The water vapor generation module is completely scalable, allowing autonomous calibrations to use N standards and providing integration times only restricted by sample availability. We document improved reproducibility in ¹⁷O-excess liquid measurements. This module makes spectroscopy measurements comparable to mass spectrometry. We document that the vapor generation module can be used to analyze instrument performance and for vapor isotope calibration during field campaign measurements.

In satellite remote sensing, estimating cloud-base height (CBH) is more challenging than estimating cloud-top height because the cloud base is obscured by the cloud itself. We developed an algorithm using the specific channel (known as the oxygen A-band channel) of the SGLI on JAXA’s GCOM-C satellite to estimate CBHs together with other cloud properties. This algorithm can provide global distributions of CBH across various cloud types, including liquid, ice, and mixed-phase clouds.

Water molecules stick to air intake tubing wall surfaces, smoothing measurements of fast isotopic variability in the atmosphere. We tested this stickiness and saw small differences in isotopic signal speed between materials, tubing inner dimensions, and isotopic switch direction, although no consistent temperature effects. Researchers can confidently compare measurements across observation systems using different commonly used tubing materials and plan for optimal inlet designs of new systems.

A method for determining drop size distributions (DSDs) for rain using radar measurements from two frequencies at two polarizations is presented. Following some preprocessing and quality control, radar measurements are incorporated into a model that uses swarm intelligence to seek the most suitable DSD to produce the input measurements.

Air pollutants such as particulate matter with diameters of 10 µm and 2.5 µm or less (PM₁₀ and PM_2.5) can cause adverse public health and environment effects; therefore their regular monitoring is crucial to keep pollutant concentrations under control. Our study demonstrates the potential of high-resolution aerosol optical depth (AOD) data from the Geostationary Environment Monitoring Spectrometer (GEMS) satellite to estimate ground-level PM concentrations using machine learning models. 

The advantage of the use of Doppler velocity in the categorization of the hydrometeors is that Doppler velocities suffer less impact from the attenuation of rain and wet attenuation on an antenna. The ground Cloud Profiling Radar observation of the radar reflectivity for the precipitation case is limited because of wet attenuation on an antenna. We found the main contribution to Doppler velocities is the terminal velocity of hydrometeors by analysis of simulation results.

This study examines newer versions of H₂O and HDO retrievals from Envisat/MIPAS and SCISAT/ACE-FTS. Results reveal a better agreement in stratospheric H₂O profiles than in HDO profiles. The H₂O tape recorder signal is consistent across databases, but δD tape recorder composites show differences that impact the interpretation of water vapour transport. These findings enhance the need for intercomparisons to refine our insights.

This paper presents a new user-friendly version of the Atmospheric Radon MONitor (ARMON). The efficiency of the instrument is of 0.0057 s^-1, obtained using different techniques at Spanish and German chambers. The total calculated uncertainty of the ARMON for hourly radon concentrations above 5 Bq m^-3 is lower than 10 % (k = 1). Results confirm that the ARMON is suitable to measure low-level radon activity concentrations and to be used as a transfer standard to calibrate in situ radon monitors.

Sonic anemometers measure wind velocity in three dimensions. They are used worldwide to help assess the trace gas exchange, critical to understanding climate change. However, their physical framework interferes with the flow they measure. We present a new way of correcting measurements from sonic anemometers of several types. The method uses measurements of vertical wind velocity and other turbulent velocities, compares their ratios, and from this yields correction factors for sonic anemometers.

Accurate and comparable measurements of oxygenated organic compounds (OVOCs) are crucial in assessing tropospheric ozone burdens and trends. However, the monitoring of many OVOCs remains challenging because of their low atmospheric abundance and lack of stable and traceable calibration standards. This paper describes the calibration standards developed for OVOCs at a low amount of substance fractions (<100 nmol mol^-1) to transfer traceability of the International System of Units to the field.

This work investigates whether machine learning (ML) can offer an alternative to existing methods to map radio occultation (RO) products, allowing the extraction of information not visible in direct observations. ML can further improve the results of Bayesian interpolation, a state-of-the-art method to map RO observations. The results display improvements in horizontal and temporal domains, at heights ranging from the planetary boundary layer up to the lower stratosphere, and for all seasons.

We describe a low-cost and small research-grade spectrometer for measurements of water vapor in the boundary layer. The instrument uses small Arduino microcontrollers and inexpensive laser diodes to reduce cost while maintaining high performance comparable to more expensive instruments. Performance was assessed with intercomparisons between commercially available instruments outdoors. The design's simplicity, performance, and price point allow it to be accessible to a variety of users.

Different ground-based ozone measurements from the last 2 decades at Lauder are compared to each other. We want to know why different trends have been observed in the stratosphere. Also, the quality and relevance of tropospheric datasets need to be evaluated. While remaining drifts are still present, our study explains roughly half of the differences in observed trends in previous studies and shows the necessity for continuous review and improvement of the measurements.

A Japanese team developed the Level 2 atmospheric radiation flux and heating rate product for EarthCARE, which offers vertical profiles of longwave and shortwave radiative fluxes and heating rates. This study outlines the algorithm for the radiative product and its comparative validation against satellite and ground-based observations. It also analyzes errors in radiative fluxes at various scales and their dependence on cloud type, with ice-containing clouds identified as a primary error source.

In this study, we present a toolkit of two Python algorithms to extract information from Doppler spectra measured by ground-based cloud radars. In these Doppler spectra, several peaks can be formed due to populations of droplets/ice particles with different fall velocities coexisting in the same measurement time and height. The two algorithms can detect peaks and assign them to certain particle types, such as small cloud droplets or fast-falling ice particles like graupel.

Most methane emissions during the life cycle of beef cattle occur during the grazing phase. Measuring methane in grazing systems is difficult due to the high mobility and low density of animals. This work investigates if dual-comb spectroscopy can measure methane emissions from small cattle herds. An enhancement of 10 nmol mol^-1 methane above the atmospheric background was measured, equivalent to 20 head located 60 m away. The calculated methane flux was within 5 % of the actual release rate.

This study systematically characterizes the temperature enhancement in the lamp-enclosed oxidation flow reactor (OFR). The enhancement varied multiple dimensional factors, emphasizing the complexity of temperature inside of OFR. The effects of temperature on the flow field and gas- or particle-phase reaction inside OFR were also evaluated with experiments and model simulations. Finally, multiple mitigation strategies were demonstrated to minimize this temperature increase.

Non-linearity (NL) correction is a critical procedure to guarantee that the calibration accuracy of a spaceborne sensor approaches a reasonable level. Different from the classical method, a new NL correction method for a spaceborne Fourier transform spectrometer is proposed. To overcome the inaccurate linear coefficient from two-point calibration influencing NL correction, an iteration algorithm is established that is suitable for NL correction of both infrared and microwave sensors.

This study proposes an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. It is suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 μm. For cloud droplets, it is 1.0–10 μm. This algorithm can quickly obtain the microphysical parameters of atmospheric particles and has better robustness.

Accurate cloud quantification is critical for climate research. We developed a novel computer vision framework using deep neural networks and clustering algorithms for cloud classification and segmentation from ground-based all-sky images. After a full year of observational training, our model achieves over 95 % accuracy on four cloud types. The framework enhances quantitative analysis to support climate research by providing reliable cloud data.

Airborne in situ measurements are of great importance to collect valuable data to improve our knowledge of the atmosphere but also present challenges which demand specific designs. This study presents an IR spectrometer for airborne trace-gas measurements with high data efficiency and a simple, compact design. Its in-flight performance is characterized with the help of a test flight and a comparison with another spectrometer. Moreover, results from its first campaign highlight its benefits.

To have better understanding of the dynamics of the lower and middle atmosphere, we installed a newly designed dual-frequency radar system that uses 53.8 MHz for near-ground to 20 km wind measurements and 35.0 MHz for 70 to 100 km wind measurements. The initial results show its good performance, along with the analysis of typical winter gravity wave activities.

In our study, we explored aerosols, tiny atmospheric particles affecting the Earth's climate. Using data from two lidar-equipped satellites, ALADIN and CALIOP, we examined a 2020 Saharan dust event. The newer ALADIN's results aligned with CALIOP's. By merging their data, we corrected CALIOP's discrepancies, enhancing the dust event depiction. This underscores the significance of advanced satellite instruments in aerosol research. Our findings pave the way for upcoming satellite missions.

Comparison between total ozone column (TOC) measurements from ground-based Dobson and Brewer spectrophotometers generally reveals seasonally varying differences of a few percent. This study recommends a new TOC retrieval approach, which effectively eliminates these seasonally varying differences by applying new ozone absorption cross sections, appropriate slit functions for the Dobson instrument, and climatological values for the effective ozone temperature.

This paper presents a design of a UV wind lidar, made with a UV fiber laser and a Quadri Mach-Zehnder interferometer as a spectral analyzer, used to measure the wind in front of future low-consumption aircraft. The article details the optimization of the different elements of the instrument with simulations. This paper also presents a method to optimize laser angles for determining wind direction and strength and shows a 50 % improvement over the current angles used.

This study demonstrates the potential of enhancing severe-hail detection through the application of convolutional neural networks (CNNs) to dual-polarization radar data. It is shown that current methods can be calibrated to significantly enhance their performance for severe-hail detection. This study establishes the foundation for the solution of a more complex problem: the estimation of the maximum size of hailstones on the ground using deep learning applied to radar data.

ATLID (atmospheric lidar) is the lidar to be flown on the Earth Clouds and Radiation Explorer satellite (EarthCARE). EarthCARE is a joint European–Japanese satellite mission that was launched in May 2024. ATLID is an advanced lidar optimized for cloud and aerosol property profile measurements. This paper describes some of the key novel algorithms being applied to this lidar to retrieve cloud and aerosol properties. Example results based on simulated data are presented and discussed.

We present a top-down approach to quantify CO₂ and CH₄ emissions at the scale of an industrial site, based on a mass balance model relying on atmospheric concentrations measurements from a new sensor embarked on board uncrewed aircraft vehicles (UAVs). We present a laboratory characterization of our sensor and a field validation of our quantification method, together with field application to the monitoring of two real-world offshore oil and gas platforms.

Radar–lidar synergy is used to retrieve ice, supercooled water and mixed-phase cloud properties, making the most of the radar sensitivity to ice crystals and the lidar sensitivity to supercooled droplets. A first analysis of the output of the algorithm run on the satellite data is compared with in situ data during an airborne Arctic field campaign, giving a mean percent error of 49 % for liquid water content and 75 % for ice water content.

Methane is a potent greenhouse gas that has been responsible for around 25 % of global warming since the industrial revolution. Consequently identifying and mitigating methane emissions comprise an important step in combating the climate crisis. We develop a new deep learning model to automatically detect methane plumes from satellite images and demonstrate that this can be applied to monitor large methane emissions resulting from the oil and gas industry.

The large fleet of Earth observation satellites in orbit currently generate huge volumes of data, requiring significant computational resources to process these data in a timely manner. We present a method for predicting poor-quality measurements using machine learning. We find that machine learning methods can accurately predict poor-quality measurements and remove them from the processing chain, saving time and computational resources. 

Currently, measurement of methane from the TROPOMI satellite is biased with respect to surface reflectance. This study demonstrates a new method of correcting for this bias on a seasonal timescale to allow for differences in surface reflectance in areas of intense agriculture where growing seasons may introduce a reflectance bias. We have successfully implemented this technique in the Denver–Julesburg basin, where agriculture and methane extraction infrastructure is often co-located.

We developed an advanced algorithm for global retrieval of TROPOspheric Monitoring Instrument (TROPOMI) HCHO and NO₂ vertical column densities with much improved consistency. Sensitivity tests demonstrate the complexity and nonlinear interactions of auxiliary parameters in the air mass factor calculation. An improved agreement is found with measurements from a global ground-based instrument network. The scientific retrieval provides a useful source of information for studies combining HCHO and NO₂.

Our study presents a new method, the solar background calibration method, which improves temperature determinations in rotational Raman lidar systems. By utilizing background solar radiation, this technique offers more continuous and reliable temperatures independent of external measuring instruments. This new method enhances our ability to monitor and understand atmospheric trends and their association with climate change with greater accuracy.

This study explores the potential of two newly launched commercial Global Navigation Satellite System (GNSS) radio occultation (RO) satellite missions for advancing Arctic lower-atmospheric studies. The products have a good sampling of the lower Arctic atmosphere and are useful to derive the planetary boundary layer (PBL) height during winter months. This research is a step towards closing the observation gap in polar regions due to the decomissioning of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-1) GNSS RO mission and the lack of high-latitude coverage by its successor (COSMIC-2).

A portable laser-flash photolysis Faraday rotation spectrometer for measuring atmospheric total hydroxyl (OH) reactivity was developed, with optical box dimensions of 130 cm × 40 cm × 35 cm. It features a pump–probe multi-pass cell with a high overlapping factor of 75.4 %. The instrument’s precision and uncertainty are 1.0 s^-1 (1σ, 300 s) and within 2 s^-1, respectively. This portable, cost-effective instrument expands current measurement capabilities and is convenient for field applications.

The radiative effect of black carbon (BC) is substantially modulated by its mixing state, which is challenging to derive physically with a single-particle soot photometer. This study establishes a machine-learning-based inversion model which can accurately and efficiently acquire the BC mixing state. Compared to the widely used leading-edge-only method, our model utilizes a broader scattering signal coverage to more accurately capture diverse particle characteristics.

The new collector BOOGIE has been designed to sample cloud droplets and evaluated. Computational fluid dynamics simulations are performed to evaluate the sampling efficiency for different droplet sizes. In situ measurements show very good water collection rates and sampling efficiency. BOOGIE is compared to other cloud collectors and the efficiency is comparable, as are the chemical and biological compositions.