This study presents, for the first time, the uncertainty analysis of the Ångström exponent assumption on the full set of retrievable optical properties obtained with Raman lidar. A quantitative comparison between the pure rotational and vibrational–rotational Raman lidar approaches is presented. A minor impact of a wrong Ångström exponent on the determined aerosol optical properties is found.

Global horizontal irradiance retrievals from satellite observations are affected by spatial displacements due to parallax and cloud shadows. We assess different approaches to correct for these displacements and quantify their added value by comparison with a network of ground-based pyranometer observations. The corrections are found to become increasingly important at higher spatial resolutions and are most relevant for variable cloud types.

Clouds affect Earth's energy balance, with high-altitude cirrus clouds contributing to atmospheric warming. While active satellite sensors are the most accurate for detecting cirrus clouds, they are not ideal for long-term studies. This study compares Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data, testing six MODIS methods, one MODIS-based test, and two International Satellite Cloud Climatology Project (ISCCP) tests. The all tests consolidation (ATC) was the most effective, achieving 72.98 % accuracy during daytime and 59.50 % at night, making it relatively accurate for creating a cirrus mask.

This study presents a data processing scheme for the detection and quantification of methane emissions using the MethaneAIR airborne spectrometer. We show that the proposed methods enable the detection of smaller plumes (compared with those detectable using other existing methods) and improve the potential of MethaneAIR to survey methane point sources across large regions.

The study highlights how aerosol measurements from aircraft can be used in synergy with ground-based observations to validate the European Space Agency's Aeolus satellite aerosol product above the tropical Atlantic. For the first time, collocated sections of the troposphere up to 626 km long are crossed. Combining measurements from satellite, aircraft, and ground-based instruments allows characterization of the optical properties of the observed dust particles emitted from the Sahara.

One important property of a cloud is the concentration of cloud drops. This property is relevant to how the cloud interacts with sunlight and how easily the cloud forms precipitation. Measuring this property from satellite observations is one important source of data, but it does require making some assumptions. This study evaluates the accuracy of satellite-derived drop concentration by a comparison with aircraft measurements.

The topic is finding methods to extend climate data records from single-instrument satellite observations, in this case the Advanced Very High Resolution Radiometer (AVHRR). Several modern instruments include AVHRR-heritage channels, but some corrections are necessary to account for some differences. We have simulated AVHRR data from the Visible Infrared Imaging Radiometer Suite (VIIIRS) sensor on National Oceanic and Atmospheric Administration (NOAA) polar satellites. We find that methods based on machine learning are capable of performing these corrections.

Nitryl chloride (ClNO₂) is a reservoir of chlorine atoms and nitrogen oxides, both of which play important roles in atmospheric chemistry. However, all ambient ClNO₂ observations so far have been made by a single technique, mass spectrometry, which needs complex calibrations. Here, we present a laser-based method that detects ClNO₂ (TD-TILDAS – thermal dissociation–tunable infrared laser direct absorption spectrometry) without the need for complicated calibrations. The results show excellent agreement between these two methods from both laboratory and ambient samples.

This study analyzed NaCl and CaCl₂ powder-type hygroscopic materials for cloud seeding, using the Korea Cloud Physics Experimental Chamber (K-CPEC) in South Korea. The aerosol chamber enabled particle analysis in an extremely dry environment, while the cloud chamber, with precise pressure and temperature control, facilitated droplet growth through quasi-adiabatic expansion. Our study provides valuable insights for the development of new seeding materials and advanced cloud seeding experiments.

The Korean Geostationary Environmental Monitoring Spectrometer (GEMS) monitors trace gases over Asia, e.g., NO₂. GEMS provides hourly data, improving the time resolution compared to the daily overpasses by other satellites. For the prediction of hourly surface NO₂ over South Korea from GEMS observations and meteorological data, this study shows that machine learning models benefit from this higher time resolution. This is achieved by using observations from previous hours as additional inputs.

Simulations of storm characteristics and associated radar signatures were improved, especially under the freezing level, using an advanced cloud scheme. Discrepancies between observations and forecasts at and above the melting layer highlighted issues in both the radar forward operator and the microphysics. To overcome some of these issues, different parameterizations of the operator were suggested. This work aligns with the future integration of polarimetric data into assimilation systems.

Clumped-isotope signals obtained through gas source mass spectrometry are typically small and require pressure baseline corrections. While such corrections have been developed for square-shaped peaks, we present an approach for correcting peaks with complex shapes. Our method is demonstrated using oxygen clumped isotopes measured in pure oxygen, where the peak tops are linearly increasing and/or negatively curved.

We present an analysis of high-resolution remote sensing measurements of nitrogen-containing trace gases emitted by wildfires. The measurements were made using an instrument on the NASA ER-2 aircraft in the summer of 2019. We find that time-resolved fire intensity is critical to quantify trace gas emissions over a fire's entire lifespan. These findings have implications for improving air pollution forecasts downwind of wildfires using computer models of atmospheric chemistry and meteorology.

TanSat-2 is designed for global carbon monitoring, offering high-resolution dual-band observations of solar-induced chlorophyll fluorescence – a key indicator of photosynthesis. Simulations show its data processing can retrieve fluorescence with high accuracy. These results suggest TanSat-2 will enhance global tracking of the carbon cycle and vegetation health, providing valuable insights for climate change research.

This study evaluates a remote calibration tool, referred to as MOment MAtching (MOMA), for calibrating PurpleAir PM_2.5 sensors, especially for varying PM sources, and compares it with the Environmental Protection Agency (EPA) correction. MOMA improved the accuracy of PurpleAir sensor data comparable to the EPA correction. Although reliant on nearby reference sites, MOMA offers valuable insights into potential PM sources, thereby increasing the overall value of PurpleAir sensor networks.

This study introduces a novel method to detect horizontally oriented ice crystals (HOICs) using two ground-based polarization lidars at different zenith angles, based on a yearlong dataset collected in Beijing. Combined with cloud radar and reanalysis data, the fine categorization results reveal HOICs occur in calm winds and moderately cold temperatures and are influenced by turbulence near cloud bases. The results enhance our understanding of cloud processes and improve atmospheric models.

Measurements of methane with vehicle-based sensors are an effective method to identify and quantify leaks from urban gas distribution systems. We deliberately released methane in different environments and calibrated the response of different methane analysers when they transected the plumes in a vehicle. We derived an improved statistical function for consistent emission estimations using different instruments. Repeated transects reduce the uncertainty in emission rate estimates.

The Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST) is a commercially available ground-based infrared spectroradiometer. It is designed for automated and passive measurement of the thermal radiation emitted by the atmosphere, providing information about the vertical distribution of temperature and humidity, trace gases, clouds, and aerosols in the boundary layer. In this paper, we outline the key characteristics of the ASSIST hardware and signal processing algorithm that yields downwelling radiance spectra in near real-time.

This study provides insight into how individual ions measured by proton-transfer-reaction (PTR) mass spectrometry are produced by multiple volatile organic compounds (VOCs). A reference table is provided for attributing the PTR signal to contributing VOC species. The signals are grouped according to the complexity of their potential identities. We find that a number of signal ions such as C₆H₇⁺ for benzene and C₅H₉⁺ for isoprene merely give an upper limit of their corresponding concentrations.

When deriving a geophysical variable from remote sensors, the uncertainty and information content are critical. The latter quantify specifies what fraction of a real perturbation would be observed in the derived variable. This paper outlines, for the first time, a methodology for propagating the information content from multiple remote sensors into a derived product using horizontal advection as an example.

Contrails, the clouds formed by aircraft, are have a substantial climate impact. Determining which flight formed each contrail is a critical step to decreasing this impact. We introduce a dataset of synthetic contrail observations with known flight attributions that can be used to develop and assess geostationary-satellite-based contrail-to-flight attribution systems. We additionally introduce a new attribution algorithm and show that it outperforms previous methods.

Planetary boundary layer height (PBLHT) is an important parameter in atmospheric process studies and numerical model simulations. We use machine learning methods to produce a best-estimate planetary boundary layer height (PBLHT-BE-ML) by integrating four PBLHT estimates derived from remote sensing measurements. We demonstrated that PBLHT-BE-ML greatly improved the comparisons against sounding-derived PBLHT.

Understanding the lowest layers of the atmosphere is crucial for climate research, especially in the Arctic. Our study introduces the T-Bird, an aircraft-towed platform designed to measure turbulence and aerosol properties at extremely low altitudes. Traditional aircraft cannot access this region, making the T-Bird a breakthrough for capturing critical atmospheric data. Its first deployment over the Arctic demonstrated its potential to improve our understanding of polar processes.

This study explores nitrous oxide (N₂O) fluxes from a nutrient-poor sub-Arctic peatland. N₂O is a potent greenhouse gas; understanding its fluxes is essential for addressing global warming. Using a new instrument and flux chambers, we introduce a system to reliably detect low N₂O fluxes and provide recommendations on chamber closure times and flux calculation methods to better quantify N₂O fluxes. We encourage researchers to further investigate N₂O fluxes in low-nutrient environments.

Acetylene mole fraction measurements from the Picarro G2203 were characterised. Abnormal sensor behaviour was observed in wet conditions. A dry linear calibration was derived, although this was invalid below 1.2 ppb due to unstable measurements. The instrument was used to measure acetylene as a methane tracer at a landfill site. Fluxes were derived from 14 downwind transects using both raw and calibrated acetylene mole fraction, with raw measurements resulting in 8 % flux underestimation.