The success of long-range forecasts for Hurricane Harvey
(Reno, NV) The 12 year drought of major hurricane landfalls in the U.S. is over, with catastrophic impacts in Texas. Predictions of Hurricane Harvey illustrate the realization of extended- and long-range hurricane forecasts.
Harvey was a very predictable storm, with genesis predicted 12 days in advance of landfall. Hints of Harvey’s formation had signals back to 7/24.
On 8/22, one day before Harvey re-formed as a tropical storm, both NOAA and ECMWF were consistently predicting a landfall in south Texas. CFAN’s calibrated GEFS tracks show high probability for the track that matched closely Harvey’s eventual track.
After re-genesis on 8/23, attention turned to the intensity forecasts. On 8/24, Harvey began to rapidly intensify. The NHC HWRF model was the first model to pick up hints of rapid intensification, on 8/24 00Z. The first model to predict Cat 3 was GFS at 8/24 12Z. The NHC HMON was the first model to approach a Cat 4 forecast 8/25 00Z.
Closer to landfall, ECMWF DET (HRES) predicted the highest intensity (approaching Cat 4). Note, after landfall, the NOAA/NHC models predict a rapid fall off in intensity, whereas ECMWF predicts reintensification about 4 days after initial landfall. For landfall intensity, GFS, HWRF, HMON, ECMWF DET performed best at different lead times. The most surprisingly good intensity forecasts were the high-resolution global model forecasts GFS and ECMWF DET.
Since Hurricane Sandy in 2012 and the exceptionally accurate forecast of ECMWF as much as 6 days in advance, there has been a battle of the hurricane forecast models: NOAA/NHC versus ECMWF. For Harvey, all of the forecast models both performed very well, albeit with some differences. The real battle of the hurricane forecast models is shifting to the global ensemble forecast models (e.g. NOAA GEFS and ECMWF) versus the mesoscale models (e.g. HWRF, HMON).
An extensive summary of the forecasts for Hurricane Harvey are provided in a blog post [here]
Bottom line: no one should have been surprised by Harvey, which had exceptionally accurate forecasts as far back as 8/23.
Climate Forecast Applications Network (CFAN) develops innovative forecast tools that give longer and more accurate warnings of extreme weather events, so clients can better prepare and recover. CFAN’s staff applies the latest research to a wide range of customer challenges, helping businesses and government around the world. Our advanced prediction tools provide clients with the confidence to make complex and difficult decisions about weather risks.
CFAN was founded in 2006 by Judith Curry and Peter Webster and launched under the Enterprise Innovation Institute’s VentureLab program at Georgia Tech. Its research has been assisted by grants from NOAA, NASA, and the Department of Energy.
Dr. Judith Curry, President firstname.lastname@example.org (404) 803-2012