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The Global Ozone Monitoring Experiment (GOME)-type Ozone Profile Essential Climate Variable (GOP-ECV) data record provides monthly mean ozone profiles with global coverage from 1995 to 2021 at a spatial resolution of 5° × 5°. Measurements from five nadir-viewing satellite sensors are first harmonized and then merged into a coherent record. The long-term stability of the data record is further improved through scaling the profiles using the GOME-type Total Ozone Essential Climate Variable (GTO-ECV) data record as a reference.

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Surveillance cameras have emerged as a new low-cost, high-resolution Near Surface Precipitation Type observer. A novel deep learning approach is developed to classify rain, snow, and graupel, achieving 93 % accuracy in real-world observations. The model remains robust to camera parameter variations and maintains reliable performance at wind speeds below 5 m s⁻¹, demonstrating strong potential for large-scale practical applications.

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Precipitable water vapor (PWV) is important for various climate and weather studies, but difficult to monitor under various weather conditions. This study shows that surface-based spectral irradiance combined with deep neural network models can accurately estimate PWV under various atmospheric conditions. Models using global, direct, and diffuse irradiances performed best, while even global-only data gave reliable results.

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Our research compares three systems of continuous flow analysis coupled with cavity ring-down spectrometry (CFA-CRD) from Venice, Paris, and Grenoble laboratories for measuring water isotopes in ice cores, crucial for reconstructing past climate. We quantify each system’s mixing and measurement noise effects, which impact the achievable resolution of isotope continuous records. Our findings reveal specific configurations and procedures to enhance measurement accuracy, providing a framework to optimise water isotope analysis.

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Accurate quantification of methane emissions is crucial in reducing greenhouse gases. This paper presents a comprehensive evaluation of several multi-source emission quantification models using data from fixed-point continuous monitoring systems (CMSs), collected during a controlled-release study. This work offers a novel contribution by evaluating multi-source emissions quantification techniques, leveraging a fixed-point CMS to capture the complexities of overlapping plumes from simultaneous releases.

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This work develops an Neural-Network-based above cloud aerosol (ACA) detection and retrieval scheme for multi-angular polarimetric (MAP) instruments. On one year of the retrieval, the retrieved aerosol properties (aerosol optical thickness, AOT, Angstrom Exponent, AE, and Single Scattering Albedo, SSA) agree well with adjacent clear-sky aerosol retrievals. The seasonal global pattern of ACA events and above cloud AOT are also within expectation.

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This study leverages machine learning models to classify cloud thermodynamic phases using multi-sensor remote sensing data collected at the Department of Energy Atmospheric Radiation Measurement North Slope of Alaska observatory. We evaluate model performance, feature importance, and application of the model to another observatory and quantify how the models respond to instrument outages.

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This manuscript details laboratory and field-based testing of a tower-based methane and ethane measurement system to address the challenge of separating methane sources in oil and gas basins. We describe methods for managing water vapor, calibration, and estimating the various components of measurement uncertainty. With appropriate engineering and calibration, the instrument shows the capability to measure CH₄ and C₂H₆ with sufficient stability to distinguish regional methane emission sources.

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We statistically analyze the hygroscopic growth characteristics of urban anthropogenic aerosols over Wuhan, a megacity over central China, using lidar observations and Hänel parameterization from 2010 to 2024. Aerosol hygroscopic parameter γ increases from 2014 to 2017 and stabilizes at high levels afterwards, aligning with the changes in NO₂-to-SO₂ concentration ratio. Moreover, no evident differences are found across seasons, as well as between the free troposphere and boundary layer.

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PUFIN (Profiling Upper altitudes For Ice Nucleation) is a lightweight sampler flown on the U.S. Department of Energy’s Atmospheric Radiation Measurement user facility’s tethered balloons to measure ice nucleating particles at multiple altitudes. Deployments in Maryland and Alabama show it can detect low concentrations in under an hour and capture changes with height. All data are publicly available, and future flights will help track seasonal and vertical patterns of these unique particles.

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We present, to our knowledge, the first relaxed eddy accumulation system explicitly tailored to a radiocarbon (¹⁴C)-based partitioning of fossil and non-fossil urban CO₂ fluxes. Laboratory tests and in-depth quality and performance checks prove that the system meets the technical requirements. A pilot application on a tall tower in the city of Zurich, Switzerland, demonstrates the ability to separate fossil and non-fossil CO₂ components within the typical precision of ¹⁴C measurements.

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Water vapor is a strong greenhouse gas, and accurate measurements of its concentration in the upper atmosphere (~8–25 km altitude) are crucial for reliable climate predictions. We investigated the performance of four airborne hygrometers, deployed on aircraft or stratospheric balloon platforms and based on different techniques, in a climate simulation chamber. The results demonstrate the high accuracy and reliability of the involved sensors for atmospheric monitoring and research applications.

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Black carbon or 'soot', generated by combustion, harms climate and health. Traditional filter-based sensors are prone to artefacts and need frequent human intervention. Our optoacoustic illumination-detection separating sensor (IDSS) is filterless and requires minimal cleaning. Flows of clean air shield the sensor's cavity, preventing contamination. We demonstrate the stable performance of the IDSS and estimate a cleaning cycle of 1.5 years in ship-board applications.

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An attention-augmented ResNet and a transfer training are implemented to derive diurnal variations in near-global planetary boundary layer height. The transfer-trained model shows superior performances compared to conventional algorithms and non-transfer trained mode. The model predicted more reliable diurnal PBLH behaviors, with daily amplitude and peak timing approaching radiosonde results.

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We present a fast, interpretable machine learning method to retrieve key aerosol parameters from ground-based Sun-sky photometer measurements. Trained on simulated data covering diverse aerosol and atmospheric conditions, ensuring robustness and physical consistency. Applied to real observations, it agrees well with AERONET products and reduces computation time by orders of magnitude, offering a practical tool for monitoring aerosols and their effects on air quality and climate.

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This paper presents, for the first time, a method for cross-calibration of spectrometers using data from the Global Ozone Monitoring Experiment (GOME) and the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), which operated concurrently for 10 years. The method addresses key challenges compared with optical imagery calibration and integrates Polarization Monitoring Device data to enhance the accuracy and reliability of the cross-calibration process.

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Methane plumes can be detected with several instruments from space at a high spatial resolution nowadays. We see a ground projection of these methane plumes from the satellites that, similarly to clouds or buildings, are distorted depending on the observation and illumination angle. Here we highlight this issue and propose a methodology to account for it using simulations that could enhance current and upcoming retrieval and quantification algorithms.

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This work presents a new simplified ground-based thermal infrared (TIR) system capable of detecting and retrieving volcanic emissions during both the day and the night. Knowing the location of the instrument and the crater, it is possible to compute the geometry (height and thickness) of a volcanic plume. Furthermore, thanks to a specific filter positioned in front of one of the TIR cameras, it is possible to compute the sulfur dioxide (SO₂) content emitted by the volcano at a safe distance from the vent.

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This research aimed to improve our understanding of cloud structure using spaceborne measurements. The study applied an optimal estimation method to determine how cloud droplet sizes change with height, using satellite data and coincident aircraft measurements for validation. It found that current space-borne spectrometers lack the accuracy to fully resolve this vertical structure, but upcoming instruments like CLARREO (Climate Absolute Radiance and Refractivity Earth Observatory) Pathfinder will significantly enhance this capability.

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Two decades of operations and performance for the MAESTRO instrument are reviewed. Topics addressed include: a) occultation measurement schemes, b) MAESTRO’s field of view (FOV) on the Sun, c) FOV changes on the solar disk during occultations and their impact on calculated transmittances, d) the relation between the MAESTRO and ACE-FTS FOVs, e) verification of wavelength assignment, and how it has been affected by thermal environment changes, and f) the determination of MAESTRO tangent heights.

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Formaldehyde is an important atmospheric species that is present at all times throughout the troposphere. Accurately measuring formaldehyde is necessary for understanding key atmospheric chemical cycles. We describe here the first wide-scale demonstration of the gas chromatographic mass spectrometric (GC/MS) technique (NSF NCAR TOGA-TOF) for quantifying formaldehyde in the atmosphere and we show this technique to be highly sensitive and selective.

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We evaluate the new radar module of the Community Radiative Transfer Model (CRTM) using EarthCARE CPR, CloudSat CPR, and GPM DPR observations. Simulated reflectivities show strong sensitivity to particle size distributions and frozen hydrometeor habits. Results highlight the role of microphysical assumptions in radar forward modeling and their importance for assimilating spaceborne radar data in NWP.

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We developed methods to reduce noise in satellite images that track air pollution, good for making faint emission signals easier to detect. By using clearer measurements of a related gas, our techniques improve image quality by up to 60 percent, allowing more accurate identification of pollution sources. Tested with simulated and real satellite data, this approach could enhance monitoring of emissions and support better environmental decisions.

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Since June 2019, an infrared camera has been scanning the nearly entire sky (diameter: 500 km) above DLR Oberpfaffenhofen (48.09° N, 11.28° E), Germany, every night providing images of the OH* airglow layer (height: 85–87 km), with a high spatial and temporal resolution (150 m, 2 min). We analysed three years of data for spatially confined small-scale wave structures with a machine learning approach. We derived seasonal variations and deduced that wave breaking is mostly observed in summer.

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Ice-nucleating particles (INPs) are important for cloud formation and properties affecting weather and climate. This article presents a method coupling an offline ice nucleus counter to an electron microscope to obtain information, not only about INP number concentrations but also about physico-chemical properties (e.g., size and chemical composition) relevant to ice nucleation on single particles. The method was evaluated on the basis of a case study at the high-altitude research station Jungfraujoch.

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This study used Polarimetric radio occultation (PRO) observations to evaluate simulations of cloud hydrometeors with five microphysics schemes for three typhoons from 2019 and 2021. The simulated cloud hydrometeors distributions varied significantly depending on model initial conditions, typhoon structures, and microphysics schemes. Results in this study demonstrate the potential for using PRO observation to evaluate the performance of different microphysics schemes in numerical models.

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Greenhouse gases like CO₂ and CH₄ drive climate change. Satellites enable monitoring of these emissions from space. Our simulations show that the upcoming Twin ANthropogenic Greenhouse gas Observers (TANGO) mission can detect about 500 targets per 4 d cycle under clear skies, but cloud cover reduces detection. Integrating cloud forecasts into TANGO’s manoeuvering boosts detection, highlighting its potential for improving global emission monitoring.

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We present PrecipBE, a multi-instrument precipitation event best-estimate data product developed at the Atmospheric Radiation Measurement (ARM) user facility, providing time series and tabular statistics of events, which could help advance model evaluation and cloud-process studies. We demonstrate PrecipBE utilization with a brief 30-year trend analysis using ARM Southern Great Plains (SGP) site data, suggesting shorter, less intense events, but rising annual rainfall, driven by rare extremes.

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This study presents an approach that enables the detection of the shape and orientation of multiple types of co-located hydrometeors in mixed-phase cloud systems. This information is key for improving the understanding of these clouds, as they do contain ice and liquid water simultaneously, making them relevant for the precipitation budget and radiative balance of the Earth's atmosphere. The retrieval is based on elevation scans of polarimetric cloud radars and can therefore be flexibly applied.

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How ice particles grow in extremely cold conditions remains poorly understood due to limited observations. This study develops a new method to identify dominant ice-particle growth from radar reflectivity and Doppler velocity. It reveals, for the first time, that key growth processes vary not only with temperature but also by region. These findings highlight EarthCARE's value for monitoring clouds and improving climate model representation.

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This paper reports the deployment of the first narrowband sodium lidar in the low-latitude region of China. The lidar observations are compared with satellite measurements and atmospheric models, confirming the scientific credibility of these results. Calculations of vertical gravity wave heat and sodium fluxes are used to investigate the influence of the space environment. This lidar system will provide a new ground-based detection device for studying atmospheric environment above the area.

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We present a new method to retrieve atmospheric particulate matter concentrations using only airborne High Spectral Resolution Lidar measurements in machine learning algorithms. Retrieved concentrations agree well with surface measurements. These concentrations and our estimates of the particle mass extinction efficiency are also consistent with those retrieved from airborne in situ measurements. This methodology can also be applied to the Atmosphere Lidar on the EarthCARE satellite.

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This study proposes a new technique to augment polarimetric radar measurements of the national German operational radar network with vertically looking cloud radars. The method is tested in two different case studies by comparison to dedicated scanning radars revealing promising results. Future usage of the method is motivated by analyzing the coverage of the operational radar network finding advantageous locations with good radar coverage.

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A new technique for the end-to-end calibration of weather radars is introduced. Highly precise artificial radar targets are generated with a radar target simulator and serve as a calibration reference for weather radar observables like reflectivity and Doppler velocity. The system allows investigating and correcting any biases associated with weather radar observations.

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Entrainment is key in understanding temperature and moisture changes within the boundary layer, but it is difficult to observe using ground-based observations. This work used simulations to verify an assumption that simplifies entrainment estimations from ground-based observational data, recognizing that entrainment is the combination of the transfer of heat and moisture from above the boundary layer into it and the change in concentration of heat and moisture as boundary layer depth changes.

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An instrumented container laboratory is operated on regular commercial passenger flights to obtain a long-term representative dataset on the composition of the upper troposphere and lower stratosphere. Here we report on the development of a fully automated aerosol mass spectrometer for this project. We present technical specifications, necessary modifications for the automation, instrument calibration and comparisons, detection limits, and the first in-flight data.

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Current knowledge of the link between clouds and climate is limited by a lack of observations of the drop size distribution (DSD) within clouds, especially for the smallest drops. We demonstrate a method of retrieving DSDs down to small drop sizes using observations of drizzling marine layer clouds captured by the CloudCube millimeter-wave Doppler radar. We compare the shape of the observed spectra to theoretical expectations of radar echoes to solve for DSDs at each time step and elevation.

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Stratospheric water vapor (SWV) influences various atmospheric processes. While the Ozone Mapping and Profiler Suite Limb Profiler (OMPS LP) was not designed to measure SWV, we utilized near-coincident measurements by the Aura Microwave Limb Sounder (MLS) and OMPS LP to develop a machine learning method to measure SWV between 11.5–40.5 km. The LP-derived SWV closely agrees with MLS. Our results suggest OMPS LP can continue the global water vapor record after MLS measurements cease in 2026.

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We developed a hybrid machine learning-optimal estimation retrieval system that efficiently and accurately mimics operational retrieval results. Crucially, this algorithm also predicts critical diagnostic variables including observation operators needed for comparison with independent data and ingestion into downstream chemical data assimilation models.

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In this work, we introduce WALL-E, a newly designed wall-free particle evaporator that enables real-time online aerosol analyses while minimizing wall interactions and fragmentation, offering a more accurate and efficient approach to studying aerosol properties. WALL-E provides molecular-level insights into aerosol composition, quantification, and volatility distributions.

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This paper introduces a framework for tracking sensor performance and comparison. We analyzed two decades of reflectance data from two well-known spectrometers over 20 desert sites, covering about 1300 spectral channels. The results lay the groundwork for cross-calibration of spectrometers by identifying which reference/constrained sensor and investigating the stability of invariant calibration sites.

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We assess the representativeness of a remote ground-based ACTRIS station, in Mindelo, Cabo Verde by utilizing the continuous observations of a ground-based PollyNET multiwavelength polarization Raman lidar, in combination with the LIVAS products. A statistical analysis of the optical properties at different radii around Mindelo was performed, in addition to case studies. Our study results indicate that overall, the ground-based station in Mindelo can be considered conditionally representative.

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Fengyun radio occultation products are useful for numerical weather prediction (NWP). However, its accuracy required to be further improved from middle stratosphere above. We developed a quality control scheme and evaluate the Fengyun bending angles’ quality. The new quality control scheme are useful in rejecting outliers and products’ quality have been well understood. The results are promising in improving the performance of Fengyun data in NWP and climate applications.

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In this study, nearly 10 years of data from a radar wind profiler data stationed in the Azores is processed. The sensitivity and dynamic range of the radar are evaluated over time, and a methodology is developed and implemented to remove degraded data. With the remaining data, measurements of turbulence are retrieved and a climatology of wind data during marine conditions is created, showing increased turbulence in the marine boundary layer during autumn and winter months.

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Entrainment of dry warm air from above the cloud into the cloud layer modulates the cloud properties and lifetime. Despite its importance, observations of entrainment remain elusive. Here, we present a technique to derive mesoscale vertical air motion and entrainment velocities using cloud top heights, and horizontal winds from the Multi-angle Imaging Spectro-Radiometer (MISR). The results motivate application of the technique to generate global climatology leveraging full MISR 23-year record.

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Tropospheric ozone from the three Infrared Atmospheric Sounding Interferometer (IASI) instruments is very consistent (<1 %). The validation against homogenized ozone sondes reveals an overall good agreement with slight biases (3 %–6 %) in tropospheric ozone and a possible temporal drift, but this is difficult to assess due to the limited number of sites. No specific trends are estimated for the tropospheric ozone column for the period 2008–2022, but persistent negative trends are observed in the lower troposphere.

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The WIVERN (WInd Velocity Radar Nephoscope) conically scanning Doppler W-band radar has the potential, for the first time, to map the mesoscale and synoptic variability of cloud dynamics and precipitation microphysics. This study shows that the oblique angle of incidence will be advantageous compared to standard nadir-looking radars due to substantial clutter suppression over the ocean surface. This feature will enable the detection and quantification of light and moderate precipitation, with improved proximity to the surface.

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A new, low-cost instrument for measuring total column ozone was tested against a MK III Brewer spectrophotometer for six months. Using the "Global Irradiance" method from the Norwegian GUV network, it showed ozone values matching Direct Sun Brewer measurements with a 1.8 % standard deviation. Despite temperature and cloud dependencies, the instrument is robust and easy to use. Fully coverage of the UV-A/B spectrum allows for further UV-radiation analysis. Total cost is less than EUR 3000.

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The European Space Agency's Aeolus satellite (2018–2023) was the first mission to measure global wind profiles from space. We analysed its performance over five years to understand data quality and coverage under different conditions. By linking instrument behaviour to wind observations, we identified strengths and limitations. These results provide essential guidance for the design and operation of the operational follow-on mission Aeolus-2.

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Due to the scarcity of offshore in situ observations, alternative data sources are essential for a reliable understanding of offshore winds. Therefore, this study delves into the world of satellite remote sensing (ASCAT) and numerical models (ERA5), exploring their capabilities and limitations in characterising offshore winds. This investigation evaluates these two datasets against measurements from a floating ship-based lidar, collected during a novel measurement campaign in the Baltic Sea.

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Air pollution management requires accurate determination of emissions and emission ratios of air pollutants. In this paper, we explore a new way to resolve excess mixing ratios in turbulent plumes, which allows aggregation of unbiased ensemble averages of air pollutant ratios that can be compared with emission models. The approach is tested in an urban environment and used to resolve emission patterns of nitrogen oxides and carbon dioxide.

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The two ozone instruments of IAGOS (In-service Aircraft for a Global Observation System) have been compared with the Ozone PhotoMeter (OPM) of the World Calibration Center of Ozone Sondes (WCCOS) in an atmospheric simulation chamber under realistic flight conditions of pressure, temperature, and ozone concentrations. The two IAGOS-instruments showed good agreement with the OPM within 5–6 %. The observed differences are small but systematic and reproducible during the intercomparison.

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Ultrafine particles (diameter of less than 100 nm) are suspected to cause significant health effects. Accurately measuring their chemical composition and physical properties in real time is challenging due to their low mass and interference from larger particles. This study proposes a method for the continuous, automated measurement of their composition, tested in a pilot field study to explore their chemical characteristics, physical properties, and sources.

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Sea spray affects air–sea interaction, cloud microphysics, and the radiative budget. However, meteorological processes at the wind-gust level complicate the physical interpretation of measured aerosol particle properties. We used meter-scale models to track the life history of thousands of particles under different conditions to show that investigators must account for key factors to link observations at aircraft level to sea-spray emissions at the ocean's surface.

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This study compares aerosol single scattering albedo data from two algorithms, CERES-MODIS and OMI, across different regions like forests, oceans, land, and deserts. It finds that CERES-MODIS tracks aerosol absorption more accurately, especially in areas with smoke and pollution. In clean regions, both algorithms perform similarly. The study helps scientists understand which satellite gives better data in different conditions, supporting improved climate and air quality research.

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Humidity transport from the Earth's surface into the atmosphere is relevant for many processes. However, knowledge of the actual distribution of humidity concentrations is sparse – mainly due to technological limitations. With the lidar presented herein, it is possible to measure humidity concentrations and their vertical fluxes up to altitudes of > 3 km with high spatiotemporal resolution, opening new possibilities for detailed process understanding and, ultimately, better model representation.

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Tropospheric gradients provide information on the moisture distribution, whereas zenith total delays provide the overall moisture information along the zenith. When both observations are used together, the model can actuate the moisture fields, correcting their dynamics. Our research shows that, in regions with very few stations, assimilating tropospheric gradients on top of zenith total delay observations can enhance the performance of existing improvements.

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In the study, we simulate spectral polarimetric variables for raindrops as observed by cloud radar. Raindrops are modeled as oblate spheroids, and backscattering properties are computed via the T-matrix method, including noise, turbulence, and spectral averaging effects. When comparing simulations with measurements, differences in the amplitudes of polarimetric variables are noted. This shows the challenge of using simplified shapes to model raindrop polarimetric variables when moving to millimeter wavelengths.

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Satellite remote sensing helps monitor dust storms. Our study compared five satellite products that use ground-based PM₁₀ data. Most products agreed on the daily dust distribution, with absorbing aerosol index (AAI) performing best under cloud cover. Overall, the Sentinel-5P AAI has the highest probability of correct detection (POCD) in dust events but is unstable and has a high probability of false detection (POFD). The FY4A/B infrared difference dust index (IDDI) has the lowest POCD, but it is relatively stable, and its POFD is low.

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Mid- and low-cost CO₂ sensors are attractive in carbon monitoring and atmospheric inversions. They are useful in both fixed stations and mobile monitoring. Yet the performance faces great challenges due to environmental impacts and long-term drifts. Here, we conducted 30 months of co-located observations using such sensors with a reference instrument. After corrections of environmental impacts and drifts, the accuracy reached 1–3 ppm. We recommend standard gas calibration within 3–6 months.

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In this study, we developed a new way to produce and measure nitrous acid, an important atmospheric gas that can be difficult to synthesize. By carefully controlling temperatures and reaction conditions, we created high-purity (<96 %) samples and showed how they can be stored and released when needed. This advance makes it easier to study how nitrous acid contributes to air pollution and atmospheric interactions, while also improving tools for laboratory experiments and instrument testing.

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1. The formation of ice crystal clouds catalyzed by dust aerosols were observed by coherent Doppler wind lidar in the Taklimakan Desert. 2. The wind provides a dynamic basis for the formation of ice crystal clouds and plays an important role in the decomposition process. 3. The special basin topography, turbulence and downdrafts keep the base height of the ice crystal clouds at around 3 km.

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The data quality of weather radar near coastlines can be affected by echoes from ships, and this interference is exacerbated when pulse compression technology is used. This study developed a hybrid ship clutter identification algorithm based on artificial intelligence and heuristic criteria, effectively mitigating the issue. The successful reproduction of ship tracks in the Gulf of Finland supports this conclusion.

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This study shows that it is possible to automatically monitor atmospheric aerosols from research vessels using automated instruments, following the same standards as AERONET (AErosol RObotic NETwork) land-based stations. By collecting 3 years of data in the Indian Ocean, we demonstrate that high-quality measurements can be made, even on a moving platform. These results open new possibilities for observing aerosols over the ocean and improving satellite data and climate studies.

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We have deployed two spectrometers on a ship traveling along the coast of Japan. By that, we were able to repeatedly measure the greenhouse gas and air pollutant emissions of power plants, large industrial facilities, and cities. Using the ratios between the different gases, we are able to identify sources based on their unique signature. In addition, we are able to show that spectrometers can be operated on a ship, while still fulfilling the high standards of land-based observation networks.

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This study proposes a novel machine learning method to estimate pollution levels (PM_2.5) on urban areas at fine scale. Our model generates hourly PM_2.5 maps with high spatial resolution, by combining satellite data, ground measurements, geophysical model data, and different geographical indicators. The model properly accounts for spatial and temporal variability of the urban pollution levels, and can be highly beneficial for air quality monitoring and health protection.

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Ice particles (e.g., cloud ice, snow, and graupel) in convective clouds play key roles in cloud and precipitation formation. This study combines satellite millimeter-wave radar and radiometer observations to estimate the vertical distributions of physical parameters of ice particles such as mass, size, and number densities. CPR radar and GPM radiometer observations together reduce the estimation errors of the physical parameters and provide information on the optimal ice particle shape.

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This study advances the field of low altitude wind field detection by systematically evaluating Doppler wind lidar performance against in situ balloon radiosonde under complex atmospheric conditions. We propose a novel machine learning framework for wind profile correction and the Aeolus satellite is used to verify the reliability of the algorithm further to enhance data reliability in meteorological remote sensing.

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In this work, a new quantitative retrieval of cloud thermodynamic phase partitioning based on multi-angle polarimetric imaging is presented. The retrieval is validated using synthetic data for idealized and realistic cloud cases and applied to measurements of the airborne specMACS instrument during the HALO-(AC)³ campaign. It provides high spatial resolution information about phase partitioning at cloud top and allows for example to study phase transitions in Arctic mixed-phase clouds.

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Many atmospheric science endeavors require temporally resolved profiles of temperature, humidity, and winds. Radiosondes are considered the gold standard for measuring these profiles, but the temporal resolution is frequently too coarse for many applications within the atmospheric boundary layer. This study proposes a new method using a normalized height grid in the temporal interpolation process that yields more accurate profiles in the convective boundary layer.

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We retrieved high-resolution maps of Arctic surface temperature and type using an airborne thermal infrared imager during an Arctic aircraft campaign. Our study highlights small-scale surface variability, complementing satellite observations. Surface temperature was retrieved via radiative transfer simulations, while surface type was classified using machine learning. Additionally, we analysed segment sizes of each surface type, presenting results based on their distance from the sea-ice edge.

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This work investigates the aerosol remote sensing capabilities offered by the new Meteosat Third Generation-Imager geostationary satellite. First, aerosol load retrieval performance is demonstrated based on realistic synthetic data. Second, the potential for aerosol type characterization is proven, with the estimation of fine mode fraction. This work opens pathways for the future study of diurnal aerosol variations from space thanks to the high temporal resolution of geostationary satellites.

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This study presents the results from a validation study on the Level 2A products (aerosol optical properties) of the ESA's (European Space Agency) Aeolus mission. Measurements from the eVe lidar, a combined linear/circular polarization and Raman lidar and ESA's ground reference system, that have been collected during the Joint Aeolus Tropical Atlantic Campaign are compared with collocated Aeolus Level 2A profiles obtained from the latest version (Baseline 16) of the Aeolus algorithms.

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The Arctic Weather Satellite (AWS), developed by the European Space Agency, highlights a new approach in satellite design, aiming to expand the network of operational microwave sensors cost-effectively. Launched in August 2024, AWS features a 19-channel microwave cross-track radiometer. Notably, it introduces groundbreaking channels at 325.15 GHz. In addition, AWS acts as the stepping stone to a suggested constellation of satellites, denoted as EUMETSAT Polar System Sterna.

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ESA's Arctic Weather Satellite (AWS) is a novel mission to demonstrate the ability of polar-orbiting satellites to improve short-time forecasts and nowcasts of Arctic weather. Although the payload consists of a state-of-the-art microwave radiometer, the mission is designed in a cost-effective manner and, if successful, will serve as a blueprint for the Sterna constellation of satellites, which is under consideration by EUMETSAT. The AWS was launched in August 2024 and this highlight paper describes the mission, the radiometer and the state of the mission towards the end of the commissioning phase.

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The SO₂ gridded point emissions calculated from satellite measurements appear spread out around the source. This is inherent to the inversion method and the resolution of the satellite measurements. We made a new deconvolution algorithm to reverse the spreading and make the emission signal clearer. With this method, the effective resolution is improved. Known sources can be located more precisely, and new ones can be found from space. The same approach also works for other gases like NO_x.

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In space-based estimates of atmospheric methane concentrations, one can often observe biases that look like imprints of surface features. We performed realistic simulation experiments and find the root cause to be unaccounted aerosols. Since good knowledge of aerosols is difficult to achieve for operational science data processing, we conclude that a comprehensive surface bias correction scheme is highly important for missions utilizing the 2.3 µm spectral band for methane retrievals.

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Long-term measurements of CH₄ emission rates at a biogas plant in Germany were performed for eight years using mobile measurements combined with a Gaussian plume model. The average CH₄ emission rate of the biogas plant was 5.9 ± 0.5 kg CH₄ h^-1. To increase the accuracy of the emission rate calculations and harmonize the dataset, the methodology was evaluated through six controlled methane release experiments demonstrating an uncertainty lower than 30 %, following several recommendations.

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This study introduces the maximum diameter (D_max) of precipitation particles measured by a two-dimensional video disdrometer (2DVD) as a novel parameter. D_max is applied in a cloud seeding study during the Cloudlab campaign and, for the first time, in a MOSAiC case to evaluate the LIRAS-ice remote-sensing retrieval of in-cloud ice crystal size and number. Both quantities agreed well with the 2DVD measurements under ideal conditions, highlighting the potential of D_max for precipitation studies.

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This study aimed to develop a calibration using a multivariate linear regression for the Clarity Node S for PM₁, PM_2.5, and PM₁₀. Calibrations for PM₁ and PM_2.5 were successfully implemented using internal temperature, relative humidity, and EDM-180 PM₁₀. Comparison of the calibration of sensors for eight months showed improvements in detecting spikes in high PM concentrations and maintained good correlation with a reference monitor.

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Reducing methane emissions is essential to tackle climate change. In this paper, we introduce MetFluxNet, a deep learning model for methane plume source rate estimation. This model is trained on a new synthetic dataset designed to avoid network overfit. MetFluxNet can accurately estimate low source rates even in the case of heterogeneous backgrounds. To demonstrate its reliability for real-world plume estimation, we validated its predictions on real plumes with known source rates.

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We validate a novel chemical ionization mass spectrometer for simultaneous measurements of volatile organic and inorganic compounds. A field comparison with a Cavity Ring-Down Spectroscopy analyzer for NH₃ validated its accuracy and superior time resolution for capturing transient pollution peaks. Versatility is demonstrated across three key applications: stationary urban monitoring, mobile source mapping, and industrial process control.

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Airplane condensation trails trap heat, but the full warming effect has been hard to measure with satellites as they quickly blend with natural cirrus clouds. Our new method isolates their long-lived impact, estimating the effect of flight paths on satellite measurements of heat leaving the Earth. We found contrails trapped 46.9 gigajoules of heat per kilometer flown over the Americas, providing a crucially missing observation-based estimate of a large portion of aviation's environmental impact.

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