Doppler Radar Data Research Articles

Improving Recognition of Road Users via Doppler Radar Data and Deep Learning Convolutional Networks

This research presents findings from laboratory experiments on a novel method for identifying and differentiating objects using radar signatures and a specialized convolutional neural network architecture. Previously introduced by the authors, this method has been validated through real-world measurements in an urban environment with a 24 GHz frequency-modulated continuous-wave radar. This study describes how radar signatures, generated in the MATLAB (R2023b) environment from I and Q signals captured by the uRAD USB v1.2 radar, were processed. A database of radar signatures for pedestrians, cyclists, and vehicles was created, and a tailored convolutional neural network was trained. The developed solution achieves an accuracy of over 95% in distinguishing between various objects. The simulation results and successful tests support the application of this system across various sectors. The key innovations include distinguishing multiple objects from a single radar signature, a custom architecture for the convolutional neural network, and an application that processes radar data to produce near-real-time recognition results.

Initiation of a Supercell by Convectively Generated Gravity Waves in the Simulated Kaiyuan Tornadic Event of 3 July 2019

Abstract An EF4-rated supercell tornado occurred on 3 July 2019 in Kaiyuan, China, causing heavy casualties. A three-level nested-grid high-resolution numerical simulation is used to investigate the initiation of the tornadic supercell. Automatic weather station (AWS) data, FY-4A visible satellite data, and Doppler radar data are used to verify the model simulation. The most important aspects of the simulated pre-supercell mesoscale convective system (MCS) and the initiation of the supercell agree with observations. Detailed investigation of the model results reveals that the initial cells form first above a convective boundary layer (CBL) on the dry side of a surface dryline. Above the CBL is a moist layer in terms of relative humidity and the layer is stable. Convectively generated gravity waves (GWs) emanating from the MCS and propagating southward along the stable layer above the CBL provide localized forcing for the actual triggering of initial cells at specific locations. The associated perturbation potential temperature and vertical velocity patterns confirm that the GWs trigger a series of cloud bands. The additional lifting by the updraft of a horizontal convective roll in the CBL underneath the GW updraft works together to promote faster growth of the initial cell that later becomes the supercell. Examination of the Scorer parameter profiles shows favorable conditions for vertical trapping of GWs along the wave guide in the stable layer, preventing the radiation of wave energy to the upper levels.

Automatic Monitoring of Rock‐Slope Failures Using Distributed Acoustic Sensing and Semi‐Supervised Learning

AbstractEffective use of the wealth of information provided by Distributed Acoustic Sensing (DAS) for mass movement monitoring remains a challenge. We propose a semi‐supervised neural network tailored to screen DAS data related to a series of rock collapses leading to a major failure of approximately 1.2 million on 15 June 2023 in Brienz, Eastern Switzerland. Besides DAS, the dataset from 16 May to 30 June 2023 includes Doppler radar data for partially ground‐truth labeling. The proposed algorithm is capable of distinguishing between rock‐slope failures and background noise, including road and train traffic, with a detection precision of over . It identifies hundreds of precursory failures and shows sustained detection hours before and during the major collapse. Event size and signal‐to‐noise ratio (SNR) are the key performance dependencies. As a critical part of our algorithm operates unsupervised, we suggest that it is suitable for general monitoring of natural hazards.

Quantitative Testing of a SOLO-Based Automated Quality Control Algorithm for Airborne Tail Doppler Radar Data

An automated quality control pre-processing algorithm for removing non-weather radar echoes from airborne Doppler radar data has been developed. The proposed algorithm can significantly reduce the time and experience level required for interactive radar data editing prior to dual-Doppler wind synthesis or data assimilation. As important as reducing the time required and skill level necessary to process an airborne Doppler dataset can be, the quality of the automated analysis is paramount. Retrieved wind data, recovered perturbation pressure data (with associated momentum check values) and correlation coefficients were computed. To quantitatively test the quality of the automated quality control algorithm, spatial Pearson correlation coefficients and momentum check values were computed. Four different (published) Electra Doppler Radar (ELDORA) datasets of convective echoes were used to stress the algorithm. Four distinct threshold levels for data removal in the automated quality control algorithm were applied to each of four ELDORA datasets. The algorithm threshold levels were labeled as follows: extremely low, low, medium, and high. Extremely low algorithm cases were deemed necessary during the data analyses and were added to the low, medium and high cases. A description of each case and the differences in the perturbation pressure momentum check values and correlation coefficients between the interactively edited fields were computed. These comparisons along with a subjective visual inspection show that the automated quality control algorithm can produce an analysis comparable—and in some cases superior—to an interactive analysis when used properly. A key benefit of this algorithm is that the skill level of a relatively inexperienced airborne radar meteorologist may be effectively increased by using the SOLO QC algorithm.

Contrasting the Effects of X-Band Phased Array Radar and S-Band Doppler Radar Data Assimilation on Rainstorm Forecasting in the Pearl River Delta

Contrasting the X-band phased array radar (XPAR) with the conventional S-Band dual-polarization mechanical scanning radar (SMSR), the XPAR offers superior temporal and spatial resolution, enabling a more refined depiction of the internal dynamics within convective systems. While both SMSR and XPAR data are extensively used in monitoring and alerting for severe convective weather, their comparative application in numerical weather prediction through data assimilation remains a relatively unexplored area. This study harnesses the Weather Research and Forecasting Model (WRF) and its data assimilation system (WRFDA) to integrate radial velocity and reflectivity from the Guangzhou SMSR and nine XPARs across Guangdong Province. Utilizing a three-dimensional variational approach at a 1 km convective-scale grid, the assimilated data are applied to forecast a rainstorm event in the Pearl River Delta (PRD) on 6 June 2022. Through a comparative analysis of the results from assimilating SMSR and XPAR data, it was observed that the assimilation of SMSR data led to more extensive adjustments in the lower- and middle-level wind fields compared to XPAR data assimilation. This resulted in an enlarged convergence area at lower levels, prompting an overdevelopment of convective systems and an excessive concentration of internal hydrometeor particles, which in turn led to spurious precipitation forecasts. However, the sequential assimilation of both SMSR and XPAR data effectively reduced the excessive adjustments in the wind fields that were evident when only SMSR data were used. This approach diminished the generation of false echoes and enhanced the precision of quantitative precipitation forecasts. Additionally, the lower spectral width of XPAR data indicates its superior detection accuracy. Assimilating XPAR data alone yields more reasonable adjustments to the low- to middle-level wind fields, leading to the formation of small-to-medium-scale horizontal convergence lines in the lower levels of the analysis field. This enhancement significantly improves the model’s forecasts of composite reflectivity and radar echoes, aligning them more closely with actual observations. Consequently, the Threat Score (TS) and Equitable Threat Score (ETS) for heavy-rain forecasts (>10 mm/h) over the next 5 h are markedly enhanced. This study underscores the necessity of incorporating XPAR data assimilation in numerical weather prediction practices and lays the groundwork for the future joint assimilation of SMSR and XPAR data.

Multi‐Task Learning for Tornado Identification Using Doppler Radar Data

AbstractTornadoes, as highly destructive weather events, require accurate detection for effective decision‐making. Traditional radar‐based tornado detection algorithms (TDA) face challenges with limited tornado feature extraction capabilities, leading to high false alarm rates and low detection probabilities. This study introduces the Multi‐Task Identification Network (MTI‐Net), leveraging Doppler radar data to enhance tornado recognition. MTI‐Net integrates tornado detection and estimation tasks to acquire comprehensive spatial and locational information. As part of MTI‐Net, we introduce a novel backbone network of Multi‐Head Convolutional Block (MHCB), which incorporates Spatial and Channel Attention Units (SAU and CAU). SAU optimizes local tornado feature extraction, while CAU reduces false alarms by enhancing dependencies among input variables. Experiments demonstrate the superiority of MTI‐Net over TDA, with a decrease in false alarm rates from 0.94 to 0.46 and an increase in hit rates from 0.23 to 0.81, highlighting the effectiveness of MTI‐Net in handling small‐scale tornado events.

The Improvement of Short-Term Quantitative Precipitation Forecast in Mountainous Areas by the Assimilation of Meteorological State Variables Retrieved by Multiple Doppler Radar Data

Abstract This study presents a sequential procedure formulated by combining a multiple-Doppler radar wind synthesis technique with a thermodynamic retrieval method, which can be applied to retrieve the three-dimensional wind, pressure, temperature, rainwater mixing ratio, and moisture over complex terrain. The retrieved meteorological state variables are utilized to reinitialize a high-resolution numerical model, which then carries out time integration using four different microphysical (MP) schemes, including the Goddard Cumulus Ensemble (GCE), Morrison (MOR), WRF single-moment 6-class (WSM6), and WRF double-moment 6-class (WDM6) schemes. It is found that through this procedure, the short-term quantitative precipitation forecast (QPF) skill of a numerical model over mountainous areas can be significantly improved up to 6 h. The moisture field plays a crucial role in producing the correct rainfall forecast. Since no specific microphysical scheme outperforms the others, a combination of various rainfall scenarios forecasted by different MP schemes is suggested in order to provide a stable and reliable rainfall forecast. This work also demonstrates that, with the proposed approach, radar data from only two volume scans are sufficient to improve the rainfall forecasts. This is because the unobserved meteorological state variables are instantaneously retrieved and directly used to reinitialize the model, thereby the model spinup time can be effectively shortened.

Sampling and misspecification errors in the estimation of observation‐error covariance matrices using observation‐minus‐background and observation‐minus‐analysis statistics

AbstractSpecification of the observation‐error covariance matrix for data assimilation systems affects the observation information content retained by the analysis, particularly for observations known to have correlated observation errors (e.g., geostationary satellite and Doppler radar data). A widely adopted approach for estimating observation‐error covariance matrices uses observation‐minus‐background and observation‐minus‐analysis residuals, which are routinely produced by most data assimilation systems. Although this approach is known to produce biased and noisy estimates, due to sampling and misspecification errors, there has been no systematic study of sampling errors with this approach to date. Furthermore, the eigenspectrum of the estimated observation‐error covariance matrix is known to influence the analysis information content and numerical convergence of variational assimilation schemes. In this work, we provide new theorems for the sampling error and eigenvalues of the estimated observation‐error covariance matrices with this approach. We also conduct numerical experiments to illustrate our theoretical results. We find that this method produces large sampling errors if the true observation‐error standard deviation is large, while the other error characteristics, including the true background‐error standard deviation and observation‐ and background‐error correlation length‐scales, have a relatively small effect. We also find that the smallest eigenvalues of the estimated matrices may be smaller or larger than the true eigenvalues, depending on the assumed and true observation‐ and background‐error statistics. These results may provide insights for practical applications: for example, in deciding on appropriate sample sizes and choosing parameters for matrix reconditioning techniques.

Study of dynamics-microphysical-lightning activity characteristics in a tropical hailstorm

Based on three-dimensional lightning locations and S-band polarimetric Doppler radar data, a hailstorm that occurred on Hainan Island on June 4, 2021 was analyzed in detail. The analysis revealed that the flash activity of hailstorms is mainly intracloud (IC) flashes, the cloud-to-ground (CG) flashes frequency were less, positive CG flashes (+CG) were domination in the CG activity before hailfall. The flash frequency appeared “lightning jumps” when hailstorms merging with convective cells, ice hydrometeors also rapidly increased in the clouds of the mixed-phase area in this period, which was beneficial for efficient electrification. The Zdr column and evolution of echo top indicated presence of an intense updraft during the mature stage of the hailstorm. Zdr column depth about 4 km close to 20 °C during the mature stage. Near the time of the hailfall, Zdr column was contraction, due to sinking hail generated downdraft within the cloud. Zdr column after hailfall may be produced by the convergence of downdraft and sea breeze. In combination with the spatiotemporal distribution of the hydrometeors in hailstorms, flash activity was most closely related to graupel and it plays a important role for flashes electrification. The hail plays a minimum role in charge separation. Radiation sources were concentrated between 50 dBZ and 55 dBZ. The spatial distribution of radiation sources indicated possible complex charge structure within hail storm clouds.

Comparison of Cold Pool Characteristics of Two Distinct Gust Fronts over Bohai Sea Bay in China

Previous studies have demonstrated that cold pools play a pivotal role in the initiation and organization of convection, yet their influence on the evolution of gust fronts (GFs) remains inadequately understood. A destructive wind event associated with a rearward gust front (RGF; 8 grade gale after passing GF) and a prior gust front (PGF; 10 grade gale before passing GF) over the north coast of China on 10 June 2016 was analyzed. Using multiple forms of observation data, as well as the four-dimensional Variational Doppler Radar Data Assimilation System (VDRAS), we found that the depth and intensity of the cold pool in RGF are relatively shallower and weaker, leading to a correspondingly reduced strength in both outflow and convergence. In contrast, the enhanced vertical shear and boundary northeaster inflow of PGF generate intensified and more organized downdrafts, resulting in a deeper cold pool, robust outflow, and convergence. Two schematic models were proposed to explain the discrepancy between GFs and associated cold pools. We further show that there is an internal correlation between meso-γ-scale vortices (MVs) and cold pools, the collision of MVs strengthened low-level convergence and updraft between these two GFs. Moreover, the consolidation of the two cold pools exacerbates low-layer instability and rotation, generating an intense horizontal vorticity that leads to rapid convective storm intensification. These findings offer novel insights into the diversity of GFs and associated cold pools.

Shear-Relative Asymmetric Kinematic Characteristics of Intensifying Hurricanes as Observed by Airborne Doppler Radar

Abstract While recent observational studies of intensifying (IN) versus steady-state (SS) hurricanes have noted several differences in their axisymmetric and asymmetric structures, there remain gaps in the characterization of these differences in a fully three-dimensional framework. To address these limitations, this study investigates differences in the shear-relative asymmetric structure between IN and SS hurricanes using airborne Doppler radar data from a dataset covering an extended period of time. Statistics from individual cases show that IN cases are characterized by peak wavenumber-1 ascent concentrated in the upshear-left (USL) quadrant at ∼12-km height, consistent with previous studies. Moderate updrafts (2–6 m s−1) occur more frequently in the downshear eyewall for IN cases than for SS cases, likely leading to a higher frequency of moderate to strong updrafts USL above 9-km height. Composites of IN cases show that low-level outflow from the eye region associated with maximum wavenumber-1 vorticity inside the radius of maximum wind (RMW) in the downshear-left quadrant converges with low-level inflow outside the RMW, forming a stronger local secondary circulation in the downshear eyewall than SS cases. The vigorous eyewall convection of IN cases produces a net vertical mass flux increasing with height up to ∼5 km and then is almost constant up to 10 km, whereas the net vertical mass flux of SS cases decreases with height above 4 km. Strong USL upper-level ascent provides greater potential for the vertical development of the hurricane vortex, which is argued to be favorable for continued intensification in shear environments.

Convective Properties and Lightning Activity in Different Categories of Thunderstorms over the Beijing Area during Five Warm Seasons

Based on comprehensive observations, including total lightning, Doppler radar, precipitation, and other meteorological data, the variations in thunderstorm properties and lightning activity of different categories for thunderstorms over the Beijing area during five warm seasons were investigated. According to the morphology of radar echo, thunderstorms were classified into five categories, including single convective cells, multi-cells, linear mesoscale convective system (MCS), nonlinear MCS, and weak convective precipitation system (WCPS). The diurnal variability of lightning, thunderstorm occurrence, and precipitation showed late-afternoon maxima, with the peak time of lightning frequency occurring before that of precipitation. Despite WCPS having the lowest lightning frequency, the percentage of +CG/CG was the highest with large peak currents. The convective available potential energy (CAPE) of linear MCS, multi-cells, nonlinear MCS, single cells, and WCPS categories followed a pattern from largest to smallest. Meanwhile, warm cloud depth (WCD) exhibited a smaller value in the well-organized thunderstorm categories and a larger value in the WCPS. The topographic forcing mechanism and large wind gradient along mountain slopes facilitated convection occurrence and enhancement, further promoting lightning production. Meanwhile, the nocturnal convection mechanism significantly impacted the activity of nonlinear MCS and WCPS.

Dual-Polarimetric Radar Applications for Investigating Severe Thunderstorms in Northern Thailand during the Pre-Monsoon Season

Understanding the physical characteristics of severe pre-monsoon thunderstorms is crucial for mitigating the adverse impacts of natural disasters on people in tropical regions. On April 23, 2020, a severe storm affected the Chiang Khong District of the Chiang Rai Province in Northern Thailand, causing devastation to more than 500 houses with hail and strong winds. This study investigated the storm's physical characteristics using dual-polarimetric radar data from the Thai Meteo-rological Department (TMD). Using a combination of polarimetric radar variables, such as reflectivity (ZH), specific differential phase (KDP), differential reflectivity (ZDR), and copolar correlation coefficient (ρhv), fuzzy logic was utilized to classify the hydrometeor types. During the severe storm that affected the Chiang Khong District, hail and large raindrops were mixed in with the rain, according to the analysis. Two severe storms were observed in the unorganized mesoscale convective systems that were analyzed, with the second storm producing more intense effects due to the radar reflectivity shape of a bow echo that generated strong wind gusts. The Doppler radar data retrieved the wind field, indicating the convergence of intense local winds during the storm, which was consistent with the analysis results of synoptic-scale weather systems from ECMWF Reanalysis v5 (ERA5). In addition, a Hovmöller diagram revealed orographic enhancement of the convective cloud as the storm passed through mountainous regions prior to approaching the Chiang Khong District. The findings of this study can provide valuable information for TMD's near-real-time warning operation in order to minimize loss of life and property from future severe storms.

On the Distribution of Convective and Stratiform Precipitation in Tropical Cyclones from Airborne Doppler Radar and Its Relationship to Intensity Change and Environmental Wind Shear Direction

Abstract Airborne Doppler radar reflectivity data collected in hurricanes on the NOAA P-3 aircraft between 1997 and 2021 were parsed into different modes of precipitation: stratiform precipitation, shallow convection, moderate convection, and deep convection. Stratiform precipitation was the most frequent precipitation mode with 82.6% of all observed precipitation while deep convection was the most infrequent at 1.3%. When stratified by 12-h intensity change, intensifying TCs had a greater areal coverage of total convection in the eyewall compared to weakening and steady-state TCs. The largest difference in the azimuthal distributions in the precipitation modes was in deep convection, which was mostly confined to the downshear-left quadrant in weakening and steady-state hurricanes and more symmetrically distributed in intensifying hurricanes. For all intensity change categories, the most symmetrically distributed precipitation mode was stratiform rain. To build upon the results of a recent thermodynamic study, the precipitation data were recategorized for hurricanes experiencing deep-layer wind shear with either a northerly component or southerly component. Like intensifying storms, hurricanes that experienced northerly component shear had a more symmetric distribution of deep convection than southerly component shear storms, which had a distribution of deep convection that resembled weakening storms. The greatest difference in the precipitation distributions between the shear direction groups were in major hurricanes experiencing moderate (4.5–11 m s−1) wind shear values. Consistent with previous airborne radar studies, the results suggest that considering the distribution of deep convection and the thermodynamic distributions associated with differing environmental wind shear direction could aid TC intensity forecasts. Significance Statement This research investigates how the distribution of different types of precipitation are related to tropical cyclone (TC) intensity change. Even though deep convection—the tallest clouds—is the least frequent type of precipitation, it has the strongest relationship to intensity change with uniform distributions around the eyewall associated with intensification. Less significant relationships were noticed for shallower clouds and stratiform (lighter) rain. The study also analyzed how change in direction of the large-scale winds with height (wind shear) influences intensity change. When wind shear is northerly, there is a more symmetric distribution of deep convection compared to when wind shear is southerly. These relationships illustrate how wind shear direction influences TC convective structure and, in turn, TC intensity change.

Rockfall monitoring with a Doppler radar on an active rockslide complex in Brienz/Brinzauls (Switzerland)

Abstract. We present and analyze a rockfall catalog from an active landslide complex in Brienz/Brinzauls of the Swiss Alps, collected with a new Doppler radar system. This radar system provides a complete and continuous time series of rockfall events with volumes of 1 m3 and greater since 2018 and serves as automatic traffic control for an important main road. In the period between January 2018 and October 2022, 6743 events were detected, which is 2 orders of magnitude higher activity than in stable continental cliffs. A few percent of all rockfall events reached the shadow area, which hosts an important road and agricultural area. The Doppler radar data set allows us to investigate the triggering factors quantitatively. We found that the background rockfall activity is controlled by seasonal climatic triggers. In winter, more rockfalls are observed during thawing periods, whereas in summer the rockfall activity increases with hourly rainfall intensity. We also found that, due to the geological setting in an active landslide complex, increased rockfall activity occurs clustered in space and time, triggered by local displacement hotspots. Thus, monitoring spatial and temporal variations of slope displacement velocity is crucial for detailed rockfall hazard assessment in similar geological settings.

Application of Doppler Radar Data in Rainstorm Forecast

Application of Doppler Radar Data in Rainstorm Forecast

Giant hail in Poland produced by a supercell merger in extreme instability – A sign of a warming climate?

Severe thunderstorms that occurred on 11 June 2019 over eastern Germany and western Poland caused severe material losses primarily due to large hail. This work documents a detailed inventorization of 79 large hail reports collected in the densely populated area of Gorzów Wielkopolski in Poland, including the largest with a diameter of 12 cm and heaviest with a weight of 380 g. Evaluation of environmental and Doppler radar data indicate that three unique aspects characterize this event. Firstly, the storm benefited from a favorable convective environment including a record-high CAPE exceeding 4000 J kg−1 that was the highest ever measured value at a proximity rawinsonde station of Lindenberg, and the highest for this region according to ERA5 since 1950. Secondly, moisture pooling along the convergence zone led to convective initiation of 3 isolated cells that merged together. Two of them with already embedded rotation. This merger subsequently evolved into a single powerful mesocyclone that was a main cause of giant hail. Subtle differences in the orography within Warta river valley could have played a role in the merging process. Third, a supercell had the biggest intensity over a densely populated area, which led to considerable material losses, but also allowed collection of a large number of hail reports. Distribution of these reports indicates that peak hail size varied significantly on very small distances. Storm has been producing giant hail for around 20-min over a distance of around 10 km and was preceded by a well-developed velocity couplet, bounded weak echo region and hook-echo signatures on the Doppler radar scans.

Environmental and Radar-Derived Predictors of Tornado Intensity within Ongoing Convective Storms

Analyses of Doppler radar data and environmental parameters for 300 tornado cases are used to propose an alternative framework for tornado intensity prediction during pretornadic stages of ongoing storms, conditional on tornadogenesis. This framework is founded on the robust relationship (R² = 0.69) between pretornadic mesocyclone width and the EF rating of the subsequent tornado. In contrast, the linear relationship between pretornadic mesocyclone intensity and EF scale is much weaker (R² = 0.29). Environmental information for each case was additionally used to explore relationships between environmental parameters and tornado intensity. Such relationships depend in part on how the tornado-intensity categories are distributed [i.e., nonsignificant (EF0–1) versus significant (EF2–5), or weak (EF0–1) versus strong (EF2–3) versus violent (EF4–5)]. Low-level shear parameters discriminate the environments of significant tornadoes from nonsignificant tornadoes, but not the environments of violent tornadoes from strong tornadoes. The converse is true for thermodynamic parameters. Operational implementation of this framework for thepurposes of impact-based warnings will require real-time, automated quantification of mesocyclone width in addition to intensity and other attributes. The information gained from the pretornadic analysis demonstrated in this study would allow an operational forecaster to be aware of—and communicate—information about potential tornado intensity in warning text to the public before a tornado develops to better protect life and property. Currently, these relationships are being utilized in machine learning models for binary prediction of non-significant versus significant tornado intensity where skill is being demonstrated.

Vertical Motions Forced by Small-Scale Terrain and Cloud Microphysical Response in Extratropical Precipitation Systems

Abstract Airborne vertically profiling Doppler radar data and output from a ∼1-km-grid-resolution numerical simulation are used to examine how relatively small-scale terrain ridges (∼10–25 km apart and ∼0.5–1.0 km above the surrounding valleys) impact cross-mountain flow, cloud processes, and surface precipitation in deep stratiform precipitation systems. The radar data were collected along fixed flight tracks aligned with the wind, about 100 km long between the Snake River Plain and the Idaho Central Mountains, as part of the 2017 Seeded and Natural Orographic Wintertime clouds: the Idaho Experiment (SNOWIE). Data from repeat flight legs are composited in order to suppress transient features and retain the effect of the underlying terrain. Simulations closely match observed series of terrain-driven deep gravity waves, although the simulated wave amplitude is slightly exaggerated. The deep waves produce pockets of supercooled liquid water in the otherwise ice-dominated clouds (confirmed by flight-level observations and the model) and distort radar-derived hydrometeor trajectories. Snow particles aloft encounter several wave updrafts and downdrafts before reaching the ground. No significant wavelike modulation of radar reflectivity or model ice water content occurs. The model does indicate substantial localized precipitation enhancement (1.8–3.0 times higher than the mean) peaking just downwind of individual ridges, especially those ridges with the most intense wave updrafts, on account of shallow pockets of high liquid water content on the upwind side, leading to the growth of snow and graupel, falling out mostly downwind of the crest. Radar reflectivity values near the surface are complicated by snowmelt, but suggest a more modest enhancement downwind of individual ridges. Significance Statement Mountains in the midlatitude belt and elsewhere receive more precipitation than the surrounding lowlands. The mountain terrain often is complex, and it remains unclear exactly where this precipitation enhancement occurs, because weather radars are challenged by beam blockage and the gauge network is too sparse to capture the precipitation heterogeneity over complex terrain. This study uses airborne profiling radar and high-resolution numerical simulations for four winter storms over a series of ridges in Idaho. One key finding is that while instantaneous airborne radar transects of the cross-mountain flow, vertical drafts, and reflectivity contain much transient small-scale information, time-averaged transects look very much like the model transects. The model indicates substantial surface precipitation enhancement over terrain, peaking over and just downwind of individual ridges. Radar observations suggest less enhancement, but the radar-based assessment is uncertain. The second key conclusion is that, even though orographic gravity waves are felt all the way up into the upper troposphere, the orographic precipitation enhancement is due to processes very close to the terrain.

Estimation of Gait Parameters From Trunk Movement Measured by Doppler Radar

This study proposes a novel method to estimate biomechanical gait parameters using the monostatic continuous wave Doppler radar data corresponding to trunk movement; the use of trunk movement leads to stable gait parameter estimations when compared to the conventional method which is based on leg movements because the received powers of the trunk echoes are larger and more stable than those of the legs. The proposed gait parameter estimation method employs trunk accelerations extracted from radar spectrograms. The accuracies of the gait parameters, such as the step length, swing time, and stance time, estimated using the proposed methods were evaluated by comparing them with the reference motion capture data. We experimentally demonstrated that the proposed trunk-based method estimated gait parameters with accuracy similar to that of the conventional leg-based method. Additionally, the proposed trunk-based method was able to estimate the swing and stance times, whereas the leg-based method failed to estimate them owing to instability of the ankle echoes. Furthermore, the combined method of the trunk and toe data achieved better accuracy in the estimation of swing and stance times. This study is the first to use data on trunk movements (not leg movements) for temporal gait parameter estimation using radar and demonstrated its practicality for realistic situations in clinical and daily gait assessment to grasp the health status of individuals such as risks of future falls and cognitive impairments.