Showing 21 - 24 results of 24 for search '"Contiguous United States"', query time: 0.04s Refine Results
  1. 21

    Decarbonization and technology cost drivers: considerations for potential future thermoelectric water use in the power sector by Stuart Cohen, Raquel Valdez, Vincent Tidwell, Saroj Khanal, Thushara Gunda

    Published 2025-01-01
    “…Specifically, we evaluate four decarbonization scenarios for the contiguous United States that vary in assumptions concerning demand growth and technology costs, with technology costs driving alternative outcomes that prioritize either technologies that require low amounts of water (such as wind, solar, and battery) or high amounts of water (such as nuclear and carbon capture and storage). …”
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  2. 22

    Machine Learning-Based Prediction of Ecosystem-Scale CO<sub>2</sub> Flux Measurements by Jeffrey Uyekawa, John Leland, Darby Bergl, Yujie Liu, Andrew D. Richardson, Benjamin Lucas

    Published 2025-01-01
    “…AmeriFlux is a network of hundreds of sites across the contiguous United States providing tower-based ecosystem-scale carbon dioxide flux measurements at 30 min temporal resolution. …”
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  3. 23

    Informing grassland ecosystem modeling with in-situ and remote sensing observations by Johny Arteaga, Melannie D Hartman, William J Parton, Maosi Chen, Wei Gao

    Published 2025-01-01
    “…We simulated historical grassland aboveground plant productivity (ANPP) across the midwestern and western contiguous United States using the DayCent-UV ecosystem model. …”
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  4. 24

    LoSI: Large Scale Location Inference Through FM Signal Integration and Estimation by Tathagata Mukherjee, Piyush Kumar, Debdeep Pati, Erik Blasch, Eduardo Pasiliao, Liqin Xu

    Published 2019-12-01
    “…The algorithm uses a large-scale model estimation phase that computes the expected FM spectrum in small rectangular cells (realized using geohashes) across the Contiguous United States (CONUS). We define and use Dominant Channel Descriptor (DCD) features, which can be used for positioning using time varying models. …”
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