Stratospheric Effects on UV, Speed of Sound, Pressure, and Temperature

The atmosphere is composed of several layers, each with its own distinct environment varying in temperature, pressure, and levels of UV radiation. Quantifying these varying parameters proves to be useful in understanding atmospheric composition in greater detail. Variance in the composition of the atmosphere allows for the study of the evolution of physical phenomena at different altitudes. Our group quantified this variance using a high-altitude weather balloon and designed an experimental method to observe the nature of sound propagation through varying altitudes. The goal was to develop an altitude-dependent model of the speed of sound by using an open-air, microcontroller-based payload. Using our platform, we found that the open-air payload design results in noisy readings. Additionally, our method was restricted to low altitude environments, unable to produce reliable data above 6,700 meters. We address possible improvements and constraints in developing an open-air payload design to derive an altitude-dependent model for sound propagation. Furthermore, we present our findings on the variations in pressure, temperature, and levels of UV radiation during balloon flights at altitudes of up to 30,000 meters. These variations included a proportional decrease in pressure, a temperature inversion at 15,000 meters, and an exceptional increase in both UVA and UVB radiation as altitude increases

Simulation-based Bayesian inference for epidemic models

A powerful and flexible method for fitting dynamic models to missing and censored data is to use the Bayesian paradigm via data-augmented Markov chain Monte Carlo (DA-MCMC). This samples from the joint posterior for the parameters and missing data, but requires high memory overheads for large-scale systems. In addition, designing efficient proposal distributions for the missing data is typically challenging. Pseudo-marginal methods instead integrate across the missing data using a Monte Carlo estimate for the likelihood, generated from multiple independent simulations from the model. These techniques can avoid the high memory requirements of DA-MCMC, and under certain conditions produce the exact marginal posterior distribution for parameters. A novel method is presented for implementing importance sampling for dynamic epidemic models, by conditioning the simulations on sets of validity criteria (based on the model structure) as well as the observed data. The flexibility of these techniques is illustrated using both removal time and final size data from an outbreak of smallpox. It is shown that these approaches can circumvent the need for reversible-jump MCMC, and can allow inference in situations where DA-MCMC is impossible due to computationally infeasible likelihoods. © 2013 Elsevier B.V. All rights reserved.Trevelyan J. McKinley, Joshua V. Ross, Rob Deardon, Alex R. Coo