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Resources for Training Machine Learning Algorithms Using CAM6 Simple Physics Packages
User Collection
- Creator:
- Limon, Garrett
- Description:
- The collection contains the code and the data used to train machine learning algorithms to emulate simplified physical parameterizations within the Community Atmosphere Model (CAM6). CAM6 is the atmospheric general circulation model (GCM) within the Community Earth System Model (CESM) framework, developed by the National Center for Atmospheric Research (NCAR). GCMs are made up of a dynamical core, responsible for the geophysical fluid flow calculations, and physical parameterization schemes, which estimate various unresolved processes. Simple physics schemes were used to train both random forests and neural networks in the interest of exploring the feasibility of machine learning techniques being used in conjunction with the dynamical core for improved efficiency of future climate and weather models. The results of the research show that various physical forcing tendencies and precipitation rates can be effectively emulated by the machine learning models.
- Keyword:
- Machine Learning, Climate Modeling, and Physics Emulators
- Discipline:
- Science and Engineering
2Works -
F3UEL: Flaring & Fossil Fuels: Uncovering Emissions & Losses
User Collection
- Creator:
- Kort, Eric and Plant, Genevieve
- Description:
- Fossil energy production, processing, flaring, and transmission all can harm climate and air quality by emitting greenhouse gases and air pollutants. Studies now show that onshore oil and gas production emit much more methane than what is inventoried, and that local air quality impacts can be significant, however, natural gas flaring and offshore systems have been largely overlooked. The F3UEL (Flaring & Fossil Fuels: Uncovering Emissions & Losses) project aims to address these gaps by improving our understanding of offshore emissions, characterizing how flares behave in the real world, identifying what portion of the offshore system is responsible for emissions, and determining how such systems can be monitored. Spanning three years (2020-2022), the project employed an aircraft platform to measure including both greenhouse gas and air quality measurements. To sample the largest regions of current and potential future offshore production and flaring, airborne measurements targeted the Gulf of Mexico, offshore California and Alaska, the Bakken Formation (North Dakota) and the Permian and Eagle Ford Basins (Texas). Data provided here includes the airborne measurements collected using Scientific Aviation’s Mooney aircraft platform, equipped with spectroscopic instrumentation to measure methane, carbon dioxide, water vapor, nitrous oxide, and nitrogen oxide, in addition to meteorological variables such as wind speed and direction. Data products from our analysis of these airborne measurements are also provided, including estimated flare destruction removal efficiency for the Bakken, Eagle Ford, and Permian basins. Each data file is in .csv format and is accompanied by a readme file with further information and descriptors of the variables included. All users should cite the papers and datasets provided in the readme files for each individual dataset. Website: https://graham.umich.edu/f3uel This project is funded by the Alfred P. Sloan Foundation with additional support from the Environmental Defense Fund, Scientific Aviation, and University of Michigan (College of Engineering, Climate and Space Sciences and Engineering; Graham Sustainability Institute).
- Keyword:
- offshore oil & gas, flaring, methane, Nitrogen oxides, natural gas flaring, and oil & gas
- Discipline:
- Science
4Works -
Survey Data
User Collection
- Creator:
- Galaty, Michael
- Description:
- All databases, field notebooks, paper maps, GIS files, photographs, and photo descriptions related to the intensive survey, of tracts and tumuli, and the collection of sites have been made available in PASH Deep Blue Data Realm 1. The data are broadly organized by team (A-K). The surveyed land was divided up into “tracts”. Tracts are labeled with team letter and a consecutive number: e.g., A-001, A-002, B-003, C-122, D-035.
- Keyword:
- Archaeology
- Discipline:
- Science
6Works -
- Creator:
- Towne, Aaron
- Description:
- This database contains six datasets intended to aid in the conception, training, demonstration, evaluation, and comparison of reduced-complexity models for fluid mechanics. The six datasets are: large-eddy-simulation data for a turbulent jet, direct-numerical-simulation data for a zero-pressure-gradient turbulent boundary layer, particle-image-velocimetry data for the same boundary layer, direct-numerical-simulation data for laminar stationary and pitching flat-plate airfoils, particle-image-velocimetry and force data for an airfoil encountering a gust, and large-eddy-simulation data for the separated, turbulent flow over an airfoil. All data are stored within hdf5 files, and each dataset additionally contains a README file and a Matlab script showing how the data can be read and manipulated. Since all datafiles use the hdf5 format, they can alternatively be read within virtually any other programing environment. An example.zip file included for each dataset provides an entry point for users. The database is an initiative of the AIAA Discussion Group on Reduced-Complexity Modeling and is detailed in the paper listed below. For each dataset, the paper introduces the flow setup and computational or experimental methods, describes the available data, and provide an example of how these data can be used for reduced-complexity modeling. All users should cite this paper as well as appropriate primary sources contained therein. Towne, A., Dawson, S., Brès, G. A., Lozano-Durán, A., Saxton-Fox, T., Parthasarthy, A., Biler, H., Jones, A. R., Yeh, C.-A., Patel, H., Taira, K. (2022). A database for reduced-complexity modeling of fluid flows. AIAA Journal 61(7): 2867-2892.
- Keyword:
- fluid dynamics, reduced-complexity models, and data-driven models
- Discipline:
- Engineering and Science
6Works -
Estimates of the water balance of the Laurentian Great Lakes using the Large Lakes Statistical Water Balance Model (L2SWBM)
User Collection
- Creator:
- Smith, Joeseph P., Fry, Lauren M., Do, Hong X., and Gronewold, Andrew D.
- Description:
- This collection contains estimates of the water balance of the Laurentian Great Lakes that were produced by the Large Lakes Statistical Water Balance Model (L2SWBM). Each data set has a different configuration and was used as the supplementary for a published peer-reviewed article (see "Citations to related material" section in the metadata of individual data sets). The key variables that were estimated by the L2SWBM are (1) over-lake precipitation, (2) over-lake evaporation, (3) lateral runoff, (4) connecting-channel outflows, (5) diversions, and (6) predictive changes in lake storage. and Contact: Andrew Gronewold Office: 4040 Dana Phone: (734) 764-6286 Email: drewgron@umich.edu
- Keyword:
- Great Lakes water levels, statistical inference, water balance, data assimilation, Great Lakes, Laurentian, Machine learning, Bayesian, and Network
- Citation to related publication:
- Smith, J. P., & Gronewold, A. D. (2017). Development and analysis of a Bayesian water balance model for large lake systems. arXiv preprint arXiv:1710.10161., Gronewold, A. D., Smith, J. P., Read, L., & Crooks, J. L. (2020). Reconciling the water balance of large lake systems. Advances in Water Resources, 103505., and Do, H.X., Smith, J., Fry, L.M., and Gronewold, A.D., Seventy-year long record of monthly water balance estimates for Earth’s largest lake system (under revision)
- Discipline:
- Science and Engineering
5Works -
R/V Melville Core Retrieval Campaign (MV0811), November 2008
User Collection
- Creator:
- Wang, Yi and Hendy, Ingrid
- Description:
- This collection represents various raw data and analysis of cores extracted during the November 2008 mission of R/V Melville in the Santa Barbara Basin., The core included is the jumbo piston core MV0811-14JC. Core photos, physical properties and magnetic susceptibility from the multisensor track (MST), and the scanning X-ray fluorescence (XRF) data are included in the collection., and Cruise DOI: 10.7284/903459 The research is funded by NSF OCE-1304327.
- Keyword:
- Santa Barbara Basin, Southern California, core photos, physical properties, scanning XRF, MV0811, and Earth Science
- Discipline:
- Science
3Works -
- Creator:
- Wang, Yi and Hendy, Ingrid
- Description:
- This collection represents various raw data and analysis of cores extracted during the January 2009 mission of the research vessel Sproul in the Santa Barbara Basin., Cores included: box core SPR0901-04BC, box core SPR0901-unnamed, and Kasten core SPR0901-03KC. Core photos, physical properties and magnetic susceptibility from the multisensor track (MST), and the scanning X-ray fluorescence (XRF) data are included in the collection., and Cruise DOI: 10.7284/901089 This research is funded by NSF-OCE 0752093.
- Keyword:
- Santa Barbara Basin, Southern California, core photos, physical properties, scanning XRF, SPR0901, and Earth Science
- Discipline:
- Science
8Works