Abstract
Ground-based microwave radiometer (MWR) observations of downwelling brightness temperature (T-B) are commonly used to estimate atmospheric attenuation at relative transparent channels for radio propagation and telecommunication purposes. The atmospheric attenuation is derived from T-B by inverting the radiative transfer equation with a priori knowledge of the mean radiating temperature (T-MR). T-MR 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 T-MR 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 T-MR 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 T-MR 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 T-MR at the K and V-W band is compared with estimates from nearly colocated and simultaneous radiosondes. The proposed method provides T-MR 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 similar to 30% for V-W-band channels.