Improving weather-forecast based model chain to optimize data-volume transfer for Ka-band deep-space downlinks

This work aims at verifying an innovative approach for link-design optimization of deep-space missions working at Ka band. The presented approach exploits a weather forecast (WF) model coupled with a radiopropagation model to maximize data-transfer during a Ka-band downlink transmission. First, we exploit radiosounding data to tune the WF model on the geographical site of interest. As second step, we use microwave radiometric measurements to verify both WF and radiopropagation models. A final goal is obtained applying the WF-based approach to optimize the link and then computing the yearly data return on the basis of the actual atmospheric scenario measured by the microwave radiometer. On a test period of three years of transmission, WF-based approach provides a gain, in terms of yearly received data-volume, of about 15% up to 24% if compared to traditional link-design techniques. This gain is combined with a corresponding reduction of yearly lost data. These interesting results make the WF-based approach an appealing alternative for deep-space applications.