###### Abstract
Based on the above analysis, it can be concluded that the situational awareness and reaction time of the aircraft crew in conditions of strong gusts of wind and other atmospheric threats related to their occurrence are an essential factor determining the safety of continuing normal procedures or the correct execution of escape maneuvers. During critical flight phases--such as long final approaches in gusty conditions or escape maneuvers--flight crews have limited ability to rely on automation. Manual flying skills and rapid pilot reactions remain essential and have not yet been fully replaced by automation. Wind components (headwind, tailwind, and crosswind) also limit the  
Figure 1: Illustration of a flight through the microburst phenomenon. (**A**)—increase in headwind speed; indicated air speed increases, rate of descent decreases, tendency to go above the assumed glide path. (**B**)—decrease in headwind speed; downdraft increases, indicated airspeed decreases, rate of descent increases, tendency to descend below the assumed glide path. (**C**)—increase in tailwind speed; the indicated air speed is constantly decreasing, the approach is interrupted at decision height, success depends on altitude, current speed and power reserve. feasibility of automatic landings, which are subject to strict ground and onboard system requirements and operational procedures. Wind estimation methods used onboard commonly analyze true airspeed (TAS) and ground speed (GS) vectors. For example, in study [18], based on the measured ground speed (GS), track angle, true airspeed (TAS), and geographic heading, the instantaneous components of wind speed in the geographic reference frame (U\({}_{\text{N}}\) and U\({}_{\text{E}}\)) are calculated. These components are then averaged using a recursive filter with a time constant \(T_{u}\) to obtain the estimated values U\({}_{\text{NE}}\) and U\({}_{\text{EE}}\). Additionally, the average drift angle is computed. To minimize estimation delays and reduce control-induced disturbances, inertial measurements and aircraft dynamics are also considered. In work [19], the disclosure pertains to devices, systems, and techniques for estimating wind speed during the operation of an unmanned aerial vehicle (UAV), based on a comparison between modeled and actual in-flight acceleration. The method involves modeling the UAV's acceleration based at least on its measured ground speed, determining the actual acceleration using one or more sensors, and estimating the wind speed by integrating the difference between the modeled and actual acceleration. The solutions, presented in works [20; 21], measures the ground speed of the aircraft and estimates its airspeed based on the aircraft's acceleration and the controlled aerodynamic forces acting upon it. Using the ground speed and estimated airspeed, the device determines the wind field affecting the aircraft. This estimated wind field is then used to support aircraft navigation. In paper [22], an algorithm for estimating wind direction using a small UAV was proposed. The selected UAV featured a relatively large delta-shaped main wing, making it particularly susceptible to wind disturbances. The algorithm was based on measuring angular velocities and accelerations relative to the body frame, as well as roll and pitch angles relative to the inertial frame. A gust of the same intensity affects small aircraft or UAVs [23; 24; 25; 26] differently than larger, heavier aircraft weighing several tons [27]. Larger airplanes exhibit slower and more delayed responses to such disturbances. In our previous work [28], headwind gust estimation was based on modelling the aircraft's complex longitudinal response. This work aims to generalize gust estimation in a three-dimensional context. Drawing from prior research, relationships were derived to estimate headwind, lateral, and vertical gusts, incorporating the aircraft's dynamics model.