Geostationary Satellite–Derived Positioning of a Tropical Cyclone Center Using Artificial Intelligence Algorithms over the Western North Pacific.

Artificial intelligence (AI) models were developed to determine the center of tropical cyclones (TCs) in the western North Pacific. These models integrated information from six channels of geostationary satellite imagery: the brightness temperature of four infrared (IR) and one shortwave IR channels, as well as the reflectivity of one visible channel. The first model is a convolutional neural network designed for spatial data processing, and the second is a convolutional long short-term memory model that effectively captures spatiotemporal information. For training, verification, and testing purposes, spatial images from six channels were obtained from the Japanese Himawari-8 satellite from 2016 to 2021. The position of the European Centre for Medium-Range Weather Forecasts 6- or 12-h prediction was assigned as an initial value to the AI models. Errors in the initial value were 20–50 km compared to the Joint Typhoon Warning Center best track, depending on TC intensity. Weak (strong) TCs exhibited large (small) errors. This error dependency was found in Automated Rotational Center Hurricane Eye Retrieval (ARCHER) product, which is currently used by several operational organizations. ARCHER errors were typically small when observations from both geostationary and polar-orbiting satellites were included. Significant errors remained in the absence of microwave channel information from polar-orbiting satellites. This study successfully developed two AI models that consistently determined the location of the TC center using only six-channel images from geostationary satellites. These models exhibited comparable or better performance than the ARCHER products. The newly developed AI models can potentially be implemented for operational use. SIGNICANCE STATEMENT: This study has developed AI models that can determine the center of TCs in the western North Pacific, which can be applied to meteorological organizations that have limited access to real-time polar-orbiting satellite data. The newly developed AI models derived positions comparable to or better than ARCHER, which is currently one of the most precise systems for determining the position of the TC center. The successful development of these AI models is very opportune as it was only possible thanks to the recent availability of high-frequency data observed from geostationary satellite, coupled with the rapid technological advances in AI algorithms. [ABSTRACT FROM AUTHOR]