TWOmethods to acquire near real-time wind information can be distinguished [1–5]. The first method uses wind observations made by aircraft systems that are relayed to the ground. The second uses secondary surveillance radar (SSR) position and speed data, possibly extendedwith downlink airborne parameters (DAPs).When DAPs are unavailable, the SSR’s position and speed data can be used to estimate wind from aircraft that completed a turn [3,4]. This paper presents a novel method to estimate wind using Automatic Dependent Surveillance-Broadcast (ADS-B) over a larger area. ADS-B-equipped aircraft broadcast their position, ground speed, track angle, and other aircraft information [6,7]. Although registers for data in the air reference frame exist, data analysis showed these are left unused by most aircraft. For wind estimation purposes, ADS-B provides velocity vector information measured using a global satellite navigation system. This information can also be derived from SSR position data. However, the latter are expected to be, in most cases, less accurate and have a lower update rate. Previous methods were based on a single aircraft and therefore required multiple measurements of that aircraft at different headings. The proposed approach is new as it circumvents this limitation by assuming that aircraft at the same altitude travel, on average, at the same true airspeed (TAS) in all directions. The remainder of this note is organized as follows. Section II discusses the theoretical model and the application of dilution of precision (DOP) and Kalman filtering techniques to integrate noisy wind estimates into a time-varying wind profile for a larger area. Section III discusses the results of simulations to assess the effect of the actual wind speed and airspeed differences between aircraft. Section IV gives the results of a comparative analysis of wind estimates made using actual ADS-B datawith forecasts of the Global Forecast System (GFS). II. Wind Estimation Algorithm