Existing hurricane prediction systems fall into two categories: hurricane track and intensity predictions on a weekly timescale; and the prediction of hurricane activity on a seasonal timescale. Substantial progress has been made in improving the predictions on the two distinct timescales in the past decade. However, the prediction of hurricane activity on a subseasonal timescale (from two weeks to two months) has not shown much advancement. Credible subseasonal hurricane predictions can have significant socioeconomic impacts, but are challenging. There is much uncertainty in the sources of predictability; furthermore, the realistic simulation of hurricanes requires high horizontal resolution (at least finer than 10 km), which is expensive when using global prediction systems.
In this study, led by AOS Associate Research Scholar Kun Gao and published in Geophysical Research Letters, the authors investigated the prediction of monthly (30-day) North Atlantic hurricane activity based on the GFDL High-Resolution Atmospheric Model (HiRAM), which is powered by the Finite-Volume Cubed-Sphere (FV3) dynamical core. The two-way nesting capacity in FV3 allows for refining horizontal resolution over a selected region in the global domain. Former AOS Postdocs Jan-Huey Chen (UCAR) and Lucas Harris (GFDL), AOS Alumnus Shian-Jiann Lin (GFDL), and AOS Postdoc Yongqiang Sun are among the study's co-authors.