Improving the background ensemble covariance at the air-sea interface toward the fully coupled data assimilation in Hurricane Analysis and Forecast System (HAFS)

Oceans are vital heat and energy sources that play a critical role in the development of hurricanes, working in conjunction with atmospheric changes. However, current data assimilation (DA) practices tend to treat the ocean and atmospheric components of numerical models independently. This approach can create significant inconsistencies at the air-sea interfaces, leading to potentially degraded hurricane predictions.

The primary goal of the study is to advance the background ensemble covariance at the air-sea interface, one critical aspect that needs to be addressed toward developing the eventual fully coupled DA system. We begin by first evaluating the background ensemble at the air-sea interface in the self-cycled HAFS EnVar system that is coupled with the HYCOM and MOM6 ocean model. To achieve this, we will incorporate a range of inner-core observations that is capable of sampling both the atmosphere and the ocean simultaneously including the dropsondes from NOAA P-3 aircraft that are paired with the AXBT probes and the SailDrone observations.

Results show that (a) the self-cycled HAFS system provides reasonable atmospheric and oceanic background predictions at the atmosphere-ocean interface as compared to both the operational HAFS system and the observations; (b) there are some systematic biases in the SST, surface air temperature (SAT), near sea surface moisture and wind fields. Additionally, we implement methods to verify and improve the ensemble covariance at the air-sea interface. The detailed results of these methods will be presented in the conference.