Improving atmospheric path attenuation estimates for radio propagation applications by microwave radiometric profiling

Ground-based microwave radiometer (MWR) observations of downwelling brightness temperature (TB) are commonly used to estimate atmospheric attenuation at relative transparent channels for radio propagation and telecommunication purposes. The atmospheric attenuation is derived from TB by inverting the radiative transfer equation with a priori knowledge of the mean radiating temperature (TMR). TMR is usually estimated by either time-variant site climatology (e.g., monthly average computed from atmospheric thermodynamical profiles) or condition-variant estimation from surface meteorological sensors. However, information on TMR may also be extracted directly from MWR measurements at channels other than those used to estimate atmospheric attenuation. This paper proposes a novel approach to estimate TMR in clear and cloudy sky from independent MWR profiler measurements. A linear regression algorithm is trained with a simulated dataset obtained by processing 1 year of radiosonde observations of atmospheric thermodynamic profiles. The algorithm is trained to estimate TMR at K- and V–W-band frequencies (22–31 and 72–82 GHz, respectively) from independent MWR observations at the V band (54–58 GHz). The retrieval coefficients are then applied to a 1-year dataset of real V-band observations, and the estimated TMR at the K and V–W band is compared with estimates from nearly colocated and simultaneous radiosondes. The proposed method provides TMR estimates in better agreement with radiosondes than a traditional method, with 32 %–38 % improvement depending on frequency. This maps into an expected improvement in atmospheric attenuation of 10 %–20 % for K-band channels and ∼30 % for V–W-band channels.