A Systematic Assessment of the Overall Dropsonde Impact during the 2017-2020 Hurricane Seasons using the Basin-Scale HWRF

Abstract This study marks the most comprehensive assessment of the overall impact of dropsondes on tropical cyclone (TC) forecasts to date. We compare two experiments to quantify dropsonde impact: one that assimilated and another that denied dropsonde observations. These experiments used a basin-scale, multi-storm configuration of the Hurricane Weather Research and Forecasting model (HWRF) and covered active North Atlantic basin periods during the 2017–2020 hurricane seasons. The importance of a sufficiently large sample size as well as thoroughly understanding the error distribution by stratifying results are highlighted by this work. Overall, dropsondes directly improved forecasts during sampled periods and indirectly impacted forecasts during unsampled periods. Benefits for forecasts of track, intensity, and outer wind radii were more pronounced during sampled periods. The forecast improvements of outer wind radii were most notable given the impact that TC size has on TC-hazards forecasts. Additionally, robustly observing the inner and near-core regionwas necessary for 64-kt-wind-radii forecasts. Yet, these benefitswere heavily dependent on the data assimilation (DA) system quality. More specifically, dropsondes only improved forecasts when the analysis used mesoscale error covariance derived from a cycled HWRF ensemble, suggesting that it is a vital DA component. Further, while forecast improvements were found regardless of initial classification and in steady-state TCs, TCs undergoing intensity change had diminished benefits. The diminished benefits during intensity change probably reflects continued DA deficiencies. Thus, improving DA-system quality and observing system limitations would likely enhance dropsonde impacts.