This study uses vertically pointing Doppler lidar and Ka-band Doppler radar to estimate drizzle droplet distributions falling below cloud base in maritime warm boundary-layer (WBL) clouds. The aerosol rich environment enabled the Doppler lidar to measure the vertical air motion, even in the presence of falling drizzle droplets. The Ka-band Doppler velocity power spectra observed the falling drizzle droplets, which are shifted upwards or downwards by the vertical air motion and broadened by turbulent broadening effects. The retrieval algorithm consists of four steps: (1) deconvolve the observed Kaband Doppler velocity spectra to remove turbulent broadening, (2) shift the spectra by the observed Doppler lidar air motion, (3) convert reflectivity spectral density in each velocity bin to number concentration, and (4) convert each velocity bin to drizzle diameter so that the discrete drizzle droplet distribution is in units of number concentration per drizzle drop diameter.

Abstract. This study investigates hydrometeor fall speeds using a dual-frequency profiling radar operating during the 2005–2006 monsoon season near Darwin, Australia. Our focus is on tropical deep convection where the observations provide a new perspective on hydrometeor fall speeds within and near intense drafts having mixed-phase media. The techniques we employ avoid undue assumptions on the air motion or media distributions, offering a convenient path to estimate bulk radar reflectivity(Z)-weighted hydrometeor fall speed Vt. While these mixed-phase media estimates are not specific to size or density, they may be replicated by models and are practical for radar-based retrievals that necessitate Vt assumptions. Tests performed under rain and snow conditions show comparable performance to disdrometer and other references. The standard deviation of residuals for rain and snow relationships are ≅ 1 ms-1 and ≅ 0.4 ms-1, respectively. In convective core regions aloft, Darwin observations align with existing graupel Vt-Z treatments, however mixed-phase media typically falls faster (> 1–2 ms-1) for Z < 35 dBZ than prior relationships. Breakdowns suggest that Active and Break monsoon conditions favor a similar Vt-Z behavior in strong cores. However, Break conditions – those more favorable to intense daytime tropical convection – potentially indicate the presence of additional lofted liquid or melting media mixed in volumes at convective core peripheries Z < 35 dBZ. Break events also show higher variability in Vt-Z pairs, with select samples having Vt faster than rain for a given Z that argues for partially-melted graupel coupled with size-sorting.

We present results from ATTO-Campina, a new permanent observational site in central Amazon, about 4 km from the ATTO towers. Operational since 2020, ATTO-Campina characterizes atmospheric, cloud and rainfall properties through remote sensing. The goal is to provide continuous, complementary measurements to the ATTO towers, addressing the rainforest’s complex gas-aerosol-cloud-precipitation dynamics. Using a 3.5-year dataset, we classified convective clouds into three types: shallow cumulus ( ShCu ), congestus ( Con ) or ( Deep ) clouds. The shallow-to-deep transition takes about three hours, starting with ShCu formation at 11:00 local time. The accumulated rainfall peak follows at about 16:00. Only weak downdrafts are present in the upper troposphere where previous studies indicate new particle formation (NPF) occurrence. Strong downdrafts are mostly limited to heights below 5 km. Con and Deep convective days have higher concentrations of ultrafine aerosol and lower concentrations of accumulation-mode particles compared to ShCu . Convective clouds also significantly modify gas mixing ratios. Deep convective clouds are associated with high near-surface O 3 , consistent with downward transport from the midtroposphere. Our results showcase the added detail achieved by integrating data from the ATTO towers and ATTO-Campina sites. Together, these sites support better understanding of interconnected gas-aerosol-cloud-precipitation processes in the Amazon and their evolution under climate change.

The use of radar in speech recognition is a growing field of interest in recent years. The inclusion of radar introduces several new variables into the scenario. One of the lesser-known variables of this process is the effect that a radar's inherent angular dependency has on the recovered speech signals. This paper presents a study into this effect seen through continuous wave (CW) Doppler radar. The vocal vibrations were detected without aid of secondary passive or active amplification to isolate the radars sensitivity to this effect. The phrases considered in this study were the words "A," "I," and "O." The readings of each phrase were compared across several angles of observation to map the difference in signal-to- noise ratio (SNR) from 80 - 100 degrees for a person.

Doppler Radar has long been considered for use in biomedical applications due to its ability to detect mu m-scale displacements. In these cases, a common concern is the ambiguity introduced in vital sign detection caused by clutters, both moving and stationary. This work proposes using multiple radars with coherent multi-view detection to reduce the impact of moving clutter and other factors, such as fundamental harmonics, on vital sign detection.

Microwave radar has been widely adopted as a wireless sensor to accurately detect mu m-scale motions. Low-cost sensors often use ac-coupled baseband amplifiers to increase the signal level for digitization, but these can increase startup times for low-frequency sensing. Recent architectures have shown that startup times can be improved using commercial diodes but have not been tested in a wide range of use cases to evaluate potential limitations. This work presents an in-depth analysis of diode-based ac-coupled fast-startup amplifiers to quantify the impacts of 2(nd) order effects on amplifier performance. The closed-form solutions are verified using SPICE software, with experimental results confirming the effects of nonidealities in realistic radar applications.

Bowing gestures are a key component of violin playing and can be analyzed to provide feedback on a violinist's performance. Radar systems have increasingly been used to recognize human movements but not yet in a musical context. In this study, a portable frequency-modulated continuous-wave (FMCW) radar is used to detect various violin bowing gestures. Range profiles and time-Doppler spectrograms are extracted from the raw signal data, and their unique characteristics allow for the differentiation of different bowing techniques and the recognition of incorrect bowing motions. The results of this study demonstrate the potential of radars in aiding musical instrument training.

In-cloud vertical air motion is a key parameter to describe cloud dynamics and lifecycles. Short-wavelength ($Ka$- and $W$-band) radars are often used to observe clouds and extract the vertical air motion from the radar-measured Doppler velocity. However, the vertical air motion retrieval techniques developed using ground-based radar Doppler spectrum may be problematic for airborne cloud radars due to nonideal radar system performance (e.g., receiver saturation) and the Doppler velocity measurement uncertainties caused by aircraft motion, aircraft speed, and the large aircraft motion induced horizontal wind. This article presents a new and simple approach for estimating the in-cloud vertical air motion using airborne $W$-band radar measurements, which is applicable to cloud measurements without precipitation or with weak precipitation. In particular, a power-law relation between cloud and precipitation particle fall speed and attenuation corrected radar reflectivity is established first. Then, the particle fall speeds estimated from radar reflectivity using the established power-law relation are compared with the radar-measured Doppler velocities to derive the vertical air motions. This technique is demonstrated with $W$-band airborne radar measurements from the National Oceanic and Atmospheric Administration Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign from January to February 2020, which was designed to investigate atmospheric shallow convection and air-sea interaction in the tropical North Atlantic east of Barbados. The retrieved in-cloud air motion is compared with results reported in the literature for a nearby domain, suggesting that this simple retrieval technique performs reasonably well. Since this approach is applicable for airborne radar measurements with high-frequency noises, it can be used as an effective tool for investigating the mean profile of vertical air motion.

This paper presents a novel frequency modulated continuous wave (FMCW) radar response simulation method with a focus on human gesture detection. Through use of 3D motion captured by an infrared (IR) camera system and data processing, a realistic and computationally efficient simulation is produced. The gestures considered are a hand raise (up-down), right-left arm sweep, and push forward. The high efficiency in simulation is due to a simplified yet effective approach to radar cross section (RCS) calculation and radar-detected signal integration. This allows for the simulation to run smoothly while still resulting in realistic approximations of both range profile and doppler response of gestures. The simulated results are validated with experimental results detected by an Infineon 60-GHz radar.

This dataset contains relevant files for the GEOS-Chem chemical transport model simulations used in the following manuscript: Graham, K. A., Friedrich, G., Rauschenberg, C. D., Williams, C. R., Bottenheim, J. W., Chavez, F. P., Halfacre, J. W., Holmes, C. D., Perovich, D. K., Shepson, P. B., Simpson, W. R., Tans, P. P., & Matrai, P. A. (2022). Variability of Atmospheric CO2 Over the Arctic Ocean. Journal of Geophysical Research: Atmospheres. In Review.

The ability to detect buried objects is critical for the Army. Therefore, this report summarizes the fourth year of an ongoing study to assess environ-mental phenomenological conditions affecting probability of detection and false alarm rates for buried-object detection using thermal infrared sensors. This study used several different approaches to identify the predominant environmental variables affecting object detection: (1) multilevel statistical modeling, (2) direct image analysis, (3) physics-based thermal modeling, and (4) application of machine learning (ML) techniques. In addition, this study developed an approach using a Canny edge methodology to identify regions of interest potentially harboring a target object. Finally, an ML method was developed to improve automatic target detection and recognition performance by accounting for environmental phenomenological conditions, improving performance by 50% over standard automatic target detection and recognition software.

Within the theoretical literature on crime control and offender therapy, little has been written about the importance of virtue ethics in the experience of human justice and in the evolution of the common good. As a theory of being, the aretaic tradition extols eudemonic existence (i.e., excellence, flourishing) as a relational habit of developing character that is both practiced and embodied over time. What this implies is that virtue justice depends on a set of assumptions and predispositions-both moral and jurisprudential-whose meanings are essential to comprehending its psychological structure. This article sets out to explore several themes that our integral to our thesis on the virtues (i.e., the being) of justice. We reclaim justice's aretaic significance, critique the common conflation of justice and law, discuss how the dominant legalistic conception of justice is rooted in a particular view of human nature, suggest how justice might be more properly grounded in natural moral sensibilities, and provide a tentative explication of the psychological character of justice as a twofold moral disposition. Given this exploratory commentary, we conclude by reflecting on how individual well-being, system-wide progress, and transformative social change are both possible and practical, in the interest of promoting the virtues of justice within the practice of crime control and offender therapy.

The present effort (Phase 3) builds on our previously published prior efforts (Phases 1 and 2), which examined methods of determining the probability of detection and false alarm rates using thermal infrared for buried object detection. Environmental phenomenological effects are often represented in weather forecasts in a relatively coarse, hourly resolution, which introduces concerns such as exclusion or misrepresentation of ephemera or lags in timing when using this data as an input for the Army’s Tactical Assault Kit software system. Additionally, the direct application of observed temperature data with weather model data may not be the best approach because metadata associated with the observations are not included. As a result, there is a need to explore mathematical methods such as Bayesian statistics to incorporate observations into models. To better address this concern, the initial analysis in Phase 2 data is expanded in this report to include (1) multivariate analyses for detecting objects in soil, (2) a moving box analysis of object visibility with alternative methods for converting FLIR radiance values to thermal temperature values, (3) a calibrated thermal model of soil temperature using thermal IR imagery, and (4) a simple classifier method for automating buried object detection.

Vertically pointing radars (VPRs) provide detailed observations of precipitating cloud systems as they pass directly over the radar site. Two VPRs operating side-by-side and at different millimeter wavelengths (mm-wave) will observe the same raindrops but will have different return signals due to wavelength dependent raindrop backscattering and attenuation characteristics. These differences enable the retrieval of raindrop size distributions and vertical air motions. Yet, as the rain rate increases, the attenuation increases. Eventually, at some combination of path length [km] and rain specific attenuation [dB/km], the attenuation [dB] will extinguish high frequency VPR return signals; limiting high frequency VPRs to studying rain processes close to the ground. In order to estimate how far VPRs can measure into rain shafts, this study simulated constant rain rate precipitation columns and then estimated the path length needed to produced enough attenuation to drop the VPR signal-to-noise ratio below the VPR’s detection limit. This study used surface disdrometer observations and publically available T-Matrix scattering code to produce realistic VPR measurements at frequencies from 3 to 200 GHz. These simulations found that in order to observe raindrops above a 3.5 km rain shaft, the constant rain rate needed to be less than 138, 67, 26, 14, and 4 mm/h for VPRs operating in the X-, Ku-, K-, Ka-, and W-bands, respectively (i.e., 9, 13.6, 24, 35.6, and 94 GHz). Additionally, due solely to atmospheric gas attenuation, the G-band (200 GHz) VPR return signal will be completely extinguished by 3.5 km. Preventing a G-band VPR from detecting raindrops above 3.5 km.

Vertically pointing radars (VPRs) operating at millimeter wavelengths measure the power return from raindrops enabling precipitation retrievals as a function of height. However, as the rain rate increases, there are combinations of rain rate and rain path length that produce sufficient attenuation to prevent the radar from detecting raindrops all the way through rain shafts. This study explores the question: Which rain rate and path length combinations completely extinguish radar return signals for VPRs operating between 3 and 200 GHz? An important step in these simulations is converting attenuated radar reflectivity factor into radar received signal-to-noise ratio (SNR) in order to determine the range where the SNR drops below the receiver detection threshold. Configuring the simulations to mimic a U.S. Department of Energy Atmospheric Radiation Mission (ARM) W-band (95 GHz) radar deployed in Brazil, the simulation results indicate that a W-band radar could observe raindrops above 3.5 km only when the rain rate was less than approximately 4 mm h−1. The deployed W-band radar measurements confirm the simulation results with maximum observed heights ranging between 3 and 4.5 km when a surface disdrometer measured 4 mm h−1 rain rate (based on 25-to-75 percentiles from over 25,000 W-band radar profiles). In summary, this study contributes to our understanding of how rain and atmospheric gas attenuation impacts the performance of millimeter-wave VPRs and will help with the design and configuration of multi-frequency VPRs deployed in future field campaigns.

This study pertains to the thermal variations caused by buried objects and their ramifications on soil phenomenology. A multitude of environmental conditions were investigated to observe the effect on thermal infrared sensor performance and detection capabilities. Correlations between these external variables and sensor contrast metrics enable determinable key factors responsible for sensor degradation. This document consists of two parts. The first part is a summary of data collected by the U.S. Army Corps of Engineers, Engineer and Research and Development Center Cold Regions Research and Engineering Laboratory (ERDC-CRREL), ERDC-Geotechnical Structures Laboratory, and Desert Research Institute at the Yuma Proving Ground (YPG) site in February 2020 and observations from this activity. The second part is a comparison of target visibility between data collected at YPG and data collected at the ERDC-CRREL test site in 2018.

Abstract. This study presents a method to identify and distinguish insects, clouds, and precipitation in 35 GHz (Ka-band) vertically pointing polarimetric radar Doppler velocity power spectra and then produce masks indicating the occurrence of hydrometeors (i.e., clouds or precipitation) and insects at each range gate. The polarimetric radar used in this study transmits a linear polarized wave and receives signals in collinear (CoPol) and cross-linear (XPol) polarized channels. The insect-hydrometeor discrimination method uses CoPol and XPol spectral information in two separate algorithms with their spectral results merged and then filtered into single value products at each range gate. The first algorithm discriminates between insects and clouds in the CoPol Doppler velocity power spectra based on the spectra texture, or spectra roughness, which varies due to the scattering characteristics of insects versus cloud particles. The second algorithm distinguishes insects from raindrops and ice particles by exploiting the larger Doppler velocity spectra linear depolarization ratio (LDR) produced by asymmetric insects. Since XPol power return is always less than CoPol power return for the same target (i.e., insect or hydrometeor), fewer insects and hydrometeors are detected in the LDR algorithm than the CoPol algorithm, which drives this need for a CoPol based algorithm. After performing both CoPol and LDR detection algorithms, regions of insect and hydrometeor scattering from both algorithms are combined in the Doppler velocity spectra domain and then filtered to produce a binary hydrometeor mask indicating the occurrence of cloud, raindrops, or ice particles at each range gate. Comparison with a collocated ceilometer indicates that hydrometeor mask column bottoms are within +/-100 meters of simultaneous ceilometer cloud base heights. Forty-seven (47) summer-time days were processed with the insect-hydrometeor discrimination method using U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program Ka-band zenith pointing radar observations in northern Oklahoma (USA). All datasets and images are available on public repositories.

Data from an upward-pointing wind profiler radar pair at Darwin in tropical Australia are used to determine the characteristics of individual convective up- and downdrafts observed at the site. Drafts are identified as vertically contiguous regions of instantaneous upward or downward motion exceeding 0.2 ms −1 . Most updrafts and downdrafts found are less than 2 km in vertical extent, and updrafts exceeding 5 km vertical length carry no more than 33% of the total upward mass flux. Updraft length correlates positively with rain rates, and on very high rain rates (greater than 20 mm/hr), average updraft lengths are ~5 km. Typical peak updraft velocities increase from ~2.5 ms −1 for the smallest to ~ 4 ms −1 for the largest drafts, while those for downdrafts remain ~ 2 ms −1 regardless of size. These results are broadly consistent with other numerical modeling studies, but contrast with the common view of deep convection as being dominated by continuous, deep drafts.

Using NOAA's S-band High-Power Snow-Level Radar (HPSLR), a technique for estimating the rain drop size distribution (DSD) above the radar is presented. This technique assumes the DSD can be described by a four parameter, generalized gamma distribution (GGD). Using the radar's measured average Doppler velocity spectrum and a value (assumed, measured, or estimated) of the vertical air motion w, an estimate of the GGD is obtained. Four different methods can be used to obtain w. One method that estimates a mean mass-weighted raindrop diameter D-m from the measured reflectivity Z produces realistic DSDs compared to prior literature examples. These estimated DSDs provide evidence that the radar can retrieve the smaller drop sizes constituting the "drizzle " mode part of the DSD. This estimation technique was applied to 19 h of observations from Hankins, North Carolina. Results support the concept that DSDs can be modeled using GGDs with a limited range of parameters. Further work is needed to validate the described technique for estimating DSDs in more varied precipitation types and to verify the vertical air motion estimates.

An approach to increasing sensor performance and detection reliability for buried objects is to better understand which physical processes are dominant under certain environmental conditions. The present effort (Phase 2) builds on our previously published prior effort (Phase 1), which examined methods of determining the probability of detection and false alarm rates using thermal infrared for buried-object detection. The study utilized a 3.05 × 3.05 m test plot in Hanover, New Hampshire. Unlike Phase 1, the current effort involved removing the soil from the test plot area, homogenizing the material, then reapplying it into eight discrete layers along with buried sensors and objects representing targets of inter-est. Each layer was compacted to a uniform density consistent with the background undisturbed density. Homogenization greatly reduced the microscale soil temperature variability, simplifying data analysis. The Phase 2 study spanned May–November 2018. Simultaneous measurements of soil temperature and moisture (as well as air temperature and humidity, cloud cover, and incoming solar radiation) were obtained daily and recorded at 15-minute intervals and coupled with thermal infrared and electro-optical image collection at 5-minute intervals.