Comparison of 2018–22 Tropical Cyclone Track Forecasts before and after NOAA G-IV Missions

Abstract This study investigates the impact of dropwindsonde data from NOAA Gulfstream IV (G-IV) synoptic surveillance missions on Atlantic basin tropical cyclone (TC) track forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) and the National Centers for Environmental Prediction Global Ensemble Forecast System (GEFS) from 2018 to 2022. Track error and forecast skill are computed for the 69 forecast initializations that assimilated G-IV data and are compared to the 712 forecast initializations from the same period, but without these data, to quantify forecast impacts. Overall, ECMWF EPS and GEFS track forecasts containing G-IV data are up to 24% more skillful than forecasts without the assimilation of G-IV data. Moreover, these results suggest that the greatest positive impact on track forecast skill occurs during the first forecast initialized with G-IV data for each TC. Additionally, two case studies, Hurricanes Marco and Zeta (2020), which are characterized by notable track forecast improvements relative to the forecast prior to assimilating G-IV data, are analyzed to quantify how the G-IV data may have altered the steering flow and hence the track forecast. The track error reduction for Hurricane Marco appears to be the result of a change in the 0-h position that placed the storm in a more westerly steering flow, resulting in a more eastern track. Meanwhile, Hurricane Zeta had a 5-kt (1 kt ≈ 0.51 m s −1 ) reduction in the along-track steering motion ahead of the storm following assimilation of the G-IV data, yielding a forecast position closer to the best track. Significance Statement When a tropical cyclone in the Atlantic Ocean is forecast to make landfall in the United States and its territories, the Gulfstream IV (G-IV) aircraft is used to collect data from the environment around the hurricane with the goal of improving the hurricane track forecast. Our study aims to evaluate whether these data improve the track forecasts of hurricanes from 2018 to 2022 by comparing forecasts with these data to forecasts without these data. Averaged over all forecasts, the track forecasts with the G-IV data were up to 24% better than track forecasts without these data. We also looked closer at two forecasts from Hurricanes Marco and Zeta to understand how the addition of the G-IV data could have resulted in better track forecasts.