This paper describes the results of the research fulfilled in TU-Delft by joint Ukrainian and Dutch team. It analyzes multi-instrument rain observations, using the instrument set, which includes W-band cloud radar, laser optical disdrometers, weather station, and microwave radiometer. New friendly interface software is developed, presented, and used as a tool for comparison and fusion of diverse sensors datasets. The results obtained demonstrate the synergy of multi-instrument measurements and corresponds to the overarching trends of big data analysis. The intricacies of combining data from various sources to enhance calibration and improve the accuracy of atmospheric studies is discussed. In particular, analysis of 94 GHz cloud radar calibration based on disdrometer measurements with application of additional multi-instrument measurements is performed.

This paper is devoted to discussing peculiarities of W-band cloud radar calibration. After a brief overview of meteorological radar calibration methods for quantitative information retrieval, we focus on problems and their possible solutions with respect to mm-wave radar calibration. The experimental part of the research is based on multi-instrument measurements performed during several years in the Cabauw experimental meteorological site in the Netherlands. The accumulated data are used for comparison of 94 GHz radar rain measurements with non-radar droplet size distribution measurements, provided by laser disdrometers. Calculations are done taking into account data of other in situ meteorological measurements. A specialized MATLAB software tool for processing such complex data and radar calibration is developed and demonstrated.

This paper proposes generalized mathematical model of different passive interferences and develops an effective algorithm of digital signal processing for detection on the background of them. Models of interferences as random process of K-distribution is used with parametrization for the unwanted reflections from atmosphere, land, and sea. Robust algorithm for signal detection on the background of such interferences, in particular in case of non-gaussian distribution, is developed. Its effectiveness is researched and confirmed.