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- Creator:
- Liemohn, Michael W, Azari, Abigail R, Ganushkina, Natalia Yu, and Rastätter, Lutz
- Description:
- Scientists often try to reproduce observations with a model, helping them explain the observations by adjusting known and controllable features within the model. They then use a large variety of metrics for assessing the ability of a model to reproduce the observations. One such metric is called the relative operating characteristic (ROC) curve, a tool that assesses a model’s ability to predict events within the data. The ROC curve is made by sliding the event-definition threshold in the model output, calculating certain metrics and making a graph of the results. Here, a new model assessment tool is introduced, called the sliding threshold of observation for numeric evaluation (STONE) curve. The STONE curve is created by sliding the event definition threshold not only for the model output but also simultaneously for the data values. This is applicable when the model output is trying to reproduce the exact values of a particular data set. While the ROC curve is still a highly valuable tool for optimizing the prediction of known and pre-classified events, it is argued here that the STONE curve is better for assessing model prediction of a continuous-valued data set. and Data and code were created using IDL, but can also be accessed with the open-source Gnu Data Language (GDL; see https://github.com/gnudatalanguage/gdl)
- Keyword:
- ROC curve, STONE curve, data-model comparison, model validation, forecasting, and statistical methods
- Citation to related publication:
- Liemohn, M. W., Azari, A. R., Ganushkina, N. Yu., & Rastätter, L. (2020). The STONE curve: A ROC-derived model performance assessment tool. Earth and Space Science, 7, e2020EA001106. https://doi.org/10.2019/2020EA001106
- Discipline:
- Science
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- Creator:
- Bougher, S. W. (CLaSP Department, U. of Michigan), Roeten, K. J. (CLaSP Department, U. of Michigan), and Sharrar, R. (Astronomy Department, U. of Michigan)
- Description:
- The NASA MAVEN (Mars Atmosphere and Volatile Evolution) spacecraft, which is currently in orbit around Mars, has been taking daily (systematic) measurements of the densities and temperatures in the upper atmosphere of Mars between about 140 to 240 km above the surface. Wind measurement campaigns are also conducted once per month for 5-10 orbits. These densities, temperatures and winds change with time (e.g. season, local time) and location, and sometimes fluctuate quickly. Global dust storm events are also known to significantly impact these density, temperature and wind fields in the Mars thermosphere. Such global dust storm period measurements can be compared to simulations from a computer model of the Mars atmosphere called M-GITM (Mars Global Ionosphere-Thermosphere Model), developed at U. of Michigan. This is the first detailed comparison between direct global dust storm period measurements in the upper atmosphere of Mars and simulated MGITM fields and is important because it can help to inform us what physical processes are acting on the upper atmosphere during such large dust events. Since the global circulation plays a role in the structure, variability, and evolution of the atmosphere, understanding the processes that drive the winds in the upper atmosphere of Mars also provides key context for understanding how the atmosphere behaves as a whole system. A basic version of the M-GITM code can be found on Github as follows: https:/github.com/dpawlows/MGITM and About 4 months of Neutral Gas and Ion Mass Spectrometer (NGIMS) measurements of densities and winds have been made by the MAVEN team during the summer of 2018 (Elrod et al., 2019). Nine reference measurement intervals during this global dust storm (1-June through 30-August 2018) are selected for detailed study (Elrod et al. 2019). The Mars conditions for these nine intervals have been used to launch corresponding M-GITM code simulations, yielding 3-D neutral density, temperature and wind fields for comparison to these NGIMS measurements. The M-GITM datacubes used to extract the density, temperature and neutral winds, along the trajectory of each orbit path between 140 and 240 km, are provided in this Deep Blue Data archive. README files are provided for each datacube, detailing the contents of each file. A general README file is also provided that summarizes the inputs and outputs of the M-GITM code simulations for this study.
- Keyword:
- Mars, MAVEN Spacecraft, Mars Thermosphere, and Mars Global Dust Storm of 2018
- Citation to related publication:
- Elrod, M. K., S. W. Bougher, K. Roeten, R. Sharrar, J. Murphy, Structural and Compositional Changes in the Upper Atmosphere related to the PEDE-2018 Dust Event on Mars as Observed by MAVEN NGIMS, Geophys. Res. Lett., (2019). doi: 10.1029/2019GL084378. and Jain, S. K., Bougher, S. W., Deighan, J., Schneider, N. M., Gonzalez‐Galindo, F., Stewart, A. I. F., et al. ( 2020). Martian thermospheric warming associated with the Planet Encircling Dust Event of 2018. Geophysical Research Letters, 47, e2019GL085302. https://doi.org/10.1029/2019GL085302
- Discipline:
- Engineering and Science
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- Creator:
- Tye, Alexander R, Niemi, Nathan A, Safarov, Rafig T, Kadirov, Fakhraddin A, Babayev, Gulam R
- Description:
- The dataset contains U-Pb radiometric ages of zircon grains from sedimentary rocks and modern river sands from the Caucasus region of western Asia. The data were collected as part of a research project investigating the effects of continental collision in the Caucasus region on regional erosion and sedimentary systems. The data are presented using the standard quantities reported for zircon U-Pb age analyses at the University of Arizona Laserchron Center.
- Keyword:
- detrital zircon, provenance, and U-Pb
- Citation to related publication:
- Tye, A. R., Niemi, N. A., Safarov, R. T., Kadirov, F. A., & Babayev, G. R. (2021). Sedimentary response to a collision orogeny recorded in detrital zircon provenance of Greater Caucasus foreland basin sediments. Basin Research, 33(2), 933–967. https://doi.org/10.1111/bre.12499
- Discipline:
- Science
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- Creator:
- University of Michigan Museum of Paleontology and CTEES
- Description:
- 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.
- Keyword:
- Paleontology, Fossil, CT, Primates, Notharctidae, UMMP, University of Michigan Museum of Paleontology, Eocene, and CTEES
- Discipline:
- Science
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- Creator:
- Auteri, Giorgia G., Knowles, L. Lacey, Marchán-Rivadeneira, Raquel M., and Olson, Deanna H.
- Description:
- This data was collected as part of a study to study population dynamics of coastal giant salamanders in Oregon. The study uses genetics to answer questions related to conservation concerns including population connectivity, sensitivity to habitat disturbances (such as logging and fires), and genetic diversity of populations.
- Keyword:
- salamander, Dicamptodon tenebrosus, conservation genetics, microsatellite , landscape genetics, population connectivity, and bottleneck
- Citation to related publication:
- Auteri, Giorgia G., M. Raquel Marchán-Rivadeneira, Deanna H. Olson, L. Lacey Knowles. Connectivity in coastal giant salamanders (Dicamptodon tenebrosus) shows no association with land-use, fire frequency, or river drainage but does not offset negative consequences of locally unstable population sizes. PLoS ONE. In review.
- Discipline:
- Science
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- Creator:
- Kort, Eric A. and Smith, Mackenzie L.
- Description:
- Data collected by Mooney aircraft over Houston and Denver in Summer 2020. Flights typically were designed to measure within the boundary layer in a raster pattern perpendicular to wind direction, thus sampling the urban plume repeatedly. Vertical profiles are conducted on each flight to capture the vertical structure and mixing depths of the atmosphere. The data file contains all merged flight data from each flight day.
- Keyword:
- Atmospheric Chemistry, Air Quality, Houston, Denver, and Covid-19
- Discipline:
- Science
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- Creator:
- Hegedus, Alexander M
- Description:
- This is the README for the LunarSynchrotronArray package, maintained by Dr. Alex Hegedus alexhege@umich.edu This code repository corresponds to the Hegedus et al. 2020 (accepted) Radio Science paper, "Measuring the Earth's Synchrotron Emission from Radiation Belts with a Lunar Near Side Radio Array". The arxiv link for the paper is https://arxiv.org/abs/1912.04482. The DOI link is https://doi.org/10.1029/2019RS006891 , The Earth's Ionosphere is home to a large population of energetic electrons that live in the balance of many factors including input from the Solar wind, and the influence of the Earth's magnetic field. These energetic electrons emit radio waves as they traverse Earth's magnetosphere, leading to short‐lived, strong radio emissions from local regions, as well as persistent weaker emissions that act as a global signature of the population breakdown of all the energetic electrons. Characterizing this weaker emission (Synchrotron Emission) would lead to a greater understanding of the energetic electron populations on a day to day level. A radio array on the near side of the Moon would always be facing the Earth, and would well suited for measuring its low frequency radio emissions. In this work we simulate such a radio array on the lunar near side, to image this weaker synchrotron emission. The specific geometry and location of the test array were made using the most recent lunar maps made by the Lunar Reconnaissance Orbiter. This array would give us unprecedented day to day knowledge of the electron environment around our planet, providing reports of Earth's strong and weak radio emissions, giving both local and global information. , This set of codes should guide you through making the figures in the paper, as well as hopefully being accessible enough for changing the code for your own array. I would encourage you to please reach out to collaborate if that is the case! Requirements: , and CASA 4.7.1 (or greater?) built on python 2.7 Example link for Red Hat 7 https://casa.nrao.edu/download/distro/casa/release/el7/casa-release-4.7.1-el7.tar.gz Users may follow this guide to download and install the correct version of CASA for their system https://casa.nrao.edu/casadocs/casa-5.5.0/introduction/obtaining-and-installing CASA executables should be fairly straightforward to extract from the untarred files. gcc 4.8.5 or above (or below?) GCC installation instructions can be found here: https://gcc.gnu.org/install/ SPICE (I use cspice here) https://naif.jpl.nasa.gov/naif/toolkit_C.html As seen in lunar_furnsh.txt which loads the SPICE kernels, you also must download KERNELS_TO_LOAD = ( '/home/alexhege/SPICE/LunarEph/moon_pa_de421_1900-2050.bpc' '/home/alexhege/SPICE/LunarEph/moon_080317.tf' '/home/alexhege/SPICE/LunarEph/moon_assoc_me.tf' '/home/alexhege/SPICE/LunarEph/pck00010.tpc' '/home/alexhege/SPICE/LunarEph/naif0008.tls' '/home/alexhege/SPICE/LunarEph/de430.bsp' ) All of which can be found at https://naif.jpl.nasa.gov/pub/naif/generic_kernels/ SLDEM2015_128_60S_60N_000_360_FLOAT.IMG for the lunar surface data by LRO LOLA found at http://imbrium.mit.edu/DATA/SLDEM2015/GLOBAL/FLOAT_IMG/
- Citation to related publication:
- Hegedus, A., Nenon, Q., Brunet, A., Kasper, J., Sicard, A., Cecconi, B., MacDowall, R., & Baker, D. (2019). Measuring the Earth's Synchrotron Emission from Radiation Belts with a Lunar Near Side Radio Array. https://arxiv.org/abs/1912.04482 and Hegedus, A., Nenon, Q., Brunet, A., Kasper, J., Sicard, A., Cecconi, B., MacDowall, R., & Baker, D. (2020). Radio Science. https://doi.org/10.1029/2019RS006891
- Discipline:
- Engineering and Science
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- Creator:
- Sergio E. Vidal-Luengo and Mark B. Moldwin
- Description:
- Multi-satellite tracking of solar wind dynamic pressure pulse observations through the Earth's magnetosphere enables us to distinguish local changes with propagation signatures.
- Keyword:
- Heliophysics, Magnetosphere, Dynamic pressure pulse, Magnetosphere, THEMIS, MMS, Cluster, SuperMag, and Heliophysics System Observatory
- Citation to related publication:
- Vidal-Luengo, S. E., & Moldwin, M. B. (2021). Global magnetosphere response to solar wind dynamic pressure pulses during northward IMF using the heliophysics system observatory. Journal of Geophysical Research: Space Physics, 126, e2020JA028587. https://doi.org/10.1029/2020JA028587
- Discipline:
- Science
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- Creator:
- Brandt, Daniel, A. and Ridley, Aaron, J.
- Description:
- The research that produced this data focused on conducting a statistical comparison between horizontal winds modeled with GITM and those derived from the accelerometer aboard the GOCE satellite. The winds from GITM and GOCE were compared by constructing their respective probability densities under different levels of geomagnetic activity, and by distributing them as a function of geomagnetic activity, magnetic latitude, magnetic local time, day-of-the-year, and solar radio flux.
- Keyword:
- Thermosphere, GITM, GOCE, Neutral winds, and Thermospheric modeling
- Discipline:
- Science and Engineering
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- Creator:
- Smith, Joeseph P., Gronewold, Andrew D., Read, Laura, Crooks, James L., School for Environment and Sustainability, University of Michigan, Department of Civil and Environmental Engineering, University of Michigan, and Cooperative Institute for Great Lakes Research, University of Michigan
- Description:
- Using the statistical programming package R ( https://cran.r-project.org/), and JAGS (Just Another Gibbs Sampler, http://mcmc-jags.sourceforge.net/), we processed multiple estimates of the Laurentian Great Lakes water balance components -- over-lake precipitation, evaporation, lateral tributary runoff, connecting channel flows, and diversions -- feeding them into prior distributions (using data from 1950 through 1979), and likelihood functions. The Bayesian Network is coded in the BUGS language. Water balance computations assume that monthly change in storage for a given lake is the difference between beginning of month water levels surrounding each month. For example, the change in storage for June 2015 is the difference between the beginning of month water level for July 2015 and that for June 2015., More details on the model can be found in the following summary report for the International Watersheds Initiative of the International Joint Commission, where the model was used to generate a new water balance historical record from 1950 through 2015: https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf. Large Lake Statistical Water Balance Model (L2SWBM): https://www.glerl.noaa.gov/data/WaterBalanceModel/, and This data set has a shorter timespan to accommodate a prior which uses data not used in the likelihood functions.
- Keyword:
- Water, Balance, Great Lakes, Laurentian, Machine Learning, Machine, Learning, Lakes, Bayesian, and Network
- Citation to related publication:
- Smith, J., Gronewald, A. et al. Summary Report: Development of the Large Lake Statistical Water Balance Model for Constructing a New Historical Record of the Great Lakes Water Balance. Submitted to: The International Watersheds Initiative of the International Joint Commission. Accessible at https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf, Large Lake Statistical Water Balance Model (L2SWBM). https://www.glerl.noaa.gov/data/WaterBalanceModel/, and Gronewold, A.D., Smith, J.P., Read, L. and Crooks, J.L., 2020. Reconciling the water balance of large lake systems. Advances in Water Resources, p.103505.
- Discipline:
- Science and Engineering