Assimilation of Small Uncrewed Aircraft System Observations(sUAS) in NOAA's Next-Generation Hurricane Analysis and Forecast System (HAFS)

Small uncrewed aircraft can be used to collect high-quality in situ observations below conventional reconnaissance aircraft altitudes, filling an important data gap in the tropical cyclone (TC) inner core. Deployments of two such vehicles, the Coyote, in hurricanes Edouard (2014), Maria (2017) and Michael (2018), and the Altius in Hurricane Ian (2022), successfully demonstrated this ability by continuously collecting observations in the turbulent TC boundary layer for up to one and a half hours. The quality of the Coyote data was demonstrated in Cionne et al. (2016) and a preliminary attempt to assimilate Coyote observations in an experimental numerical model was presented in Cione et. al (2020). Sellwood et al. (2022) showed that, when assimilated in an optimal manner, Coyote observations can help to improve TC track and intensity forecasts in operational HWRF. Since NOAA’s new Hurricane Analysis and Forecast System (HAFS) is expected to replace HWRF as the operational regional/hurricane model and Altius and other new sUAS are in development, it is important to determine whether the Coyote results can be extended to these newer platforms within HAFS. To this end, we use data collected with Altius in Hurricane Ian (2022) and possibly a yet-to-be-determined 2023 TC, and HAFS-B operational configuration. The data are first assimilated using the same quality control, grid-relative resolution and global GDAS covariances as in Sellwood et al. (2022). We then explore methods for optimizing the use of these data specifically for HAFS. This includes incorporating the novel online quality control technique introduced in Aksoy et al. (2022) and using a HAFS self-cycled ensemble for providing the background covariances used for DA. Provided there is a sufficiently amount of data collected in 2023, the impact of data distribution will also be explored.