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Kinetic energy spectra derived from observations in the free atmosphere possess a wavenumber dependence of k Ϫ3 for large scales, characteristic of 2D turbulence, and transition to a k Ϫ5/3 dependence in the mesoscale. Kinetic energy spectra computed using mesoscale and experimental near-cloud-scale NWP forecasts from the Weather Research and Forecast (WRF) model are examined, and it is found that the model-derived spectra match the observational spectra well, including the transition. The model spectra decay at the highest resolved wavenumbers compared with observations, indicating energy removal by the model's dissipation mechanisms. This departure from the observed spectra is used to define the model's effective resolution. Various dissipation mechanisms used in NWP models are tested in WRF model simulations to examine the mechanisms' impact on a model's effective resolution. The spinup of the spectra in forecasts is also explored, along with spectra variability in the free atmosphere and in forecasts under different synoptic regimes.

Two time-splitting methods for integrating the elastic equations are presented. The methods are based on a third-order Runge-Kutta time scheme and the Crowley advection schemes. The schemes are combined with a forward-backward scheme for integrating high-frequency acoustic and gravity modes to create stable splitexplicit schemes for integrating the compressible Navier-Stokes equations. The time-split methods facilitate the use of both centered and upwind-biased discretizations for the advection terms, allow for larger time steps, and produce more accurate solutions than existing approaches. The time-split Crowley scheme illustrates a methodology for combining any pure forward-in-time advection schemes with an explicit time-splitting method. Based on both linear and nonlinear tests, the third-order Runge-Kutta-based time-splitting scheme appears to offer the best combination of efficiency and simplicity for integrating compressible nonhydrostatic atmospheric models.

Since its initial release in 2000, the Weather Research and Forecasting (WRF) Model has become one of the world’s most widely used numerical weather prediction models. Designed to serve both research and operational needs, it has grown to offer a spectrum of options and capabilities for a wide range of applications. In addition, it underlies a number of tailored systems that address Earth system modeling beyond weather. While the WRF Model has a centralized support effort, it has become a truly community model, driven by the developments and contributions of an active worldwide user base. The WRF Model sees significant use for operational forecasting, and its research implementations are pushing the boundaries of finescale atmospheric simulation. Future model directions include developments in physics, exploiting emerging compute technologies, and ever-innovative applications. From its contributions to research, forecasting, educational, and commercial efforts worldwide, the WRF Model has made a significant mark on numerical weather prediction and atmospheric science.