The advent of satellite limb scatter measurements has allowed the stratosphere to be studied at a scope unparalleled by previous observational techniques, affording the opportunity to study structures on both small spacial and temporal scales. Utilizing these measurements to their fullest has fueled the development of radiative transfer models to simulate the measurements, but also inversion techniques to retrieve atmospheric parameters. The limb scatter instrument OSIRIS, onboard the Odin satellite, is currently used in conjunction with the SASKTRAN radiative transfer model and multiplicative algebraic reconstruction technique to retrieve stratospheric aerosol extinction. In this work, the aerosol information content of limb scatter measurements is explored and an improved version of the aerosol retrieval is developed through the simultaneous retrieval of a second aerosol parameter, the Angstrom coefficient, which is related to particle size. The sensitivity of limb scatter measurements to aerosol is investigated through forward modelling of OSIRIS measurements as a function of wavelength, satellite geometry and particle size. Information content of the measurements is investigated to determine the feasibility of retrieving various aerosol size parameters and a simple linear inversion technique is tested. Results from this study are used to develop a non-linear inversion technique with minimal sensitivity to the required assumptions. Incorporation of longer wavelength data into the retrieval allows for the determination of the wavelength dependence of the scattered signal, which when combined with a lognormal particle size distribution of constant mode width allows for the retrieval of aerosol number density and mode radius. Conversion of these parameters to extinction and the Angstrom coefficient provides retrieved quantities with minimal dependence on the assumed size distribution. Application of this technique to the OSIRIS data set shows improved extinction results through both internal comparisons of the data and when compared with other results from SAGE II, III and CALIPSO satellite measurements. Although the retrieved Angstrom coefficient shows some bias due to the required assumptions, comparisons with the SAGE II data set show considerable improvement over the apriori estimate.

In this thesis, we assess various climatic impacts of stratospheric aerosol injection (SAI), a geoengineering proposal that aims to cool Earth by enhancing the sunlight-reflecting aerosol layer in the lower stratosphere. To this end, we employ simpleradiative transfer models, a detailed radiative transfer code (SOCRATES), and two Hadley Centre general circulation models (HadGEM2-CCS and HadGEM2-ES). We find that the use of a light-absorbing aerosol (black carbon) for SAI would result in significant stratospheric warming and an unprecedented weakening of the hydrological cycle. Conversely, we find that SAI with sulphate or titania aerosol could counteract many of the extreme climate changes exhibited by a business-as-usual scenario (RCP8.5) by the end of this century. In a separate investigation, we show that volcanic aerosol dispersion following low-altitude volcanic eruptions can exhibit high sensitivity to the ambient weather state. Volcanic aerosol may get 'trapped' in a single hemisphere or transported to the opposite hemisphere depending simply on the meteorological conditions on the day of the eruption. In a final study, we investigate the impacts of SAI on North Atlantic tropical storm frequency. We find that SAI exclusively promoted in the southern hemisphere would increase North Atlantic storm frequency, and vice versa for northern hemisphere SAI. The results of this thesis should promote further research into SAI, which could conceivably be deployed to maintain global-mean temperature below the COP21 target of +1.5 K above pre-industrial levels, whilst society transitions onto a sustainable energy pathway. Conversely, the possibility of SAI being weaponised, for instance, to specifically increase North Atlantic tropical storm frequency, should motivate policymakers to implement effective regulation and governance to deter unilateral SAI deployments.

A new retrieval algorithm has been developed based on the Optimal Estimation (OE) approach, which retrieves lognormal aerosol size distribution parameters from multiwavelength aerosol extinction data, as measured by the Stratospheric Aerosol and Gas Experiment (SAGE) II in the lower stratosphere. Retrieving these aerosol properties becomes increasingly more difficult under aerosol background conditions, when tiny particles (« 0.1 µm) prevail, to which the experiment is nearly or entirely insensitive. A successful retrieval algorithm must then be able (a) to fill the 'blind spot' with suitable information about the practically invisible particles, and (b) to identify 'the best' of many possible solutions. The OE approach differs from other previously used aerosol retrieval techniques by taking a statistical approach to the multiple solution problem, in which the entire range of possible solutions are considered (including the smallest particles) and characterized by probability density functions. The three main parts of this thesis are (1) the development of the new OE retrieval algorithm, (2) the validation of this algorithm on the basis of synthetic extinction data, and (3) application of the new algorithm to SAGE II measurements of stratospheric background aerosol. The validation results indicate that the new method is able to retrieve the particle size of typical background aerosols reasonably well, and that the retrieved uncertainties are a good estimate of the true errors. The derived surface area densities (A), and volume densities (V ) tend to be closer to the correct solutions than the directly retrieved number density (N), median radius (R), and lognormal distribution width (S). Aerosol properties as retrieved from SAGE II measurements (recorded in 1999) are observed to be close to correlative in situ data. In many cases the OE and in situ data agree within the (OE and/or the in situ ) uncertainties. The retrieved error estimates are of the order of 69% (σN), 33% (σR), 14% (σS), 23% (σA), 12% (σV), and 13% (σReff ). The OE number densities are generally larger, and the OE median particle sizes are generally smaller than those N and R retrieved by Bingen et al. (2004a), who suggest that their results underestimate (N) or overestimate (R) correlative in situ data due to the 'small particle problem'. The OE surface area estimates are generally closer to correlative in situ profiles (courtesy of T. Deshler, University of Wyoming), and larger than Principal Component Analysis (PCA) retrieval solutions of A (courtesy of L. W. Thomason, NASA LaRC) that have been observed to underestimate correlative in situ data by 40-50%. These observations suggest that the new OE retrieval algorithm is a successful approach to the aerosol retrieval problem, which is able to add to the current knowledge by improving current estimates of aerosol properties in the lower stratosphere under low aerosol loading conditions.

A new retrieval algorithm has been developed based on the Optimal Estimation (OE) approach, which retrieves lognormal aerosol size distribution parameters from multiwavelength aerosol extinction data, as measured by the Stratospheric Aerosol and Gas Experiment (SAGE) II in the lower stratosphere. Retrieving these aerosol properties becomes increasingly more difficult under aerosol background conditions, when tiny particles (« 0.1 µm) prevail, to which the experiment is nearly or entirely insensitive. A successful retrieval algorithm must then be able (a) to fill the 'blind spot' with suitable information about the practically invisible particles, and (b) to identify 'the best' of many possible solutions. The OE approach differs from other previously used aerosol retrieval techniques by taking a statistical approach to the multiple solution problem, in which the entire range of possible solutions are considered (including the smallest particles) and characterized by probability density functions. The three main parts of this thesis are (1) the development of the new OE retrieval algorithm, (2) the validation of this algorithm on the basis of synthetic extinction data, and (3) application of the new algorithm to SAGE II measurements of stratospheric background aerosol. The validation results indicate that the new method is able to retrieve the particle size of typical background aerosols reasonably well, and that the retrieved uncertainties are a good estimate of the true errors. The derived surface area densities (A), and volume densities (V ) tend to be closer to the correct solutions than the directly retrieved number density (N), median radius (R), and lognormal distribution width (S). Aerosol properties as retrieved from SAGE II measurements (recorded in 1999) are observed to be close to correlative in situ data. In many cases the OE and in situ data agree within the (OE and/or the in situ ) uncertainties. The retrieved error estimates are of the order of 69% (σN), 33% (σR), 14% (σS), 23% (σA), 12% (σV), and 13% (σReff ). The OE number densities are generally larger, and the OE median particle sizes are generally smaller than those N and R retrieved by Bingen et al. (2004a), who suggest that their results underestimate (N) or overestimate (R) correlative in situ data due to the 'small particle problem'. The OE surface area estimates are generally closer to correlative in situ profiles (courtesy of T. Deshler, University of Wyoming), and larger than Principal Component Analysis (PCA) retrieval solutions of A (courtesy of L. W. Thomason, NASA LaRC) that have been observed to underestimate correlative in situ data by 40-50%. These observations suggest that the new OE retrieval algorithm is a successful approach to the aerosol retrieval problem, which is able to add to the current knowledge by improving current estimates of aerosol properties in the lower stratosphere under low aerosol loading conditions.

This paper aims to study how varying crossflow burning temperatures from 1100 C to 1800 C affect the liquid droplet breakup, size distribution, and atomization of a liquid water jet injected into a vitiated crossflow. The LJIC injection mechanism was implemented using the high-pressure axially staged combustion facility at the University of Central Florida. The measurement devices used to gather particle data from the exhaust plume were the TSI Aerodynamic Particle Sizer (APS), which measures particles between 0.523 µm and 20 µm, and the Sensirion SPS30 (SPS30), which measures particles between 0.3 µm and 10 µm. Both measurement devices were placed 3 ft away from the choked exit. Table 3 shows that the 1800 C crossflow temperature behaved as predicted by having the largest particle distribution of 67.97% and the largest particle count of 19,301 at 0.523 µm. The 1100 C crossflow produced the second-largest normalized particle count of 66.69% and raw particle count of 20,209 at 0.523 µm. This result is contrary to the original hypothesis because it shows that the relationship between temperature and particle count is non-linear and that many other factors must be at play in the atomization process, such as the droplet distribution at the nano level. The SPS30 was used to compare the particle size distributions between a 1500 C and 1800 C crossflow. Acquiring number concentration data for particles up to 10 µm in size, the 1800 C crossflow had a distribution peak at 802.76416 N/cm3, and the 1500 C crossflow had a peak of 867.28272 N/cm3. For the 0.5 µm peak, The 1800 C had a 10 µm particle size distribution peak at 674.27.76416 N/cm3, and the 1500C crossflow had a peak of 730.501 N/cm3. The decreased number concentration from 1500 C to 1800 C case grants the water particles in the 1800 C crossflow increased surface area, which allows for increased heat exposure from the vitiated crossflow [7]. Despite some nonlinear particle count results, the highest crossflow temperature of 1800 C produces the best atomization results by reducing the total particle count and having the largest collection of particles at the lowest detectable particle size of 0.523 µm.