An Improved One-Dimensional Variational Method for a Ground-Based Microwave Radiometer - Hualong Yan; Di Zhou; Renxin Ji; Rongmei Geng (Atmosphere)
Temperature and water vapor density profiles in the troposphere (from the surface to 10 km) can be retrieved from a ground-based microwave radiometer (MWR) at high temporal and moderate vertical resolution. The back-propagation neural network (BPNN) algorithm is commonly deployed in ground-based microwave radiometers. Some studies have shown that the accuracy of the BPNN retrieval algorithm is affected by training data with large deviations. In this paper, an improved 1D-VAR method is proposed, which can effectively correct the bias; the results show that the improved 1D-VAR method can provide more accurate inversion results. Compared to the BPNN and 1D-VAR methods, the root mean square errors of temperature for the improved 1D-VAR method are reduced by <semantics>60.8%</semantics> and <semantics>29.4%</semantics> during daytime and by <semantics>54.2%</semantics> and <semantics>49.7%</semantics> during nighttime, respectively.