Energy Research and Forecasting
ERF is open source and freely available at the ERF GitHub.
At LBNL, I worked as a project scientist to help write the Energy Research and Forecasting (ERF) code for mesoscale weather modeling.
Mesoscale modeling is exactly the calculations you need to do to figure out where along a coastline is the most efficient place to generate power with a wind turbine farm.
To optimize your wind turbine output, you’ll need to calculate the expected wind speeds year round as the wind currents around the Earth shift throughout the year. You’ll also need to account for any nearby mountains and the unique interactions between the ocean and Earth’s atmosphere.
We wrote the ERF code to help researchers answer these exact questions and enable engineers to design efficient wind power generation solutions.
We built ERF on top of the adaptive mesh refinement (AMR) library AMReX, which our CCSE group at LBNL develops.
AMReX allows computational scientists to focus our computing resources on the parts of our simulations that require greatest accuracy.
Our success developing ERF enabled our team to simulate a three-dimensional supercell storm, capturing the storm winds along with the water cycle of rainfall, evaporation, and cloud condensation that keeps the storm active.
We published our ERF results along with a suite of verification and validation tests in Lattanzi et al. (2024).
We also published the ERF code itself in the Journal of Open Source Software (JOSS) in Almgren et al. (2023).