Litcius/Paper detail

Science Applications of Phased Array Radars

Pavlos Kollias, Robert D. Palmer, David M. Bodine, Toru Adachi, Howie Bluestein, John Y. N. Cho, Casey B. Griffin, Jana B. Houser, Pierre‐Emmanuel Kirstetter, Matthew R. Kumjian, James M. Kurdzo, Wen Chau Lee, Edward Luke, Steve Nesbitt, Mariko Oue, Alan Shapiro, Angela K. Rowe, Jorge L. Salazar-Cerreño, Robin L. Tanamachi, Kristofer S. Tuftedal, Xuguang Wang, Dušan S. Zrnić, Bernat Puigdomènech Treserras

2022Bulletin of the American Meteorological Society31 citationsDOIOpen Access PDF

Abstract

Abstract Phased array radars (PARs) are a promising observing technology, at the cusp of being available to the broader meteorological community. PARs offer near-instantaneous sampling of the atmosphere with flexible beam forming, multifunctionality, and low operational and maintenance costs and without mechanical inertia limitations. These PAR features are transformative compared to those offered by our current reflector-based meteorological radars. The integration of PARs into meteorological research has the potential to revolutionize the way we observe the atmosphere. The rate of adoption of PARs in research will depend on many factors, including (i) the need to continue educating the scientific community on the full technical capabilities and trade-offs of PARs through an engaging dialogue with the science and engineering communities and (ii) the need to communicate the breadth of scientific bottlenecks that PARs can overcome in atmospheric measurements and the new research avenues that are now possible using PARs in concert with other measurement systems. The former is the subject of a companion article that focuses on PAR technology while the latter is the objective here.

Topics & Concepts

Atmosphere (unit)Phased arrayComputer scienceSystems engineeringRemote sensingEnvironmental scienceMeteorologyGeologyEngineeringTelecommunicationsGeographyAntenna (radio)Meteorological Phenomena and SimulationsPrecipitation Measurement and AnalysisOcean Waves and Remote Sensing