Chemical ionization mass spectrometry (CIMS) is widely used in atmospheric chemistry studies. We still have a limited understanding of the complex functioning of the instrument; therefore, we applied machine learning to provide insights from CIMS analyses. We were able to predict both detection and signal intensity with a fair error, and we found out the most important structural fragments for negative ionization schemes (NH and OH) and positive ones (nitrogen-containing groups).

Although machine learning technology is advanced in the field of satellite remote sensing, the physical inversion algorithm based on cloud base height can better capture the daily variation in the characteristics of the cloud base.

This study shows the derived organic aerosol hygroscopicity under high-humidity conditions based on a simple optical scatter monitor system, including two nephelometric monitors (pDR-1500), when the aerosol chemical composition is already known.

We developed a continuous flow analysis system to analyze an ice core from northwestern Greenland and coupled it with an improved refractory black carbon (rBC) measurement technique. This allowed accurate high-resolution analyses of size distributions and concentrations of rBC particles with diameters of 70 nm–4 μm for the past 350 years. Our results provide crucial insights into rBC's climatic effects. We also found previous ice core studies substantially underestimated rBC mass concentrations.

Understanding lightning activity is important for meteorology and atmospheric chemistry. However, the occurrence of lightning activity in clouds is uncertain. In this study, we quantified the difference between isolated thunderstorms and non-thunderstorms. We showed that lightning activity was more likely to occur with more graupel volume and/or riming. A deeper Z_DR column was associated with lightning occurrence. This information can aid in a deeper understanding of lighting physics.

The assumption of chemical equilibrium is widely used to derive information about poorly measured characteristics of the mesosphere–lower thermosphere from rocket and satellite data and to study the physicochemical processes at these altitudes. In this work, we analyze the fundamental aspects of chemical equilibria of two important trace gases and discuss their possible applications.

The Copernicus Atmosphere Monitoring Service (CAMS) provides global analyses and forecasts of aerosols and trace gases in the atmosphere. On 27 June 2023 a major upgrade, Cy48R1, became operational. Comparisons with in situ, surface remote sensing, aircraft, and balloon and satellite observations show that the new CAMS system is a significant improvement. The results quantify the skill of CAMS to forecast impactful events, such as wildfires, dust storms and air pollution peaks.

To serve the uncertainty quantification (UQ) needs of 4D-Var data assimilation (DA) practitioners, we describe and justify a UQ algorithm from carbon flux inversion and incorporate its sampling uncertainty into the final reported UQ. The algorithm is mathematically proved, and its performance is shown for a carbon flux observing system simulation experiment. These results legitimize and generalize this algorithm's current use and make available this effective algorithm to new DA domains.