Assimilation of Coyote Small Uncrewed Aircraft System Observations in Hurricane Maria (2017) Using Operational HWRF

Abstract

Abstract This study presents an initial demonstration of assimilating small uncrewed aircraft system (sUAS) data into an operational model with a goal to ultimately improve tropical cyclone (TC) analyses and forecasts. The observations, obtained using the Coyote sUAS in Hurricane Maria (2017), were assimilated into the operational Hurricane Weather Research and Forecast (HWRF) system as they could be in operations. Results suggest that the Coyote data can benefit HWRF forecasts. A single-cycle case study produced the best results when the Coyote observations were assimilated at greater horizontal resolution with more relaxed quality control (QC) than comparable flight-level high-density observations currently used in operations. The case study results guided experiments that cycled HWRF for a roughly 4-day period that covered all Coyote flights into Maria. The cycled experiment that assimilated the most data improved initial inner-core structure in the analyses and better agreed with other aircraft observations. The average errors in track and intensity decreased in the subsequent forecasts. Intensity forecasts were too weak when no Coyote data were assimilated, and assimilating the Coyote data made the forecasts stronger. Results also suggest that a symmetric distribution of Coyote data around the TC center is necessary to maximize its benefits in the current configuration of operational HWRF. Although the sample size was limited, these experiments provide insight for potential operational use of data from newer sUAS platforms in future TC applications. Significance Statement This study represents the first time that observations from a small uncrewed aircraft system (sUAS) have been assimilated into an operational numerical model. Including these data was shown to have potential for improving forecasts of tropical cyclone track and intensity. The data were obtained using the Coyote sUAS, but these results are expected to be applicable to newer platforms that will be operational soon.