FMCW Radar Performance for Atmospheric Measurements

Frequency-modulated continuous-wave radars (FMCW) have been used in the investigation of the atmosphere since the late 1960’s. FMCW radars provide tremendous sensitivity and spatial resolution compared to their pulsed counterparts and are therefore attractive for clear-air remote-sensing applications. However, these systems have some disadvantages and performance limitations that have prevented their widespread use by the atmospheric science community. In this study, system performance of atmospheric FMCW radar is analyzed and some measurement limitations for atmospheric targets are discussed. The effects of Doppler velocities and spectral widths on radar performance, radar’s near-field operation, and parallax errors for two-antenna radar systems are considered. Experimental data collected by the highresolution atmospheric FMCW radar is used to illustrate typical performance qualitatively based on morphological backscattered power information. A post-processing based on single-lag covariance differences between the Bragg and Rayleigh echo is applied to estimate clear-air component from refractive index turbulence and perform quantitative analysis of FMCW radar reflectivity from atmospheric targets

Retrieval of vertical air motion in precipitating clouds using Mie scattering and comparison with in situ measurements

The article of record as published may be located at http://dx.doi.org/10.1175/JAMC-D-16-0158.1For the first time, the Mie notch retrieval technique is applied to airborne cloud Doppler radar observations in warm precipitating clouds to retrieve the vertical air velocity profile above the aircraft. The retrieval algorithm prescribed here accounts for two major sources of bias: aircraft motion and horizontal wind. The retrieval methodology is evaluated using the aircraft in situ vertical air velocity measurements. The standard deviations of the residuals for the retrieved and in situ measured data for an 18-s time segment are 0.21 and 0.24 m s−1, respectively; the mean difference between the two is 0.01 m s−1. For the studied cases, the total theoretical uncertainty is less than 0.19 m s−1 and the actual retrieval uncertainty is about 0.1 m s−1. These results demonstrate that the Mie notch technique combined with the bias removal procedure described in this paper can successfully retrieve vertical air velocity from airborne radar observations with low spectral broadening due to Doppler fading, which enables new opportunities in cloud and precipitation research. A separate spectral peak due to returns from the cloud droplets is also observed in the same radar Doppler spectra and is also used to retrieve vertical air motion. The vertical air velocities retrieved using the two different methods agree well with each other, and the correlation coefficient is as high as 0.996, which indicates that the spectral peak due to cloud droplets might provide another way to retrieve vertical air velocity in clouds when the Mie notch is not detected but the cloud droplets’ spectral peak is discernable.ONR N000140810465

Progress toward characterization of the atmospheric boundary layer over northern Alabama using observations by a vertically pointing, S-band profiling radar during VORTEX-Southeast

During spring 2016 and spring 2017, a vertically pointing, S-band FMCW radar (UMass FMCW) was deployed in northern Alabama under the auspices of the Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX) – Southeast. In total, ~14 weeks’ worth of data were collected, in conditions ranging from quiescent clear skies to severe thunderstorms. The principal objective of these deployments was to characterize the boundary layer evolution near the VORTEX-Southeast domain. In this paper, we describe intermediate results in service of this objective. Specifically, we describe updates to the UMass FMCW system, document its deployments for VORTEX-Southeast, and apply three automated algorithms: (1) an dealiasing algorithm to the Doppler velocities, (2) a fuzzy logic scatterer classification scheme to separate precipitation from non-precipitation observations, (3) a bright band / melting layer identification algorithm for stratiform precipitation, and (4) an extended Kalman filter-based convective boundary layer depth (mixing height) measurement algorithm for non-precipitation observations. Results from the latter two applications are qualitatively verified against retrieved soundings from a collocated thermodynamic profiling system.Peer ReviewedPostprint (author's final draft

Improved Micro Rain Radar snow measurements using Doppler spectra post-processing

The Micro Rain Radar 2 (MRR) is a compact Frequency Modulated Continuous Wave (FMCW) system that operates at 24 GHz. The MRR is a low-cost, portable radar system that requires minimum supervision in the field. As such, the MRR is a frequently used radar system for conducting precipitation research. Current MRR drawbacks are the lack of a sophisticated post-processing algorithm to improve its sensitivity (currently at +3 dBz), spurious artefacts concerning radar receiver noise and the lack of high quality Doppler radar moments. Here we propose an improved processing method which is especially suited for snow observations and provides reliable values of effective reflectivity, Doppler velocity and spectral width. The proposed method is freely available on the web and features a noise removal based on recognition of the most significant peak. A dynamic dealiasing routine allows observations even if the Nyquist velocity range is exceeded. Collocated observations over 115 days of a MRR and a pulsed 35.2 GHz MIRA35 cloud radar show a very high agreement for the proposed method for snow, if reflectivities are larger than −5 dBz. The overall sensitivity is increased to −14 and −8 dBz, depending on range. The proposed method exploits the full potential of MRR's hardware and substantially enhances the use of Micro Rain Radar for studies of solid precipitation

Numerical solver for vertical air motion estimation

We present preliminary research on a method to estimate Vertical Air Motion (VAM) at a particular height by comparing the measured rain-rate (RR) by a vertically-pointing S-band Frequency-Modulated Continuous-Wave (FMCW) radar with that of a ground-based disdrometer. The method is based on a constrained parametric solver, assuming high correlation between 5-min averaged rain rates measured by the radar and disdrometer. The method is tested over disdrometer and radar observations during the Verification of the ORigins Tornado EXperiment in South East US (VORTEX-SE) project. Finally, the results are partially validated by means of fitting a gamma distribution to the VAM-corrected DSD profiles and studying its parameters.This research is part of the projects PGC2018-094132-B-I00 and MDM2016-0600 (“CommSensLab” Excellence Unit) funded by Ministerio de Ciencia e Investigación (MCIN)/ Agencia Estatal de Investigación (AEI)/10.13039/501100011033/ FEDER “Una manera de hacer Europa”. The work of A. Salcedo-Bosch was supported under grant 2020 FISDU 00455 funded by Generalitat de Catalunya—AGAUR. The European Commission collaborated under projects H2020 ACTRIS-IMP (GA-871115) and H2020 ATMOACCESS (GA-101008004).Peer ReviewedPostprint (author's final draft

The dynamics of surges in the 3 February 2015 avalanches in Vallee de la Sionne

Five avalanches were artificially released at the Vallée de la Sionne test site in the west of Switzerland on 3 February 2015 and recorded by the GEOphysical flow dynamics using pulsed Doppler radAR Mark 3 radar system. The radar beam penetrates the dilute powder cloud and measures reflections from the underlying denser avalanche features allowing the tracking of the flow at 111 Hz with 0.75 m downslope resolution. The data show that the avalanches contain many internal surges. The large or “major” surges originate from the secondary release of slabs. These slabs can each contain more mass than the initial release, and thus can greatly affect the flow dynamics, by unevenly distributing the mass. The small or “minor” surges appear to be a roll wave-like instability, and these can greatly influence the front dynamics as they can repeatedly overtake the leading edge. We analyzed the friction acting on the fronts of minor surges using a Voellmy-like, simple one-dimensional model with frictional resistance and velocity-squared drag. This model fits the data of the overall velocity, but it cannot capture the dynamics and especially the slowing of the minor surges, which requires dramatically varying effective friction. Our findings suggest that current avalanche models based on Voellmy-like friction laws do not accurately describe the physics of the intermittent frontal region of large mixed avalanches. We suggest that these data can only be explained by changes in the snow surface, such as the entrainment of the upper snow layers and the smoothing by earlier flow fronts

Identifying cloud droplets beyond lidar attenuation from vertically pointing cloud radar observations using artificial neural networks

In mixed-phase clouds, the variable mass ratio between liquid water and ice as well as the spatial distribution within the cloud plays an important role in cloud lifetime, precipitation processes, and the radiation budget. Data sets of vertically pointing Doppler cloud radars and lidars provide insights into cloud properties at high temporal and spatial resolution. Cloud radars are able to penetrate multiple liquid layers and can potentially be used to expand the identification of cloud phase to the entire vertical column beyond the lidar signal attenuation height, by exploiting morphological features in cloud radar Doppler spectra that relate to the existence of supercooled liquid. We present VOODOO (reVealing supercOOled liquiD beyOnd lidar attenuatiOn), a retrieval based on deep convolutional neural networks (CNNs) mapping radar Doppler spectra to the probability of the presence of cloud droplets (CD). The training of the CNN was realized using the Cloudnet processing suite as supervisor. Once trained, VOODOO yields the probability for CD directly at Cloudnet grid resolution. Long-term predictions of 18 months in total from two mid-latitudinal locations, i.e., Punta Arenas, Chile (53.1 S, 70.9 W), in the Southern Hemisphere and Leipzig, Germany (51.3 N, 12.4 E), in the Northern Hemisphere, are evaluated. Temporal and spatial agreement in cloud-droplet-bearing pixels is found for the Cloudnet classification to the VOODOO prediction. Two suitable case studies were selected, where stratiform, multi-layer, and deep mixed-phase clouds were observed. Performance analysis of VOODOO via classification-evaluating metrics reveals precision > 0.7, recall ≈ 0.7, and accuracy ≈ 0.8. Additionally, independent measurements of liquid water path (LWP) retrieved by a collocated microwave radiometer (MWR) are correlated to the adiabatic LWP, which is estimated using the temporal and spatial locations of cloud droplets from VOODOO and Cloudnet in connection with a cloud parcel model. This comparison resulted in stronger correlation for VOODOO (≈ 0.45) compared to Cloudnet (≈ 0.22) and indicates the availability of VOODOO to identify CD beyond lidar attenuation. Furthermore, the long-term statistics for 18 months of observations are presented, analyzing the performance as a function of MWR-LWP and confirming VOODOO's ability to identify cloud droplets reliably for clouds with LWP > 100 g m-2. The influence of turbulence on the predictive performance of VOODOO was also analyzed and found to be minor. A synergy of the novel approach VOODOO and Cloudnet would complement each other perfectly and is planned to be incorporated into the Cloudnet algorithm chain in the near future

Acoustic sounding of snow water equivalent

An acoustic frequency-swept wave was investigated as a means for determining Snow Water Equivalent (SWE) in cold wind-swept prairie and sub-alpine environments. Building on previous research conducted by investigators who have examined the propagation of sound in snow, digital signal processing was used to determine acoustic pressure wave reflection coefficients at the interfaces between 'layers' indicative of changes in acoustic impedance. Using an iterative approach involving boundary conditions at the interfaces, the depth-integrated SWE was determined using the Berryman equation from porous media physics. Apparatuses used to send and receive sound waves were designed and deployed during the winter season at field sites situated near the city of Saskatoon, Saskatchewan, and in Yoho National Park, British Columbia. Data collected by gravimetric sampling was used as comparison for the SWE values determined by acoustic sounding. The results are encouraging and suggest that this procedure is similar in accuracy to SWE data collected using gravimetric sampling. Further research is required to determine the applicability of this technique for snow situated at other geographic locations

Radar Reflectivity of Micro Rain Radar (MRR2) At 16.44180N, 80.620E of India

To improve accurate standards of Radar Reflectivity Z, Rainfall Rates RR, and even to monitor small size precipitation particles Z-R relation is derived in this work with the help of Micro Rain Radar. Formerly, taking rain/precipitation data from ground-based rain gauges (Cylindrical, Optical, Weighing, Tipping Bucket Rain Gauges, Disdrometers, etc.,) currently, in this work using METEK MRR (Micro Rain Radar) of Frequency Modulated Continuous Wave System which reads the vertical structure of precipitation particles and hence named as vertical profile radar which operates at 24.2 GHz and height up to 6000m/6kms with an increment step size of 200m respectively to monitor the frozen hydrometeors. It is installed at (16.440 N, 80.620 E) K L University, 29 meters above the sea level (ASL) in CARE LAB. To know the Radar Reflectivity of precipitation, it is observing the amount of power received to the radar receiver after hitting the precipitation particles with respect to the transmitted power of the radar. This proposed work considered distinctive rain conditions at different heights ASL ranging from 200m 1200m 2200m, and derived Z-R relations respectively