A Novel Method for Estimating the Supercooled Liquid Water Content Using In Situ Aircraft Observation Data and Gated Recurrent Unit

This study aims to estimate the objective amount of the supercooled liquid water content (SLWC) using in situ aircraft observation data to construct an objective and consistent long-term dataset of aircraft icing intensity. SLWC was estimated using two conventional calibration methods and a newly proposed Gated Recurrent Unit (GRU) model, based on measurements from the Rosemount Icing Detector (RICE) and collocated in situ aircraft observations. The observations were collected by NARA research aircraft operated by the National Institute of Meteorological Sciences in South Korea, which has conducted regular atmospheric observations since February 2018. The GRU-based approach demonstrated substantially improved performance compared to the calibration methods, achieving a Pearson correlation coefficient of 0.945 and a Nash–Sutcliffe efficiency of 0.891 when evaluated against independent observations not used in model training. In particular, the proposed method enables a more detailed representation of SLWC evolution by providing time-series SLWC estimates, whereas calibration-based approaches typically provide a single representative value for each icing event. The GRU-based estimates closely reproduce the observed temporal variability of SLWC in NARA icing cases, further demonstrating the capability of the proposed method to capture realistic SLWC evolution. The estimated SLWC from the proposed model were subsequently used to classify icing intensity based on operationally established SLWC thresholds for each icing intensity category, resulting in a robust long-term icing intensity dataset spanning over six years. The outcomes of this study are expected to contribute not only to aircraft icing research but also to a broad spectrum of applications including remote-sensing-based hydrometeor detection, cloud microphysical processes, and numerical weather prediction model parameterizations.

Acknowledgement: This research is supported by the Korea Meteorological Administration Research and Development Program under Grant RS-2022-KM220310 and RS-2022-KM220410.