![]() ![]() ![]() OFCL=Official NHC forecast GFS=Global Forecast System model HMON=Hurricanes in a Multi-scale Ocean-coupled Non-hydrostatic regional model HWRF=Hurricane Weather Research and Forecasting model Euro=European Center for Medium-range Weather Forecasting (ECMWF) model UKMET=United Kingdom Met Office model COAMPS=COAMPS-TC regional model Navy=Navy Global Environmental Model (NAVGEM). Skill of computer model forecasts of Atlantic named storms in 2019, compared to a “no skill” model called “CLIPER5” that uses just climatology and persistence to make a hurricane track forecast (persistence means a storm will tend to keep going in the direction it is currently going). ![]() Improved consensus modeling techniques are one major reason NHC track forecasts have seen such a large improvement in the past 20 years. These seven models are used as input to various “consensus” models, such as “TVCN”, often referenced in NHC discussions for a storm. ![]() If one averages together the track forecasts from three or more of these models, the NHC official forecast will rarely depart much from it. Navy: Navy Global Environmental Model (NAVGEM). HWRF: Hurricane Weather and Research Forecasting regional model, initialized using GFS dataĬOAMPS: COAMPS-TC regional model, initialized using GFS data HMON: Hurricanes in a Multi-scale Ocean-coupled Non-hydrostatic regional model, initialized using GFS data UKMET: The United Kingdom Met Office’s global forecast model GFS: The National Oceanic and Atmospheric Administration (NOAA) Global Forecast System model Here is a list of some of the main hurricane forecast models used by NHC:Įuro: The European Center for Medium-range Weather Forecasting (ECMWF) global forecast model The performance of the Navy model (NAVGEM) and NOAA’s GFS, HWRF, and HMON models lagged behind. The UKMET model was the second-best model in 2019, closely followed by the COAMPS-TC model. Best track model in 2019: the EuropeanĪs usual, in 2019 the official NHC track forecasts for Atlantic storms were tough to beat, though the European Center (ECMWF) model did outperform the official NHC forecast for one-day and two-day forecasts. The average NHC three-day track forecast error for Sebastien was 390 miles – much worse than the typical NHC three-day track error of 118 miles used in the cone of uncertainty. The NHC track forecasts for Humberto and Lorenzo were particularly excellent, but the overall track forecast errors would have been much better had it not been for one oddball storm that caused major frustrations for forecasters – Tropical Storm Sebastien. The 2019 NHC Atlantic track forecasts tended to have a south to southwest bias of 31 – 95 miles for three- to five-day forecasts (i.e., the official forecast tended to fall to the south or southwest of the verifying position). That amounts to an extraordinary accomplishment, one undoubtedly leading to huge savings in lives, damage, and emotional angst. Over the past 30 years, one- to three-day track forecast errors have decreased by 70 – 75% over the past 15 years, four-day and five-day track forecast errors have decreased by 60%. (Image credit: 2019 National Hurricane Center Forecast Verification Report)ĭuring the 2019 Atlantic hurricane season, NHC track forecasts had accuracies near the five-year average, with five-day track forecasts setting a new record for accuracy. Verification of official NHC hurricane track forecasts for the Atlantic, 1990 – 2019. While an individual model may outperform the official NHC forecast in some situations – for example, the European model’s track forecasts for Hurricane Isaias did better than the NHC’s forecast at all forecast time periods – the 2019 National Hurricane Center Forecast Verification Report documented that overall, it is very difficult for any one model to consistently beat the NHC forecasts for track and for intensity. Put your trust in the National Hurricane Center, or NHC, forecast. For those puzzling over the various hurricane computer forecast models to figure out which one to believe, the best answer is: Don’t believe any of them. ![]()
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