A Study of Twenty Years of Advanced Water Vapor Radiometer Data at Goldstone, California

Water vapor radiometers are used to measure the sky brightness along a path through the atmosphere. This sky brightness includes contributions of atmospheric noise temperature and cosmic background. After removal of the cosmic background contribution, the remaining atmospheric noise temperature contribution has been used to generate statistics of atmospheric attenuation and atmospheric noise temperature used in telecommunications link budgets at frequency bands allocated for deep space communications such as Ka-band (32 GHz). The data also have been used to calibrate or experimentally characterize atmospheric error sources in phase data gathered from radio science and very long baseline interferometry (VLBI) experiments. One can also extract meteorological data types (integrated precipitable water vapor, liquid water content, and path delay) from the multi-frequency AWVR sky brightness temperature measurements. In this study, we will examine and cross-compare the statistics and time-variability of the AWVR brightness temperatures and of the meteorological data types extracted from AWVR measurements acquired between 2001 and 2021. The calibrated and validated AWVR data in this study are used for training and testing in a Machine Learning forecast system to predict atmospheric noise temperature at Deep Space Network (DSN) tracking sites in support of deep-space missions.