What question did this study set out to answer?

This research aims to evaluate background ensemble forecasts using real-time observations from Hurricane Fiona.

March 10, 2026Journal of Geophysical Research Atmospheres0 citations

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)

TLTsung‐Han LiUniversity of Oklahoma XWXuguang WangUniversity of Oklahoma XLXu LuNOAA National Centers for Environmental Prediction

Key Points

This research aims to evaluate background ensemble forecasts using real-time observations from Hurricane Fiona.
Used paired dropsondes, Airborne EXpendable BathyThermographs, gliders, and saildrones for data collection.
Measured atmospheric and oceanic parameters such as wind speed, temperature, and humidity.
Evaluated the self-cycled Hurricane Analysis and Forecast System data assimilation outputs.
Analyzed cross-correlation of surface air temperature, humidity, wind speed, and sea surface temperature.
The deterministic forecast accurately captured key variations in surface air temperature, humidity, and wind speed.
Systematic biases in humidity and sea surface temperature were identified.
The SST ensemble spread was underestimated, showing an inverse relationship with forecast error variance.
Time-lagged relationships indicated atmospheric changes preceded sea surface temperature responses by 24-36 hours.

Abstract

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.

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