Use Case

Application of the Weather Research and Forecasting (WRF) Model for Climate-Relevant Simulations on the Cloud

S.C. Pryor (Cornell) with R. Levy (NASA Goddard), F. Yu (SUNY Albany), A. Hodzic (NCAR), P. Crippa (University of Newcastle), H. Matsui (Japan Agency for Marine-Earth Science and Technology), and B. Barker, P. Vaillancourt, B. Wineholt (Cornell CAC)

What?

• Model the variability of wind speeds and atmospheric properties

Why Aristotle?

• Bursting to process new data
• Sharing of a high-value processed dataset of general interest

Accomplishments

• Built a physics-only version of the Weather Research and Forecasting (WRF) model using Docker and compiled it with parallel NetCDF to evaluate cloud-based performance.
• Ran high-resolution simulations to quantify wind climate and analyze the impact of large wind turbine developments on downstream climate (local to mesoscale).
• Evaluated 10-minute wind speeds from simulations relative to in-situ measurements from the National Weather Service Automated Surface Observing System (ASOS) on Jetstream to allow Aristotle to continue to focus on the numerical simulations.
• Analyzed high-resolution numerical simulations of the effects of wind turbines (WT) on regional climate.
• Completed simulations to test the sensitivity of the climate impacts to the precise description of the WT aerodynamics (the extraction of momentum and introduction of turbulence behind the turbine rotor).
• Analyzed long-term simulations with the WRF model to examine inter-annual variability of annual mean wind speeds at/near typical wind turbine hub-heights, and applied the power curve of the most commonly deployed WT to post-process the 10-minute wind speed output into estimated annual energy production (goal is to create a more robust prediction of the value of wind energy projects: analyses rendered possible by mounting a 100TB hard drive on an Aristotle instance).
• Analyzed output from simulation of WRF model at 12Km over eastern N. America for 2001-2016 for the assessment of year-to-year variability in the wind resource (150TB WRF runs).
• Enhanced high-resolution simulations of wind farm wakes from 2 parameterizations to advance methods to optimized wind turbine arrays and maximize system-wide power production. Analysis of output from simulation on Aristotle were conducted on Jetstream.
• Began new simulations for a domain centered over the Southern Great Plains on a single VM; this case would make an exceptional candidate for a trial of simulations across multiple VMs.
• Used machine learning and Red Cloud's large RAM and multi-processors to detect and quantify wind gusts at Newark, Boston, and Chicago O'Hare Airports. Found Artificial Neural Networks exhibit a higher skill than logistic and linear regression models for wind gust occurrence and magnitude.
• Simulated wind farm wakes from the east-coastal offshore lease areas using ultra-high resolutions with WRF on NERSC-Cori. Performed the data analyses on Aristotle's Red Cloud.
• Ran an 11-member ensemble of a derecho that impacted Washington, DC on Aristotle with varying lateral boundary conditions and microphysics schemes to identify the simulation configuration that yielded the greatest fidelity.
• Used machine learning approaches for wind gust detection and quantification.
• Analyzed wind farm wake simulations from the east-coastal offshore lease areas using ultra-high resolutions with WRF. The goal is to define the optimal density of wind farms to optimize system-wide power production and minimize the levelized cost-of-energy.
• Benefitted from Aristotle's large RAM, multi-processors for analysis speed using parallel processors, and large disk volumes that enabled all WRF output to be hosted for analysis.

Plans

• Complete simulations of derechos, fast-moving damaging deeply convective systems, associated with tornadoes, wind gusts, very heavy precipitation and hail.
• Under a new grant, examine the wind resources and optimize wind farm layouts for the offshore wind turbines along the U.S. east coast. This work strongly leverages our previous simulations on Aristotle and will entail additional WRF simulations with wind farm parameterizations enabled. The goal is to define the optimal density of wind farms to optimize system-wide power production and minimize the levelized cost-of-energy.

Products

• Aird, J.A., Barthelmie, R.J., Shepherd, T.J. & Pryor, S. C. (2022). Occurrence of low-level jets over the Eastern U.S. coastal zone at heights relevant to wind energy.. Energies.
• Aird, J.A., Barthelmie, R.J, Shepherd, T.J. & Pryor, S.C. (2021). WRF-simulated low-level jets over Iowa: characterization and sensitivity studies. Wind Energy Science.
• Barthelmie, R.J., Shepherd, T.J. & Pryor, S.C. (2021). Offshore wakes in the U.E. east coast lease areas. Presentation at the Wind Energy Science Conference (WESC), Hanover, Germany.
• Letson, F., Shepherd, T.J., Barthelmie, R.J. & Pryor, S.C. (2020). WRF modeling of deep convection and hail for wind power applications. Journal of Applied Meteorology and Climatology.
• Pryor, S.C., Shepherd, T., Volker, P., Hahmann, A. & Barthelmie, R.J. (2020). Diagnosing systematic differences in predicted wind turbine array-array interactions. Journal of Physics: Conference Series.
• Aird, J., Barthelmie, R.J., Shepherd, T.J. & Pryor, S.C. (2020). WRF-simulated springtime low-level jets over Iowa: Implications for wind energy. Journal of Physics: Conference Series.
• Barthelmie, R.J., Shepherd, T.J. & Pryor, S.C. (2020). Increasing turbine dimensions: Impact on shear and power. Journal of Physics: Conference Series.
• Shepherd, T.J., Barthelmie, R.J. & Pryor, S.C. (2020). Sensitivity of wind turbine array downstream effects to the parameterization used in WRF. Journal of Applied Meteorology and Climatology.
• Letson, F.W., Barthelmie, R.J. & Pryor, S.C. (2020). RADAR-derived precipitation climatology for wind turbine blade leading edge erosion.. Wind Energy Science.
• Pryor, S.C., Barthelmie, R.J. & Shepherd, T.J. (2020). 20% of US electricity from wind will have limited impacts on system efficiency and regional climate. Nature: Scientific Reports.
• Pryor, S.C., Shepherd, T.J., Volker, P., Hahmann, A.N. & Barthelmie, R.J. (2020). 'Wind theft' from onshore wind turbine arrays: Sensitivity to wind farm parameterization and resolution. Journal of Applied Meteorology and Climatology.
• Pryor, S.C., Shepherd, T.J., Bukovsky, M. & Barthelmie, R.J. (2020). Assessing the stability of wind resource and operating conditions. Journal of Physics: Conference Series.
• Letson, F., Shepherd, T.J., Barthelmie, R.J. & Pryor, S.C. (2020). Modelling hail and convective storms with WRF for wind energy applications. Journal of Physics: Conference Series.
• Pryor, S.C., Shepherd, T.J., Bukovsky, M. & Barthelmie, R.J. (2019) Assessing the stability of wind resource and operating conditions. Presentation at the North American Wind Energy Academy WindTech Conference, Amherst, MA.
• Leston, F., Shepherd, T.J., Barthelmie, R.J. & Pryor, S.C. (2019). Modelling hail and convective storms with WRF for wind energy applications. Presentation at the North American Wind Energy Academy WindTech Conference, Amherst, MA.
• Shepherd, T.J., Barthelmie, R.J. & Pryor, S.C. (2019). Assessment of wind turbine impact on future climate in GCM-driven WRF simulations. Presentation at the North American Wind Energy Academy WindTech Conference, Amherst, MA.
• Shepherd, T.J., Barthelmie, R.J. & Pryor, S.C. (2019). Quantifying array-array effects using WRF model simulations: A sensitivity analysis. Presentation at the North American Wind Energy Academy WindTech Conference, Amherst, MA.
• Shepherd, T., Wineholt, B., Barthelmie, R. & Pryor, S.C. (2019). Quantifying weather and climate simulation reproducibility in the cloud. Presentation at 99th American Meteorological Society Annual Meeting: 5th Symposium on High Performance Computing for Weather, Water, and Climate, Phoenix, AZ.
• Pryor, S.C., Shepherd, T.J., Barthelmie, R.J., Hahmann, A.N. & Volker, P. (2019) Wind farm wakes simulated using WRF. Journal of Physics.
• Barthelmie, R., Pryor, S.C. & Shepherd, T. J. (2019). Wakes from wind turbine arrays. Presentation at National Renewable Energy Laboratory, Golden, CO.
• Pryor, S.C., Shepherd, T.J., Bukovsky, M. & Barthelmie, R.J. (2019). Wind energy scenarios for climate change mitigation. Presentation at The Scenarios Forum 2019, Denver, CO.
• Letson, F., Barthelmie, R.J. & Pryor, S.C. (2019). RADAR-derived precipitation climatology for wind turbine blade leading edge erosion. Wind Energy Science.
• Pryor, S.C. & Hahmann, A.N. (2018). Downscaling wind: Oxford Research Encyclopedias: Climate Science. Oxford University Press, Von Storch, H., editor.
• Aristotle WRF Container video (2018) demonstrates the pulling or caching of Docker images to get the WRF app running quickly.
• Pryor, S.C., Shepherd, T.J. & Barthelmie, R.J. (2018). Inter-annual variability of wind climates and wind turbine annual energy production. Wind Energy Science.
• Pryor, S.C., Barthelmie, R.J. & Shepherd, T.J. (2018). The influence of real-world wind turbine deployments on regional climate. Journal of Geophysical Research: Atmospheres 123(11).
• Pryor, S.C., Barthelmie, R.J., Hahmann, A., Shepherd, T. & Volker, P. (2018): Downstream effects from contemporary wind turbine deployments. Journal of Physics: Conf. Series 1037.
• Pryor, S.C., Barthelmie, R.J., Hahmann, A., Shepherd, T. & Volker, P. (2018). Contemporary wind turbine deployments have a minor impact on regional climate. Presentation at the Science of Making Torque from Wind, Milan Italy.
• Shepherd, T.J., Volker, P., Barthelmie, R.J., Hahmann, A. & Pryor, S.C. (2018). Sensitivity of wind turbine array downstream effects to the parameterization used in WRF. Presentation at WRF/MPAS User’s Workshop, Boulder, CO.
• Pryor, S.C., Barthelmie, R.J. & Shepherd, T. (2018). Do current and near-term future wind turbine deployments have a substantial impact on regional climate? Presentation at European Geosciences Union General Assembly 2018, Vienna, Austria.
• Pryor, S.C., Barthelmie, R.J. & Shepherd, T. (2018). Assessing the fidelity of the North American wind climate and impacts of wind farms using high resolution modeling. Presentation (recorded) at the 98th American Meteorological Society Annual Meeting (21st Conference on Planned and Inadvertent Weather Modification), Austin, TX.
• Pryor, S.C., Barthelmie, R.J. & Shepherd, T. (2018). Improved characterization of the magnitude and causes of spatio-temporal variability in wind resources. Presentation at the 98th American Meteorological Society Annual Meeting (31st Conference on Climate Variability and Change), Austin, TX.
• Pryor, S.C. (2017). High resolution WRF simulations for resource assessment and quantification of downstream impacts of high density wind turbine deployments. Presentation at DTU Wind Energy, Roskilde, Denmark.