Tropical Cyclone Data Assimilation with Coyote Uncrewed Aircraft System Observations, Very Frequent Cycling, and a New Online Quality Control Technique

A unique dataset obtained from the Coyote small uncrewed aircraft system (sUAS) in the inner-core boundary layer of Hurricane Maria (2017) is assimilated using NOAA's Hurricane Ensemble Data Assimilation System (HEDAS) for data assimilation and Hurricane Weather Research and Forecasting (HWRF) system for model advances. The case of study is 1800 UTC 23 September 2017 when Maria was a category-3 hurricane. In addition to the Coyote observations, measurements collected by the NOAA Lockheed WP-3D Orion and U.S. Air Force C-130 aircraft were also included. To support the assimilation of this unique dataset, a new online quality control (QC) technique in HEDAS scales the observation-background difference by the total uncertainty during data assimilation and uses the interquartile range outlier method to identify outlier observations. Experimental setup includes various very frequent cycling scenarios for a control that does not assimilate Coyote observations, assimilation of Coyote observations in addition to the control observations, and the application of online QC. Findings suggest progressively improved analyses with more-frequent cycling, Coyote assimilation, and application of online QC. This applies to verification statistics computed at the locations of both Coyote and non-Coyote observations. In terms of the storm structure, only experiments that assimilated the Coyote observations were able to reproduce the double-eyewall structure that was observed at the time of the analysis, which is more consistent with the intensity of the storm according to the observations that were collected. Limitations of the study and future plans are also discussed. Significance StatementFindings from this study illustrate the significant impact difficult-to-obtain, near-surface observations can have on improving the accuracy of tropical cyclone structure and intensity. Adding these novel measurements in a way that also includes advanced cycling and quality control techniques in data assimilation has the potential to improve public forecasts that are reliant upon detailed depictions of storm strength and boundary layer structure prior to landfall. The results speak to the importance of parallel and consistent advancements in modeling, data assimilation, and observational capabilities to improve the depiction of the tropical cyclone inner-core structure in numerical models.