Search Results (101)

Convective updrafts are one of the main characteristics of convective clouds, responsible for the convective mass flux and the redistribution of energy and condensate in the atmosphere. During the early stages of their lifecycle, convective clouds experience rapid cloud-top ascent manifested by a decrease in the geostationary IR brightness temperature ($T{B}_{IR}$). Under the assumption that the convective cloud top behaves like a black body, the ascent rate of the convective cloud top can be estimated as (${\displaystyle \frac{\partial T{B}_{IR}}{\partial t}}$), and it can be used to infer the near cloud-top convective updraft. The temporal resolution of the geostationary IR measurements and non-uniform beam-filling effects can influence the convective updraft estimation. However, the main shortcoming until today was the lack of independent verification of the strength of the convective updraft. Here, Doppler radar observations from the ESCAPE and TRACER field experiments provide independent estimates of the convective updraft velocity at higher spatiotemporal resolution throughout the convective core column and can be used to evaluate the updraft velocity estimates from the IR cooling rate for limited samples. Isolated convective cells were tracked with dedicated radar (RHIs and PPIs) scans throughout their lifecycle. Radial Doppler velocity measurements near the convective cloud top are used to provide estimates of convective updrafts. These data are compared with the geostationary IR and VIS channels (from the GOES satellite) to characterize the convection evolution and lifecycle based on cloud-top cooling rates. Full article

The high spatial resolution of radar data enables the detailed resolution of typhoon vortices and their embedded structures; the assimilation of radar data in the initialization of numerical weather prediction exerts an important influence on the forecasting of typhoon track, intensity, and structures up to at least 12 h. For the case of typhoon Morakot (2009), Taiwan radar data was assimilated to adjust the dynamic and thermodynamic structures of the vortex in the model initialization by the three-dimensional variation data assimilation system in the Advanced Region Prediction System (ARPS). The radial wind was directly assimilated to tune the original unbalanced velocity fields through a 3-dimensional variation method, and complex cloud analysis was conducted by using the reflectivity data. The influence of radar data assimilation on typhoon prediction was examined at the stages of Morakot landing on Taiwan Island and subsequently going inland. The results showed that the assimilation made some improvement in the prediction of vortex intensity, track, and structures in the initialization and subsequent forecast. For example, besides deepening the central sea level pressure and enhancing the maximum surface wind speed, the radar data assimilation corrected the typhoon center movement to the best track and adjusted the size and inner-core structure of the vortex to be close to the observations. It was also shown that the specific humidity adjustment in the cloud analysis procedure during the assimilation time window played an important role, producing more hydrometeors and tuning the unbalanced moisture and temperature fields. The neighborhood-based ETS revealed that the assimilation with the specific humidity adjustment was propitious in improving forecast skill, specifically for smaller-scale reflectivity at the stage of Morakot landing, and for larger-scale reflectivity at the stage of Morakot going inland. The calculation of the intensity-scale skill score of the hourly precipitation forecast showed the assimilation with the specific humidity adjustment performed skillful forecasting for the spatial forecast-error scales of 30–160 km. Full article

This research investigates how incorporating both radar radial velocity ($V_r$) and radar reflectivity influences the accuracy of tropical cyclone (TC) prediction. Different control variables are introduced to analyze their roles in $V_r$ data assimilation, while background-dependent radar reflectivity assimilation methods are also applied. Using Typhoon “Doksuri” (2023) as a primary case study and Typhoon “Kompasu” (2021) as a supplementary case, the Weather Research and Forecasting (WRF) model’s three-dimensional variational assimilation (3DVAR) is utilized to assimilate $V_r$ and reflectivity observations to improve TC track, intensity, and precipitation forecasts. Three experiments were conducted for each typhoon: one with no assimilation, one with $V_r$ assimilation using $\psi -\chi$ control variables and background-dependent radar reflectivity assimilation, and one with $V_r$ assimilation using $U-V$ control variables and background-dependent radar reflectivity assimilation. The results show that assimilating $V_r$ enhances small-scale dynamics in the TC core, leading to a more organized and stronger wind field. The experiment involving $U-V$ control variables consistently showed advantages over the $\psi -\chi$ scheme in aspects such as overall track prediction, initial intensity representation, and producing more stable or physically plausible intensity trends, particularly evident when comparing both typhoon events. These findings highlight the importance of optimizing control variables and assimilation methods to enhance the prediction of TCs. Full article

High-resolution radar observations are essential to improving the numerical predictions of high-impact weather systems with data assimilation techniques. The numerical simulations of the landfall of Typhoon Lekima (2019) are conducted in the framework of the WRF model, investigating the impact of assimilating radar radial velocity observations via the Ensemble Kalman Filter (EnKF) on the typhoon’s analysis and forecast performance. The results demonstrate that the EnKF method significantly improves forecast accuracy for Typhoon Lekima, including track, intensity and the 24 h cumulative precipitation. To be specific, the control experiment significantly underestimated typhoon intensity, while EnKF-based radar radial velocity assimilation markedly improved near-surface winds (>48 m/s) in the typhoon core, refined vortex structure and reduced track forecast errors by 50–60%. Compared with the control and 3DVAR experiments, EnKF assimilation better captured typhoon precipitation patterns, with the highest ETS scores, especially for moderate-to-high precipitation intensities. Moreover, the detailed analysis and diagnostics of Lekima show that the warm core structure is better captured in the assimilation experiment. The typhoon system is also improved, as reflected by enhanced potential temperature and a more robust wind field analysis. Full article

Ocean surface radial current velocities can be derived from synthetic aperture radar (SAR) Doppler shift observations using the Doppler centroid technique and a recently developed Doppler velocity model. However, comprehensive evaluations of the accuracy and reliability of these retrievals remain limited. To address this gap, we analyzed 6341 Sentinel-1 SAR scenes acquired over the South China Sea (SCS) between December 2017 and October 2023, in conjunction with drifting buoy observations, to systematically validate the retrieved radial current velocities. A linear fitting method and the dual co-polarization Doppler velocity (DPDop) model were applied to correct for the influence of non-geophysical factors and sea state effects. The validation against the drifter data yielded a bias of 0.01 m/s, a root mean square error (RMSE) of 0.18 m/s, and a mean absolute error (MAE) of 0.16 m/s. Further comparisons with the Surface and Merged Ocean Currents (SMOC) dataset revealed bias, RMSE, and MAE values of 0.07 m/s, 0.14 m/s, and 0.12 m/s in the Beibu Gulf, and −0.06 m/s, 0.23 m/s, and 0.19 m/s in the Kuroshio intrusion area. These results demonstrate that SAR Doppler measurements have a strong potential to complement existing ocean observations in the SCS by providing high-resolution (1 km) ocean surface current maps. Full article

Cross-Track Interferometric Synthetic Aperture Radar (XTI-SAR) can utilize variations in interferometric phase to measure sea surface velocity along radar radial direction and sea surface height, which can be used for ocean wave parameter inversion. However, research on the imaging mechanisms of XTI-SAR systems for ocean waves remains understudied, and there are still some problems in its perception. To further study the imaging mechanism of XTI-SAR measurement systems for ocean waves, this paper describes research based on the nonlinear integral transform model and the quasi-linear integral transform model derived by Bao in 1999, which relate the XTI-SAR ocean wave spectrum to the phase spectrum. Firstly, this work derived another quasi-linear integral transform model based on the nonlinear integral transform model, and also optimized the quasi-linear integral transform model derived by Bao. The optimized quasi-linear integral transform model eliminates the need for complex calculations of cross-correlation functions between sea surface height and radar radial orbital velocity components of ocean waves, as well as the radar line-of-sight velocity transfer function, while maintaining high integral transform accuracy. Secondly, based on two-dimensional sea surface simulations, we analyzed the differences between the quasi-linear integral transform models and the nonlinear integral transform model corresponding to different XTI-SAR system configurations and different sea states. The numerical simulation results show that, for the XTI-SAR system, in general, the difference between the quasi-linear integral transform model derived in this work and the nonlinear integral transform model is greater than that of the quasi-linear integral transform model derived by Bao. However, the difference between the optimized quasi-linear integral transform model and the nonlinear integral transform model in this study is smaller, and it is more convenient when transforming the ocean wave spectrum to the phase spectrum. Full article

This paper proposes a hybrid deep learning network architecture (Inception-VGG16) to address the challenge of accurate aircraft wake vortex identification. The model first employs a Feature0 module for preliminary feature extraction of two-dimensional Doppler radar radial velocity data. This module comprises convolution, batch normalization, ReLU activation, and max pooling operations. Subsequently, improved InceptionB and InceptionC modules are utilized for parallel extraction of multi-scale features. The InceptionB former module adopts two parallel branches, combining 1 × 1 and 3 × 3 convolutions, and outputting 64-channel feature maps, while the InceptionC latter module expands the number of channels number to 128, enhancing the model’s feature representation capability. The backend employs the VGG16’s hierarchical structure, performing deep feature extraction through multiple convolution and pooling operations, and ultimately achieving wake vortex classification through fully connected layers. Experimental validation based on 3530 wind field samples collected at the Chengdu Shuangliu Airport demonstrates that compared to traditional methods (SVM, KNN, RF) and single deep networks (VGG16), the proposed hybrid model achieves a classification accuracy of 98.8%, significantly outperforming comparative traditional methods (SVM, KNN, RF) and single deep networks (VGG16). The model not only overcomes the limitations of single networks in processing multi-scale wake features but also enhances the model’s ability to identify wake vortices in complex backgrounds through deep feature hierarchies, providing a new technical solution for aviation safety monitoring systems based on deep learning. Full article

High-frequency (HF) ocean radars have become essential tools for monitoring surface currents, offering real-time, wide-area coverage with cost-effectiveness. This study compares the compact CODAR system (MABT, 13 MHz) and the square-array phased-array radar (KNTN, 8 MHz) deployed at Cape Maobitou, Taiwan. Radial velocity measurements were evaluated against data from the Global Drifter Program (GDP), and a quality control (QC) mechanism was applied to improve the data’s reliability. The results indicated that KNTN provides broader spatial coverage, whereas MABT demonstrates higher precision in radial velocity measurements. Baseline velocity comparisons between MABT and KNTN revealed a correlation coefficient of 0.77 and a root-mean-square deviation (RMSD) of 0.23 m/s, which are consistent with typical values reported in previous radar performance evaluations. Drifter-based velocity comparisons showed an initial correlation of 0.49, with an RMSD of 0.43 m/s. In more stable oceanic regions, the correlation improved to 0.81, with the RMSD decreasing to 0.24 m/s. To clarify, this study does not include multiple environmental scenarios but focuses on cases where both radar systems operated simultaneously and where surface drifter data were available within the overlapping area. Comparisons are thus limited by these spatiotemporal conditions. Radar data may still be affected by environmental or human factors, such as ionospheric variations, interference from radio frequency management issues, or inappropriate parameter settings, which could reduce the accuracy and consistency of the observations. International ocean observing programs have developed quality management procedures to enhance data reliability. In Taiwan, the Taiwan Ocean Research Institute (TORI) has established a data quality management mechanism based on international standards for data filtering, noise reduction, and outlier detection, improving the accuracy and stability of radar-derived velocity measurements.To eliminate the effects caused by different center frequencies between MABT and KNTN, this study used the same algorithms and parameter settings as much as possible in all steps, from Doppler spectra processing to radial velocity calculation, ensuring the comparability of the data. This study highlights the strengths and limitations of compact and phased-array HF radar systems based on co-observed cases under consistent operational conditions. Future research should explore multi-frequency radar integration to enhance spatial coverage and measurement precision, improving real-time coastal current monitoring and operational forecasting. Full article

Every day, millions of meteoroids enter the atmosphere and ablate, forming a long plasma trail. It is a strongly scattering object for electromagnetic waves and can be effectively detected by meteor radar at altitudes between 70 km and 140 km. Its echo typically has Fresnel oscillation characteristics. Most of the traditional detection methods rely on determining the threshold value of the signal-to-noise ratio (SNR) and solving parameters to recognize meteor echoes, making them highly susceptible to interference. In this paper, a neural network model, YOLOv8n-BP, was proposed for detecting the echoes of underdense meteors by identifying them from their echo characteristics. The model combines the strengths of both YOLOv8 and back propagation (BP) neural networks to detect underdense meteor echoes from Range-Time-Intensity (RTI) plots where multiple echoes are present. In YOLOv8, the n-type parameter represents the lightweight version of the model (YOLOv8n), which is the smallest and fastest variant in the YOLOv8 series, specifically designed for resource-constrained scenarios. Experiments show that YOLOv8n has excellent recognition ability for underdense meteor echoes in RTI plots and can automatically extract underdense meteor echoes without the influence of radio-frequency interference (RFI) and disturbance signals. Limited by the labeling error of the dataset, YOLOv8 is not precise enough in recognizing the head and tail of meteors in the radar echograms, which may result in the extraction of imperfect echoes. Utilizing the Fresnel oscillation properties of meteor echoes, a BP network based on a Gaussian activation function is designed in this paper to enable it to detect meteor head and tail positions more accurately. The YOLOv8n-BP model can quickly and accurately detect and extract underdense meteor echoes from RTI plots, providing correct data for meteor parameters such as radial velocities and diffusion coefficients, which are used to allow wind field calculations and estimate atmospheric temperature. Full article

The validation of HF radar systems remains an area with significant scope for advancement, particularly in terms of linking data quality with system operational parameters, fully utilizing the potential of redundant data (e.g., overlapping radial measurements), and accurately capturing the spatiotemporal variability observed by independent devices, such as drifters. In this study, we conducted a large-scale Lagrangian measurement campaign in the Tuscan Archipelago, aimed at validating surface current data from the HF radar network. This radar network, a recent addition to the area, monitors an oceanographic region critical to Mediterranean dynamics. The validation was executed using different approaches: a Eulerian method, comparing the radial velocities measured by radar with drifter-derived velocities along radial directions; a Lagrangian method, contrasting the observed drifter trajectories with the synthetic virtual trajectories generated from radar-based flow fields; and radar-to-radar comparisons with the concurrent utilization of two radars in same point. Through fine-tuning of the quality control parameters and an analysis of the impact of different thresholds of such parameters, we assessed the radar’s ability to capture dynamic processes, identifying both strengths and limitations. Our results not only confirm the utility of HF radar in coastal monitoring but also provide a basis for improving calibration strategies, ultimately supporting more accurate, high-resolution radar observations in complex marine environments. Full article

(1) The aliasing of radial velocities from weather radars is a known challenge in meteorology. It may also occur during bird migration if the unambiguous velocity threshold is below the birds’ ground speed. High variability in birds’ radial velocities and high flight speeds lead to multiple aliasing (folding) and challenge meteorological dealiasing approaches. Unfolded radial velocities are essential for calculating flight directions and speed and derived migration traffic rates for aeroecological applications. (2) We study the occurrence of aliasing in measurements of different pulse repetition frequencies (PRF) in C-band weather radars in bird and insect cases and test the efficiency of a dealiasing algorithm widely used in biological weather radar software. We use dual-PRF measurements as a reference to avoid the folding of radial velocities in quantitative and qualitative bird migration outputs. (3) The dealiasing algorithm performed poorly in single-PRF measurements during bird migration, though not in insect and precipitation cases. In contrast, dual-PRF velocities yielded proper flight speeds, flight directions and migration traffic rates. (4) The study unveils severe biases in aeroecological analyses of C-band weather radars from imperfectly dealiased single-PRF radial velocities. Dual-PRF measurements with appropriate dealiasing postprocessing offer a valid alternative to single PRF and should be preferred whenever available. Full article

In this work, we present the results of a comparative analysis between the first-generation Advanced Synthetic Aperture Radar (ASAR) sensor mounted on board the ENVISAT platform and the novel ICEYE micro-satellite synthetic aperture radar (SAR) sensor in measuring the radial velocity of ocean currents through the Doppler Centroid Anomaly (DCA) technique. First, the basic principles of DCA and the theoretical precision of the Doppler Centroid (DC) estimates are introduced. Subsequently, the role of the DC measurements in retrieving the sea surface current velocity is addressed. To achieve this goal, two sets of SAR data gathered by ASAR (C-band) and from the X-band ICEYE instruments, respectively, are exploited. The standard deviation of DCA measurements is derived and tested against what is expected by theory. The presented analysis results are beneficial to evaluate the pros and cons of the new-generation X-band to the first-generation ASAR/ENVISAT system, which has been extensively exploited for ocean currents monitoring applications. As an outcome, we find that with inherently selected methods for DC estimates, the performance offered by ICEYE is comparable to, or even better than (with specific parameters selection), the consolidated approaches based on the ASAR sensor. Nonetheless, new SAR constellations offer an undoubted advantage regarding improved spatial resolution and time repeatability. Full article

Measurements of downburst outflows using standard meteorological instruments (e.g., anemometers) are rare due to their transient and localized nature. However, video recordings of such events are becoming more frequent. This short communication (Technical Note) study presents a new approach to estimating the kinematics of a downburst event using video footage recordings of the event. The main geometric dimensions of the event, such as downdraft diameter, cloud base height, outflow depth, and the radius of the outflow at a given moment in time, are estimated by sizing them against reference structures of known dimensions that are present in the video footage. From this analysis, and knowing the frame rate of the video recording, one can estimate the characteristic velocities in the downburst event, such as the mean downdraft velocity and the mean velocity of the radial outflow propagation. The proposed method is tested on an August 2015 downburst event that hit Tucson, Arizona, United States. The diameter of the downburst outflow increased with the time from approximately 1.10 km to 3.35 km. This range of values indicates that the event was a microburst. The mean descending velocity of downburst downdraft was 8.9 m s⁻¹ and the horizontal velocity of outflow propagation was 17.7 m s⁻¹. The latter velocity is similar to the measured wind gust at the nearby weather station and Doppler radar. The outflow depth is estimated at 160 m, and the cloud base height was approximately 1.24 km. Estimating the kinematics of downbursts using video footage, while subject to certain limitations, does yield a useful estimation of the main downburst kinematics that contribute to a better quantification of these localized windstorms. Full article

Interactions between ocean surface currents, winds and waves at the atmosphere-ocean interface are key controls of lateral and vertical exchanges of water, heat, carbon, gases and nutrients in the global Earth System. The SeaSTAR satellite mission concept proposes to better quantify and understand these important dynamic processes by measuring two-dimensional fields of total surface current and wind vectors with unparalleled spatial and temporal resolution (1 × 1 km² or finer, 1 day) and unmatched precision over one continuous wide swath (100 km or more). This paper presents a comprehensive numerical analysis of the expected performance of the Earth Explorer 11 (EE11) SeaSTAR mission candidate in the case of idealised and realistic 2D ocean currents and wind fields. A Bayesian framework derived from satellite scatterometry is adapted and applied to SeaSTAR’s bespoke inversion scheme that simultaneously retrieves total surface current vectors (TSCV) and ocean surface vector winds (OSVW). The results confirm the excellent performance of the EE11 SeaSTAR concept, with Root Mean Square Errors (RMSE) for TSCV and OSVW at 1 × 1 km² resolution consistently better than 0.1 m/s and 0.4 m/s, respectively. The analyses highlight some performance degradation in some relative wind directions, particularly marked at near range and low wind speeds. Retrieval uncertainties are also reported for several variations around the SeaSTAR baseline three-azimuth configuration, indicating that RMSEs improve only marginally (by ∼0.01 m/s for TSCV) when including broadside Radial Surface Velocity or broadside dual-polarisation data in the inversion. In contrast, our results underscore (a) the critical need to include broadside Normalised Radar Cross Section data in the inversion; (b) the rapid performance degradation when broadside incidence angles become steeper than 20° from nadir; and (c) the benefits of maintaining ground squint angle separation between fore and aft lines-of-sight close to 90°. The numerical results are consistent with experimental performance estimates from airborne data and confirm that the EE11 SeaSTAR concept satisfies the requirements of the mission objectives. Full article

This research advances millimeter-wave (mmWave) altimetry for unmanned aerial systems (UASs) by optimizing performance metrics within the constraints of inexpensive automotive radars. Leveraging the software-defined architecture, this study encompasses the intricacies of frequency modulated continuous waveform (FMCW) design for three distinct stages of UAS flight: cruise, landing approach, and touchdown within a signal processing framework. Angle of arrival (AoA) estimation, traditionally employed in terrain mapping applications, is largely unexplored for UAS radar altimeters (RAs). Time-division multiplexing multiple input–multiple output (TDM-MIMO) is an efficient method for enhancing angular resolution without compromising the size, weight, and power (SWaP) characteristics. Accordingly, this work argues the potential of AoA estimation using TDM-MIMO to augment situational awareness in challenging landing scenarios. To this end, two corner cases comprising landing a small-sized drone on a platform in the middle of a water body are included. Likewise, for the touchdown stage, an improvised rendition of zoom fast Fourier transform (ZFFT) is investigated to achieve millimeter (mm)-level range accuracy. Aptly, it is proposed that a mm-level accurate RA may be exploited as a software redundancy for the critical weight-on-wheels (WoW) system in fixed-wing commercial UASs. Each stage is simulated as a radar scenario using the specifications of automotive radar operating in the 77–81 GHz band to optimize waveform design, setting the stage for field verification. This article addresses challenges arising from radial velocity due to UAS descent rates and terrain variation through theoretical and mathematical approaches for characterization and mandatory compensation. While constant false alarm rate (CFAR) algorithms have been reported for ground detection, a comparison of their variants within the scope UAS altimetry is limited. This study appraises popular CFAR variants to achieve optimized ground detection performance. The authors advocate for dedicated minimum operational performance standards (MOPS) for UAS RAs. Lastly, this body of work identifies potential challenges, proposes solutions, and outlines future research directions. Full article