Neural network model for atmospheric attenuation retrieval between 20 and 50 GHz by means of dual-frequency microwave radiometers

The propagation of signals through the atmosphere plays a major role in the quality of communications between ground terminals and satellites. Its characteristics have to be known accurately for appropriate communications equipment to be selected. In the band of frequencies used by operators in the future generation of satellites (beyond 20 GHz), the quality of transmission is especially affected by the attenuation of received signals because of rain, and by other less significant but much more frequent effects due to atmospheric gases, and nonprecipitating water. These phenomena have a direct impact on the availability ratio of a link between a ground terminal and a satellite. Our main goal in this study is to measure the atmospheric attenuation, using dual-frequency ground-based radiometers measuring the sky radiation at different pointing directions, so as to perform a statistical study. A new algorithm, based on a neural approach, is thus developed for estimating atmospheric attenuation, in various meteorological conditions, for several elevation angles and for frequencies between 20 and 50 GHz, from dual-frequency radiometric measurements. A validation of the obtained algorithm is performed on Olympus experimental data for the 20 and 30 GHz channels. At the end of this paper some applications are then presented to underline the usefulness of this new algorithm. The applicability of the algorithm to satellite beacon calibration in Ka or Q band with accuracy of 0.1 dB is shown. Preliminary joint statistics between attenuation at various pointing directions obtained at 40 GHz show what improvement can be expected from satellite diversity in the case of satellite constellations.