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RadTraQ is a python library housing routines related to assessing and monitoring the quality of a radar's quality and calibration. This library is a work in progress, currently housing scripts to plot radar CFADs, plot and analyze corner reflector scans, calculate noise floors, create cloud masks, and plot up average profiles for comparisons.
This dataset contains radar track data of avian targets from DeTect's 7360 s-band radar during the large barge deployment from June 2024 though September 2024.
This dataset contains radar track data of avian targets from DeTect's 7360 s-band radar during the large barge deployment from June 2024 though September 2024.
Abstract The depth of convective clouds affects vertical transport of atmospheric constituents, influencing downstream weather and climate. Atmospheric controls on the maximum depth reached by moist convection are investigated with radar‐tracked convective cells tagged with sounding‐derived atmospheric parameters from a field campaign in central Argentina. Regression analyses show that narrow (<12‐km diameter) and wide (>16‐km diameter) cell depths respond to disparate factors, where cell areas are defined using composite reflectivity signatures. Undiluted lifted parcel indices including convective available potential energy (CAPE) and level of neutral buoyancy (LNB) are top predictors of wide cell maximum depth while mid‐tropospheric relative humidity is the top predictor of narrow cell maximum depth. Because narrow cells are more numerous than wide cells, the overall outcome of the full cell population does not strongly correlate with CAPE and LNB conditions. Tracked cells and atmospheric conditions in a simulation with 3‐km grid spacing covering the field campaign produce similar results to those observed. Narrow cells that are relatively deep have a cooler and moister mid‐troposphere with weaker free tropospheric subsidence, while relatively deep wide cells have much warmer and moister lower tropospheric conditions. These atmospheric differences are present 1 hr before cell initiation at both a fixed observing site and variable cell initiation locations. Simulated narrow cell maximum equivalent potential temperature decreases with height at a rate similar to the ambient vertical gradient, causing these cells to fall short of their LNB and supporting the view that entrainment‐driven dilution is a dominant control on their depth.
Abstract. Optimizing radar observation strategies is one of the most important considerations in pre-field campaign periods. This is especially true for isolated convective clouds that typically evolve faster than the observations captured by operational radar networks. This study investigates uncertainties in radar observations of the evolution of the microphysical and dynamical properties of isolated deep convective clouds developing in clean and polluted environments. It aims to optimize the radar observation strategy for deep convection through the use of high-spatiotemporal cloud-resolving model simulations, which resolve the evolution of individual convective cells every 1 min, coupled with a radar simulator and a cell tracking algorithm. The radar simulation settings are based on the Tracking Aerosol Convection Interactions ExpeRiment (TRACER) and Experiment of Sea Breeze Convection, Aerosols, Precipitation and Environment (ESCAPE) field campaigns held in the Houston, TX, area but are generalizable to other field campaigns focusing on isolated deep convection. Our analysis produces the following four outcomes. First, a 5–7 m s−1 median difference in maximum updrafts of tracked cells is shown between the clean and polluted simulations in the early stages of the cloud lifetimes. This demonstrates the importance of obtaining accurate estimates of vertical velocity from observations if aerosol impacts are to be properly resolved. Second, tracking of individual cells and using vertical cross section scanning every minute capture the evolution of precipitation particle number concentration and size represented by polarimetric observables better than the operational radar observations that update the volume scan every 5 min. This approach also improves multi-Doppler radar updraft retrievals above 5 km above ground level for regions with updraft velocities greater than 10 m s−1. Third, we propose an optimized strategy composed of cell tracking by quick (1–2 min) vertical cross section scans from more than one radar in addition to the operational volume scans. We also propose the use of a single-RHI (range height indicator) updraft retrieval technique for cells close to the radars, for which multi-Doppler radar retrievals are still challenging. Finally, increasing the number of deep convective cells sampled by such observations better represents the median maximum updraft evolution with sample sizes of more than 10 deep cells, which decreases the error associated with sampling the true population to less than 3 m s−1.
The Ka-band ARM Zenith Radar Active Remote Sensing of CLouds CloudSat-aligned (KAZRARSCL-CLOUDSAT) Value-Added Product (VAP) applies satellite-based reflectivity calibrations to KAZRARSCL data sets. The Atmospheric Radiation Measurement (ARM) user facility has primarily used radar subsystem calibration monitoring to track cloud radar reflectivity drift over time, since reliable external calibration sources or other absolute references (such as corner reflectors) have historically been unavailable or impracticable. A study by Kollias et al. (2019) examined cloud reflectivity profiles observed with a well-characterized spaceborne downward-pointing millimeter cloud radar, operating as part of NASA’s CloudSat satellite mission (Tanelli et al. 2008). The Kollias team derived monthly statistical reflectivity offsets between CloudSat and the various generations of ARM cloud radars (millimeter wavelength cloud radar [MMCR], W-Band ARM Cloud Radar [WACR[, and Ka-band ARM Zenith Radar [KAZR]) for many, but not all, months at most fixed and mobile ARM sites over the period 2007-2017. These offsets, when available, are applied to the existing KAZRARSCL VAP products using the KAZRARSCL-CLOUDSAT VAP.
Abstract Human identification and activity recognition (HIAR) is crucial for many applications, such as surveillance, smart homes, and assisted living. As a sensing modality, radar has many unique characteristics including privacy protection, and contactless sensing. Single classification systems have shown to be accurate, but for long‐term solutions both human identification (ID) and human activity recognition (HAR) will need to be integrated in one system where it can be utilised simultaneously. In this article, a novel radar‐based human tracking system is presented where three classifiers are utilised to identify the subject and his/her behaviour. For any kind of motion, the system tracks the subject and detect the type of his/her motion. Based on the detected type of motion, the three classifiers are utilised for identification and activity recognition. The classifiers are built utilising deep transfer learning where three radar datasets are established to train and validate each of the deep networks. To recognise six activities and 10 human subjects, the three classifiers, namely, HAR, Gait ID, and Heart sound ID, achieve superior performance compared to the best reported results in literature with classification accuracies of 97.6%, 100%, and 41.8% respectively. Three successful examples are presented to demonstrate the introduced concept.
Entrainment of surrounding cooler and drier air into convective updrafts is one of the key processes that influence deep convection initiation and growth. Numerous studies have investigated the effect of entrainment on isolated convective cloud growth in idealized simulations, but the importance of this effect in realistic conditions with many interacting convective clouds remains uncertain. We examine the impact of entrainment on the depth reached by convective clouds in realistic large-eddy simulations (LES) over central Argentina during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign. Cloudy updrafts and their associated properties are assigned to convective cells tracked with radar reflectivity signatures. Several thousand convective cells are tracked over two high convective available potential energy (CAPE) and two low CAPE cases that support cells of varying depths and intensities. Entrainment is calculated explicitly as the fluxes of air into the outer surface of each cloudy updraft. Single-predictor logistic regression models are used to determine the relative importance of updraft, near-updraft, and preconvective initiation atmospheric conditions in predicting whether convective cells become deep. We then build a multiple-predictor regression model pairing important updraft and meteorological metrics with fractional entrainment rate. The probability of cells transitioning to deep convection is most sensitive to ambient 600-hPa relative humidity (42% of total metric contribution to cloud depth predictability), followed by low-level CAPE (28%), cloud-base updraft width (19%), and fractional entrainment (11%). Thus, the initial width of the updraft along with potential buoyancy and its dilution through the midtroposphere collectively determine whether deep convection will result from shallower clouds.
This study evaluates the latest Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG V07B) against its predecessor V06B, for studying mesoscale convective systems (MCSs). Both versions are compared using ground-based radar and rain gauge data from five meteorologically diverse regions: the contiguous United States (including eastern coastlines), Amazon rainforest, central Argentina mountains, equatorial Indian Ocean, and northern Australia across multiple temporal (0.5–6 hours) and spatial scales (0.1°–0.25°). An updated global MCS tracking dataset is developed by integrating satellite-observed infrared brightness temperature with IMERG V07B. Comparation of IMERG against radar observations reveals that IMERG demonstrates better performance in capturing the probability distribution and quantitative contributions of rainfall (from no-rain to intense-rain conditions) over tropical oceans than over land, with marked improvements in IMERG V07B for heavy-to-intense rain (> 10 mm h-1). Over land, systematic biases persist: IMERG tends to overestimate light-to-moderate rain (1–10 mm h-1) while underestimating heavy-to-intense rain. Additionally, aggregating IMERG to coarser resolutions (3-hourly or 0.25°) improves consistency with radar observations, outperforming the 1-hourly/0.1° resolution. The new IMERG V07B-based global MCS dataset exhibits consistent statistical characteristics with the V06B-based dataset, despite lower mean rain rates and reduced heavy precipitation contributions. These findings offer valuable insights for utilizing IMERG V07B in global precipitation studies, MCS characterization, and model evaluation.
Recent advancements in sensor technologies, in conjunction with signal processing and machine learning, have enabled real-time traffic control systems to adapt to varying traffic conditions. This paper introduces a new sensor fusion approach that combines data from a single camera and radar to achieve cost-effective and efficient vehicle detection and tracking. Initially, vehicles are independently detected and classified using the camera and radar. Then, the constant-velocity model within a Kalman filter is employed to predict vehicle locations, while the Hungarian algorithm is used to associate these predictions with sensor measurements. Finally, vehicle tracking is accomplished by merging kinematic information from predictions and measurements through the Kalman filter. A case study conducted at an intersection demonstrates the effectiveness of the proposed sensor fusion method for traffic detection and tracking, including performance comparisons with individual sensors.
Abstract Multisensor Agile Adaptive Sampling (MAAS), a smart sensing framework, was adapted to increase the likelihood of observing the vertical structure (with little to no gaps), spatial variability (at subkilometer scale), and temporal evolution (at ∼2-min resolution) of convective cells. This adaptation of MAAS guided two mechanically scanning C-band radars (CSAPR2 and CHIVO) by automatically analyzing the latest NEXRAD data to identify, characterize, track, and nowcast the location of all convective cells forming in the Houston domain. MAAS used either a list of predetermined rules or real-time user input to select a convective cell to be tracked and sampled by the C-band radars. The CSAPR2 tracking radar was first tasked to collect three sector plan position indicator (PPI) scans toward the selected cell. Edge computer processing of the PPI scans was used to identify additional targets within the selected cell. In less than 2 min, both the CSAPR2 and CHIVO radars were able to collect bundles of three to six range–height indicator (RHI) scans toward different targets of interest within the selected cell. Bundles were successively collected along the path of cell advection for as long as the cell met a predetermined set of criteria. Between 1 June and 30 September 2022 over 315 000 vertical cross-section observations were collected by the C-band radars through ∼1300 unique isolated convective cells, most of which were observed for over 15 min of their life cycle. To the best of our knowledge, this dataset, collected primarily through automatic means, constitutes the largest dataset of its kind.
This is a combined data set of 67,410 bird/bat tracks from an avian radar system deployed on a research barge (MERLIN True3D, DeTect, Panama City, Florida, USA) and concurrent wind measurements from two scanning lidars (WindCube v2.1, Vaisala, Vantaa, Finland, and Halo XR+, Halo Photonics, Lannion, France). The research barge (16.5 m x 61 m) was deployed as part of the Wind Forecast Improvement Project (WFIP-3) off the northeast coast of the United States south of Massachusetts (40.9 deg N, 70.79 deg W). This data set comprises 5 weeks of data between August 27th 2024 and September 27th 2024. Radar data were provided by DeTect and Lidar data were accessed through the Wind Data Hub (wfip3/barg.WINDPROF.z01.a0) The data have been filtered and sorted into two size groups ("big" and "small") based on a clustering approach. See Snortland, A., Clerc, J., Hein, C., & Cotter, E. (2025). Wind as Driver of Bird and Bat Abundance, Flight Direction, Altitude, and Speed on the North Atlantic Shelf. arXiv preprint arXiv:2511.14983 for complete details. Data are provided in 2 files: "Birds" and "Birds_hourly" Birds: This file contains information about each of the 67,410 flying animal tracks detected by the radar during the data collection period, including parameters measured by the radar and wind information interpolated from the lidar wind measurements. We note that the raw radar dataset contained 301,618 tracks; tracks in this processed dataset were filtered based on the requirements described in Snortland et al. (2025). Birds_hourly: This file contains timeseries of the number of tracks detected per hour over the course of the data collection period, including wind conditions and sun position for each hour. These data were used for generalized additive modeling in Snortland et al. (2025).
C-Band Scanning ARM Precipitation Radar, CF/Radial Formatted, Quality Controlled Tobac Tracking
Millions of vehicles are on the road with RADAR sensors in use for adaptive cruise control (ACC), and RADAR sen- sors are not tracking all of the objects in the field of view. This work shows a work-in-progress tool to improve track- ing from RADAR and controller area network (CAN) which should be vitally useful for safety of transportation systems and automated vehicle development. The CAN data provides object detections, but there is a lingering data association problem. The contribution of this work in progress is the solution to the data association problem by posing the data association as a minimum cost network flow problem, and doing it at low cost with an eye toward scalable CPS research.
Properties of 7488 thunderstorms are summarized for June–September 2022 during the Tracking Aerosol Convection Interactions Experiment (TRACER) field campaign Houston, Texas, using polarimetric weather radar and VHF 3D Lightning Mapping Array data. Automated tracking of storms linked each instrument’s measurements to a data-defined, time-evolving storm footprint. Within each storm, the depth and magnitude of episodic columns of radar differential reflectivity and specific differential phase quantified the prevalence of updrafts that activated mixed-phase precipitation pathways. Lightning measurements further distinguished the degree of rimed precipitation formation: the fraction of tracks with lightning varied from day to day and cells with lightning had stronger polarimetric columns. Track-level correlation of the lightning flash rate with radar polarimetric measures had substantial spread, showing that lightning provides an additional signal of mixed-phase precipitation processes that can complement future studies of thermodynamic and aerosol controls on cloud microphysics in the Houston region.
C-Band Scanning ARM Precipitation Radar, CF/Radial Formatted, Quality Controlled Tobac Tracking Cloud Mask
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 (𝑇𝐵 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 ($\frac{∂𝑇𝐵_{𝐼𝑅}}{∂𝑡}$), 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.
Abstract During the DOE Atmospheric Radiation Measurement (ARM) Tracking Aerosol Convection Interactions Experiment (TRACER) IOP spanning June–September 2022, two fixed ARM sites and a mobile team concurrently sampled the airmass heterogeneity across sea- and bay-breeze fronts around the greater Houston metropolitan region. Here, we quantify the spatiotemporal variability between maritime (coastal/bay side of breeze fronts) and continental (inland side of breeze fronts) air masses over 15 IOP days characterized by strong sea-breeze forcing. We analyze environmental profile data from 177 radiosondes and use S- and C-band radar data to track and quantify the variability in attributes of more than 2300 shallow and transitioning cells across different air masses. The composite analysis of environmental profiles indicates that during the early afternoon, the sea-breeze maritime air mass exhibits lower convective available potential energy (CAPE) than the bay-breeze maritime air mass. As the sea breeze advances inland with time, CAPE within the maritime air mass exceeds that of the continental air mass to the north of the breeze fronts. In general, maritime cells have a larger mean composite reflectivity and cell widths than continental cells; however, the response varies between shallow and transitioning cells. Mean composite 20-dB Z echo-top heights, however, are similar across air masses for both shallow and transitioning cells. The continental and maritime inflow air mass for transitioning cells has significantly different mean values for mixed-layer entrainment CAPE, lifted condensation level, level of free condensation, boundary layer depth, and diluted equilibrium level. For shallow cells, only total precipitable water shows a significant difference. Significance Statement The greater Houston metropolitan area is a natural laboratory for understanding the individual impacts of background meteorology and aerosols on convective clouds. Due to its proximity to the Gulf Coast and Galveston Bay, the Houston region experiences a diurnal precipitation cycle in the summer, driven by convection triggered from sea- and bay-breeze fronts. These fronts act as a boundary between air masses with distinct thermodynamic and environmental characteristics. Convergence along these fronts and interactions between storm outflow and the fronts facilitate convection initiation in different mesoscale air masses. This study quantifies the heterogeneity among these air masses while investigating their influence on cloud microphysics. We find that the effect of airmass heterogeneity is more pronounced for the bulk microphysical properties in shallow clouds.