Implementation of the Online Quality Control Technique in NOAA’s Next-Generation Hurricane Analysis and Forecast System (HAFS)

The online Quality Control (QC) technique proposed by Altug et al. (2022) is a new tool to filter outlier observations before assimilation. In this method, the innovations for all observations are normalized by their total uncertainty. The observations associated with the outlier normalized innovations are excluded from assimilation in order to avoid strongly nonlinear updates. This method has already been implemented in the Hurricane Weather Research and Forecasting (HWRF) system and showed promising results. Since the Hurricane Analysis and Forecast System (HAFS) has replaced HWRF as NOAA’s next-generation operational numerical model and data assimilation system in 2023, we introduced this new method into HAFS and carried out a self-cycled experiment of Hurricane Ian (2022) for testing. Observations from the experimental Altius Small Uncrewed Aircraft System (sUAS) were obtained in Ian’s inner core before its landfall in Florida. The impact of the assimilation of these high-quality in situ observations was also examined in this experiment, in addition to the regularly collected reconnaissance aircraft observations.