Data Fusion of Multi-Frequency Radar, Radiometer, and in Situ Sensors for Rain and Cloud Research

Abstract

Accurate characterization of the vertical structure of rain and clouds is essential for environmental monitoring and for the calibration and validation of cloud and precipitation radars used in climate research. It is also increasingly important for ensuring the safety and operational reliability of light air vehicles and unmanned aerial vehicles (UAVs), which are highly sensitive to small-scale variability in atmospheric conditions. This study presents a multi-instrument data fusion framework for longterm monitoring of precipitation processes based on three years of simultaneous ground-based observations in the Netherlands. The dataset combines vertically pointing radars operating at 24, 35, and 94 GHz with a microwave radiometer, laser disdrometers, and standard meteorological measurements. The developed processing approach emphasizes physical consistency across heterogeneous observations rather than optimized retrievals. The results demonstrate pronounced frequency-dependent signatures in reflectivity structure and attenuation, while key precipitation features, including the melting layer, are consistently observed across frequencies. The combined radar-disdrometer analysis provides a physically constrained reference for precipitation monitoring and supports calibration and performance assessment of cloud and precipitation radars, particularly at W-band, as well as emerging applications in aviation safety and low-altitude atmospheric operations.