AbstractThe Prediction of Intensity Model Error (PRIME) forecasting scheme uses various large-scale meteorological parameters as well as proxies for initial condition uncertainty and atmospheric flow stability to provide operational forecasts of tropical cyclone intensity forecast error. PRIME forecasts of bias and absolute error are developed for the Logistic Growth Equation Model (LGEM), Decay Statistical Hurricane Intensity Prediction Scheme (DSHP), Hurricane Weather Research and Forecasting Interpolated Model (HWFI), and Geophysical Fluid Dynamics Laboratory Interpolated Hurricane Model (GHMI). These forecasts are evaluated in the Atlantic and east Pacific basins for the 2011–15 hurricane seasons. PRIME is also trained with retrospective forecasts (R-PRIME) from the 2015 version of each model. PRIME error forecasts are significantly better than forecasts that use error climatology for a majority of forecast hours, which raises the question of whether PRIME could provide more than error guidance. PRIME bias forecasts for each model are used to modify intensity forecasts, and the corrected forecasts are compared with the original intensity forecasts. For almost all basins, forecast intervals, and versions of PRIME, the bias-corrected forecasts achieve significantly lower errors than the original intensity forecasts. PRIME absolute error and bias forecasts are also used to create unique ensembles of the four models. These PRIME-modified ensembles are found to frequently outperform the intensity consensus (ICON), the equally weighted ensemble of DSHP, LGEM, GHMI, and HWFI.
Weather and Forecasting – American Meteorological Society
Published: Aug 2, 2017
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera