ERF: Energy Research and Forecasting Model

TL;DR

ERF is a GPU-enabled, open-source regional atmospheric model that integrates fully compressible and anelastic dynamics with moisture and terrain, built on AMReX for performance portability. The authors detail the governing equations, numerical discretization (RK methods with acoustic substepping for compressible and projection-based updates for anelastic), AMR capabilities, and physics options (turbulence closures and Eulerian microphysics), then validate ERF across mesoscale to microscale cases. The results demonstrate good agreement with benchmark data, reveal meaningful CPU-GPU performance gains, and showcase ERF’s flexibility through multi-physics coupling and scalable workflows. Collectively, ERF provides a scalable, modular platform for exploring atmospheric processes relevant to weather prediction and renewable-energy applications, with a clear path toward real-world validation and enhanced coupling to ocean-wave systems and column physics modules.

Abstract

High performance computing (HPC) architectures have undergone rapid development in recent years. As a result, established software suites face an ever increasing challenge to remain performant on and portable across modern systems. Many of the widely adopted atmospheric modeling codes cannot fully (or in some cases, at all) leverage the acceleration provided by General-Purpose Graphics Processing Units (GPGPUs), leaving users of those codes constrained to increasingly limited HPC resources. Energy Research and Forecasting (ERF) is a regional atmospheric modeling code that leverages the latest HPC architectures, whether composed of only Central Processing Units (CPUs) or incorporating GPUs. ERF contains many of the standard discretizations and basic features needed to model general atmospheric dynamics as well as flows relevant to renewable energy. The modular design of ERF provides a flexible platform for exploring different physics parameterizations and numerical strategies. ERF is built on a state-of-the-art, well-supported, software framework (AMReX) that provides a performance portable interface and ensures ERF's long-term sustainability on next generation computing systems. This paper details the numerical methodology of ERF and presents results for a series of verification and validation cases.

Figures (21)

• Figure 1: Density current time histories of minimum potential temperature (a,b), maximum outflow velocity (c,d), and front position (e,f) for compressible ("comp") and anelastic ("anel") simulation modes with coarse, medium, fine, and AMR grid configurations. The right panels show the normalized error of each simulated quantity (with hat symbols) relative to the compressible, fine-grid result in the left panels.
• Figure 2: Density current potential temperature fields after 900 s for the reference compressible (fine grid, $\Delta = \Delta x = \Delta z = 25$ m), anelastic ($\Delta = 25$ m), and AMR simulations in both compressible and anelastic simulation modes. Both AMR examples shown here used one level of factor four refinement that locally increased the mesh resolution from $\Delta = 100 \to 25$ m --- only the finest grid level is shown. Temperature contours are plotted at 1 K intervals.
• Figure 3: Dry bubble rise with (left) $\theta_{d}^{\prime}$ contoured every $0.4$ K and (right) $w$ velocity contoured every $2$ m/s. Black contours are positive and white contours are negative.
• Figure 4: Moist bubble rise with (left) $\theta_{e}^{\prime}$ contoured every $0.4$ K and (right) $w$ velocity contoured every $2$ m/s. Black contours are positive and white contours are negative.
• Figure 5: The squall line simulation at (top) $3000$ s, (middle) $6000$ s, and (bottom) $9000$ s. The orange contour denotes the cloud $q_{c}$=$10^{-5}$ kg/kg and the perturbation potential temperature is given by $\theta_{d}^{\prime}$ = $\theta_{d}(t)-\theta_{d}(0)$ K. Note, the horizontal $x$-direction has been clipped to $\pm 60$ km to highlight the region of interest around the cloud.