Air-sea exchange is a major uncertainty source for many volatile organic compounds (VOCs). Aircraft-based eddy covariance can be used to quantify these fluxes over large regions but such applications have been limited. We used observations over the Atlantic to characterize VOC fluxes and elucidate the drivers of measurement error. Results show how sensor noise and turbulent variability interact to determine flux detectability, and define instrumental priorities for future use of this technique.

Particles in the atmosphere vary in size and concentration, forming what is known as a particle size distribution (PSD). These distributions rarely consist of a single group of similar particles, known as a mode. Instead, they are often made up multiple overlapping modes. This work introduces an open-source algorithm to separate complex PSDs into their individual modes, enabling characterization of each particle population to provide insight into properties relevant to human health and climate.

To analyse long-term trends in atmospheric trace gas concentrations, it is important to identify data points minimally affected by local pollution sources or air masses carried from other latitudes or altitudes. Traditional methods for detecting these “baselines” are computationally expensive or lack a basis in physical principles. This paper introduces a machine-learning method that uses meteorological data and offers significantly lower computational costs compared to physics-based techniques.

A solid state power amplifier (SSPA) was integrated into an existing C-band magnetron weather radar. This unique setup allows for an easy comparison of the data quality of both transmitters. Reflectivity, Doppler and Dual Pol moments are compared. The SSPA shows better data quality in areas with low signal strength. The gap between short and long pulse is treated well, range sidelobes around strong targets are an issue. SSPA is considered as a possible replacement for the magnetron transmitters.

Greenhouse gas column measurements from space using reflected sunlight have to make assumptions about the Earth's surface elevation. Usually, it assumed that the elevation representing the satellite's spatial sample is the unweighted average of high-resolution surface elevation data. We find and show with many examples that the surface elevation should be weighted by the surface reflectance in the respective wavelength bands. This improves errors in the retrieval of trace gases.

We describe ASISKYCOVER, a new machine learning algorithm for pixel segmentation of all-sky imager (ASI-16) data used by the Atmospheric Radiation Measurement (ARM) User Facility. ASISKYCOVER provides cloud cover and thickness estimates, detects artifacts, and reports uncertainties. Using one year of data from the ARM Southern Great Plains site and comparisons with other ARM datasets, we demonstrate its use and robustness, which will improve cloud cover analyses and data evaluation efforts.

We tested whether satellites accurately measure clouds affected by ship pollution. Using computer simulations, we found that satellites overestimate how much cloud droplet numbers rise in polluted ship tracks compared to nearby clean clouds. This means the cooling effect of pollution on clouds may be overstated, which matters for strategies like marine cloud brightening that aim to boost this cooling effect deliberately.

We investigated how well common measurement approaches capture atmospherically relevant organic molecules formed from plant emissions. By synthesizing reference compounds and comparing different ionization methods, we show that widely used mass spectrometric techniques underestimate the abundance of important peroxide‑containing molecules. This bias influences how particle formation and growth rates are derived from measurements and affects subsequent calculations used in atmospheric science.

This study investigated whether short-range weather forecasting over India could be improved by incorporating GNSS (Global Navigation Satellite System) radio occultation (RO) data from ROMEX (Radio Occultation Modelling Experiment). Data assimilation experiments using the WRF (Weather Research and Forecasting) model were conducted for September 2022. The findings demonstrate how GNSS-RO measurements improved predictions for temperature, humidity, wind, and precipitation. The biggest gains came from better initial water vapour fields, which reduced forecast error and improved rainfall skill.

Hailstorms cause significant damage, yet measuring hail accurately remains difficult because current sensors have small sampling area and often confuse hail with rain. To solve this, we developed HailCam, a smart camera system, that counts and measures hail size every minute. Tests of the system prove that it is highly accurate and better than existing tools at ignoring rain. This technology is crucial to improve forecasts, warn communities earlier, and help scientists better understand storms.

Ground-based spectrometers measuring HCHO and NO₂ were compared to airborne in situ observations in the Summer 2023. Total column HCHO is within uncertainty, but with high biases near the surface. NO₂ results are highly sensitive to the viewing angle of the ground monitor which must be considered during validation studies. Spectrometers in NYC show good agreement with a geostationary satellite on clear-sky days. Further quantified uncertainty for the monitors is necessary.