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An important prerequisite for the design, assessment and certification of aircraft and their associated control systems is a quantitative specification of the environment in which the aircraft is intended to operate, for example, atmospheric gust. Gust loads on aircraft may induce detrimental influences such as increased aerodynamic and structural loads, structural deformation and decreased flight dynamic performance. This paper presents a systematic and comprehensive overview of important concepts and applications of gust loads on aircraft. This overview includes a brief research background, concepts, research techniques, influences and load alleviation measures of gust. Finally, we summarise some potential improvements in the future work. It is also recommended to learn from previous experiences to avoid aviation accidents due to flight through atmospheric gusts and turbulence.

Postprocessing ensemble weather predictions to correct systematic errors has become a standard practice in research and operations. However, only a few recent studies have focused on ensemble postprocessing of wind gust forecasts, despite its importance for severe weather warnings. Here, we provide a comprehensive review and systematic comparison of eight statistical and machine learning methods for probabilistic wind gust forecasting via ensemble postprocessing that can be divided in three groups: state-of-the-art postprocessing techniques from statistics [ensemble model output statistics (EMOS), member-by-member postprocessing, isotonic distributional regression], established machine learning methods (gradient-boosting extended EMOS, quantile regression forests), and neural network–based approaches (distributional regression network, Bernstein quantile network, histogram estimation network). The methods are systematically compared using 6 years of data from a high-resolution, convection-permitting ensemble prediction system that was run operationally at the German weather service, and hourly observations at 175 surface weather stations in Germany. While all postprocessing methods yield calibrated forecasts and are able to correct the systematic errors of the raw ensemble predictions, incorporating information from additional meteorological predictor variables beyond wind gusts leads to significant improvements in forecast skill. In particular, we propose a flexible framework of locally adaptive neural networks with different probabilistic forecast types as output, which not only significantly outperform all benchmark postprocessing methods but also learn physically consistent relations associated with the diurnal cycle, especially the evening transition of the planetary boundary layer.

The ERA5 reanalysis product has been compared with hourly near-surface wind speed and gust observations across Sweden for 2013–2017. ERA5 shows closer agreement than the previous ERA-Interim reanalysis with regard to both mean wind speed and gust measurements, although significant discrepancies are still found for inland and mountainous regions. Therefore, attempts have been made to improve formulations of the gust parametrization used in ERA5 by adding an elevation-dependency and by adjusting the convective gust contribution. Major improvements, especially over mountain regions, are achieved when the elevation differences among the stations are considered. Closer agreement between the observed and parametrized gusts is reached when the convective gust contribution is also tuned. The newly designed gust parametrization was also tested for Norway, which is characterized by more complex topography. Wind gusts from the selected Norwegian stations are more realistically simulated when both the elevation-dependency and the tuned convective contribution are implemented, although the parametrized gusts are still negatively biased. Such biases are not explained by the different in gust duration in recorded wind gusts between Sweden and Norway.

This study presents rare measurements and analysis of a nocturnal thunderstorm downburst on the 213 m tall Cabauw tower in The Netherlands. The event occurred on 12 March 2008 between 02:00 and 03:00 UTC and was measured using four ultrasonic 10-Hz anemometers positioned at 3, 60, 100, and 180 m above ground level. 1-second gusts in the outflow exceeded 30 m s −1 at 60 m and above. This wind event was accompanied by an abrupt change of wind direction from southwest to west. While the shift in wind direction corresponded with the change of upwind surface roughness, the time series of turbulence intensity and other turbulence characteristics were not affected. The statistical properties of this event were compared against the largest European database of thunderstorm winds measured in the Mediterranean. The study also demonstrated that primary and secondary vortex structures—secondary vortex being rarely observed in actual downbursts—developed at the forward edge of the cold outflow. The estimated diameter of the downdraft was 1200 m at 70 m above ground. The measured velocity profiles and friction velocity were compared against theoretical predictions of the Monin-Obukhov Similarity Theory (MOST). MOST without stratification adjustment overestimated measured friction velocity twofold. Alternative values for surface roughness during the outflow were derived based on the measured friction velocity and MOST-based fit of measured velocity profiles. Ceilometer and radar measurements were supplementary data in this analysis.

Investigating Amazonian intense wind gusts and their environments is essential to better understand the drivers and impacts of severe convection that can reshape forest structure, increase tree mortality, and threaten ecosystems and communities. This study presents the first multi‐decadal (2000–2024) assessment of intense convective wind gusts ≥15ms−1 $\\left(\\ge 15\\,\\mathrm{m}\\,{\\mathrm{s}}^{-\\mathrm{1}}\\right)$ across the entire Brazilian Amazon, using hourly observations from surface weather stations. Intense gusts occur frequently across the Amazon, particularly during the dry‐to‐wet transition months of September and October, peaking in the mid‐ to late afternoon. Thermodynamic factors favor intense gust generation during the dry and transition seasons, with environments characterized by higher downdraft convective available potential energy, steeper low‐level lapse rates, and higher lifting condensation levels, particularly in southern Amazon.

Increased resolution has enabled kilometer‐scale weather and climate models to partially resolve secondary circulations, including horizontal convective rolls (HCRs) and cold pool gust fronts. Although these circulations are ubiquitous in convective boundary layers over land, their impacts on the surface energy balance are largely unknown. Doppler lidar and surface observations were combined with DOE E3SM land model experiments, revealing increased surface winds (5 m/s) and heat fluxes (50 W/m2) in convergent branches of HCRs. Larger wind‐driven flux responses (up to 150 W/m2) were found along gust fronts. Surface energy balance shifts to accommodate wind‐driven fluxes, reducing ground heat conduction and longwave cooling. Our findings from the US Southern Great Plains are broadly relevant to modeling convective boundary layers. In particular, widely used subgrid wind gust parameterizations were found to be physically inconsistent with resolved secondary circulations and could worsen climate prediction biases at kilometer‐scales. Plain Language Summary Earth's surface is heated by solar radiation, and this energy is transferred to the overlying air in the form of sensible and latent heat fluxes. Surface heat fluxes are generated by turbulent motions that are too small to be directly simulated in weather and climate models. Instead, models use mathematical functions, known as parameterizations, to predict surface fluxes from simulated winds and surface‐to‐air differences in moisture and temperature. Observed winds near Earth's surface are known to organize into patterns referred to as secondary circulations, creating frequently observed “cloud streets,” and influencing the soaring patterns of birds. With increasing computational power, weather, and climate models have begun to resolve these circulations in winds simulated at kilometer scales. Although they are widely observed, this study provides new evidence that secondary circulations significantly alter surface heat fluxes and the energy balance of the land surface. It is also shown that current parameterizations of wind‐driven heat fluxes can be made more realistic to improve predictions in weather and climate models that are run at kilometer‐scale spatial resolutions. Key Points Lidar and surface wind measurements provide evidence linking widely observed secondary circulations to surface wind gusts The circulations alter the land surface energy balance and increase surface heat fluxes in convergent branches of circulation updrafts Land model parameterizations are inconsistent with resolved circulations, pointing to needed improvements at kilometer‐scale resolutions

Severe wind gusts produced by squall lines are difficult to monitor and forecast. This paper assessed and improved the physics-based Brasseur WGE (wind gust estimate) method for diagnosing wind gust of squall lines by coupling the WGE methods with the WRF (Weather Research and Forecasting) model. The simulation results show that the Brasseur WGE method accurately captured the strong gust feature with 32 m·s −1 maximum wind speed during the disastering Shipwreck event occurred over Yangtze River on 1 June 2015, but overestimated the extended area of severe gust speeds. Analysis of the kinematic structure and boundary-layer conditions of the squall line confirmed the theoretical applicability of the Brasseur WGE method for squall lines. A novel gust-front-area limiting method was introduced to modify the Brasseur WGE method, which effectively reduces its gust wind overestimation area. Furthermore, five squall line events occurred in the middle China during 2021 were simulated to test the modified WGE method and the results exhibit significant improvements to the wind gust forecasts, with an average false alarm rate decreased from 0.89 to 0.54, and the critical success index(CSI) increased from 0.1 to 0.4.

On the local afternoon of 29 May 2012, a long-lived, right-moving (RM) supercell formed over northwestern Oklahoma and turned roughly southeastward. For >3 h, as it moved toward the Oklahoma City, Oklahoma, metro area, this supercell remained nontornadic and visually high-based, producing a nearly tornadic gustnado and a swath of significantly severe, sometimes giant hail up to 5 in. (12.7 cm) in diameter. Meanwhile, a left-moving (LM) supercell formed over southwestern Oklahoma about 100 mi (161 km) south-southwest of the RM storm, and moved northeastward, with a rear-flank gust front that became well defined on radar imagery as the LM storm approached southern and central parts of the metro. The authors, who had been observing the RM supercell in the field since genesis, surmised its potential future interaction with the LM storm’s trailing gust front about 1 h beforehand. We repositioned to near the gust front’s extrapolated collision point with the RM mesocyclone, in anticipation of maximized tornado potential, then witnessed a small tornado from the RM mesocyclone immediately following its interception of the boundary. Synchronized radar and photographic images of this remarkable sequence are presented and discussed in context of more recent findings on tornadic supercell–boundary interactions, with implications for operational utility.

This article introduces in-situ learning (ISL), a paradigm in which drones adapt directly through real-world interaction rather than relying on simulation-to-reality (sim-to-real) transfer. Whereas sim-to-real separates training from embodiment, ISL integrates the physical interaction features into the learning loop, treating disturbances such as wind gusts, ground effect, and collisions not as nuisances but as informative signals. We formulate the in-situ learning problem for quadrotor UAVs and develop an adaptive reservoir critic with continuous-time weight updates. The framework admits formal guarantees of boundedness, convergence, and input-to-state stability, and introduces interaction-gated adaptation , where natural disturbances provide the persistent excitation required for learning. Experimental studies via both simulations and a real-world quadrotor demonstrate emergent behaviors such as disturbance anticipation, collision-driven adaptation, and context-sensitive thrust regulation. These results support a philosophical shift: robustness and intelligence emerge not from increasingly complex simulators but from embodied interaction itself. We argue that ISL reframes physical interaction as a renewable computational resource and motivates new benchmarks and design principles for embodied intelligence beyond the sim-to-real paradigm.