Potential of AI classification of the weather radar observations for aeroecological research

The benefits of the application of weather radar observations for aeroecological research are already well known to the scientific community. The advantages of long-term polarimetric weather radar observations for the detection of bird and insect migration or estimation of their abundances are used by different teams all over the world. In this context, a correct, timely, and meaningful interpretation of polarimetric weather radar observations is an important part of these studies. This interpretation requires a well-developed technique that automates the recognition of separate classes in both spatial and temporal dimensions of the data.

The study presents a novel data-driven technique for identifying different classes in Quasi-Vertical Profiles (QVPs) and in Columnar Vertical Products (CVP) based on observations made by a dual-polarization Doppler weather radar. The top-down optimal clustering is applied to the detection and identification of aeroecological classes in the QVPs and CVPs. We demonstrate its application to the NCAS X-band dual-polarization Doppler weather radar (NXPol) data and the potential of its application to the C-band data of the Met Office radar network. This technique is generally applicable to similar multivariate data from other observational instruments and will improve quantitative observation and monitoring of biodiversity in the UK.