• deficiencies in the current generation of operational forecast models, which are key tools used by hurricane forecasters;
• a lack of sufficient observations within the hurricane vortex (that is, the inner-core area) or on larger scales over the ocean; and
• the limited ability of operational forecast models to efficiently ingest available observations for initializing the model.
• the development of better numerical models, with improved understanding and parameterization of subgrid scale physical processes;
• the necessity of running the model at cloud-resolving resolutions;
• the need for enhanced surveillance through ground-based and airborne Doppler radars, rawindsondes and dropsondes, and satellite observations;
• the design of better data assimilation techniques to more effectively use observations;
• the need for probabilistic analysis and forecasting given hurricane predictability's inherent limit; and
• the demand for massively parallel advanced computing capabilities.