A long-term dataset of tropical cyclone radius of maximum wind in the Western North Pacific Based on a Bayesian consensus of multiple estimates

Abstract The radius of maximum wind (RMW) is one of the key parameters for characterizing the structure of tropical cyclones (TCs). Although multiple methods have been developed to estimate the RMW, their estimates can sometimes differ substantially and are not always available, making it difficult to construct a comprehensive long-term RMW dataset. This study develops a Bayesian consensus method to derive a single RMW estimate from multiple RMW estimates. In this framework, the posterior probability distribution of the RMW is estimated using likelihoods constructed from method-specific error distributions combined with an outlier distribution, and the final RMW is determined as the maximum a posteriori (MAP) estimate. By explicitly accounting for an outlier distribution for each estimation method, the proposed method can determine a reasonable RMW close to one of the clusters even when the estimates split into two clusters. Using this consensus method and, as a primary source, estimates from satellite-based global precipitation data and from infrared and microwave satellite data for TCs with an eye, a long-term RMW dataset is constructed at 3-h intervals for 631 TCs in the western North Pacific during 2000–2025. Verification of the consensus RMW estimates shows that errors decrease with increasing TC intensity and posterior probability, and that posterior probability itself serves as an indicator of estimate reliability. This new RMW dataset enables a realistic representation of the inner-core structure, particularly small RMWs, which were difficult to estimate before the advent of synthetic aperture radar (SAR)-derived winds and in the absence of aircraft observations.