A Novel Framework for Automatic Scanning Radar Pointing Calibration Using the Sun

Abstract. A high accuracy of antenna beam pointing is essential for weather and cloud radars in order to precisely locate cloud and precipitation. It is also a critical requirement for estimating the horizontal wind field or to retrieve particle's vertical motions. We present a general framework for radar pointing calibration using the sun as a reference target. The workflow is structured into three steps: (i) measurement and analysis of individual Sun scans, (ii) estimation of scanner inaccuracies from a series of scans, and (iii) correction of these inaccuracies. Our approach is radar-agnostic and applicable to any instrument equipped with a two-axis pan-tilt scanner and a parabolic antenna. General recommendations for Sun scan implementation are given, and the full calibration process is demonstrated using a Mira-35 cloud radar. The method allows retrieval of a comprehensive set of parameters, including beamwidth in two orthogonal directions, pedestal tilt, axis misalignments, encoder offsets, gear backlash, and the receiver-scanner time offset. With this approach, absolute pointing accuracy better than 0.1° can be achieved, and relative changes as small as 0.01° can be detected. To facilitate the automatic application, we provide the open-source Python library SunscanPy for radar pointing calibration. This toolset is especially valuable for stationary radars and radar networks, where it enables automatic monitoring of long-term pointing stability. Finally, we introduce a novel automatic pointing correction scheme based on inverse kinematics. Once the scanner inaccuracies are estimated, the required motor positions can be computed to compensate for the inaccuracies, without mechanical adjustments. Such functionality is particularly advantageous for mobile radars, research campaigns, or remote deployments, where frequent mechanical leveling is necessary but often difficult to perform.