Single-Pass Tropical Cyclone Detector and Scene-Classified Wind Speed Retrieval Model for Spaceborne GNSS Reflectometry

Abstract

Spaceborne global navigation satellite system reflectometry has been used to retrieve wind speed, but few studies have directly detected tropical cyclones from spaceborne GNSS-R observations. Moreover, it is challenging to solve the multivalued dependence of the spaceborne observable on wind speed to unambiguously and accurately retrieve wind speed in cyclone conditions. This paper presents a single-pass cyclone detector and coarse estimation approach of the tropical cyclone position using a spaceborne GNSS-R full delay-Doppler map (FDDM). When the specular point passes through a tropical cyclone, the FDDM asymmetry experiences an abnormal change. From the FDDM asymmetry sequence along the specular point trajectory, two subsequence features, including the slope and extremum difference, are defined, from which the tropical cyclone detector is proposed. The results from the simulation and the Cyclone GNSS (CYGNSS) data both indicate a good detection performance for tropical cyclones. The location corresponding to the peak detector is considered as a coarse estimation of the tropical cyclone position. The test result from the CYGNSS data shows that the mean error between the detected cyclone center and the International Best Track Archive for Climate Stewardship (IBTrACS) cyclone center is approximately 124.50 km. From the detector of tropical cyclones, a scene-classified wind speed retrieval model is proposed. The simulated and experimental results show that a better retrieval performance can be obtained at high wind speed (> 30 m/s) using the scene-classified model. Reductions of 10 m/s and 4 m/s in the root mean square errors (RMSEs) are obtained for the simulation and CYGNSS data, respectively. This work is meaningful for directly detecting tropical cyclones and retrieving high wind speed data using spaceborne GNSS-R in real time.