What can we learn from the unique 3D measurements they collect.
Quadruple frequency radar observations of clouds and precipitation
References: Lamer, K. and coauthors (in press) Multi-frequency Radar Observations of Cloud and Precipitation Including the G-band. Journal of Atmospheric Measurement Techniques
Over the past 20 years, millimeter-wavelength radars have become the instrument of choice for the study of cloud and precipitation. Today, radars operating at 35- and 94-GHz frequencies are routinely operated at ground-based observatories. In space, the CloudSat 94-GHz cloud profiling radar has been operating since May 2006 (Stephens et al., 2002) and the Earth Cloud Aerosols and Radiation Explorer (EarthCARE), the first spaceborne Dopplerized cloud profiling radar, is expected to be launched in 202. Reasons for the popular use of millimeter-wavelength radars include that this frequency range is much more sensitive (in contrast to cm-wavelength radars) to cloud droplets and small ice crystals and that it allows for the collection of observations at excellent spatial resolution (~30m; (Kollias et al., 2020a). It remains challenging to extract quantitative information about the sizes and mass of small hydrometeors using observations from stand-alone single-frequency millimeter-wave radars. For the most part, challenges arise since signal at any one frequency experiences both attenuation (related to particle mass) and scattering (related to particle habit and size) making it nearly impossible to disentangle these effects.
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, 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 don’t, 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 with 1) sensitivity sufficient 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) sensitivity sufficient to overcome liquid attenuation, to reveal the larger differential signals generated from using G-band as part of a multifrequency deployment, and 3) capable of monitoring atmospheric gases to reduce related uncertainty.
3D cloud structure and lifetime
References: Kollias P. and coauthors (2020). Agile adaptive radar sampling of fast-evolving atmospheric phenomena guided by satellite imagery and surface cameras. Geophysical Research Letters, 47.14 e2020GL08844
Kollias, P., and coauthors (2020). The ARM radar network: At the leading-edge of cloud and precipitation observations. Bulletin of the American Meteorological Society, 101(5), E588-E607.
Berg L. and coauthors (2016). The Two‐Column Aerosol Project: Phase I—Overview and impact of elevated aerosol layers on aerosol optical depth. JGR: Atmospheres, 121(1), 336-361. Lamer K., A. Tatarevic, I. Jo, and P. Kollias (2014). Evaluation of gridded scanning ARM cloud radar reflectivity observations and vertical Doppler velocity retrievals. AMT, 7(4), 1089-1103.
Kollias P. and coauthors (2014). Scanning ARM cloud radars. Part II: Data quality control and processing. Journal of Atmospheric and Oceanic Technology, 31(3), 583-598.
- ARM/ASR report: Advancing the Use of ARM Observations for Large-Scale Earth System Model Development breakout session summary. 07/2019 - ARM/ASR report: End-to-end Forward Simulators breakout session summary. 4/2018 - Second recommendation on the optimum configuration of the two Scanning ARM Cloud Radars at the SGP. 01/2016 - Recommendation on the optimum configuration of the two Scanning ARM Cloud Radars at the SGP. 05/2015
Opportunities: The Center for Multiscale Applied Sensing (CMAS) operates an array of weather sensors including a Ka-band Scanning Radar and is open to test innovative scan strategy ideas.
In contrast to weather radars, the potential of scanning cloud radars remains underexploited. Early challenges were associated with determining how best to deploy them. That is because these systems while highly precise, cover small volumes at once and as such must also scan slowly to collect 3D observations.
Work as lead to the development of a set of scan strategies for scanning cloud radars each designed to capture different aspects of evolving cloud systems.
First light observations revealed that extracting scientifically valuable information from these measurement would require intensive post-processing which led to the development of value-added products SACRCORR which removes noise and corrects the measured Doppler velocity and SACRADV3D3C which maps post-processed radar reflectivity to cartesian coordinates and provides estimates of domain cloud cover.
Our group composed of Stony Brook University and Brookhaven National Lab members developed a new paradigm for collecting observations one where radars are guided not by their own surveillance observations but rather from information from external sensors. Developing this framework we named the Multisensor Agile Adaptive Sampling (MAAS) was made possible by our group establishing our own ground-based cloud observatory in the North East. See the Radar Science group page for more information.
Ongoing work seeks to use this type of observations for 3D cloud structure and life cycle studies.
Domain rain rate estimates
References: Lamer, K., and coauthors (2019). Characterization of Shallow Oceanic Precipitation using Profiling and Scanning Radar Observations at the Eastern North Atlantic ARM Observatory, AMT, 12(9), 4931-4947.
Vertically pointing radars are typically the pillar of cloud observatories. They provide insight about cloud morphology and can be used to characterize the internal structure of clouds at a resolution of only a few seconds and a few tens of meters. That being said, it is often unclear whether or not these point observations are representative of the surrounding.
Scanning precipitation radars offers new insights into precipitation variability and organization over a domain of 40-60 km radius around ground-based sites. On the other hand, scanning sensors typically cannot benefit for information from other collocated sensors and as such geophysical quantities retrieved from their measurements are typically more uncertain.
I have worked on constraining rain rate retrieval estimated from scanning sensor observations using vertically pointing radar and lidar observations for a more precise quantification of domain rain rate.
Although domain rate rate is best quantified by scanning sensors, decadal precipitation variability both temporal and structural can only currently be extracted from vertically pointing sensors which have a longer data record. However, with vertically pointing observations, it is near impossible to disentangle temporal evolution from horizontal structure. Classical approaches rely on Taylor hypothesis of frozen turbulence to convert elapsed time to horizontal dimension using the horizontal wind speed responsible for advecting cloud and precipitation overhead. While widely used, little research has been conducted to determine the validity and limitations of this assumption (see Oue et al. (2016) for a discussion on cloud fraction). In our work, we have attempted to determine how long does one need to observe precipitation advected overhead to gather statistical precipitation information equivalent to that of an 80 km wide domain.
We concluded that when the character of precipitation varies rapidly with height for instance owing to an active evaporation process, zenith-pointing radars are more suited for precipitation characterization; Shorter term domain precipitation rate variability can only be capture by scanning precipitation radars.
Scanning sensors are also better suited to document sporadic and horizontal homogeneous precipitation including precipitation presenting mesoscale organization.