Plain Language Summary

A tropical cyclone typically moves with the environmental wind, which is generated by several large‐scale pressure systems (e.g., Bermuda High, Continental High) in the atmosphere. Weather models can predict the path of tropical cyclones, but these forecasts have errors. Tropical cyclones often bring devastation along their path, so it is important to mitigate track errors to provide better warnings for impacted communities. Here, we use a diagnostic technique called “piecewise potential vorticity inversion” to understand how the environmental wind causes errors in tropical cyclone tracks. In three different examples of hurricane track forecasts, we show that errors in the predicted track are caused by errors in the environmental wind from specific pressure systems. By considering numerous cases, we can also identify model biases, or errors that are consistent throughout many forecasts. These errors are a result of errors in the models themselves. Overall, our results show that piecewise potential vorticity inversion is a useful diagnostic tool that has the potential to improve track forecasts through the identification of model biases.

Abstract

Tropical cyclone (TC) track forecasting provides essential guidance for coastal communities. However, track forecast errors still occur, highlighting the need for continued research into error sources. Piecewise potential vorticity (PV) inversion is used systematically to quantitatively diagnose errors in track forecasts in four models during the 2017 Atlantic hurricane season. The deep layer mean steering flow (DLMSF) provides a sufficient proxy for hurricane movement, and DLMSF errors are correlated with TC track errors. Analysis of track forecasts for Hurricanes Harvey, Irma, and Maria reveals that their track errors are attributed to steering errors caused by misrepresentations of specific pressure systems. Harvey's westward track error in the GFS resulted from zonal wind errors from the Continental High, while Irma's northward track error in the SHiELD gfsIC resulted from meridional wind errors in the Bermuda High and Continental High. Maria's southward track error in the IFS resulted from meridional wind errors in the Bermuda High and a misrepresentation of Jose to Maria's northwest. The mean absolute error of the DLMSF shows that the Bermuda High contributed the most to steering flow errors in the cases examined. Our results show that piecewise PV inversion can identify the sources of biases in TC track forecasts. The correction of these biases may lead to improved track forecasts. Quantitative diagnostics presented here provide useful information for future model development.