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Assessments of Surface Winds and Waves from the NCEP Ensemble Forecast System

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The error characteristics of surface waves and winds produced by ensemble forecasts issued by the National Centers for Environmental Prediction are analyzed as a function of forecast range and severity.… Click to show full abstract

The error characteristics of surface waves and winds produced by ensemble forecasts issued by the National Centers for Environmental Prediction are analyzed as a function of forecast range and severity. Eight error metrics are compared, separating the scatter component of the error from the systematic bias. Ensemble forecasts of extreme winds and extreme waves are compared to deterministic forecasts for long lead times, up to 10 days. A total of 29 metocean buoys is used to assess 1 year of forecasts (2016). The Global Wave Ensemble Forecast System (GWES) performs 10-day forecasts four times per day, with a spatial resolution of 0.5° and a temporal resolution of 3 h, using a 20-member ensemble plus a control member (deterministic) forecast. The largest errors in GWES, beyond forecast day 3, are found to be associated with winds above 14 m s−1 and waves above 5 m. Extreme percentiles after the day-8 forecast reach 30% of underestimation for both 10-m-height wind (U10) and significant wave height (Hs). The comparison of probabilistic wave forecasts with deterministic runs shows an impressive improvement of predictability on the scatter component of the errors. The error for surface winds drops from 5 m s−1 in the deterministic runs, associated with extreme events at longer forecast ranges, to values around 3 m s−1 using the ensemble approach. As a result, GWES waves are better predicted, with a reduction in error from 2 m to less than 1.5 m for Hs. Nevertheless, under extreme conditions, critical systematic and scatter errors are identified beyond the day-6 and day-3 forecasts, respectively.

Keywords: forecast system; winds waves; surface winds; forecast; ensemble forecast; day

Journal Title: Weather and Forecasting
Year Published: 2018

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