http://hdl.handle.net/2027.42/58625 http://deepblue.lib.umich.edu/retrieve/213533 http://hdl.handle.net/2027.42/58625 Search the Channel search http://deepblue.lib.umich.edu/simple-search http://hdl.handle.net/2027.42/58181 Title: Three dimensional adaptive mesh refinement on a spherical shell for atmospheric models with lagrangian coordinates<br/><br/>Authors: Penner, Joyce E; Andronova, Natalia; Oehmke, Robert C; Brown, Jonathan; Stout, Quentin F; Jablonowski, Christiane; Leer, Bram van; Powell, Kenneth G; Herzog, Michael<br/><br/>Abstract: One of the most important advances needed in global climate models is the development of atmospheric General Circulation Models (GCMs) that can reliably treat convection. Such GCMs require high resolution in local convectively active regions, both in the horizontal and vertical directions. During previous research we have developed an Adaptive Mesh Refinement (AMR) dynamical core that can adapt its grid resolution horizontally. Our approach utilizes a finite volume numerical representation of the partial differential equations with floating Lagrangian vertical coordinates and requires resolving dynamical processes on small spatial scales. For the latter it uses a newly developed general-purpose library, which facilitates 3D block-structured AMR on spherical grids. The library manages neighbor information as the blocks adapt, and handles the parallel communication and load balancing, freeing the user to concentrate on the scientific modeling aspects of their code. In particular, this library defines and manages adaptive blocks on the sphere, provides user interfaces for interpolation routines and supports the communication and load-balancing aspects for parallel applications. We have successfully tested the library in a 2-D (longitude-latitude) implementation. During the past year, we have extended the library to treat adaptive mesh refinement in the vertical direction. Preliminary results are discussed. This research project is characterized by an interdisciplinary approach involving atmospheric science, computer science and mathematical/numerical aspects. The work is done in close collaboration between the Atmospheric Science, Computer Science and Aerospace Engineering Departments at the University of Michigan and NOAA GFDL. Sun, 01 Jul 2007 00:00:00 GMT http://hdl.handle.net/2027.42/57824 Title: SPACE WEATHER FORECASTING: IDENTIFYING PERIODICALLY SWITCHING BLOCK-STRUCTURED MODELS TO PREDICT MAGNETIC-FIELD FLUCTUATIONS<br/><br/>Authors: Palanthandalam-Madapusi, Harish J.; Bernstein, Dennis S.; Ridley, Aaron J. Mon, 01 Oct 2007 00:00:00 GMT http://hdl.handle.net/2027.42/55422 Title: Recent exposure to particulate matter and C-reactive protein concentration in the Multiethnic Study of Atherosclerosis (MESA)<br/><br/>Authors: Diez Roux, AV; Auchincloss, A; Astor, B; Barr, RG; Cushman, M; Dvonch, T; Jacobs, DR Jr.; Kaufman, J; Lin, X; Samson, P Sun, 01 Jan 2006 00:00:00 GMT http://hdl.handle.net/2027.42/55251 Title: Scaling Methods of Sediment Bioremediation Processes and Applications<br/><br/>Authors: Li, M.-Y.; Michalak, A. M.<br/><br/>Abstract: Bioremediation has been argued to be one of the most cost-effective remediation technologies available to reduce soil, sediment, or groundwater contamination, particularly because this approach may allow for the implementation of in-place strategies. Recent trends have advocated the application of innovative sediment stabilization strategies through placement of (reactive) capping material to allow long-term biodegradation of contaminants in these complex biogeochemical environments. The potential long-term risk reduction associated with this approach requires a demonstration of causal relationships between sediment or contaminant stability on the one hand, and microbial reactivity on the other. The spatial analysis needed to fully understand and quantify these correlations requires sensitive probabilistic techniques. Geostatistics has been used for the characterization of multi-scale spatial patterns for the last few decades, and the analysis of microbial attributes has shown significant spatial structures on microbial abundance and activity. However, there is a dearth of information on the applicability of geostatistics to quantitatively describe the interaction between the microorganisms and their environment. Using the Passaic River (NJ) dioxin data as a model dataset, multiple scaling models were applied to scale and interpolate sampled dioxin data and derive dechlorination signatures in sediments. Unlike conventional geostatistic tools that are based on the point-to-point spatial structures, the new multi-scale model (M-Scale) introduces a new framework for spatial analysis in which regional values at different scales are anchored by the correlations to each other. Spatial dioxin distributions and microbial dechlorination signatures were used as benchmarks for comparison of M-Scale to ordinary kriging. The results from cross-validation and jackknifing approaches applied to these datasets were analyzed and compared using Quantile-Quantile (Q–Q) plots and reproduction coefficients. These plots indicated that the M-Scale better preserves the local features of hotspots during data interpolation to a basin-wide scale. Current efforts focus on mapping microbial abundance and respiratory competence in the Anacostia River, based on measurements at three different scales. The outcomes of this work will be used to develop an uncertainty-based spatial decision tool for site remediation in this watershed using various capping strategies. Thu, 01 Jun 2006 00:00:00 GMT