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In a comparative study contrasting new and traditional clustering techniques, the capabilities of K-means, the hierarchal clustering algorithm (HCA), and GenieClust were examined. Both K-means and HCA demonstrated strong consistency in cluster profiles and sizes, emphasizing their effectiveness in differentiating particle types and confirming that the fundamental patterns within the data were captured reliably. An added dimension to the study was the integration of an autoencoder (AE). When coupled with K-means, the AE enhanced outlier detection, particularly in identifying compositional loadings of each cluster. Conversely, whilst the AE's application to all methods revealed a potential for noise reduction by removing infrequent, larger particles, in the case of HCA, this information distortion during the encoding process may have affected the clustering outcomes by reducing the number of observably distinct clusters. The findings from this study indicate that GenieClust, when applied both with and without an AE, was effective in delineating a notable number of distinct clusters. Furthermore, each cluster's compositional loadings exhibited greater internal variability, distinguishing up to 3× more particle types per cluster compared to traditional means, and thus underscoring the algorithms' ability to differentiate subtle data patterns. The work here postulates that the application of GenieClust both with and without an AE may provide important information through initial outlier detection and enriched speciation with an AE applied, evidenced by a greater number of distinct clusters within the main body of the data. [ABSTRACT FROM AUTHOR]

Biological particulate matter (BioPM) is a poorly constrained, ubiquitous, and diverse subset of atmospheric aerosols. They influence climate, air quality, and health via many mechanisms, spurring renewed interest in constraining their emissions to elucidate their impacts. In order to build the framework required to assess the role of BioPM in these multidisciplinary areas, it is necessary to develop robust, high time-resolution detection methodologies so that BioPM emissions can be understood and characterized. In this study, we present ambient results from intensive monitoring at UK peri-urban and coastal ground sites using high time-resolution real-time bioaerosol spectrometers. We demonstrate the utility of a new dimensional reduction-driven BioPM classification scheme, where laboratory sample training data collected at the ChAMBRe facility were used to generate broad taxonomic class time series data of key species of interest. We show the general trends of these representative classes, spanning spring, early summer, and autumn periods between 2019 and 2021. Diurnal behaviors and meteorological relationships were investigated and contextualized; a key result arising from this study was the demonstration of rainfall-induced enhancement of nighttime Penicillium-like aerosol, where rainfall crucially only acts to enhance the quantity emitted without significantly influencing the early morning timing of peak spore liberation. [ABSTRACT FROM AUTHOR]

Scale-aware parameterizations of subgrid scale physics are essentials for multiscale atmospheric modeling. A single-ice (SI) microphysics scheme and Gaussian probability-density-function (Gauss-PDF) macrophysics scheme were implemented in the single-column Global-to-Regional Integrated forecast System model (SGRIST) and they were tested using the Tropical Warm Pool-International Cloud Experiment (TWP-ICE) and the Atmospheric Radiation Measurement Southern Great Plains Experiment in 1997 (ARM97). Their performance was evaluated against observations and other reference schemes. The new schemes simulated reasonable precipitation with proper fluctuations and peaks, ice, and liquid water contents, especially in lower levels below 650 hPa during the wet period in the TWP-ICE. The root mean square error (RMSE) of the simulated cloud fraction was below 200 hPa was 0.10/0.08 in the wet/dry period, which showed an obvious improvement when compared to that, i.e., 0.11/0.11 of original scheme. Accumulated ice water content below the melting level decreased by 21.57% in the SI. The well-matched average liquid water content displayed between the new scheme and observations, which was two times larger than those with the referencing scheme. In the ARM97 simulations, the SI scheme produced considerable ice water content, especially when convection was active. Low-level cloud fraction and precipitation extremes were improved using the Gauss-PDF scheme, which displayed the RMSE of cloud fraction of 0.02, being only half of the original schemes. The study indicates that the SI and Gauss-PDF schemes are promising approaches to simplify the microphysics process and improve the low-level cloud modeling. [ABSTRACT FROM AUTHOR]

Observations of high-altitude cirrus clouds are reported from measurements made during the routine monitoring of cloud properties on commercial aircraft as part of the In-Service Aircraft for a Global Observing System. The increasing global scale of the measurements is revealed, with 7 years of in situ data producing a unique and rapidly growing dataset. We find that cloud fractions measured ≥ 10 km at aircraft cruise altitude are representative of seasonal trends associated with the mid-latitude jet stream in the Northern Hemisphere, and the relatively higher cloud fractions are found in tropical regions such as the Inter-Tropical Convergence Zone and South East Asia. Both stratospheric and tropospheric data were used to calculate the cloud fractions routinely experienced by commercial aircraft. Further work is needed for a direct comparison with previous studies that limit cloud fraction calculations to tropospheric data only. The characteristics of these clouds are discussed and the potential different formation mechanisms in different regions assessed. [ABSTRACT FROM AUTHOR]