gas loss and to predict and improve gas sampling efficiency quantitatively. An aircraft gas inlet for aircraft-based laminar sampling of condensable vapors is described in Part 1 (Yang et al., 2024), which describes the inlet dimensions, flow analysis and modelling, along with initial gas transmission estimates. Here we test and characterize the complete inflight sampling system for gas-phase measurements of H2SO42SO4αi = 0.70 ± 0.05 for H2SO4Re ∼ 2300, the overall inlet transmission is 16 ± 6 % for H2SO4∼ 25 % transmission at Re ∼ 6000. A decrease in transmission is predicted only for higher Re2SO4∼ 2) by minimizing residence time, rather than maintaining laminar flow; this benefit extends to other condensable vapors and applies over the full range of operating conditions of the aircraft inlet system. For a sticky species (αi > 0.25), the laminar diffusivity is important to predict the transmission efficiency via the aircraft inlet section, while for less sticky species (αi < 0.25) the gas-phase diffusivity plays a minor role in predicting the gas transmission efficiency in the sampling line.
Atmospheric SO222222222222
The joint ESA and JAXA Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) satellite, launched on 28 May 2024, carries the first spaceborne 94 GHz Cloud Profiling Radar (CPR) with Doppler velocity measurement capability. As a successor to the highly successful NASA CloudSat CPR, the EarthCARE CPR offers an additional 7 dB of sensitivity largely due to its larger antenna size (2.5 m vs. 1.8 m) and lower orbit (400 km vs. 700 km), and a receiver point target response that significantly improves our ability to detect clouds in the lowest km of the atmosphere. The EarthCARE CPR measurements can also be indirectly used to estimate the Path-Integrated Attenuation (PIA, in dB), a measure of two-way attenuation caused by hydrometeors by quantifying the depression in the measured normalized radar cross section (NRCS) relative to a reference NRCS in the absence of hydrometeors. PIA is a key constraint for improving the accuracy of cloud and precipitation retrievals.This paper presents the PIA estimation methodology currently operationally implemented in the EarthCARE CPR L2A C-PRO data product. The retrieval approach follows a hybrid strategy, where the reference unattenuated NRCS is either estimated using calibration points surrounding the cloudy profile where PIA is estimated or a model-based estimation that uses a geophysical model that calculates NRCS as a function of wind speed and sea surface temperature (SST). The methodology provides a full characterization of the uncertainty in PIA estimates and is expected to lead to improved estimates of PIA compared to the methodology adopted for the CloudSat CPR. This method is particularly useful in PIA estimation in the commissioning phase of the mission, as it is robust for radar miscalibration and bias of gas attenuation or NRCS modeling.
The stable carbon isotopic ratio (δ13C) of atmospheric carbon dioxide (CO2) is a key tracer for understanding terrestrial carbon dynamics, yet its application in volume-limited systems remains constrained by analytical and sampling requirements. Here, we present a methodology for high-precision δ13C-CO22−80 °C), and cryogenic pre-concentration coupled to continuous-flow isotope-ratio mass spectrometry (IRMS). The workflow is rapid, low-cost, relies on widely available materials, and avoids laborious sample preparation steps (i.e. purification), enabling other laboratories to reproduce the method easily. Using this approach, a precision of ±0.1 ‰2-free air. Longer storage times or storage at ambient temperature reduces both precision and accuracy, emphasising the importance of short-term storage at negative temperature. This methodology allows high sampling frequency δ13C-CO2
Clouds are critical in the Arctic's water balance and energy budget. Especially, the cloud liquid water path (CLWP) modifies the cloud radiative properties and affects the surface energy balance. Spaceborne microwave radiometers provide a high sensitivity to CLWP at pan-Arctic scales, but extracting this information over sea ice requires separation of surface and cloud emission. Here, we assess CLWP detectability and retrieval accuracy over sea ice from a physical optimal estimation retrieval applied to airborne passive microwave observations during the HALO–(𝒜𝒞)3GHzg m−2g m−2. The CLWP retrieval accuracy increases with increasing CLWP, with a relative root mean squared error below 50 % for CLWP above 100 g m−2. Retrieval uncertainties occur due to ambiguities between cloud liquid water emission and scattering in the snowpack and emission by newly formed sea ice. We further analyze the impact of surface melt and a rain-on-snow event associated with the warm air intrusion on the surface parameters. Finally, we show CLWP distributions along the flight track for all airborne observations in comparison to ERA5 for different cloud regimes. The retrieval algorithm enhances the understanding of Arctic clouds and allows for an improved use of passive microwave satellite data in polar regions.
The vertical distribution of PM2.52.5
In this study, the particle maximum diameter is introduced and evaluated as a new variable of the two-dimensional video disdrometer (2DVD). Vertically resolved remote-sensing measurements meanwhile allow to retrieve the microphysical properties of precipitation. However, opportunities for a direct evaluation of those retrievals are still lacking. One possible approach is the ground-based observation of precipitation particles with in-situ sensors such as the 2DVD. In this context, the suitability of the 2DVD for contributing to cloud microphysics studies is being assessed. First, the retrieval of the particle maximum diameter as a new parameter is described, followed by an explanation about the procedure of the determination of dominant particle shapes done in this study. The capabilities of the 2DVD are demonstrated by means of measurements performed in a pre-alpine region of Switzerland which show that the instrument could detect signatures from cloud seeding experiments. Moreover, ice crystal number concentration and, for the first time, mean maximum diameter derived from the remote-sensing based LIRAS-ice retrieval are evaluated against ground-based in-situ measurements from the 2DVD. In the frame of a case study from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2019, LIRAS-ice and 2DVD data were found to agree well during suitable meteorological conditions that allow to relate surface observations to the higher-level remote sensing measurements. This study shows that the maximum diameter from 2DVD observations enhances the instrument's capability to contribute to precipitation-related research.
Particulate matter (PM) emitted by aircraft engines represents a major non-CO2
Carbon dioxide (CO2) is the most significant anthropogenic greenhouse gas. However, tracking CO2CO2CO2CO2
Aerosol source apportionment is a key tool for understanding the origins of atmospheric particulate matter and for guiding effective air quality management strategies. However, source apportionment techniques still struggle to properly separate highly correlated sources without relying on restrictive a priori information, possibly skewing the solution and adding subjective operator input, with varying degrees of benefit. This study introduces sparsity into the Bayesian Autocorrelated Matrix Factorisation (BAMF) model with the aim of removing non-essential species contribution in the unconstrained profiles, which is expected to improve the separation of factors compared to BAMF. The regularised horseshoe prior (HS) has been added to BAMF (BAMF+HS) to promote composition matrix F+HS was evaluated using three synthetic datasets designed to reflect increasing levels of data complexity (Toy, representing a highly simplified dataset; Offline, representing a filter dataset; and Online, representing an Aerosol Chemical Speciation Monitor (ACSM)-like dataset), and a real-world multi-site filter dataset. The results demonstrate that BAMF+HS effectively enforces sparsity in offline datasets and that this improves accuracy in reconstructing source profiles and time series compared to BAMF and Positive Matrix Factorisation (PMF). However, its application to higher-complexity ACSM datasets revealed sensitivity to sampling instability hindering sparsification. With that, even though sparsity was not achieved, the quality of the BAMF+HS solution metrics were not deprecated compared to BAMF. Overall, this work underscores the value of incorporating profile sparsity as a solution property in Bayesian source apportionment, and positions BAMF+HS as a promising model for source apportionment.
The 17 operational German C-band polarimetric weather radars routinely perform a vertical “birdbath” scan, which has so far primarily been used for calibration of differential moments. In this study, we transfer a retrieval algorithm for the rime fraction of snowflakes – originally developed for Ka-band cloud research radars – to the operational birdbath scan. This retrieval, which relies on the increase in detected mean Doppler velocity, serves as our benchmark. To validate the transfer of the retrieval, we apply it to a resampled birdbath dataset, constructed by downsampling cloud radar data to match the resolution of the operational birdbath scan. In addition, we present a new clutter filter and a melting layer detection algorithm for the operational birdbath scan. Finding good agreement between resampled and benchmark datasets, we apply the new retrieval to radar data recorded during the winters of 2021 to 2024. This results in a nationwide map of riming events in wintertime clouds. There is a north-south gradient in the riming distribution, which can be linked to Germany's precipitation climatology. Notably, we show that the occurrence of riming events correlates more strongly with precipitation intensity than with the total number of precipitation hours across sites. The temperature distribution associated with riming is consistently between −150 °C
The oxygen emission band at 60 GHz is a commonly used frequency band for atmospheric temperature sounding. The fine structure emission lines used to retrieve temperature in the stratosphere and mesosphere are affected by the Zeeman effect, which has a characteristic influence on the spectral shape of different polarization states. As a consequence of this effect, a V-Stokes component is generated, indicating symmetry breaking between right and left circular polarized radiation. In this study, we present the full-rank Stokes vector of the fine structure emission lines at 53.067 and 53.596 GHz, measured with a fully polarimetric radiometer. We discuss the advantages of the fully polarimetric approach compared to single-polarization observations for temperature sounding by comparing both simulations and observations. Our findings show that using circular polarization in the retrieval algorithm improves both the upper altitude limit and vertical resolution by several kilometers. Additionally, we introduce an operational calibration method and present calibrated spectra for the four components of the Stokes polarization vector. We also provide a continuous series of retrieved temperature profiles, demonstrating that the calibration is valid for continuous observations.
The Western Mediterranean is a climatic hotspot with strong variability in cloud processes. However, Cloudnet sites there are scarce compared to northern Europe. This study presents for the first time a five-year cloud statistical analysis at the AGORA ACTRIS-CCRES station in Granada (Spain), using 94 GHz Doppler radar, microwave radiometer, and ceilometer data. Analyses focus on single-layer clouds and their interannual variability in macrophysical and microphysical properties. A new cluster-based algorithm (CBA) is introduced for cloud classification, reducing spurious correlations found in earlier methods. The CBA shows single-layer cloud minima in summer, with annual occurrences of 5.0 % for ice, 3.6 % for precipitating ice, 3.4 % for mixed-phase, 3.2 % for precipitating mixed-phase, and 1.4 % (1.2 %) for liquid (precipitating liquid) clouds. Liquid clouds are observed at 1–2 km, thin (∼ 200–300 m), with a droplet radius of 5 µm and liquid water paths of 12 g m−2. Mixed-phase clouds occur at 5–6 km, nearly 1 km thicker, with larger droplets (10.8 µm) and ice water paths of 3.5 g m−2. Ice clouds dominate at 7–8 km, the thickest type, with higher ice water paths (8.5 g m−2) but smaller particles (∼ 39 µm) than mixed-phase (∼ 45 µm). Across all phases, precipitating clouds have lower bases, greater thickness, and higher water content and particle sizes than non-precipitating clouds. These results provide benchmark data for satellite and model evaluation. The algorithm can be applied to other Cloudnet sites, supporting consistent European cloud statistics.
To investigate the application of deep learning in satellite remote sensing, this study employs brightness temperature observations from the remapped Micro-Wave Radiation Imager-Rainfall Mission (MWRI-RM) onboard the Fengyun-3G (FY-3G) satellite as input data, while temperature and relative humidity profiles (ranging from 1000 to 100 hPa) obtained from ERA5 reanalysis data are used as label data. An Advanced Residual Convolutional Neural Network (AR-CNN) model was developed to retrieve atmospheric temperature and relative humidity profile data. The results show that: (1) The retrieval of temperature profiles achieves a root-mean-square error (RMSE) of approximately 1.24 K, and the RMSE for relative humidity profiles is 12.98 %. (2) A comparison between retrieved and labeled samples reveals consistent results for temperature retrieval but some discrepancies in extreme high and low humidity regions, suggesting the need for further refinement. (3) Gradient-based analyses and perturbation experiments confirm that 118 GHz oxygen channels are critical for mid-to-upper tropospheric temperature (500–200 hPa), indirectly impacting upper-level humidity (200–100 hPa) through thermal coupling, while 183 GHz water vapor channels dominate lower-to-mid tropospheric humidity retrievals (1000–500 hPa) and constrain temperature via moisture-radiation feedbacks. (4) Additional channel ablation experiments demonstrate that channels with smaller frequency offsets mainly affect upper atmospheric layers, whereas larger-offset channels have stronger impacts on lower layers, supporting the spectral contribution patterns identified in previous studies. These findings highlight the model's ability to capture temperature-humidity coupling and confirm the complementary roles of 118 and 183 GHz channels in improving vertical profile retrievals.
High levels of bromine monoxide (BrO) observed in volcanic plumes indicate significant catalytic destruction of tropospheric ozone (O3) at local to regional scales. The underlying chemical mechanisms are still incompletely understood and the quantification of O332), which is low in the atmospheric background but a main constituent of volcanic plumes (ppmv levels). This problem can be circumvented by using chemiluminescence (CL) O32333323
Although important, direct retrievals of entrainment rates in cloud-topped planetary boundary layer (PBL) remain elusive. Here we present a novel technique for retrieving cloud-top entrainment velocities using only Multi-angle Imaging Spectro-Radiometer (MISR) stereoscopic retrievals of cloud-motion vectors (CMVs) and cloud-top heights (CTHs). Mesoscale vertical air velocity at CTH is diagnosed from the continuity equation and then used to derive entrainment velocities from the PBL mass-budget equation. The algorithm is demonstrated through a case of marine stratocumulus deck off the California coast, with comparisons made against data from the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis (ERA5) and the data from other satellites. MISR low-cloud CTH for this case were lower than the ERA5 reported PBL depth by 189 ± 87 m. These differences in cloud top heights partly modulate the differences in the ERA5 and MISR horizontal winds, with larger differences in meridional over zonal wind components. Average difference between ERA5 and MISR derived mesoscale vertical air motion at cloud top was 0.14 ± 0.73 cm s−1, while the same for entrainment rate was −0.09 ± 0.46 cm s−1. The uncertainties in the utilized CTHs and CMVs are propagated to derive systematic and random retrieval uncertainties. Fractional uncertainty is lower than 25 % when the retrieved mesoscale vertical air motion is stronger than ±0.04 cm s−1±0.03 cm s−1. These results showcase the ability to derive mesoscale vertical air motion and entrainment rates from MISR observations and motivate its extension to generate a global climatology leveraging its full 23-year record (2000–2022). Nonetheless comprehensive validation of the retrievals is warranted through comparisons with estimates from an independent dataset across diverse weather conditions.
MAESTRO (Measurement of Aerosol Extinction in the Stratosphere and Troposphere Retrieved by Occultation), a dual UV (ultraviolet) and Visible-NIR (visible-near-infrared) spectrometer, has been operating aboard the Canadian satellite SCISAT for over 22 years. Recently, MAESTRO version 4.5 volume-mixing-ratio profile data for NO233