Phase partitioning during freezing of hydrometeors affects the transport and distribution of volatile chemical species in convective clouds. Here, the development, evaluation, and application of a mechanistic model for the study and prediction of partitioning of volatile chemical during steady-state hailstone growth are discussed. The model estimates the fraction of a chemical species retained in a two-phase growing hailstone. It is based upon mass rate balances over water and solute for constant accretion under wet-growth conditions. Expressions for the calculation of model components, including the rates of super-cooled drop collection, shedding, evaporation, and hail growth were developed and implemented based on available cloud microphysics literature. A modified Monte Carlo simulation approach was applied to assess the impact of chemical, environmental, and hail specific input variables on the predicted retention ratio for six atmospherically relevant volatile chemical species, namely, SO2, H2O2, NH3, HNO3, CH2O, and HCOOH. Single input variables found to influence retention are the ice-liquid interface supercooling, the mass fraction liquid water content of the hail, and the chemical specific effective Henry's constant (and therefore pH). The fraction retained increased with increasing values of all these variables. Other single variables, such as hail diameter, shape factor, and collection efficiency were found to have negligible effect on solute retention in the growing hail particle. The mean of separate ensemble simulations of retention ratios was observed to vary between 1.0x10-8 and 1, whilst the overall range for fixed values of individual input variables ranged from 9.0x10-7 to a high of 0.3. No single variable was found to control these extremes, but rather they are due to combinations of model input variables.

The purpose of the research presented here is to discover the central concept of "The Rhyming Poem," an Old English Christian work known only from a 10th-century manuscript, and to establish the poem's natural place in the body of Old English poetry. Existing critical literature shows little agreement about the poem's origin, vocabulary, plot, or first-person narrator, and no single translation has satisfactorily captured a sense of the poem's unity or of the purposeful vision behind it. The examination of text and context here shows that the Old English poet has created a unified vision in which religious teachings are artistically related through imagery and form. He worked in response to a particular set of conditions in early Church history, employing both pagan and Christian details to convey a message of the superiority of Christianity to idol-worship and, as well, of the validity of the Augustinian position on Original Sin over that of the heretical Pelagians.

Mixed-phase clouds (MPCs) are a key component of the Arctic climate system by contributing to surface warming. The representation of MPC processes in climate models is currently incomplete, leading to high uncertainties in the prediction of future climate change. One such MPC process is riming, which describes the freezing of liquid droplets on ice particles upon contact. The occurrence and drivers of riming are poorly understood, particularly in the Arctic. This dissertation aims at gaining a better understanding of riming in the Arctic and its influence on ice water variability in MPCs. A novel framework to consistently estimate physical and scattering properties of rimed ice particles is presented. Two methods for quantifying riming have been developed and applied to airborne cloud radar and in situ measurements. Spatial scales of ice clustering in Arctic MPCs and their relation to riming were analyzed and compared to mid-latitude MPCs. First, mass-size, cross-sectional area-size, and backscattering cross-section relations were developed as a function of the normalized rime mass M for simulated rimed aggregates. The proposed framework (“riming-dependent parameterization”) allows to consistently simulate scattering properties of rimed aggregated ice particles. For radar frequencies of 35.6 GHz and 94.0 GHz, the resulting biases are less than 1 dB assuming exponential particle size distributions when using the particle masses and scattering properties of the individual simulated particles as a reference. Further, two methods for quantifying ice particle riming are presented. The first method is based on an inverse Optimal Estimation algorithm to retrieve M from cloud radar and in situ measurements ('combined method'). The second method relies on in situ observations only, relating M to optical particle shape measurements ('in situ method'). The methods were applied to data obtained during the HALO-(AC)³ aircraft campaign, which took place west of Svalbard in spring 2022. Median M values of 0.024 and 0.021 (mean values of 0.035 and 0.033) were derived using the combined and in situ method, respectively. Compared to other temperatures, the least amount of riming occurred at − 15°C. Rimed particles were observed at low liquid water path conditions. Finally, the influence of riming on the spatial variability of ice water content (IWC) was investigated. The analysis of HALO-(AC)³ data was extended to data collected during the IMPACTS aircraft campaign, which focused on mid-latitude winter storms. Riming accounted for 66% and 63% of the total IWC during IMPACTS and HALO-(AC)³, respectively. Riming increased the probability of ice cluster occurrence at similar scales as liquid water content variability. In cold air outbreak MPCs observed during HALO-(AC)³, riming led to additional IWC clustering at spatial scales of 3-5 km, which could be lined to the presence of mesoscale updraft features. These results help to improve our understanding of riming and the link between riming and IWC variability, and can be used to evaluate and constrain models of MPCs.