Radar Remote Sensing: Retrieval Algorithms and Applications for Characterizing Precipitation

Dear Colleagues,

Radar remote sensing deals with the full vector nature of electromagnetic waves scattered from targets of interest, from which it is possible to perform hydrometeor identification, rainfall microphysical retrievals, and quantitative precipitation estimation and nowcasting in the remote sensing of atmosphere. The vector nature comes from the fact that multiple polarization or frequencies are used in the observation process. In the 1990s, the implementation of polarimetric measurement options for operational WSR-88D radars resulted in a widespread application of polarimetric techniques by a broad segment of the meteorological community. Since the 1990s, many polarimetric radars have been deployed not just at S-band frequency used by the US weather service but also at higher frequencies including C, X, Ku, Ka, and W. Similarly, multiple frequency radars and vertically pointing radars have also been deployed. This rich base of information also attracts advanced processing techniques, in particular, the use of machine learning or Bayesian retrievals.

This Special Issue focuses on recent advances in radar remote sensing of precipitation, including rain microphysics, hydrometeor classification, quantitative precipitation estimation, and nowcasting. Papers focused on the radar remote sensing of non-precipitation phenomena, such as clouds, winds, and lightning, are also welcome.

Dr. Yingzhao Ma
Dr. Robert Cifelli
Dr. Seppo Pulkkinen
Prof. V. Chandrasekar
Guest Editors

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