Using Novel Observations to Evaluate Background Ensemble Forecasts at the Air‐Sea Interface from a Self‐Cycled HAFS Data Assimilation System for Hurricane Fiona (2022)

Abstract A field campaign during Hurricane Fiona (2022) collected uniquely coordinated observations at the air‐sea interface using paired dropsondes, Airborne EXpendable BathyThermographs, gliders, and saildrones. These platforms measured key atmospheric and oceanic parameters, including wind speed, temperature, and humidity, to quantify air‐sea fluxes and characterize the atmospheric boundary layer and upper ocean. This study is the first to leverage these observations to evaluate the background ensemble at the air‐sea interface from a self‐cycled Hurricane Analysis and Forecast System data assimilation (DA) system. Verification against observations demonstrated that the background deterministic forecast captures key spatial and temporal variability of surface air temperature (SAT), humidity, wind speed, and sea surface temperature (SST), although systematic biases in humidity and SST were identified due to a combination of model and initialization errors. Evaluation of the 6‐hr background ensemble showed skillful spread for SAT, humidity, and wind speed relative to the forecast error variance. In contrast, the SST ensemble spread was markedly underestimated and inversely related to forecast error variance, emphasizing the need for sampling the oceanic state uncertainty. For longer background forecasts, temporal cross‐correlation of SST with SAT and surface wind speed revealed physically coherent time‐lagged relationships, with atmospheric changes preceding SST responses by approximately 24–36 hr. Similar correlations were preserved in ensemble perturbations, reflecting the ability of the background ensemble in maintaining the dynamic consistency across the coupled interface. These findings provide insights into improving the coupled background ensemble covariances, paving the way for the development of strongly coupled ocean‐atmosphere DA for hurricane forecasting.