Optimizing Data Volume Return for Ka-Band Deep Space Links Exploiting Short-Term Radiometeorological Model Forecast

The goal of this work is to demonstrate how the use of short-term radio-meteorological forecasts can aid the optimization of transferred data volumes from deep-space (DS) satellite payloads to Earth receiving stations. To this aim, a weather forecast (WF) numerical model is coupled with a microphysically oriented radiopropagation scheme in order to predict the atmospheric effects on Ka-band signals in DS links. A regional WFs model is exploited to obtain short-term predictions of the atmospheric state. The microphysically oriented radiopropagation scheme consists in a 3-D radiative transfer model which is used to compute the slant path attenuation and the antenna noise temperature at Ka-band in order to predict the signal-to-noise ratio at the receiving station. As a baseline, the BepiColombo mission to Mercury is chosen. Two prediction methods, statistical and maximization, are introduced and tested in two scenarios: 1) full-numerical scenario, where simulated data are used for evaluating the performances of the prediction techniques; 2) semiempirical scenario, where measured meteorological data are exploited to simulate beacon measurements in clear and rainy conditions. The results are shown in terms of received and lost data volumes and compared with benchmark scenarios. Using short-term radio-meteorological forecasts, yearly data volume return can be increased more than 20% if daily WFs, rather than monthly climatological statistics, are exploited.