Developing physical capability standards that are predictive of success on special forces selection courses

Free to read This study aimed to develop minimum standards for physical capability assessments (vertical jump, sit and reach, push-ups, seven-stage sit-ups, heaves, agility, 20-m shuttle run, loaded 5-km pack march, and 400-m swim) that candidates must pass before they can commence Australian Army Special Forces (SF) selection courses. Soldiers (Part A: n = 104; Part B: n = 92) completed the physical capability assessments before commencing a SF selection course. At the beginning of these selection courses, participants attempted two barrier assessments (3.2-km battle run and 20-km march). Statistical analysis revealed several physical capability assessments were associated with performance on the barrier assessments and selection course outcome (Part A); however, these statistical models were unable to correctly classify all candidates as likely to pass or fail the selection course. Alternatively, manual analysis identified a combination of physical capability standards that correctly classified 14% to 18% of candidates likely to fail, without excluding any candidates able to pass (Part A). The standards were applied and refined through Part B and included completing the 5-km pack march in ≤45:45 minutes : seconds, achieving ≥level five on the sit-up test, or completing ≥66 push-ups. Implementation of these standards may reduce attrition rates and enhance the efficiency of the SF recruitment process

Assessing the Accuracy of Complex Refractive Index Retrievals from Single Aerosol Particle Cavity Ring-Down Spectroscopy

<p>Cavity ring-down spectroscopy (CRDS) of single, optically manipulated aerosol particles affords quantitative retrieval of refractive indices for particles of fixed or evolving composition with high precision. Here, we quantify the accuracy with which refractive index determinations can be made by CRDS for single particles confined within the core of a Bessel laser beam and how that accuracy is degraded as the particle size is progressively reduced from the coarse mode (>1 μm radius) to the accumulation mode (<500 nm radius) regime. We apply generalized Lorenz–Mie theory to the intra-cavity standing wave to explore the effect of particle absorption on the distribution of extinction cross section determinations resulting from stochastic particle motion in the Bessel beam trap. The analysis provides an assessment of the accuracy with which the real, <i>n</i>, and imaginary, κ, components of the refractive index can be determined for a single aerosol particle.</p> <p>Published with license by American Association for Aerosol Research</p> <p><a href="https://www.tandfonline.com/pb-assets/tandf/Migrated/UAST_VideoAbstract_Transcript.pdf" target="_blank">Read the transcript</a></p> <p><a href="https://vimeo.com/263371383" target="_blank">Watch the video on Vimeo</a></p

Reduced dimensionality spin-orbit dynamics of CH3 + HCl reversible arrow CH4 Cl on ab initio surfaces

A reduced dimensionality quantum scattering method is extended to the study of spin-orbit nonadiabatic transitions in the CH3 + HCl reversible arrow CH4 + Cl(P-2(J)) reaction. Three two-dimensional potential energy surfaces are developed by fitting a 29 parameter double-Morse function to CCSD(T)/IB//MP2/cc-pV(T+d)Z-dk ab initio data; interaction between surfaces is described by geometry-dependent spin-orbit coupling functions fit to MCSCF/cc-pV(T+d)Z-dk ab initio data. Spectator modes are treated adiabatically via inclusion of curvilinear projected frequencies. The total scattering wave function is expanded in a vibronic basis set and close-coupled equations are solved via R-matrix propagation. Ground state thermal rate constants for forward and reverse reactions agree well with experiment. Multi-surface reaction probabilities, integral cross sections, and initial-state selected branching ratios all highlight the importance of vibrational energy in mediating nonadiabatic transition. Electronically excited state dynamics are seen to play a small but significant role as consistent with experimental conclusions. (C) 2011 American Institute of Physics. [doi:10.1063/1.3592732

A comparison of two statistical postprocessing methods for heavy‐precipitation forecasts over India during the summer monsoon

Accurate ensemble forecasts of heavy precipitation in India are vital for many applications and essential for early warning of damaging flood events, especially during the monsoon season. In this study we investigate to what extent Quantile Mapping (QM) and Ensemble Model Output Statistics (EMOS) statistical postprocessing reduce errors in precipitation ensemble forecasts over India, in particular for heavy precipitation. Both methods are applied to day‐1 forecasts at 12‐km resolution from the 23‐member National Centre for Medium Range Weather Forecasting (NCMRWF) global ensemble prediction system (NEPS‐G). By construction, QM leads to distributions close to the observed ones, while EMOS optimizes the ensemble spread, and it is not a priori clear which is better suited for practical applications. The methods are therefore compared with respect to several key aspects of the forecasts: local distributions, ensemble spread, and skill for forecasting precipitation amounts and the exceedance of heavy‐precipitation thresholds. The evaluation includes rank histograms, Continuous Ranked Probability Skill Scores (CRPSS), Brier Skill Scores (BSS), reliability diagrams, and receiver operating characteristic. EMOS performs best not only with respect to correcting under‐ or overdispersive ensembles, but also in terms of forecast skill for precipitation amounts and heavy precipitation events, with positive CRPSS and BSS in most regions (both up to about 0.4 in some areas), while QM in many regions performs worse than the raw forecast. QM performs best with respect to the overall local precipitation distributions. Which aspects of the forecasts are most relevant depends to some extent on how the forecasts are used. If the main criteria are the correction of under‐ or overdispersion, forecast reliability, match between the forecasted distribution for individual days and observations (CRPSS), and the skill in forecasting heavy‐precipitation events (BSS), then EMOS is the better choice for postprocessing NEPS‐G forecasts for short lead times

Reproducing the CO-to-H₂ conversion factor in cosmological simulations of Milky-Way-mass galaxies

We present models of CO(1–0) emission from Milky-Way-mass galaxies at redshift zero in the FIRE-2 cosmological zoom-in simulations. We calculate the molecular abundances by post-processing the simulations with an equilibrium chemistry solver while accounting for the effects of local sources, and determine the emergent CO(1–0) emission using a line radiative transfer code. We find that the results depend strongly on the shielding length assumed, which, in our models, sets the attenuation of the incident UV radiation field. At the resolution of these simulations, commonly used choices for the shielding length, such as the Jeans length, result in CO abundances that are too high at a given H₂ abundance. We find that a model with a distribution of shielding lengths, which has a median shielding length of ∼3 pc in cold gas (T < 300 K) for both CO and H₂, is able to reproduce both the observed CO(1–0) luminosity and inferred CO-to-H₂ conversion factor at a given star formation rate compared with observations. We suggest that this short shielding length can be thought of as a subgrid model, which controls the amount of radiation that penetrates giant molecular clouds

A multi-wavelength classification method for polar stratospheric cloud types using infrared limb spectra

The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument on board the ESA Envisat satellite operated from July 2002 until April 2012. The infrared limb emission measurements represent a unique dataset of daytime and night-time observations of polar stratospheric clouds (PSCs) up to both poles. Cloud detection sensitivity is comparable to space-borne lidars, and it is possible to classify different cloud types from the spectral measurements in different atmospheric windows regions. Here we present a new infrared PSC classification scheme based on the combination of a well-established two-colour ratio method and multiple 2-D brightness temperature difference probability density functions. The method is a simple probabilistic classifier based on Bayes' theorem with a strong independence assumption. The method has been tested in conjunction with a database of radiative transfer model calculations of realistic PSC particle size distributions, geometries, and composition. The Bayesian classifier distinguishes between solid particles of ice and nitric acid trihydrate (NAT), as well as liquid droplets of super-cooled ternary solution (STS). The classification results are compared to coincident measurements from the space-borne lidar Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument over the temporal overlap of both satellite missions (June 2006–March 2012). Both datasets show a good agreement for the specific PSC classes, although the viewing geometries and the vertical and horizontal resolution are quite different. Discrepancies are observed between the CALIOP and the MIPAS ice class. The Bayesian classifier for MIPAS identifies substantially more ice clouds in the Southern Hemisphere polar vortex than CALIOP. This disagreement is attributed in part to the difference in the sensitivity on mixed-type clouds. Ice seems to dominate the spectral behaviour in the limb infrared spectra and may cause an overestimation in ice occurrence compared to the real fraction of ice within the PSC area in the polar vortex. The entire MIPAS measurement period was processed with the new classification approach. Examples like the detection of the Antarctic NAT belt during early winter, and its possible link to mountain wave events over the Antarctic Peninsula, which are observed by the Atmospheric Infrared Sounder (AIRS) instrument, highlight the importance of a climatology of 9 Southern Hemisphere and 10 Northern Hemisphere winters in total. The new dataset is valuable both for detailed process studies, and for comparisons with and improvements of the PSC parameterizations used in chemistry transport and climate models