Cloud Hosting

Zhou, E. & Mai, T. Electrification Futures Study: Operational Analysis of U.S. Power Systems with Increased Electrification and Demand-Side Flexibility (US National Renewable Energy Laboratory, 2021); https://www.nrel.gov/docs/fy21osti/79094.pdf

Xexakis, G. & Trutnevyte, E. Consensus on future EU electricity supply among citizens of France, Germany, and Poland: implications for modeling. Energy Strategy Rev. 38, 100742 (2021).

Article Google Scholar

Steggals, W., Gross, R. & Heptonstall, P. Winds of change: how high wind penetrations will affect investment incentives in the GB electricity sector. Energy Policy 39, 13891396 (2011).

Article Google Scholar

Brinkman, G. et al. The North American Renewable Integration Study: A U.S. Perspective (US National Renewable Energy Laboratory, 2021); https://www.nrel.gov/docs/fy21osti/79224.pdf

Boie, I., Fernandes, C., Fras, P. & Klobasa, M. Efficient strategies for the integration of renewable energy into future energy infrastructures in European analysis based on transnational modeling and case studies for nine European regions. Energy Policy 67, 170185 (2014).

Article Google Scholar

Sun, X., Zhang, B., Tang, X., McLellan, B. C. & Hk, M. Sustainable energy transitions in China: renewable options and impacts on the electricity system. Energies 9, 980 (2016).

Article Google Scholar

Carvallo, J. et al. A Guide for Improved Resource Adequacy Assessments in Evolving Power Systems: Institutional and Technical Dimensions (Ernest Orlando Lawrence Berkeley National Laboratory, 2023); https://eta-publications.lbl.gov/sites/default/files/ra_project_-_final.pdf

Stenclik, D. et al. Redefining Resource Adequacy for Modern Power Systems (Energy Systems Integration Group, 2021); https://www.esig.energy/wp-content/uploads/2022/12/ESIG-Redefining-Resource-Adequacy-2021-b.pdf

Auffhammer, M., Baylis, P. & Hausman, C. H. Climate change is projected to have severe impacts on the frequency and intensity of peak electricity demand across the United States. Proc. Natl Acad. Sci. USA 114, 18861891 (2017).

Article Google Scholar

Huang, J. & Gurney, K. R. Impact of climate change on U.S. building energy demand: sensitivity to spatiotemporal scales, balance point temperature, and population distribution. Clim. Change 137, 171185 (2016).

Article Google Scholar

Craig, M. T. et al. A review of the potential impacts of climate change on bulk power system planning and operations in the United States. Renew. Sustain. Energy Rev. 98, 255267 (2018).

Article Google Scholar

Bloomfield, H. C., Brayshaw, D. J., Shaffrey, L. C., Coker, P. J. & Thornton, H. E. Quantifying the increasing sensitivity of power systems to climate variability. Environ. Res. Lett. 11, 124025 (2016).

Article Google Scholar

Yalew, S. G. et al. Impacts of climate change on energy systems in global and regional scenarios. Nat. Energy 5, 794802 (2020).

Article Google Scholar

Craig, M. T., Jaramillo, P., Hodge, B.-M., Nijssen, B. & Brancucci, C. Compounding climate change impacts during high stress periods for a high wind and solar power system in Texas. Environ. Res. Lett. 15, 024002 (2020).

Article Google Scholar

Dowling, P. The impact of climate change on the European energy system. Energy Policy 60, 406417 (2013).

Article Google Scholar

Craig, M. T. et al. Overcoming the disconnect between energy system and climate modeling. Joule 6, 14051417 (2022).

Article Google Scholar

Tapiador, F. J., Navarro, A., Moreno, R., Snchez, J. L. & Garca-Ortega, E. Regional climate models: 30 years of dynamical downscaling. Atmos. Res. 235, 104785 (2020).

Article Google Scholar

Pierce, D. W., Cayan, D. R. & Thrasher, B. L. Statistical downscaling using localized constructed analogs (LOCA). J. Hydrometeorol. 15, 25582585 (2014).

Article Google Scholar

Wood, A. W., Leung, L. R., Sridhar, V. & Lettenmaier, D. P. Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim. Change 62, 189216 (2004).

Article Google Scholar

Kaczmarska, J., Isham, V. & Onof, C. Point process models for fine-resolution rainfall. Hydrol. Sci. J. 59, 19721991 (2014).

Article Google Scholar

Vandal, T., Kodra, E. & Ganguly, A. R. Intercomparison of machine learning methods for statistical downscaling: the case of daily and extreme precipitation. Theor. Appl. Climatol. 137, 557570 (2019).

Article Google Scholar

Stengel, K., Glaws, A., Hettinger, D. & King, R. N. Adversarial super-resolution of climatological wind and solar data. Proc. Natl Acad. Sci. USA 117, 1680516815 (2020).

Article Google Scholar

Tran, D. T. et al. GANs enabled super-resolution reconstruction of wind field. J. Phys. Conf. Ser. 1669, 012029 (2020).

Article Google Scholar

Kim, J., Lee, J. K. & Lee, K. M. Deeply-recursive convolutional network for image super-resolution. in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 16371645 (2016).

Hess, P., Drke, M., Petri, S., Strnad, F. M. & Boers, N. Physically constrained generative adversarial networks for improving precipitation fields from Earth system models. Nat. Mach. Intell. https://doi.org/10.1038/s42256-022-00540-1 (2022).

Goodfellow, I. et al. Generative adversarial nets. In Proc. Advances in Neural Information Processing Systems Vol. 27 (eds Ghahramani, Z. et al.) (Curran Associates, Inc., 2014).

Di Luca, A., de Ela, R. & Laprise, R. Potential for small scale added value of RCMs downscaled climate change signal. Clim. Dyn. 40, 601618 (2013).

Article Google Scholar

Flato, G. et al. in IPCC Climate Change 2013: The Physical Science Basis Ch. 9 (eds Stocker, T. F. et al.) (IPCC, Cambridge Univ. Press, 2013).

Yukimoto, S. et al. MRI MRI-ESM2.0 Model Output Prepared for CMIP6 C4MIP esm-ssp585 Version 20191108 (WDC Climate, 2019); https://doi.org/10.22033/ESGF/CMIP6.6811

EC-Earth Consortium (EC-Earth). EC-Earth-Consortium EC-Earth3 Model Output Prepared for CMIP6 CMIP esm-ssp585, Version 20200310 (Earth System Grid Federation, 2019); https://doi.org/10.22033/ESGF/CMIP6.4700

Kao, S.-C. et al. The Third Assessment of the Effects of Climate Change on Federal Hydropower (OSTI, 2022); https://www.osti.gov/biblio/1887712/

Martinez, A. & Iglesias, G. Climate change impacts on wind energy resources in North America based on the CMIP6 projections. Sci. Total Environ. 806, 150580 (2022).

Article Google Scholar

Sengupta, M. et al. The National Solar Radiation Data Base (NSRDB). Renew. Sustain. Energy Rev. 89, 5160 (2018).

Article Google Scholar

Draxl, C., Clifton, A., Hodge, B.-M. & McCaa, J. The Wind Integration National Dataset (WIND) Toolkit. Appl. Energy 151, 355366 (2015).

Article Google Scholar

James, E. P. et al. The High-Resolution Rapid Refresh (HRRR): an hourly updating convection-allowing forecast model. Part II: forecast performance. Weather Forecast. 37, 13971417 (2022).

Article Google Scholar

Jafari, S., Sommer, T., Chokani, N. & Abhari, R. S. Wind resource assessment using a mesoscale model: the effect of horizontal resolution. in Proc. ASME Turbo Expo 2012: Turbine Technical Conference and Exposition (eds Bainier, F. et al.) 987995 (American Society of Mechanical Engineers Digital Collection, 2013).

Perez, R., David, M. & Hoff, T. E. in Foundations and Trends in Renewable Energy (eds Norton, B. et al.) 144 (Now Publishers Inc., 2016).

Kolmogorov, A. N. Dissipation of energy in the locally isotropic turbulence. Proc. Math. Phys. Sci. 434, 1517 (1991).

MathSciNet Google Scholar

Holttinen, H. et al. Design and Operation of Power Systems with Large Amounts of Wind Power: Final Summary Report, IEA WIND Task 25, Phase Four 20152017 (VTT Technical Research Centre of Finland, 2019); https://doi.org/10.32040/2242-122X.2019.T350

Dobos, A. P. PVWatts Version 5 Manual (OSTI, 2014); https://www.osti.gov/biblio/1158421

Gueymard, C. A. REST2: high-performance solar radiation model for cloudless-sky irradiance, illuminance, and photosynthetically active radiationvalidation with a benchmark dataset. Sol. Energy 82, 272285 (2008).

Article Google Scholar

Maxwell, E. L. A Quasi-Physical Model for Converting Hourly Global Horizontal to Direct Normal Insolation (OSTI, 1987); https://www.osti.gov/biblio/5987868

Olea, R. A. in Geostatistics for Engineers and Earth Scientists (ed. Olea, R. A.) 6790 (Springer, 1999).

Stull, R. Wet-bulb temperature from relative humidity and air temperature. J. Appl. Meteorol. Climatol. 50, 22672269 (2011).

Article Google Scholar

Gelaro, R. et al. The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). J. Clim. 30, 54195454 (2017).

Article Google Scholar

Atmospheric Radiation Measurement (ARM). Data Quality Assessment for ARM Radiation Data (QCRADBRS1LONG). 2015-01-01 to 2021-12-31, Southern Great Plains (SGP) Central Facility, Lamont, OK (C1) (eds Shi, Y. & Riihimaki, L.) (ARM Data Center, 1993); https://doi.org/10.5439/1027745

Brinkman, G. et al. The North American Renewable Integration Study (NARIS): A U.S. Perspective (OSTI, 2021); https://www.osti.gov/biblio/1804701

Peacock, J. A. Two-dimensional goodness-of-fit testing in astronomy. Mon. Not. R. Astron. Soc. 202, 615627 (1983).

Article Google Scholar

Novacheck, J. et al. The Evolving Role of Extreme Weather Events in the U.S. Power System with High Levels of Variable Renewable Energy (OSTI, 2021); https://www.osti.gov/biblio/1837959

IPCC Climate Change 2023: Synthesis Report Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Lee, H. & Romero, J.) 184 (IPCC, 2023).

Ralston Fonseca, F. et al. Climate-induced tradeoffs in planning and operating costs of a regional electricity system. Environ. Sci. Technol. 55, 1120411215 (2021).

Article Google Scholar

Avery, C. W. et al. in Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment Vol. II (eds Reidmiller, D. R. et al.) 14131430 (US Global Change Research Program, 2018).

Draxl, C., Hodge, B. M., Clifton, A. & McCaa, J. Overview and Meteorological Validation of the Wind Integration National Dataset Toolkit (OSTI, 2015); https://www.osti.gov/biblio/1214985

Hassanaly, M., Glaws, A., Stengel, K. & King, R. N. Adversarial sampling of unknown and high-dimensional conditional distributions. J. Comput. Phys. 450, 110853 (2022).

Article MathSciNet Google Scholar

Wootten, A., Terando, A., Reich, B. J., Boyles, R. P. & Semazzi, F. Characterizing sources of uncertainty from global climate models and downscaling techniques. J. Appl. Meteorol. Climatol. 56, 32453262 (2017).

Article Google Scholar

Karnauskas, K. B., Lundquist, J. K. & Zhang, L. Southward shift of the global wind energy resource under high carbon dioxide emissions. Nat. Geosci. 11, 3843 (2018).

Article Google Scholar

Cohen, J. et al. Divergent consensuses on Arctic amplification influence on midlatitude severe winter weather. Nat. Clim. Chang. 10, 2029 (2020).

Article Google Scholar

Voigt, A. et al. Clouds, radiation, and atmospheric circulation in the present-day climate and under climate change. WIREs Clim. Change 12, e694 (2021).

Article Google Scholar

Springenberg, J. T., Dosovitskiy, A., Brox, T. & Riedmiller, M. A. Striving for simplicity: the all convolutional net. in CoRR Vol. abs/1412.6806 (2014).

He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. in Proc. IEEE Conference on Computer Vision and Pattern Recognition 770778 (2016).

He, K., Zhang, X., Ren, S. & Sun, J. Identity mappings in deep residual networks. in Proc. Computer VisionECCV 2016 (eds Leibe, B. et al.) 630645 (Springer International Publishing, 2016).

Shi, W. et al. Is the deconvolution layer the same as a convolutional layer? Preprint at arXiv http://arxiv.org/abs/1609.07009 (2016).

Federal Aviation Administration. in Pilots Handbook of Aeronautical Knowledge Ch. 4 (FAA, US Government, 2023).

Ho, C. K., Stephenson, D. B., Collins, M., Ferro, C. A. T. & Brown, S. J. Calibration strategies: a source of additional uncertainty in climate change projections. Bull. Am. Meteorol. Soc. 93, 2126 (2012).

Article Google Scholar

Read the original post:
High-resolution meteorology with climate change impacts from global climate model data using generative machine ... - Nature.com