Abstract
The ability to provide accurate forecasts and improve situational awareness of atmospheric rivers (ARs) is key to impact-based decision support services and applications such as forecast-informed reservoir operations. The purpose of this study is to quantify the cool-season water year skill for 2017–20 of the NCEP Global Ensemble Forecast System forecasts of integrated water vapor transport along the U.S. West Coast commonly observed during landfalling ARs. This skill is summarized for ensemble probability-over-threshold forecasts of integrated water vapor transport magnitudes ≥ 250 kg m−1 s−1 (referred to as P250). The P250 forecasts near North-Coastal California at 38°N, 123°W were reliable and successful at lead times of ~8–9 days with an average success ratio > 0.5 for P250 forecasts ≥ 50% at lead times of 8 days and Brier skill scores > 0.1 at a lead time of 8–9 days. Skill and accuracy also varied as a function of latitude and event characteristics. The highest (lowest) success ratios and probability of detection values for P250 forecasts ≥ 50% occurred on average across Northern California and Oregon (Southern California), whereas the average probability of detection of more intense and longer duration landfalling ARs was 0.1–0.2 higher than weaker and shorter duration events at lead times of 3–9 days. The potential for these forecasts to enhance situational awareness may also be improved, depending on individual applications, by allowing for flexibility in the location and time of verification; the success ratios increased 10%–30% at lead times of 5–10 days allowing for flexibility of ±1.0° latitude and ±6 h in verification.
SIGNIFICANCE STATEMENT
The purpose of this study is to evaluate the efficacy of the Global Ensemble Forecast System model in forecasting enhanced water vapor transport along the U.S. West Coast commonly associated with landfalling atmospheric rivers and heavy precipitation. The model allows us to calculate the probability that enhanced water vapor transport will occur, thereby providing situational awareness for decision-making, such as in water resource management. The results of this study indicate that the model provides useful forecast information relative to climatology at lead times of 8–9 days with noticeable variability north and south along the coast and from one year to the next. Future work is aimed at evaluating the skill of additional ensemble model systems.
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