Spatiotemporal Analyses of Tornado Risk and Exposure in the Contiguous United States: A Modeling Study

Tornadoes represent a significant threat to both life and property across the United States. It is unclear exactly how climate change may influence the occurrence and intensity of small-scale phenomenon (below the resolution of current climate models), such as tornadoes. However, changes have already been observed in the spatial and temporal variability of tornadoes. Regardless of whether climate change results in substantial changes in tornado frequency or severity, continued population growth and the expansion of urban areas will likely lead to an increasing amount of exposure of people and buildings to tornadoes. Potential future changes in tornado risk and exposure require new methods for studying tornado impacts through simulation. This dissertation discusses the development and application of a new tornado impacts model, the Tornado Daily Impacts Simulator (TorDIS). First, we conducted a spatiotemporal analysis of near-miss violent tornadoes as a justification for the use of spatial models in tornado impact analysis. Second, we discussed the development of TorDIS and showcased its utility via comparisons of annual tornado exposures between six metropolitan areas. We also show an example of using TorDIS to assess potential tornado impacts on individual high-risk days. Finally, we describe a case study, using TorDIS, over the Oklahoma City Metropolitan Area of the combined and isolated effects of climate change and urban development on tornado impacts. Models such as TorDIS can be used by emergency managers to pre-allocate resources to the areas of greatest risk/potential impact, by city planners to assess how changes in land use could affect the potential tornado risk and impacts, and as a justification for the placement of public storm shelters in the areas of highest potential tornado risk and impacts

Fostering effective and sustainable scientific collaboration and knowledge exchange: a workshop-based approach to establish a national ecological observatory network (NEON) domain-specific user group

The decision to establish a network of researchers centers on identifying shared research goals. Ecologically specific regions, such as the USA’s National Ecological Observatory Network’s (NEON’s) eco-climatic domains, are ideal locations by which to assemble researchers with a diverse range of expertise but focused on the same set of ecological challenges. The recently established Great Lakes User Group (GLUG) is NEON’s first domain specific ensemble of researchers, whose goal is to address scientific and technical issues specific to the Great Lakes Domain 5 (D05) by using NEON data to enable advancement of ecosystem science. Here, we report on GLUG’s kick off workshop, which comprised lightning talks, keynote presentations, breakout brainstorming sessions and field site visits. Together, these activities created an environment to foster and strengthen GLUG and NEON user engagement. The tangible outcomes of the workshop exceeded initial expectations and include plans for (i) two journal articles (in addition to this one), (ii) two potential funding proposals, (iii) an assignable assets request and (iv) development of classroom activities using NEON datasets. The success of this 2.5-day event was due to a combination of factors, including establishment of clear objectives, adopting engaging activities and providing opportunities for active participation and inclusive collaboration with diverse participants. Given the success of this approach we encourage others, wanting to organize similar groups of researchers, to adopt the workshop framework presented here which will strengthen existing collaborations and foster new ones, together with raising greater awareness and promotion of use of NEON datasets. Establishing domain specific user groups will help bridge the scale gap between site level data collection and addressing regional and larger ecological challenges

The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis. © 2010 Nature America, Inc. All rights reserved.0SCOPUS: ar.jinfo:eu-repo/semantics/publishe