Water is a precious resource, and it is essential to monitor and predict the current and future occurrence of precipitation-producing clouds. We investigate the cloud characteristics related to precipitation using several cloud cases in the United Arab Emirates with data from aircraft measurements, satellite observations, and weather radar observations. This study provides scientific support for the development of an applicable framework to examine cloud precipitation processes.

Long-term records of plant fluorescence offer vital insights into changing vegetation activity. The GOME-2A sensor provides extensive global observations but suffers from calibration and instrument degradation, which affects data consistency. This study presents the SIFTER v3 algorithm, which effectively resolves these issues and includes other improvements, resulting in robust, accurate, and consistent GOME-2A fluorescence measurements from 2007 to 2017.

Detection of NO₂ via cavity ring-down spectroscopy (CRDS) after thermal dissociation (TD) of alkyl and peroxy nitrates can be used to detect total atmospheric organic nitrates originating from the interaction between biogenic emissions of volatile organic compounds and nitrogen oxides of anthropogenic origin. Here we present an improved TD-CRDS technique that avoids systematic bias resulting from secondary chemistry in the heated inlets and that can be deployed in regions with strong biogenic emissions.

Fog and low stratus (FLS) are both persistent clouds close to the Earth's surface. This study introduces a new machine-learning-based algorithm developed for the Meteosat Second Generation geostationary satellites that can provide a coherent and detailed view of FLS development over large areas over the 24 h day cycle.

Traveling ionospheric disturbances (TIDs) are manifestations of atmospheric waves that are significant for the transfer of energy and momentum between atmospheric layers and regions. This work demonstrates that velocities and directions of TIDs can be measured by monitoring the tiny shift in frequency of AM radio signals when they reflect from a moving ionosphere and that this method can be scaled to use large numbers of radio receivers and transmitters to monitor TIDs on a continental scale.

CO₂ column-averaged dry-air mole fractions can be retrieved from space using spectrometers like OCO-2. However, nearby clouds induce spectral distortions that bias these retrievals beyond the accuracy needed for global CO₂ source and sink assessments. This study employs a physics-based linearization approach to represent 3D cloud effects and introduces radiance-level mitigation techniques for actual OCO-2 data, enabling the operational implementation of these corrections.

We characterize the optical properties of various aerosols using a compact dual-wavelength depolarization lidar (CIMEL CE376) at 532 and 808 nm. Through a modified two-wavelength Klett inversion method, we assess the vertical distribution and temporal evolution of Saharan dust, volcanic aerosols and wildfire smoke in the subtropical North Atlantic from August 2021 to August 2023. The study confirms the CE376 lidar's effectiveness in monitoring and characterizing atmospheric aerosols over time.

Long-term field testing across diverse climatic environments is conducted to identify the optimized calibration conditions for NO₂, NO, CO and O₃ electrochemical sensors. The results uncovered three factors that influence calibration performance: calibration period, concentration range and time averaging. We developed a comprehensive framework for the best sensor calibration practices, which serves as a valuable reference for calibrating various sensor types used in air quality monitoring.

CLIMCAPS extends the Aqua AIRS+AMSU record with retrievals from CrIS+ATMS on Suomi National Polar-orbiting Partnership (SNPP) and Joint Polar Satellite System series (JPSS-1 to JPSS-4). With “continuous”, we mean a data record that is consistent in its characterization of natural variation despite changes in source instrumentation. Here we investigate how sounding continuity can improve across the full CLIMCAPS record (2002 to the present day), spanning multiple instruments and satellites.

This study analyzes three years of wind lidar measurements to explore the dynamics of the urban planetary boundary layer in Hefei, China. Results reveal that nocturnal low-level jets are most frequent in spring and intensify in summer, significantly enhancing turbulence and shear near the surface, particularly at night. Additionally, cloud cover raises the mixing layer height by approximately 100 m at night due to the greenhouse effect but reduces it by up to 200 m in the afternoon.

Using observations from the Visible Infrared Imaging Radiometer Suite day–night band, we developed a method for constructing gridded nighttime aerosol optical thickness (AOT) data based on the spatial derivative of measured top-of-atmosphere attenuated upwelling artificial lights at night. The gridded nighttime AOT retrievals, compared against Aerosol Robotic Network data, show reasonable skill levels for potential data assimilation, air quality, and climate studies of significant events.

This study revealed the existence of significant species-specific adsorptive and reactive losses of monoterpenes and sesquiterpenes in dynamic chambers. The deuterated α-pinene-d3 and β-caryophyllene-d2 were proven as effective surrogates in tracing these losses for some key monoterpenes and sesquiterpenes. The findings highlight the importance of selecting internal surrogates that closely match the adsorptive and reactive behaviors of target compounds for precise loss correction.

We evaluated how well we can infer air pollutant levels (ozone, carbon monoxide, and nitrogen oxides) between air quality stations throughout South Korea, finding good representation in most densely measured cities in spite of intense small-scale emission hotspots. Comparing observed air quality with gridded model output is desirable, and so we created gridded datasets over South Korea using air quality station measurements, which agreed with airborne measurements around Seoul.

Standard gases are essential for accurate greenhouse gas measurements. However, exchanging cylinders at remote sites presents logistical challenges, requiring systems that minimize gas consumption. We developed methods for calculating greenhouse gas mole fractions and uncertainties using our original system designed to reduce standard gas use. We validated its long-term stability through instrument comparisons. The system has proven effective for maintaining observations at remote sites.

This paper provides a comprehensive evaluation of the quality of radar-based precipitation estimation in mountainous areas and presents a method to mitigate the main shortcomings identified. It then compares three different ensemble analysis methods that combine radar-based precipitation estimates with forecasts from an ensemble numerical weather prediction model.

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.

A relative calibration technique is developed for the cloud radar by monitoring the intercept of the wet-radome attenuation log-linear behavior as a function of rainfall rates in light and moderate rain conditions. This resulting reflectivity offset during the recent field campaign is compared favorably with the traditional disdrometer comparison near the rain onset, while it also demonstrates similar trends with respect to collocated and independently calibrated reference radars.

This paper presents our latest development in tomographic reconstruction algorithms that use multi-angle (sub)millimeter-wave brightness temperature to reconstruct the spatial distribution of ice clouds. Compared to nadir-only retrievals, the tomography technique provides a detailed reconstruction of ice clouds’ inner structure with high spatial resolution and significantly improves retrieval accuracy. Also, the technique effectively increases detection sensitivity for small ice cloud particles.

The study examines the use of the spheroidal shape approximation for calculating cloud radar observables in rain and identifies some limitations. To address these, it introduces the empirical scattering model (ESM) based on high-quality Doppler spectra from a 94 GHz radar. The ESM offers improved accuracy and directly incorporates natural rain's microphysical effects. This new model can enhance retrieval and calibration methods, benefiting cloud radar polarimetry experts and scattering modelers.

We developed an automated, flexible atmospheric sampling device for various platforms. During a 5 d field campaign in the Mount Qomolangma region, we performed 15 flights using the device mounted on a hexacopter unoccupied aerial vehicle (UAV). A total of 139 samples were analyzed using an Agilent 7890A gas chromatograph. Vertical profiles of four greenhouse gases (carbon dioxide (CO₂), methane (CH₄), nitrous oxide (N₂O), and sulfur hexafluoride (SF₆)) were analyzed and discussed.