Conducting quantitative metrics-based performance analysis of first-principles-based global magnetosphere models is an essential step in understanding their capabilities and limitations, and providing scope for improvements in order to enhance their space weather prediction capabilities for a range of solar conditions. In this study, a detailed comparison of the performance of three global magnetohydrodynamic (MHD) models in predicting the Earth’s magnetopause location and ionospheric cross polar cap potential (CPCP) has been presented. Using the Community Coordinated Modeling Center’s Run-on-Request system and extensive database on results from various magnetospheric scenarios simulated for a variety of solar wind conditions, the aforementioned model predictions have been compared for magnetopause standoff distance estimations obtained from six empirical models, and with cross polar cap potential estimations obtained from the Assimmilative Mapping of Ionospheric Electrodynamics (AMIE) Model and the Super Dual Auroral Radar Network (SuperDARN) observations. We have considered a range of events spanning different space weather activity to analyze the performance of these models. Using a fit performance metric analysis for each event, we have quantified the models’ reproducibility of magnetopause standoff distances and CPCP against empirically-predicted observations, and identified salient features that govern the performance characteristics of the modeled magnetospheric and ionospheric quantities.
Citation to related publication:
Mukhopadhyay, A., et al. (2020). Global Magnetohydrodynamic Simulations: Performance Quantification of Magnetopause Distances and Convection Potential Predictions. Forthcoming.
This Ph.D. research focuses on two subject areas: experimental and numerical
model, which serves as two essential parts of a digital twin. A digital twin contains
models of real-world structures and fuses data from observations of the structures
and scale experiment to pull the models into better agreement with the real world.
Digital twin models have the promise of representing complex marine structures and
providing enhanced lifecycle performance and risk forecasts. Experimentally verifying
the updating approaches is necessary but rarely performed. Thus, the proposed
work is designing an experiment and developing a numerical model updated by the experimental data.
The dataset contains all the data collected in the experiment of a four-crack hexagon-
shaped specimen is presented, designed to mimic many of the properties of complex
degrading marine structural systems, such as crack interaction, component inter-
dependence, redundant load path, and non-binary failure.
Kaihua, Zhang (2020) "Development and Experimental Validation of Dynamic Bayesian Networks for System Reliability Prediction" Doctoral Dissertation, University of Michigan. Deep Blue. http://hdl.handle.net/2027.42/155231, "Evaluating Crack Growth Prediction in Structural Systems with Dynamic Bayesian Networks", submitted to Computers and Structure, and "Experimental Investigation of Structural System Capacity with Multiple Fatigue Cracks", submitted to Marine Structures
Johnson, J. E., & Molnar, P. H. ( 2019). Widespread and persistent deposition of iron formations for two billion years. Geophysical Research Letters, 46, 3327– 3339. https://doi.org/10.1029/2019GL081970
Please refer to the "README.txt" for more details., MATLAB R2018a (Mathworks, Natick, MA, USA) was used to process this data., and Excel (Microsoft Office) was used to store survey data on the comfort of both systems and also to provide absolute and relative intraobserver variablities for the DM device.
Comparison of anorectal function measured using wearable digital manometry and a high resolution manometry system Attari A, Chey WD, Baker JR, Ashton-Miller JA (2020) Comparison of anorectal function measured using wearable digital manometry and a high resolution manometry system. PLOS ONE 15(9): e0228761. https://doi.org/10.1371/journal.pone.0228761
We collected hours of functional magnetic resonance imaging data from human subjects listening to natural stories. We developed a predictive model of the voxel-wise response and further applied it to thousands of new words to understand how the brain stores and connects different concepts. and This is a dataset for the paper:
Zhang, Y., Han, K., Worth, R., & Liu, Z. (2020). Connecting concepts in the brain by mapping cortical representations of semantic relations. Nature communications, 11(1), 1-13. https://doi.org/10.1038/s41467-020-15804-w. This project is also documented at https://osf.io/eq2ba/.
Zhang, Y., Han, K., Worth, R., & Liu, Z. (2020). Connecting concepts in the brain by mapping cortical representations of semantic relations. Nature communications, 11(1), 1-13. https://doi.org/10.1038/s41467-020-15804-w
The precipitation data itself is the output of the models/datasets that we analyze in our paper. Most of it is in .nc or .nc4 format, although we provide code to extract the data into time series .mat files.
We used MATLAB to perform our analysis.
Reconstructed CT slices for a right medial cuneiform (entocuneiform) of Cantius mckennai (University of Michigan Museum of Paleontology catalog number UMMP VP 81820), as a series of TIFF images. Raw projections are not included in this dataset.
Reconstructed CT slices for a right navicular of Cantius mckennai (University of Michigan Museum of Paleontology catalog number UMMP VP 81831), as a series of TIFF images. Raw projections are not included in this dataset.
Reconstructed CT slices for a right astragalar [astragalus] body of Cantius mckennai (University of Michigan Museum of Paleontology catalog number UMMP VP 81827), as a series of TIFF images. Raw projections are not included in this dataset.
Reconstructed CT slices for a right calcaneum of Cantius mckennai (University of Michigan Museum of Paleontology catalog number UMMP VP 81821), as a series of TIFF images. Raw projections are not included in this dataset.