Statistical Post-Processing Techniques to Improve Hurricane Forecast Improvement Project (HFIP) Model Guidance (Invited)

The NOAA Hurricane Forecast Improvement Project (HFIP) is a 10 year program with goals to improve tropical cyclone track and intensity forecasts by 50%. Advanced coupled ocean-atmosphere prediction models, data assimilation systems and ensemble techniques are being utilized to accomplish these goals. Experience gained in the first half of HFIP indicates that statistical post-processing techniques are needed to help achieve these goals, especially with regard to intensity and wind structure forecasts. In this paper the progress of HFIP will be briefly summarized and the motivation for the use of statistical post-processing will be provided. The statistical methods being developed range from simple techniques to correct forecast biases due to model initialization limitations to more advanced methods for combining results from multiple forecast models and ensembles to provide optimal predictions of track, intensity and wind structure. Statistical methods are also being developed by HFIP to provide probabilistic forecasts of tropical cyclone formation and wind impacts. These activities will also be described.

Disclaimer: The views, opinions, and findings contained in this paper are those of the authors and should not be construed as an official National Oceanic and Atmospheric Administration (NOAA) or U.S. Government position, policy, or decision.