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Potential Vorticity Diagnosis of Hurricane Track Forecasts in IFS, GFS, and GFDL SHiELD models
  • Tyler Barbero,
  • Jan-Huey Chen,
  • Michael Bell
Tyler Barbero
Colorado State University

Corresponding Author:[email protected]

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Jan-Huey Chen
Geophysical Fluid Dynamics Laboratory,UCAR Community Programs
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Michael Bell
Colorado State University
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In this study, we used the potential vorticity (PV) diagnosis technique to investigate the key factors that affect the track forecasts of Hurricane Maria (2017) in the NCEP GFS v14, ECMWF IFS and GFDL SHiELD models. In Chen et al. (2019), it showed that a slow bias of Maria’s translation speed in the IFS 5-day forecasts was significantly improved by GFDL SHiELD with IFS initial conditions (SHiELD_IFS). Our results found that the slow moving bias in the IFS is mainly due to a strong, westerly steering flow contribution from a cutoff low from the northeast quadrant and another low system from the southwest quadrant of Maria. On the other hand, the SHiELD_IFS improves on the IFS by better simulating the strength of the Bermuda High, and low systems in the southwest, northwest, and northeast quadrants allowing for better track alignment with observations. We also found that the northward track bias of Maria in the legacy GFS and SHiELD with the GFS initial conditions (SHiELD_GFS) was associated with a weaker Continental High which contributed less northerly steering flow compared to that in the IFS and SHiELD_IFS. Furthermore, the Bermuda High was relatively weak in the SHiELD_GFS, while the two low systems in the northwest and northeast quadrants contributed steering flow opposing Maria’s moving direction, causing a slowdown of translation speed of Maria in the SHiELD_GFS. By performing this piecewise potential vorticity diagnosis on all of the storms in the 2017 North Atlantic Hurricane Season, we could possibly identify the key elements that generate the biases in TC track forecasts in these models.