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- Creator:
- Hong, Yi, Fry, Lauren M., Orendorf, Sophie, Ward, Jamie L., Mroczka, Bryan, Wright, David, and Gronewold, Andrew
- Description:
- Accurate estimation of hydro-meteorological variables is essential for adaptive water management in the North American Laurentian Great Lakes. However, only a limited number of monthly datasets are available nowadays that encompass all components of net basin supply (NBS), such as over-lake precipitation (P), evaporation (E), and total runoff (R). To address this gap, we developed a daily hydro-meteorological dataset covering an extended period from 1979 to 2022 for each of the Great Lakes. The daily P and E were derived from six global gridded reanalysis climate datasets (GGRCD) that include both P and E estimates, and R was calculated from National Water Model (NWM) simulations. Ensemble mean values of the difference between P and E (P – E) and NBS were obtained by analyzing daily P, E, and R. Monthly averaged values derived from our new daily dataset were validated against existing monthly datasets. This daily hydro-meteorological dataset has the potential to serve as a validation resource for current data and analysis of individual NBS components. Additionally, it could offer a comprehensive depiction of weather and hydrological processes in the Great Lakes region, including the ability to record extreme events, facilitate enhanced seasonal analysis, and support hydrologic model development and calibration. The source code and data representation/analysis figures are also made available in the data repository.
- Keyword:
- Great Lakes, Hydrometeorological, National Water Model, Daily, Overlake precipitation, Overlake evaporation, Total runoff, Net Basin Supply, and Water Balance
- Discipline:
- Science and Engineering
-
- Creator:
- Hutson, Abby , Fujisaki-Manome, Ayumi, and Glassman, Ryan
- Description:
- The data herein resulted from a study documenting the characteristics of extratropical cyclones that pass through the Great Lakes Region (GLR) and how the cyclones are trending with time. All scripts used to create these data can be found in the Github repository https://github.com/abkenyon/GLStormTrends_2024. storm_track_slp_xxxx.npz - Structured numpy files containing all storm tracks identified over one cold season, regardless of whether the storm encountered the GLR, with the file name indicating the year on which the season ended. storm_composite_xxxx-xxxx.nc - NetCDF files containing one seasonal cyclone composite with different atmospheric variables. A composite is storm-centered, and covers a 20 degree square area.
- Keyword:
- Extratropical Cyclones, Climate Trends, Great Lakes Climate
- Citation to related publication:
- Hutson A, Fujisaki-Manome A, Glassman R.: Historical Trends in Cold-Season Mid-Latitude Cyclones. Geophysical Research Letters. In press..
- Discipline:
- Science
-
- Creator:
- Nasser, Ahmad and Gumise, Wonder
- Description:
- The work on accelerating authenticated boot for embedded system resulted in designing an algorithm in python to perform the random address generation and cryptographic MAC calculation. The Sampled Boot schemes implemented in this package allow a significant reduction of the time needed to authenticate firmware images during startup, while still retaining a high degree of trust. This is particularly useful for automotive applications in which startup time constraints make secure boot a time prohibitive process. and Citation for this dataset: Nasser, A., Gumise, W. (2019). Authenticated Boot Acceleration Algorithm [Code and data]. University of Michigan Deep Blue Data Repository. https://doi.org/10.7302/yeh1-1x17
- Keyword:
- Trusted Computing, IOT security, Embedded Security, and Cyber Physical Systems
- Citation to related publication:
- Nasser, A., Gumise, W., and Ma, D., "Accelerated Secure Boot for Real-Time Embedded Safety Systems," SAE Int. J. Transp. Cyber. & Privacy 2(1) : 35-48, 2019, https://doi.org/10.4271/11-02-01-0003
- Discipline:
- Science
-
- Creator:
- Lee, Shih Kuang, Tsai, Sun Ting, and Glotzer, Sharon C.
- Description:
- The trajectory data and codes were generated for our work "Classification of complex local environments in systems of particle shapes through shape-symmetry encoded data augmentation" (amidst peer review process). The data sets contain trajectory data in GSD file format for 7 test systems, including cubic structures, two-dimensional and three-dimensional patchy particle shape systems, hexagonal bipyramids with two aspect ratios, and truncated shapes with two degrees of truncation. Besides, the corresponding Python code and Jupyter notebook used to perform data augmentation, MLP classifier training, and MLP classifier testing are included.
- Keyword:
- Machine Learning, Colloids Self-Assembly, Crystallization, and Order Parameter
- Citation to related publication:
- https://doi.org/10.48550/arXiv.2312.11822
- Discipline:
- Other, Science, and Engineering
-
- Creator:
- Zhang, Yizhen
- Description:
- 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/.
- Keyword:
- fMRI, natural story comprehension, neural encoding, semantic processing, word relations, and naturalistic stimuli
- Citation to related publication:
- 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
- Discipline:
- Science
-
- Creator:
- Gottesman, Ari
- Description:
- This study analyzes correlations between magnetic field data from closely-spaced pairs of ground magnetometers to observe the spatial scale of ionospheric current signatures. Correlations were mainly calculated in 7.5 minute intervals for periods of multiple days. Distributions were taken from the collection of these 7.5 minute intervals to identify the amount of time where the magnetometers were observing "similar" or "different" ionospheric signatures. The raw magnetometer data was taken from two geomagnetic storms: one taking place on 7-8 September, 2017, and the other taking place on 23-24 March, 2023. These periods were selected due to the presence of both high and low geomagnetic activity. The final distributions calculated from this analysis are available in Correlation_Distributions.csv.
- Keyword:
- Space Weather Impacts, Geomagnetically Induced Currents, and GIC
- Discipline:
- Science
-
- Creator:
- Liemohn, Michael W, Adam, Joshua G, and Ganushkina, Natalia Y
- Description:
- Many statistical tools have been developed to aid in the assessment of a numerical model’s quality at reproducing observations. Some of these techniques focus on the identification of events within the data set, times when the observed value is beyond some threshold value that defines it as a value of keen interest. An example of this is whether it will rain, in which events are defined as any precipitation above some defined amount. A method called the sliding threshold of observation for numeric evaluation (STONE) curve sweeps the event definition threshold of both the model output and the observations, resulting in the identification of threshold intervals for which the model does well at sorting the observations into events and nonevents. An excellent data-model comparison will have a smooth STONE curve, but the STONE curve can have wiggles and ripples in it. These features reveal clusters when the model systematically overestimates or underestimates the observations. This study establishes the connection between features in the STONE curve and attributes of the data-model relationship. The method is applied to a space weather example.
- Keyword:
- space physics, statistical methods, and STONE curve
- Citation to related publication:
- Liemohn, M. W., Adam, J. G., & Ganushkina, N. Y. (2022). Analysis of features in a sliding threshold of observation for numeric evaluation (STONE) curve. Space Weather, 20, e2022SW003102. https://doi.org/10.1029/2022SW003102
- Discipline:
- Science
-
- Creator:
- Malhotra, Garima and Ridley, Aaron
- Description:
- This research aims to understand the importance of lower thermospheric atomic oxygen on the upper thermosphere. O number densities between 95-100 km from WACCM-X are much closer to the observations from SABER instrument on TIMED satellite as compared to those from MSIS. We show in this study that the correction of the lower boundary atomic oxygen yields better agreement between GITM and GUVI O/N2 in the upper thermosphere .
- Keyword:
- Lower Thermosphere Atomic Oxygen, Thermospheric Dynamics, Thermospheric composition and mixing, Lower-Upper Thermosphere Vertical Coupling, GITM - WACCMX coupling, and Global Ionosphere Thermosphere Model
- Citation to related publication:
- Malhotra, G., Ridley, A. J., Marsh, D. R., Wu, C., Paxton, L. J., & Mlynczak, M. G. (2020). Impacts of Lower Thermospheric Atomic Oxygen on Thermospheric Dynamics and Composition Using the Global Ionosphere Thermosphere Model. Journal of Geophysical Research: Space Physics, e2020JA027877. https://doi.org/10.1029/2020JA027877
- Discipline:
- Science
-
- Creator:
- Swiger, Brian M., Liemohn, Michael W., and Ganushkina, Natalia Y.
- Description:
- We sampled the near-Earth plasma sheet using data from the NASA Time History of Events and Macroscale Interactions During Substorms mission. For the observations of the plasma sheet, we used corresponding interplanetary observations using the OMNI database. We used these data to develop a data-driven model that predicts plasma sheet electron flux from upstream solar wind variations. The model output data are included in this work, along with code for analyzing the model performance and producing figures used in the related publication. and Data files are included in hdf5 and Python pickle binary formats; scripts included are set up for use of Python 3 to access and process the pickle binary format data.
- Keyword:
- neural network, plasma sheet, solar wind, machine learning, keV electron flux, deep learning, and space weather
- Citation to related publication:
- Swiger, B. M., Liemohn, M. W., & Ganushkina, N. Y. (2020). Improvement of Plasma Sheet Neural Network Accuracy With Inclusion of Physical Information. Frontiers in Astronomy and Space Sciences, 7. https://doi.org/10.3389/fspas.2020.00042
- Discipline:
- Science and Engineering
-
- Creator:
- Liemohn, Michael W
- Description:
- Earth’s upper atmosphere above 500 km altitude constantly loses charged particles to outer space in a process called ionospheric outflow. This outflow is important for the dynamics of the near-Earth space environment (“space weather”) yet is poorly understood on a global scale. A mission is needed to observe the global patterns of ionospheric outflow and its relation to space weather driving conditions. The science objectives of such a mission could include not only the reconstruction of global outflow patterns but also the relation of these patterns to geomagnetic activity and the spatial and temporal nature of outflow composition. A study is presented to show that four well-placed spacecraft would be sufficient for reasonable outflow reconstructions.
- Keyword:
- ionosphere, magnetosphere, satellite mission concept, and space weather
- Citation to related publication:
- Liemohn, M. W., Jörg-Micha Jahn, Raluca Ilie, Natalia Y. Ganushkina, Daniel T. Welling, Heather Elliott, Meghan Burleigh, Kaitlin Doublestein, Stephanie Colon-Rodriguez, Pauline Dredger, & Philip Valek (2024). Reconstruction analysis of global ionospheric outflow patterns. Journal of Geophysical Research Space Physics, 129, e2023JA032238. https://doi/org/10.1029/2024JA032238
- Discipline:
- Science
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