The Decline in German Output Volatility: A Bayesian Analysis

Empirical evidence suggests a sharp volatility decline of the growth in U.S. gross domestic product (GDP) in the mid-1980s. Using Bayesian methods, we analyze whether a volatility reduction can also be detected for the German GDP. Since statistical inference for volatility processes critically depends on the specification of the conditional mean we assume for our volatility analysis different time series models for GDP growth. We find across all specifications evidence for an output stabilization around 1993, after the downturn following the boom associated with the German reunification. However, the different GDP models lead to alternative characterizations of this stabilization : In a linear AR model it shows up as smaller shocks hitting the economy, while regime switching models reveal as further sources for a stabilization, a narrowing gap between growth rates during booms and recessions or flatter trajectories characterizing the GDP growth rates. Furthermore, it appears that the reunification interrupted an output stabilization emerging already around 1987. --business cycle models,Gibbs sampling,Markov Chain Monte Carlo,regime switching,structural breaks

Secure and authenticated data communication in wireless sensor networks

© 2015 by the authors; licensee MDPI, Basel, Switzerland. Securing communications in wireless sensor networks is increasingly important as the diversity of applications increases. However, even today, it is equally important for the measures employed to be energy efficient. For this reason, this publication analyzes the suitability of various cryptographic primitives for use in WSNs according to various criteria and, finally, describes a modular, PKI-based framework for confidential, authenticated, secure communications in which most suitable primitives can be employed. Due to the limited capabilities of common WSN motes, criteria for the selection of primitives are security, power efficiency and memory requirements. The implementation of the framework and the singular components have been tested and benchmarked in our tested of IRISmotes

Assessing Ozone-Related Health Impacts under a Changing Climate

Climate change may increase the frequency and intensity of ozone episodes in future summers in the United States. However, only recently have models become available that can assess the impact of climate change on O(3) concentrations and health effects at regional and local scales that are relevant to adaptive planning. We developed and applied an integrated modeling framework to assess potential O(3)-related health impacts in future decades under a changing climate. The National Aeronautics and Space Administration–Goddard Institute for Space Studies global climate model at 4° × 5° resolution was linked to the Penn State/National Center for Atmospheric Research Mesoscale Model 5 and the Community Multiscale Air Quality atmospheric chemistry model at 36 km horizontal grid resolution to simulate hourly regional meteorology and O(3) in five summers of the 2050s decade across the 31-county New York metropolitan region. We assessed changes in O(3)-related impacts on summer mortality resulting from climate change alone and with climate change superimposed on changes in O(3) precursor emissions and population growth. Considering climate change alone, there was a median 4.5% increase in O(3)-related acute mortality across the 31 counties. Incorporating O(3) precursor emission increases along with climate change yielded similar results. When population growth was factored into the projections, absolute impacts increased substantially. Counties with the highest percent increases in projected O(3) mortality spread beyond the urban core into less densely populated suburban counties. This modeling framework provides a potentially useful new tool for assessing the health risks of climate change

Trace gas/aerosol boundary concentrations and their impacts on continental-scale AQMEII modeling domains

Copyright 2011 Elsevier B.V., All rights reserved.Over twenty modeling groups are participating in the Air Quality Model Evaluation International Initiative (AQMEII) in which a variety of mesoscale photochemical and aerosol air quality modeling systems are being applied to continental-scale domains in North America and Europe for 2006 full-year simulations for model inter-comparisons and evaluations. To better understand the reasons for differences in model results among these participating groups, each group was asked to use the same source of emissions and boundary concentration data for their simulations. This paper describes the development and application of the boundary concentration data for this AQMEII modeling exercise. The European project known as GEMS (Global and regional Earth-system Monitoring using Satellite and in-situ data) has produced global-scale re-analyses of air quality for several years, including 2006 (http://gems.ecmwf.int). The GEMS trace gas and aerosol data were made available at 3-hourly intervals on a regular latitude/longitude grid of approximately 1.9° resolution within 2 "cut-outs" from the global model domain. One cut-out was centered over North America and the other over Europe, covering sufficient spatial domain for each modeling group to extract the necessary time- and space-varying (horizontal and vertical) concentrations for their mesoscale model boundaries. Examples of the impact of these boundary concentrations on the AQMEII continental simulations are presented to quantify the sensitivity of the simulations to boundary concentrations. In addition, some participating groups were not able to use the GEMS data and instead relied upon other sources for their boundary concentration specifications. These are noted, and the contrasting impacts of other data sources for boundary data are presented. How one specifies four-dimensional boundary concentrations for mesoscale air quality simulations can have a profound impact on the model results, and hence, this aspect of data preparation must be performed with considerable care.Peer reviewedFinal Accepted Versio

Sensitivity of Present and Future Surface Temperatures to Precipitation Characteristics

A model simulation study shows that different diurnal cycles of precipitation are consistent with radically different present and future climate characteristics. In projected future climate scenarios, divergence in the time of day and type of precipitation had very divergent impacts on the radiation balance and consequently on surface temperatures. The relationship between the diurnal cycle of precipitation versus the present and future climate was examined using the GISS-MM5 (Goddard Institute for Space Studies Mesoscale Model 5) regional climate modeling system with 2 alternative moist convection schemes. June-August (JJA) mean surface temperatures of the 1990s, 2050s, and 2080s were simulated over the eastern US on a double nested 108/36 km domain, with the 36 km domain centered over the eastern US. In the 1990s, one model version simulated maxima in (convective) precipitation during the early morning, while the second model simulated the hour of precipitation maxima with considerable spatial variability (in better agreement with observations). In the futuristic climate scenarios, differences in the time of day of precipitation had very important impacts on the radiation balance at the surface. One version gave more precipitation at night and fewer clouds during the day, promoting higher surface temperatures. The alternative version created more precipitation during the day, consistent with diminished absorption of solar radiation at the surface and consequently lower surface temperatures. The results demonstrate the importance of improving cumulus parameterizations in regional mesoscale and global climate models and suggest that such improvements would lead to greater confidence in model projections of climate change