Abstract

The skill of operational deterministic turbulence forecasts is impacted by the uncertainties in both weather forecasts from the underlying numerical weather prediction (NWP) models and diagnoses of turbulence from the NWP model output. This study compares various probabilistic turbulence forecast... Show moreThe skill of operational deterministic turbulence forecasts is impacted by the uncertainties in both weather forecasts from the underlying numerical weather prediction (NWP) models and diagnoses of turbulence from the NWP model output. This study compares various probabilistic turbulence forecasting approaches to quantify these uncertainties and provides recommendations on the most suitable approach for operational implementation. The 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). Both case studies and statistical analyses are provided. The case studies show that the MDE approach that represents the uncertainty in turbulence diagnostics provides a larger ensemble spread than the TLE and FME approaches that represent the uncertainty in NWP forecasts. The larger spreads of MDE, TMDE, and FMDE allow for higher probabilities of detection for low percentage thresholds at the cost of increased false alarms. The small spreads of TLE and FME result in either hits with higher confidence or missed events, highly dependent on the performance of the underlying NWP model. Statistical evaluations reveal that increasing the number of diagnostics in MDE is a cost-effective and powerful method for describing the uncertainty of turbulence forecasts, considering trade-offs between accuracy and computational cost associated with using NWP ensembles. Combining either time-lagged or forecast-model NWP ensembles with MDE can further improve prediction skill and could be considered if sufficient computational resources are available. Show less