Analysis of the microphysical properties of snowfall using scanning polarimetric and vertically pointing multi-frequency Doppler radars

Radar dual-wavelength ratio (DWR) measurements from the Stony Brook Radar Observatory Ka-band scanning polarimetric radar (KASPR, 35 GHz), a W-band profiling radar (94 GHz), and a next-generation K-band (24 GHz) micro rain radar (MRRPro) were exploited for ice particle identification using triple-frequency approaches. The results indicated that two of the radar frequencies (K and Ka band) are not sufficiently separated; thus, the triple-frequency radar approaches had limited success. On the other hand, a joint analysis of DWR, mean Doppler velocity (MDV), and polarimetric radar variables indicated potential in identifying ice particle types and distinguishing among different ice growth processes and even in revealing additional microphysical details. We investigated all DWR pairs in conjunction with MDV from the KASPR profiling measurements and differential reflectivity (ZDR) and specific differential phase (KDP) from the KASPR quasi-vertical profiles. The DWR-versus-MDV diagrams coupled with the polarimetric observables exhibited distinct separations of particle populations attributed to different rime degrees and particle growth processes. In fallstreaks, the 35–94 GHz DWR pair increased with the magnitude of MDV corresponding to the scattering calculations for aggregates with lower degrees of riming. The DWR values further increased at lower altitudes while ZDR slightly decreased, indicating further aggregation. Particle populations with higher rime degrees had a similar increase in DWR but a 1–1.5 m s−1 larger magnitude of MDV and rapid decreases in KDP and ZDR. The analysis also depicted the early stage of riming where ZDR increased with the MDV magnitude collocated with small increases in DWR. This approach will improve quantitative estimations of snow amount and microphysical quantities such as rime mass fraction. The study suggests that triple-frequency measurements are not always necessary for in-depth ice microphysical studies and that dual-frequency polarimetric and Doppler measurements can successfully be used to gain insights into ice hydrometeor microphysics

Multifrequency radar observations of clouds and precipitation including the G-band

Observatory clearly demonstrate the potential of G-band radars for cloud and precipitation research, something that until now was only discussed in theory. The field experiment, which coordinated an X-, Ka-, W- and G-band radar, revealed that the Ka–G pairing can generate differential reflectivity signal several decibels larger than the traditional Ka–W pairing underpinning an increased sensitivity to smaller amounts of liquid and ice water mass and sizes. The observations also showed that G-band signals experience non-Rayleigh scattering in regions where Ka- and W-band signal do not, thus demonstrating the potential of G-band radars for sizing sub-millimeter ice crystals and droplets. Observed peculiar radar reflectivity patterns also suggest that G-band radars could be used to gain insight into the melting behavior of small ice crystals. G-band signal interpretation is challenging, because attenuation and non-Rayleigh effects are typically intertwined. An ideal liquid-free period allowed us to use triple-frequency Ka–W–G observations to test existing ice scattering libraries, and the results raise questions on their comprehensiveness. Overall, this work reinforces the importance of deploying radars (1) with sensitivity sufficient enough to detect small Rayleigh scatters at cloud top in order to derive estimates of path-integrated hydrometeor attenuation, a key constraint for microphysical retrievals; (2) with sensitivity sufficient enough to overcome liquid attenuation, to reveal the larger differential signals generated from using the G-band as part of a multifrequency deployment; and (3) capable of monitoring atmospheric gases to reduce related uncertainty

The Second ARM Training and Science Application Event : Training the Next Generation of Atmospheric Scientists

Non peer reviewe

Stony Brook University Ka-band Polarimetric Radar and Mobile Sounding Data from Snowstorm Observations

Snowbands in the comma head of winter storms are responsible for much of the heavy snowfall over the northeast United States. Because of the large societal impacts of these winter storms, they have been studied for decades using both numerical models and observations. However, limited knowledge still exists about ~100 m scale precipitation processes within U.S. Northeast coastal snowstorms because of a lack of high-resolution observations. We collected observational data for the U.S. Northeast coastal snowstorms using high-resolution, high-sensitivity Ka-band cloud radar and mobile sounding system. The observed snowstorm cases includes 4 January 2018, 18 January 2020, 16-17 December 2020, and 31 January - 1 February 2021

Stony Brook Radar Observatory radar data for February 20, 2019

Stony Brook Radar Observatory (SBRO) has been in operation since March in 2017 (-73.127E, 40.897N, https://you.stonybrook.edu/radar/). The flagship radar of SBRO is a very sensitive, sophisticated, and well-calibrated Ka-band (35-GHz) scanning fully-polarimetric radar (KASPR). The radar measurements are complemented by two profiling radar systems operating at W-band (94-GHz, ROGER) and K-band (24-GHz, MRRPro). KASPR, ROGER, and MRRPro at SBRO collected triple frequency data during a snow event on February 20, 2019. Analyses of dual wavelength ratio, mean vertical Doppler velocity, and polarimetric radar variables from the triple frequency measurements can be used to identify ice particle types, distinguish among different ice growth processes, and even reveal additional microphysical details

Stony Brook Radar Observatory radar and lidar data for February 25, 2020

Observations collected during the 25-February-2020 deployment of the Vapor In-Cloud Profiling Radar at the Stony Brook Radar Observatory clearly demonstrate the potential of G-band radars for cloud and precipitation research. The field experiment, which coordinated an X-, Ka, W-and G-band radar, revealed that the differential reflectivity from Ka-G-band pair provides larger signals than the traditional Ka-W pairing underpinning an increased sensitivity to smaller amounts of liquid and ice water mass and sizes. The data include: 1) Vapor In-Cloud Profiling Radar (VIPR) collected on 24-26 February 2020 2) W-band profiling radar (ROGER) collected on 25 February 2020 3) Ka-band Scanning Polarimetric Radar (KASPR) vertically-pointing measurements on 25 February 2020 4) SBU Phased Array Radar (SKYLER) vertically-pointing measurements on 25 February 2020 5) Lufft CHM lidar collected on 25 February 202

Multi-Doppler Radar Wind Retrieval Data for Deep Convective Cloud Observed on May 11, 2020

The US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program’s Southern Great Plains (SGP) site includes a heterogeneous distributed scanning Doppler radar network suitable for collecting coordinated Doppler velocity measurements in deep convective clouds. The surrounding National Weather Service (NWS) Next Generation Weather Surveillance Radar 1988 Doppler (NEXRAD WSR-88D) further supplements this network. The multi-Doppler radar reflectivity and velocity measurements are assimilated in a three-dimensional variational (3DVAR) algorithm to retrieve horizontal and vertical air motions in deep convective clouds. The data includes the 3D wind fields retrieved over a large analysis domain (100 km x 100 km) at storm-scale resolutions (500 m in horizontal and 250 m in vertical) for deep convective clouds observed on May 11, 2011

Multi-Doppler Radar Wind Retrieval Data for Deep Convective Clouds Observed in the Southern Great Plains on May 11, 2011

The US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program’s Southern Great Plains (SGP) site includes a heterogeneous distributed scanning Doppler radar network suitable for collecting coordinated Doppler velocity measurements in deep convective clouds. The surrounding National Weather Service (NWS) Next Generation Weather Surveillance Radar 1988 Doppler (NEXRAD WSR-88D) further supplements this network. The multi-Doppler radar reflectivity and velocity measurements are assimilated in a three-dimensional variational (3DVAR) algorithm to retrieve horizontal and vertical air motions in deep convective clouds. The data includes the 3D wind fields retrieved over a large analysis domain (100 km x 100 km) at storm-scale resolutions (500 m in horizontal and 250 m in vertical) for deep convective clouds observed on May 11, 2011

Configuration files and input data used for radar simulations described in The Cloud Resolving Model Radar Simulator (CR-SIM) Version 3.2: Description and Applications of a Virtual Observatory

Ground-based observatories use multi-sensor observations to characterize cloud and precipitation properties. A challenge is how to design strategies to best use these observations to understand the atmosphere and evaluate atmospheric numerical prediction models. We have developed the Cloud resolving model Radar SIMulator (CR-SIM), which uses output from high-resolution atmospheric models to emulate multi-wavelength, zenith-pointing, and scanning radar observables and multi-sensor (multi-radar and radar-lidar) integrated products. Here, we publish configuration files used for radar simulations using CR-SIM and atmospheric simulation outputs from the Weather Research Forecasting (WRF) model, which were used as inputs for the radar simulations