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This study examines the boundary layer wind profile and turbulence variables during the landfalls of Hurricanes Ida (2021) and Zeta (2020) using ground‐based Doppler radar observations and a nearby anemometer's wind measurements. While the radar sampled different parts of the hurricane circulation of the two cases, the observed maximum near‐surface wind and frictional velocity were similar. Radar‐retrieved wind profiles in both hurricanes revealed a boundary‐layer jet generally >1 km AGL, descending toward smaller radii as the hurricanes moved inland. A “knee‐like” structure in most wind profiles below the jet suggests an internal boundary layer (IBL) below 200 m and a log layer above it. Among the three methods for estimating near‐surface sustained winds from radar‐retrieved winds, leveraging low‐level IBL winds improves estimation accuracy and reduces the uncertainty to the selection of upstream surface roughness length. These findings offer valuable guidance for developing future probabilistic near‐surface wind products.

Accurate wind measurement is critical for atmospheric and environmental sciences; however, achieving high spatiotemporal resolution with operational flexibility remains challenging. This study develops and validates an approach for estimating horizontal wind speed and direction based on the flight dynamics of an unmanned aerial vehicle (UAV). Through controlled wind wall experiments, we established a relationship between UAV attitude (e.g., roll, pitch, and yaw) and wind speed. This relationship varies significantly with relative wind direction (with respect to UAV orientation) and payload configuration due to the built-in flight control system and asymmetric airframe of the UAV deployed, demonstrating the necessity of platform-specific calibration for practical application. The performance of this attitude-based method was compared against measurements from a calibrated onboard ultrasonic anemometer. While the sensor-based method achieved good accuracy for hovering and low-speed vertical flights, its performance degraded at higher vertical speeds (>2 m s−1) due to rotor-induced airflow interference. In contrast, the attitude-based method maintained robust accuracy across all flight regimes. Furthermore, a machine learning model was developed to deliver high-fidelity wind estimates (R2>0.90). The model integrated attitude data, flight dynamics, and environmental parameters (e.g., air pressure) and was trained on data from UAV flights during a 20 d field campaign. Validation against measurements from a meteorological tower confirmed the reliability of the machine learning method. This work presents a sensor-free, computationally efficient framework for obtaining high-resolution wind data. By addressing the critical, platform-specific factors affecting estimation accuracy, our approach enhances the applicability of UAVs for advanced environmental monitoring, atmospheric research, and safety assessments in the emerging low-altitude economy.

Accurate estimation of coastal near‐surface winds during hurricane landfalls remains challenging, partly attributable to an insufficient understanding of the wind profiles within the internal boundary layer (IBL) induced by an abrupt surface roughness change. This study addresses this issue by performing three semi‐idealized large‐eddy simulations. Results indicate that a nascent log layer emerges within the IBL, and its depth gradually increases from ∼60 m near the coast to ∼400 m 12 km inland, where the boundary layer transition is nearly complete. This nascent log layer is superimposed by another log layer originating from the upstream marine boundary layer. While turbulence kinetic energy (TKE) is maximized near the surface over both water and land, peak TKE values over land are a factor of 2 greater due to the amplified near‐surface vertical wind shear. The capability and uncertainty of coastal radars and radiosondes to detect IBL and estimate 10‐m winds are discussed. Plain Language Summary Hurricane landfalls are typically associated with severe wind‐related compound hazards (infrastructure damage, blackout, subsequent heatwaves during power outages, etc.) in the coastal region. Knowing exactly how strong near‐surface winds will be during hurricane landfalls is crucial for risk communication, effective preparation of coastal communities, and post‐storm rescue and assistance (e.g., by FEMA). However, this intention is compromised by our limited understanding of the evolution of near‐surface winds during landfalls, partly attributable to the scarcity of the coordinated observations of low‐level winds over both water and land. Using specially configured turbulence‐resolving computer model simulations, this study provides insights into the effects of land surface types and distance inland on the near‐surface wind profile. This study also quantifies the uncertainty of the 10‐m wind estimate derived from different observation‐based approaches for the first time. These findings can guide future field campaigns and hurricane landfall studies. Key Points A novel large‐eddy simulation framework was further improved to study the internal boundary layer (IBL) during hurricane landfalls Twin log layers coexist within a narrow coastal transition zone Radar wind retrievals in principle can capture the IBL wind profile while estimating 10‐m winds using radiosonde data is highly uncertain

Unmanned aerial vehicles are increasingly used to study atmospheric structure and dynamics. While much emphasis has been on the development of fixed-wing unmanned aircraft for atmospheric investigations, the use of multirotor aircraft is relatively unexplored, especially for capturing atmospheric winds. The purpose of this article is to demonstrate the efficacy of estimating wind speed and direction with 1) a direct approach using a sonic anemometer mounted on top of a hexacopter and 2) an indirect approach using attitude data from a quadcopter. The data are collected by the multirotor aircraft hovering 10 m above ground adjacent to one or more sonic anemometers. Wind speed and direction show good agreement with sonic anemometer measurements in the initial experiments. Typical errors in wind speed and direction are smaller than 0.5 and 30°, respectively. Multirotor aircraft provide a promising alternative to traditional platforms for vertical profiling in the atmospheric boundary layer, especially in conditions where a tethered balloon system is typically deployed.

Tropical cyclones (TCs) pose significant global hazards due to their intense winds, heavy rainfall, and associated storm surges. While in situ observations from aircraft are crucial for understanding TC structures, these measurements are often spatially sparse, limiting the characterization of the wind speed structure. In this study, we introduce a deep learning (DL) framework based on generative adversarial networks to reconstruct multi‐level wind‐speed (including that at the 10 m surface layer) from sparse dropsonde inputs within seconds. Our simulation experiments incorporate realistic flight parameters and account for the horizontal drift of dropsondes. We present a case study that reconstructs TC structures using a combination of observations from Hurricane Hunter missions. This approach demonstrates the potential of DL for reconstructing the multi‐level wind‐speed structure of TCs from sparse data. The current framework focuses on reconstructing wind speed, showing significant promise for surface wind estimation and operational storm surge modeling.

Unmanned Aerial Vehicles (UAVs) have benefited from a tremendous increase in popularity over the past decade, which has inspired their application toward many novel and unique use cases. One of them is the use of UAVs in meteorological research, in particular for wind measurement. Research in this field using quadcopter UAVs has shown promising results. However, most of the results in the literature suffer from three main drawbacks. First, experiments are performed as numerical simulations or in wind tunnels. Such results are limited in their validity in real-life conditions. Second, it is almost always assumed that the drone is stationary, which limits measurements spatially. Third, no attempts at estimating vertical wind are made. Overcoming these limitations offer an opportunity to gain significant value from using UAVs for meteorological measurements. We address these shortcomings by proposing a new dynamic model-based approach, that relies on the assumption that thrust can be measured or estimated, while drag can be related to air speed. Moreover, the proposed method is tested on empirical data gathered on a DJI Phantom 4 drone. During hovering, our method leads to precision and accuracy comparable to existing methods that use tilt to estimate the wind. At the same time, the method is able to estimate wind while the drone is moving. This paves the way for new uses of UAVs, such as the measurement of shear wind profiles, knowledge of which is relevant in Atmospheric Boundary Layer (ABL) meteorology. Additionally, since a commercial off-the-shelf drone is used, the methodology can be replicated by others without any need for custom hardware development or modifications.

Accurate wind profiles and radius of maximum wind (Rmax${R}_{\\max }$ ) are critical for reconstructing realistic tropical cyclones (TCs) to predict extreme ocean conditions, including storm surges and waves. Leveraging the expanding Synthetic Aperture Radar (SAR) database, we conducted the first comprehensive evaluation of commonly used TC wind profile models, demonstrating that a theory‐based model captures a broad range of TCs, outperforming semi‐empirical and empirical models. We then propose inferring Rmax${R}_{\\max }$with the theory‐based model, with quadrant‐maximum wind radii as inputs to account for TC asymmetry. Evaluated against SAR‐derived Rmax${R}_{\\max }$ , our best estimates are obtained using the wind radius closest to Rmax${R}_{\\max }$(64‐kt), consistent with the wind profile model's performance. These estimates outperform best‐track and semi‐empirical methods across all ocean basins, and we show that accounting for TC asymmetry significantly improves Rmax${R}_{\\max }$estimates. Finally, we highlight the challenge of representing the substantial variability observed in TC wind profile shapes.

We present a model-based approach to estimate the vertical profile of horizontal wind velocity components using motion perturbations of a multirotor unmanned aircraft system (UAS) in both hovering and steady ascending flight. The state estimation framework employed for wind estimation was adapted to a set of closed-loop rigid body models identified for an off-the-shelf quadrotor. The quadrotor models used for wind estimation were characterized for hovering and steady ascending flight conditions ranging between 0 and 2 m/s. The closed-loop models were obtained using system identification algorithms to determine model structures and estimate model parameters. The wind measurement method was validated experimentally above the Virginia Tech Kentland Experimental Aircraft Systems Laboratory by comparing quadrotor and independent sensor measurements from a sonic anemometer and two SoDAR instruments. Comparison results demonstrated quadrotor wind estimation in close agreement with the independent wind velocity measurements. However, horizontal wind velocity profiles were difficult to validate using time-synchronized SoDAR measurements. Analysis of the noise intensity and signal-to-noise ratio of the SoDARs proved that close-proximity quadrotor operations can corrupt wind measurement from SoDARs, which has not previously been reported.

High-frequency parts of ocean wave spectra are strongly coupled to the local wind. Measurements of ocean wave spectra can be used to estimate sea surface winds. In this study, two deep neural networks (DNNs) were used to estimate the wind speed and direction from the first five Fourier coefficients from buoys. The DNNs were trained by wind and wave measurements from more than 100 meteorological buoys during 2014–2018. It is found that the wave measurements can best represent the wind information about 40 min previously because the high-frequency portion of the wave spectrum integrates preceding wind conditions. The overall root-mean-square error (RMSE) of estimated wind speed is ∼1.1 m s−1, and the RMSE of the wind direction is ∼ 14∘ when wind speed is 7–25 m s−1. This model can be used not only for the wind estimation for compact wave buoys but also for the quality control of wind and wave measurements from meteorological buoys.

Meteor radars have become widely used instruments to study atmospheric dynamics, particularly in the 70 to 110 km altitude region. These systems have been proven to provide reliable and continuous measurements of horizontal winds in the mesosphere and lower thermosphere. Recently, there have been many attempts to utilize specular and/or transverse scatter meteor measurements to estimate vertical winds and vertical wind variability. In this study we investigate potential biases in vertical wind estimation that are intrinsic to the meteor radar observation geometry and scattering mechanism, and we introduce a mathematical debiasing process to mitigate them. This process makes use of a spatiotemporal Laplace filter, which is based on a generalized Tikhonov regularization. Vertical winds obtained from this retrieval algorithm are compared to UA-ICON model data. This comparison reveals good agreement in the statistical moments of the vertical velocity distributions. Furthermore, we present the first observational indications of a forward scatter wind bias. It appears to be caused by the scattering center's apparent motion along the meteor trajectory when the meteoric plasma column is drifted by the wind. The hypothesis is tested by a radiant mapping of two meteor showers. Finally, we introduce a new retrieval algorithm providing a physically and mathematically sound solution to derive vertical winds and wind variability from multistatic meteor radar networks such as the Nordic Meteor Radar Cluster (NORDIC) and the Chilean Observation Network De meteOr Radars (CONDOR). The new retrieval is called 3DVAR+DIV and includes additional diagnostics such as the horizontal divergence and relative vorticity to ensure a physically consistent solution for all 3D winds in spatially resolved domains. Based on this new algorithm we obtained vertical velocities in the range of w = ± 1–2 m s−1 for most of the analyzed data during 2 years of collection, which is consistent with the values reported from general circulation models (GCMs) for this timescale and spatial resolution.