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Microscale turbulence in the atmospheric boundary layer (ABL) is characterized by significant spatiotemporal variability. Consequently, a change in the turbulence forecasting paradigm needs to occur, moving beyond average turbulence estimates at mesoscale grid resolutions (several kilometers) to eddy-resolving forecasts. To that end, the viability of dynamic downscaling to large-eddy simulation scales is evaluated. We present for the first time, multiday dynamic downscaling from currently available numerical weather prediction forecasts to a high-resolution grid spacing of 25 m. It is found that these eddy-resolving forecasts can realistically reproduce turbulence levels and peak events in the bulk of the daytime ABL, adequately capturing turbulence variability at subminute intervals. Moreover, probability distributions of turbulence quantities are in very good agreement when compared to in situ sonic-anemometer observations. These results demonstrate the feasibility of eddy-resolving forecasts to derive accurate probabilistic estimates of turbulence in the ABL and provide a path toward real-time large-eddy simulation scale prediction.

Plain Language Summary Forecasting of turbulence for near-surface applications including wind energy and wind power production, atmospheric transport and dispersion, and wildland fire requires high-fidelity spatiotemporal information. State-of-the-art turbulence forecasting algorithms rely on numerical weather prediction models, which have spatial resolutions of several kilometers. These operational numerical weather prediction models are therefore not able to capture the relevant variability of turbulence in the atmospheric boundary layer. Herein, we demonstrate the feasibility of dynamic downscaling from operational weather forecasts to large-eddy simulation scales (25 m), with which the most relevant turbulent structures are explicitly resolved. Comparison to sonic-anemometer observations at the Boulder Atmospheric Observatory tower reveals that these eddy-resolving forecasts can be used to derive accurate probabilistic predictions of turbulence in the atmospheric boundary layer and provides a path toward real-time large-eddy simulation scale prediction in the near future.