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Andrew T. Hazelton, Lucas Harris, and Shian-Jiann Lin

Abstract

A nested version of the cubed-sphere finite-volume dynamical core (FV3) with GFS physics (fvGFS) is capable of tropical cyclone (TC) prediction across multiple space and time scales, from subseasonal prediction to high-resolution structure and intensity forecasting. Here, a version of fvGFS with 2-km resolution covering most of the North Atlantic is evaluated for its ability to simulate TC track, intensity, and finescale structure. TC structure is evaluated through a comparison of forecasts with three-dimensional Doppler radar from P-3 flights by NOAA’s Hurricane Research Division (HRD), and the structural metrics evaluated include the 2-km radius of maximum wind (RMW), slope of the RMW, depth of the TC vortex, and horizontal vortex decay rate. Seven TCs from the 2010–16 seasons are evaluated, including 10 separate model runs and 38 individual flights. The model had some success in producing rapid intensification (RI) forecasts for Earl, Edouard, and Matthew. The fvGFS model successfully predicts RMWs in the 25–50-km range but tends to have a small bias at very large radii and a large bias at very small radii. The wind peak also tends to be somewhat too sharp, and the vortex depth occasionally has a high bias, especially for storms that are observed to be shallow. Composite radial wind shows that the boundary layer tends to be too deep, although the outflow structure aloft is relatively consistent with observations. These results highlight the utility of the structural evaluation of TC forecasts and also show the promise of fvGFS for forecasting TCs.

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Jeffrey S. Gall, Isaac Ginis, Shian-Jiann Lin, Timothy P. Marchok, and Jan-Huey Chen

Abstract

This paper describes a forecasting configuration of the Geophysical Fluid Dynamics Laboratory (GFDL) High-resolution Atmospheric Model (HiRAM). HiRAM represents an early attempt in unifying, within a global modeling framework, the capabilities of GFDL’s low-resolution climate models for Intergovernmental Panel on Climate Change (IPCC) type climate change assessments and high-resolution limited-area models for hurricane predictions. In this study, the potential of HiRAM as a forecasting tool is investigated by applying the model to the near-term and intraseasonal hindcasting of tropical cyclones (TCs) in the Atlantic basin from 2006 to 2009. Results demonstrate that HiRAM provides skillful near-term forecasts of TC track and intensity relative to their respective benchmarks from t = 48 h through t = 144 h. At the intraseasonal time scale, a simple HiRAM ensemble provides skillful forecasts of 21-day Atlantic basin TC activity at a 2-day lead time. It should be noted that the methodology used to produce these hindcasts is applicable in a real-time forecasting scenario. While the initial experimental results appear promising, the HiRAM forecasting system requires various improvements in order to be useful in an operational setting. These modifications are currently under development and include a data assimilation system for forecast initialization, increased horizontal resolution to better resolve the vortex structure, 3D ocean model coupling, and wave model coupling. An overview of these ongoing developments is provided, and the specifics of each will be described in subsequent papers.

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Andrew T. Hazelton, Morris Bender, Matthew Morin, Lucas Harris, and Shian-Jiann Lin

Abstract

The 2017 Atlantic hurricane season had several high-impact tropical cyclones (TCs), including multiple cases of rapid intensification (RI). A high-resolution nested version of the GFDL finite-volume dynamical core (FV3) with GFS physics (fvGFS) model (HifvGFS) was used to conduct hindcasts of all Atlantic TCs between 7 August and 15 October. HifvGFS showed promising track forecast performance, with similar error patterns and skill compared to the operational GFS and HWRF models. Some of the larger track forecast errors were associated with the erratic tracks of TCs Jose and Lee. A case study of Hurricane Maria found that although the track forecasts were generally skillful, a right-of-track bias was noted in some cases associated with initialization and prediction of ridging north of the storm. The intensity forecasts showed large improvement over the GFS and global fvGFS models but were somewhat less skillful than HWRF. The largest negative intensity forecast errors were associated with the RI of TCs Irma, Lee, and Maria, while the largest positive errors were found with recurving cases that were generally weakening. The structure forecasts were also compared with observations, and HifvGFS was found to generally have wind radii larger than the observations. Detailed examination of the forecasts of Hurricanes Harvey and Maria showed that HifvGFS was able to predict the structural evolution leading to RI in some cases but was not as skillful with other RI cases. One case study of Maria suggested that the inclusion of ocean coupling could significantly reduce the positive bias seen during and after recurvature.

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