BOX 3.4

The NOAA-led Hurricane Forecast Improvement Program (HFIP) is a 10-year program started in 2007 with the overarching goal of improving hurricane forecast skill, with an emphasis on hurricane intensity and structure.a Hurricane forecasts have improved significantly in the last 10 years, but rapid intensification remains a significant challenge.

The specific goals of HFIP are to: 1) improve the accuracy of hurricane intensity and track forecasts and 2) increase the forecast confidence of customers and decision makers, especially those in the emergency management community (NOAA, 2010). To help facilitate these goals, the National Oceanic and Atmospheric Association has partnered with other federal agencies, the National Center for Atmospheric Research, universities, and the Naval Research Laboratory (NRL). These collaborations help address the challenges associated with transitioning new forecasting research and technology into operations. Furthermore, there has been an effort within HFIP to ensure open access to the data involved (NOAA, 2010).

differences among models and, in some cases, the component(s) of the model that is the source of those differences. To date, however, MIP efforts have had limited success in pinpointing the variables or processes that are the root cause of simulation errors, and any conclusions have often not efficiently fed back to the model developers to improve the models.

A new strategy for model intercomparison is needed that will identify specific, key processes of importance to sea ice prediction; incorporate lessons learned from model sensitivity studies; and collaborate closely with model developers to identify approaches to resolve unrealistic model behavior. Regional models and ice-ocean coupled systems will likely be an essential part of the strategy, given the greater control achieved in these approaches by prescribed (e.g., observationally- or reanalysis-derived) lateral and/or surface forcing of the Arctic. Interestingly, one outcome of these studies, along with the identification of factors that influence sea ice prediction skill, may be to realize simplifications that can be applied to coupled models. This result would allow for models with reduced complexity to be used for seasonal-scale sea ice prediction.

At the decadal timescale, where predictions are largely influenced by forcing, model sensitivity studies explore and quantify the impact of a range of parameters or representations of physical processes on predicted model outputs. These experiments show how a particular scenario may be affected by multiple parameters. Performing

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