Browsing by Subject "atmospheric science"

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  • Roudsari, Golnaz (Helsingin yliopisto, 2022)
    Pure water can remain in liquid phase in the atmosphere even at -38 ℃. However, ice nucleation can occur at higher temperatures up to -3 ℃ in the presence of atmospheric particles. Numerous studies have been conducted to identify and characterize particles with efficient nucleation enhancing properties. Silver iodide (AgI) is known for excellent ice nucleating capabilities and has long been used in rain seeding applications. In this thesis, we study the influence of various AgI structures on the ice nucleation efficiency using both atomistic and coarse grained molecular dynamics simulations. In particular, we aim to identify which characteristics of the AgI particles contribute to the ice nucleation. Specifically, we investigate ice nucleation in the presence of AgI surfaces with different defects. We also study the effects of confined geometries on ice nucleation using AgI systems with wedge and slit structures. We consider AgI wedges of different angles and AgI slit systems of varying widths. The simulation results show that AgI surfaces with defects lead to ice nucleation at low supercooling, but with a lower intensity than the perfect AgI surface. Moreover, it is observed that confined wedges (systems with angles less than 90°) almost always accelerate the formation of ice. Furthermore, ice nucleation occurs significantly faster when the wedge angle corresponds to that of the ice lattice. The simulation results for the slit structures show that as the slit's gap widens, the nucleation is periodically promoted and hindered: the nucleation process is accelerated when the gap width is an integer multiple of the width of an ice bilayer, and slowed down otherwise. Computer simulations of ice nucleation require efficient algorithms for distinguishing between the liquid and ice phases as well as identifying different types of ice crystals. In this thesis, we develop a novel ice recognition algorithm based on conformation template-matching for the identification of different ice polymorphs and interfacial ice structures. Our algorithm is robust in classifying non-ideal structures or structures with defects making it ideal for studying ice nucleation in the presence of foreign materials. Keywords: heterogeneous ice nucleation, molecular dynamics simulations, silver iodide, ice structure recognition.
  • Fung, Pak Lun (Helsingin yliopisto, 2022)
    Air pollution is one of the biggest environmental health challenges in the world, especially in the urban regions where about 90% of the world’s population lives. Black carbon (BC) has been demonstrated to play an important role in climate change, air quality and potential risk for human beings. BC has also been suggested to associate better with health effects of aerosol particles than the commonly monitored particulate matter, which does not solely originate from combustion sources. Furthermore, BC has been recommended to be included as one of the parameters in air quality index (AQI) which is communicated to citizens. However, due to financial constraints and the lack of the national legislation, BC has yet been measured in every air quality monitoring station. Therefore, some researchers developed low-cost sensors which give indicative ambient BC concentrations as an alternative. Even so, due to instrument failure or data corruption, measurements by physical sensors are not always possible and long data gaps can exist. With missing data, the data analysis of interactions between air pollutants becomes more uncertain; therefore, air quality models are needed for data gap imputation and, moreover, for sensor virtualization. To complement the current deficiency, this thesis aims to derive statistical proxies as virtual sensors to estimate BC by using the current air quality monitoring network in Helsinki metropolitan area (HMA). To achieve this, we first characterized the ambient BC concentrations in four types of environments in HMA: traffic site (TR: 0.77–2.08 μg m−3), urban background (UB: 0.51–0.53 μg m−3), detached housing (DH: 0.64–0.80 μg m−3) and regional background (RB: 0.27–0.28 μg m−3). TR, in general, had higher BC concentrations due to the close proximity to vehicular emission but decreasing trends (–10.4 % yr−1) likely thanks to the fast renewal of the city bus fleet in HMA. UB, on the other hand, had a more diverse source of BC, including biomass burning and traffic combustion. Its trend had also been decreasing, but at a smaller rate (e.g. UB1: –5.9 % yr−1). We then narrowed down the dataset to a street canyon site and an urban background site for BC proxy derivation. At both sites, despite the low correlation with meteorological factors, BC correlated well with other commonly monitored air pollutant parameters by both reference instruments and low-cost sensors, such as NOx and PM2.5. Based on this close association, we developed a statistical proxy with adaptive selection of input variables, named input-adaptive proxy (IAP). This white-box model worked better in terms of accuracy at the street canyon site (R2 = 0.81–0.87) than the urban background site (R2 = 0.44–0.60) because of the scarce missing gaps in data in the street canyon. When compared with other white- and black-box models, IAP is preferred because of its flexibility and architectural transparency. We further demonstrated the feasibility of sensor virtualization by using statistical proxies like IAP at both sites. We also stressed that such virtual sensors are location specific, but it might be possible to extend the models from one street canyon site to another with a calibration factor. Similarly, the proposed methodology can also be applied to estimate other air pollutant parameters with scarcity of data, such as lung deposited surface area and ultrafine particles, to complement the existing AQI.
  • Li, Haoran (Helsingin yliopisto, 2021)
    Majority of precipitation in mid- to high-latitudes originates from ice clouds. In these clouds, atmospheric ice particles grow through various microphysical processes and may precipitate to the surface in the form of snowfall or rainfall. A large fraction of these clouds contain supercooled liquid water, which affects microphysical properties of ice particles. However, despite the importance of ice microphysics in mixed-phase clouds to the development of precipitation, our understanding of underlying processes is still lacking. In past decades, long-term continuous observations of clouds and precipitation have shown promise for addressing this challenge. To provide such observations, remote sensing instruments, such as weather and research cloud radars, have been widely utilized. In this thesis, operational weather radars and cloud radars are used to address some challenges specific to ice microphysics. Using dual-polarization weather radar observations collected over four years, we show how the shape of ice particles depends on rime mass fraction and present the parametrization of this dependence. This study also investigates the potential of using radar dual-polarization signatures to identify riming extent. Furthermore, the complexity of ice microphysics and the ambiguity of corresponding radar signatures motivate search for additional information, which can be used to infer ice microphysics. This work illustrates how radar characteristics of the melting layer can be linked to ice growth processes such as riming and aggregation. In natural clouds, ice particles are usually characterized by a large variety of habits. However, our interpretation of the melting layer usually assumes presence of a single class of ice particles with a certain shape. This study reports that two types of ice particles can produce different radar polarimetric signals in the melting layer. The melting signal of ice needles is employed to evaluate current melting layer detection methods. The melting layer of precipitation also plays a negative role, because it attenuates radio waves. Due to this largely unknown attenuation at milimeter wavelengths, cloud properties in rainfall are poorly documented by ground-based cloud radars. In this study, the melting layer attenuation at Ka- and W-bands is quantified using the differential attenuation technique based on multifrequency radar Doppler spectra observations. In addtion, the retrievals are used to evaluate previous modelling results.