WETLAND OCCUPANCY OF POND-BREEDING AMPHIBIANS IN YOSEMITE NATIONAL PARK, USA

We estimated wetland occupancy and population trends for three species of pond-breeding anurans in Yosemite National Park from 2007 – 2011. We used a double survey technique in which two observers independently surveyed each site on the same day. Double surveys allowed us to calculate detectability for the three most common anurans within the park: Rana sierrae, Anaxyrus canorus, and Pseudacris regilla. Annual estimates of detectability were generally high; mean detectability ranged from 73.7% + 0.6 (SE) for any life history stage of A. canorus to 86.7% + 0.7 for sites with P. regilla reproduction (eggs or larvae present). Detectability was most variable for Anaxyrus canorus, which ranged from 45.9% to 99.7%. The probability of occupancy for R. sierrae was highest in larger, low-elevation wetlands that lacked fish. Anaxyrus canorus were more common in shallow high-elevation ponds; their occurrence was minimally impacted by the presence of fish. Finally, occurrence of P. regilla was largely unrelated to wetland size and elevation, but like R. sierrae, they were less likely to occupy sites with fish. Occupancy showed no trend over the five years of our study for R. sierrae or A. canorus when considering either sites with any life stage or only sites with reproduction. However, P. regilla showed a modest downward trend for sites with any life stage and sites with reproduction. Our results for R. sierrae run counter to expectations given recent concern about the decline of this species, while our findings for P. regilla raise concerns for this widespread and generally common species

Beethoven\u27s Eroica Sketchbooks: From Scribbles to Symphony

This paper will help readers to gain new insights on Beethoven’s Symphony No. 3 in E-flat Op. 55, also known as Eroica, by exploring the sketches related to this symphony. The background of the piece is discussed, and the sketches have been compared to the completed symphony. Beethoven’s sketching process was dramatically different from other composers of his time, and his sketches included even more musical material than was eventually included in the symphony

Multi-generational oxidation model to simulate secondary organic aerosol in a 3-D air quality model

Multi-generational gas-phase oxidation of organic vapors can influence the abundance, composition and properties of secondary organic aerosol (SOA). Only recently have SOA models been developed that explicitly represent multi-generational SOA formation. In this work, we integrated the statistical oxidation model (SOM) into SAPRC-11 to simulate the multi-generational oxidation and gas/particle partitioning of SOA in the regional UCD/CIT (University of California, Davis/California Institute of Technology) air quality model. In the SOM, evolution of organic vapors by reaction with the hydroxyl radical is defined by (1) the number of oxygen atoms added per reaction, (2) the decrease in volatility upon addition of an oxygen atom and (3) the probability that a given reaction leads to fragmentation of the organic molecule. These SOM parameter values were fit to laboratory smog chamber data for each precursor/compound class. SOM was installed in the UCD/CIT model, which simulated air quality over 2-week periods in the South Coast Air Basin of California and the eastern United States. For the regions and episodes tested, the two-product SOA model and SOM produce similar SOA concentrations but a modestly different SOA chemical composition. Predictions of the oxygen-to-carbon ratio qualitatively agree with those measured globally using aerosol mass spectrometers. Overall, the implementation of the SOM in a 3-D model provides a comprehensive framework to simulate the atmospheric evolution of organic aerosol

Simulating secondary organic aerosol in a regional air quality model using the statistical oxidation model – Part 1: Assessing the influence of constrained multi-generational ageing

Multi-generational oxidation of volatile organic compound (VOC) oxidation products can significantly alter the mass, chemical composition and properties of secondary organic aerosol (SOA) compared to calculations that consider only the first few generations of oxidation reactions. However, the most commonly used state-of-the-science schemes in 3-D regional or global models that account for multi-generational oxidation (1) consider only functionalization reactions but do not consider fragmentation reactions, (2) have not been constrained to experimental data and (3) are added on top of existing parameterizations. The incomplete description of multi-generational oxidation in these models has the potential to bias source apportionment and control calculations for SOA. In this work, we used the statistical oxidation model (SOM) of Cappa and Wilson (2012), constrained by experimental laboratory chamber data, to evaluate the regional implications of multi-generational oxidation considering both functionalization and fragmentation reactions. SOM was implemented into the regional University of California at Davis / California Institute of Technology (UCD/CIT) air quality model and applied to air quality episodes in California and the eastern USA. The mass, composition and properties of SOA predicted using SOM were compared to SOA predictions generated by a traditional two-product model to fully investigate the impact of explicit and self-consistent accounting of multi-generational oxidation. Results show that SOA mass concentrations predicted by the UCD/CIT-SOM model are very similar to those predicted by a two-product model when both models use parameters that are derived from the same chamber data. Since the two-product model does not explicitly resolve multi-generational oxidation reactions, this finding suggests that the chamber data used to parameterize the models captures the majority of the SOA mass formation from multi-generational oxidation under the conditions tested. Consequently, the use of low and high NOx yields perturbs SOA concentrations by a factor of two and are probably a much stronger determinant in 3-D models than multi-generational oxidation. While total predicted SOA mass is similar for the SOM and two-product models, the SOM model predicts increased SOA contributions from anthropogenic (alkane, aromatic) and sesquiterpenes and decreased SOA contributions from isoprene and monoterpene relative to the two-product model calculations. The SOA predicted by SOM has a much lower volatility than that predicted by the traditional model, resulting in better qualitative agreement with volatility measurements of ambient OA. On account of its lower-volatility, the SOA mass produced by SOM does not appear to be as strongly influenced by the inclusion of oligomerization reactions, whereas the two-product model relies heavily on oligomerization to form low-volatility SOA products. Finally, an unconstrained contemporary hybrid scheme to model multi-generational oxidation within the framework of a two-product model in which ageing reactions are added on top of the existing two-product parameterization is considered. This hybrid scheme formed at least 3 times more SOA than the SOM during regional simulations as a result of excessive transformation of semi-volatile vapors into lower volatility material that strongly partitions to the particle phase. This finding suggests that these hybrid multi-generational schemes should be used with great caution in regional models

Measures of observation impact in non-Gaussian data assimilation

ABSTRACT Non-Gaussian/non-linear data assimilation is becoming an increasingly important area of research in the Geosciences as the resolution and non-linearity of models are increased and more and more non-linear observation operators are being used. In this study, we look at the effect of relaxing the assumption of a Gaussian prior on the impact of observations within the data assimilation system. Three different measures of observation impact are studied: the sensitivity of the posterior mean to the observations, mutual information and relative entropy. The sensitivity of the posterior mean is derived analytically when the prior is modelled by a simplified Gaussian mixture and the observation errors are Gaussian. It is found that the sensitivity is a strong function of the value of the observation and proportional to the posterior variance. Similarly, relative entropy is found to be a strong function of the value of the observation. However, the errors in estimating these two measures using a Gaussian approximation to the prior can differ significantly. This hampers conclusions about the effect of the non-Gaussian prior on observation impact. Mutual information does not depend on the value of the observation and is seen to be close to its Gaussian approximation. These findings are illustrated with the particle filter applied to the Lorenz ’63 system. This article is concluded with a discussion of the appropriateness of these measures of observation impact for different situations

Children and Virtual Reality: Emerging Possibilities and Challenges

Virtual Reality is fast becoming a reality, with estimates that over 200m headsets will have been sold by 2020, and the market value for VR hardware and software reaching well over $20bn by then. Key players in the market currently include PlayStation with PSVR, Facebook with Oculus Rift, Google Cardboard and Daydream, Mattel with Viewmaster, and many other brands investing in content production for various audiences. One of those audiences is young people and children. “Children and Virtual Reality” is a collaboration between Dubit, Turner, WEARVR and the COST (European Cooperation in Science and Technology) Action DigiLitEY. Dubit, Turner and WEARVR are companies that specialise in digital, TV and VR content, with an interest in developing best practices around VR and children. DigiLitEY is a five year (2013-2017) academic network that focuses on existing and emerging communicative technologies for young children. This includes wearable technologies, 3D printers, robots, augmented reality, toys and games and relevant aspects of the Internet of Things. This report brings together industry research into the effects of VR on 8 to 12 year olds, and ideas that arose from a COST funded Think Tank to explore what the research findings might mean for the use of VR by under 8s

Data assimilation in slow-fast systems using homogenized climate models

A deterministic multiscale toy model is studied in which a chaotic fast subsystem triggers rare transitions between slow regimes, akin to weather or climate regimes. Using homogenization techniques, a reduced stochastic parametrization model is derived for the slow dynamics. The reliability of this reduced climate model in reproducing the statistics of the slow dynamics of the full deterministic model for finite values of the time scale separation is numerically established. The statistics however is sensitive to uncertainties in the parameters of the stochastic model. It is investigated whether the stochastic climate model can be beneficial as a forecast model in an ensemble data assimilation setting, in particular in the realistic setting when observations are only available for the slow variables. The main result is that reduced stochastic models can indeed improve the analysis skill, when used as forecast models instead of the perfect full deterministic model. The stochastic climate model is far superior at detecting transitions between regimes. The observation intervals for which skill improvement can be obtained are related to the characteristic time scales involved. The reason why stochastic climate models are capable of producing superior skill in an ensemble setting is due to the finite ensemble size; ensembles obtained from the perfect deterministic forecast model lacks sufficient spread even for moderate ensemble sizes. Stochastic climate models provide a natural way to provide sufficient ensemble spread to detect transitions between regimes. This is corroborated with numerical simulations. The conclusion is that stochastic parametrizations are attractive for data assimilation despite their sensitivity to uncertainties in the parameters.Comment: Accepted for publication in Journal of the Atmospheric Science

Evaluation of an Air Quality Model for the Size and Composition of Source-Oriented Particle Classes

Air quality model predictions of the size and composition of atmospheric particle classes are evaluated by comparison with aerosol time-of-flight mass spectrometry (ATOFMS) measurements of single-particle size and composition at Long Beach and Riverside, CA, during September 1996. The air quality model tracks the physical diameter, chemical composition, and atmospheric concentration of thousands of representative particles from different emissions classes as they are transported from sources to receptors while undergoing atmospheric chemical reactions. In the model, each representative particle interacts with a common gas phase but otherwise evolves separately from all other particles. The model calculations yield an aerosol population, in which particles of a given size may exhibit different chemical compositions. ATOFMS data are adjusted according to the known particle detection efficiencies of the ATOFMS instruments, and model predictions are modified to simulate the chemical sensitivities and compositional detection limits of the ATOFMS instruments. This permits a direct, semiquantitative comparison between the air quality model predictions and the single-particle ATOFMS measurements to be made. The air quality model accurately predicts the fraction of atmospheric particles containing sodium, ammonium, nitrate, carbon, and mineral dust, across all particle sizes measured by ATOFMS at the Long Beach site, and in the coarse particle size range (D_a ≥ 1.8 μm) at the Riverside site. Given that this model evaluation is very likely the most stringent test of any aerosol air quality model to date, the model predictions show impressive agreement with the single-particle ATOFMS measurements