Probabilistic Forecasts of Upper-Level Turbulence at Convection-Permitting Resolutions

The performance of operational deterministic turbulence forecasts is impacted by the uncertainty in the underlying operational numerical weather prediction (NWP) model that provides large-scale weather information and turbulence diagnostics to estimate unresolved small-scale turbulence from resolved large-scale weather. In efforts to represent these uncertainties, different probabilistic forecasting methods have been developed by expressing either the uncertainty in NWP model (Gill and Buchanan 2014; Kim et al. 2015; Storer et al. 2019; Lee et al. 2020) or the uncertainty in turbulence diagnostics (Kim et al. 2018), or both (Storer et al. 2020; Shin et al. 2023).

A study of Shin et al. (2023) compared these various probabilistic forecasting approaches in predicting clear-air turbulence (CAT) and/or mountain-wave turbulence (MWT), using a pseudo-Global Ensemble Forecast System (pseudo-GEFS) at a 13-km grid spacing as the NWP model. It was shown that those approaches that represent the uncertainty in diagnosing turbulence provides a larger ensemble spread of eddy dissipation rate (EDR) than those approaches that represent the uncertainty in NWP forecasts. Such a smaller EDR spread in the methods based on the ensembles of NWP forecasts is at least in part due to the limited spread of the pseudo-GEFS NWP model that is designed for medium-range forecasts.

In this study, we extend the comparative study of probabilistic turbulence forecasts to a convection-permitting model­­ — i.e., the Rapid Refresh Forecast System (RRFS)-Ensemble (RRFS-E) NWP model at a 3-km grid spacing — and discuss differences in probabilistic turbulence forecasts between the RRFS-E and the pseudo GEFS. The probabilistic turbulence forecasting approaches considered are all based on ensembles of NWP forecasts and/or turbulence diagnostics, and include a multi-diagnostic ensemble (MDE), a time-lagged NWP ensemble (TLE), a forecast model NWP ensemble (FME), and combined time-lagged MDE (TMDE) and forecast-model MDE (FMDE).

This research is in response to requirements and funding by the Federal Aviation Administration (FAA). The views expressed are those of the authors and do not necessarily represent the official policy or position of the FAA.

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