Individual aerosol particles in ambient and updraft conditions below convective cloud bases in the Oman mountain region

Abstract

An airborne study of cloud microphysics provided an opportunity to collect aerosol particles in ambient and updraft conditions of natural convection systems for transmission electron microscopy (TEM). Particles were collected simultaneously on lacey carbon and calcium‐coated carbon (Ca‐C) TEM grids, providing information on particle morphology and chemistry and a unique record of the particle's physical state on impact. In total, 22 particle categories were identified, including single, coated, aggregate, and droplet types. The fine fraction comprised up to 90% mixed cation sulfate (MCS) droplets, while the coarse fraction comprised up to 80% mineral‐containing aggregates. Insoluble (dry), partially soluble (wet), and fully soluble particles (droplets) were recorded on Ca‐C grids. Dry particles were typically silicate grains; wet particles were mineral aggregates with chloride, nitrate, or sulfate components; and droplets were mainly aqueous NaCl and MCS. Higher numbers of droplets were present in updrafts (80% relative humidity (RH)) compared with ambient conditions (60% RH), and almost all particles activated at cloud base (100% RH). Greatest changes in size and shape were observed in NaCl‐containing aggregates (>0.3 µm diameter) along updraft trajectories. Their abundance was associated with high numbers of cloud condensation nuclei (CCN) and cloud droplets, as well as large droplet sizes in updrafts. Thus, compositional dependence was observed in activation behavior recorded for coarse and fine fractions. Soluble salts from local pollution and natural sources clearly affected aerosol‐cloud interactions, enhancing the spectrum of particles forming CCN and by forming giant CCN from aggregates, thus, making cloud seeding with hygroscopic flares ineffective in this region.

Processing of aerosol particles within the Habshan pollution plume

“…While size and crystal morphologies of CaSO 4 components were used to distinguish between gypsum (larger size, robust crystal shape, and distinct diffraction pattern) and recrystallized MCS droplets (lath crystal shape). Table 3 of Semeniuk et al [] also shows the correspondence of particle types for different grid types; e.g., NaCl‐rich droplets on Ca‐C grids are equivalent to NaCl or NaCl/MCS on LC grids. Errors associated with classifying particles into types are difficult to assess for such a labor intensive manual task.…”

“…= silicate). Particle types are derived from the 22 different baseline types described in Semeniuk et al []. Those types with frequencies less than 1% are not shown, e.g., Fe oxides, NaNO 3 , silicate/carbonate aggregates, etc.…”

“…Specifically, we found that industrially derived chloride‐bearing particles significantly affected artificial rainfall enhancement practices in updrafts by forming GCCN. While industrially derived sulfate‐bearing particles formed smaller CCN close to cloud base [ Semeniuk et al , ]. Similar enhancement of CCN was recorded during the Kuwait oil‐fires [ Hudson and Clarke , ] and in clouds forming above dust storms in the Arabian Desert [ Pósfai et al , ].…”

“…Using data for our reference set of 22 particle types (see types listed in Table 3 of Semeniuk et al []) as a baseline, compositional data were compiled using both morphology and some additional EDX spectra to classify 100 to 150 particles per grid, resulting in the classification of 300–400 particles per sample. For example, sulfate‐rich droplets or particles were classified as mixed cation sulfates (MCS; pollution derived) when they did not show Cl peaks in their spectra; otherwise, they were classified as NaCl/MCS (derived from processed sea salt or brine components).…”

Developing the Framework for Integrating Autonomous Unmanned Aircraft Systems into Cloud Seeding Activities

“…The data collected from the payload sensors, and seeding apparatus, during an entire flight would be collected and downloaded for use by others to improve and validate model parameterizations especially when applied to simulating seeding agent dispersion Large datasets collected during airborne cloud seeding experiments already exist [e.g. 17, [23][24][25][26][27][28][29][30][31][32][33][34][35][36] and provide valuable sources of data to develop and constrain the algorithms that guide the UAS. These data can be mined, analyzed and features extracted to locate representative time-series of key sensors from research aircraft flying at or below cloud base (e.g.…”