This paper investigates the use of an acoustic glider to perform acoustical meteorology. This discipline consists of analyzing ocean ambient noise to infer above-surface meteorological conditions. The paper focuses on wind speed estimation, in a near-shore marine environment. In such a shallow water context, the ambient noise field is complex, with site-dependent factors and a variety of nonweather concurrent acoustic sources. A conversion relationship between sound pressure level and wind speed is proposed, taking the form of an outlier-robust nonlinear regression model learned with in situ data. This method is successfully applied to experimental data collected in Massachusetts Bay (MA, USA) during four glider surveys. An average error in wind speed estimation of 1.3 m · s -1 (i.e., average relative error of 14%) over wind speed values up to 17 m · s -1 is reported with this method, which outperformed results obtained with relationships from the literature. Quantitative results are also detailed on the dependence of wind speed error estimation on the environment characteristics, and on the classification performance of observations contaminated by acoustic sources other than wind. Passive acoustic-based weather systems are a promising solution to provide long-term in situ weather data with fine time and spatial resolutions. These data are crucial for satellite calibration and assimilation in meteorological models. From a broader perspective, this paper is the first step toward an operationalization of acoustic weather systems and their on-board embedding in underwater monitoring platforms such as gliders.