Signatures of Smoke, Clear-Air, Birds, and Insects on Solid-State Radars

Detection of non-meteorological targets, such as fires, clear-air turbulence, and biological targets (e.g., insects and birds), using solid-state weather radar is crucial across various industries. These include agriculture, the construction of wind farms in areas outside bird migration corridors, and air traffic control. Each of these fields requires reliable identification of such targets to support operations, mitigate risks, and ensure safety. We have conducted a comprehensive study and in-depth analysis of the unique signatures of these non-meteorological targets using our solid-state radars, which are capable of capturing detailed polarimetric information.

In our presentation, we will explore the typical characteristics of polarimetric variables and several derived products for clear-air conditions, birds, bats, and fire plumes as measured by solid-state weather radars. We will present not only typical values and their distribution in histograms, but also the spatial variability of these polarimetric variables for each non-meteorological target type. These variations in polarimetric properties provide insight into the structure and movement of different targets. Additionally, we will highlight the differences between the various categories, allowing for a clearer distinction in classification. Special attention will also be given to the change in the values of polarimetric variables in areas where water vapor condenses on particles from fire, which creates unique radar signatures.

Particular emphasis will also be placed on the scanning strategy we’ve adopted for clear-air detection, aimed at improving the identification of clear-air echoes and turbulence. The results presented have significant potential for further application, including the development of algorithms using traditional classification methods, such as K-Means or the Random Forest Algorithm, as well as new AI-based methods. These approaches are expected to enable more effective classification and detection of non-meteorological targets, advancing radar technology and its practical use across multiple domains.