We propose a new method to quantify particulate sodium and potassium salts (nitrate, chloride, and sulfate) by using a refractory aerosol thermal desorption mass spectrometer (rTDMS). The combination of a graphite particle collector and a carbon dioxide laser enables high desorption temperature. Laboratory experiments showed that major ion signals originating from sodium or potassium salts were clearly detected, associated with the increase in the desorption temperature by laser heating.

Existing measurements of spectral aerosol absorption are limited, particularly in the UV region. We use the synergy of satellite and ground measurements to derive spectral single scattering albedo of aerosols from the UV–visible spectrum. The resulting spectral SSAs are used to investigate seasonality in absorption for carbonaceous, dust, and urban aerosols. Regional aerosol absorption models that could be used to make reliable assumptions in satellite remote sensing of aerosols are derived.

A new method for cloud-correcting observations of surface albedo is presented for AVHRR data. Instead of a binary cloud mask, it applies cloud probability values smaller than 20% of the A3 edition of the CLARA (CM SAF cLoud, Albedo and surface Radiation dataset from AVHRR data) record provided by the Satellite Application Facility on Climate Monitoring (CM SAF) project of EUMETSAT. According to simulations, the 90% quantile was 1.1% for the absolute albedo error and 2.2% for the relative error.

We have developed a machine-learning-based model that can be used to correct the Sentinel-3 satellite-based aerosol parameter data of the Synergy data product. The strength of the model is that the original satellite data processing does not have to be carried out again but the correction can be carried out with the data already available. We show that the correction significantly improves the accuracy of the satellite aerosol parameters.

This study uses collocated ship-based, ground-based, and spaceborne radar observations to validate the concept of using the GPM spaceborne radar observations to calibrate national weather radar networks to the accuracy required for operational severe weather applications such as rainfall and hail nowcasting.

We propose a suite of Bayesian algorithms for synergistic radar and radiometer retrievals to evaluate the next-generation NASA Cloud, Convection and Precipitation (CCP) observing system. The algorithms address pixel-level retrievals using active-only, passive-only, and synergistic active–passive observations. Novel techniques in developing synergistic algorithms are presented. Quantitative assessments of the CCP observing system's capability in retrieving ice cloud microphysics are provided.

Atmospheric chambers, like SAPHIR in Jülich (Germany), are used to experimentally simulate specific atmospheric scenarios to improve our understanding of the complex chemical reactions occurring in our atmospheres. These facilities hence require cutting-edge gas-sensing capabilities to detect trace gases at the lowest level and in a short time. One important trace gas is HONO, for which we custom-built an optical sensing setup, capable of detecting one HONO molecule in ~40 billion in 1 min.

To compare two instruments, a VisiSize D30 shadowgraph system and a phase Doppler interferometer (PDI-FPDR), we performed a series of measurements of cloud droplet size and number concentration in orographic clouds. After applying essential modifications and filters to the data, the results from the two instruments showed better agreement in droplet sizing and velocimetry than droplet number concentration or liquid water content. Discrepancies were observed for droplets smaller than 13 µm.

We present a formalism that relates the vertical column density (VCD) of the oxygen collision complex O₄ to surface values of temperature and pressure, based on physical laws. In addition, we propose an empirical modification which also accounts for surface relative humidity (RH). This allows for simple and quick but still accurate calculation of the O₄ VCD without the need for constructing full vertical profiles, which is expected to be useful in particular for MAX-DOAS applications.

This work develops a methodology and software to study and analyze the cloud-droplet-forming ability of aerosols with an aerodynamic aerosol classifier (AAC). This work quantifies the uncertainties in size-resolved measurements and subsequent uncertainties propagated to cloud droplet parameterizations. Lastly, we present the best practices for AAC cloud droplet measurement.

While pollen impacts public health and the Earth’s climate, the identification of each pollen taxon remains challenging. In this context, a laboratory evaluation of the polarimetric light-scattering characteristics of ragweed, ash, birch and pine pollen, when embedded in ambient air, is here performed at two wavelengths. Interestingly, the achieved precision of the retrieved scattering matrix elements allows unequivocal light scattering characteristics of each studied taxon to be identified.

We present a setup for systematic characterization of differences between numerical weather models and radar observations for convective weather situations. Radar observations providing dual-wavelength and polarimetric variables to infer information about hydrometeor shapes and sizes are compared against simulations using microphysics schemes of varying complexity. Differences are found in ice and liquid phase, pointing towards issues of some schemes in reproducing particle size distributions.

Space-borne lidars have been providing invaluable information of atmospheric optical properties since 2006, and new lidar missions are on the way to ensure continuous observations. In this work, we compare the clouds estimated from space-borne ALADIN and CALIOP lidar observations. The analysis of collocated data shows that the agreement between the retrieved clouds is good up to 3 km height. Above that, ALADIN detects 40 % less clouds than CALIOP, except for polar stratospheric clouds (PSCs).

This paper uses a machine learning algorithm to infer high-resolution maps of particulate air quality in eastern China, Japan, and the Korean peninsula, using data from a geostationary satellite along with meteorology. We then perform an extensive evaluation of this inferred air quality and use it to diagnose trends in the region. We hope this paper and the associated data will be valuable to other scientists interested in epidemiology, air quality, remote sensing, and machine learning.