Optimal estimation of water vapour profiles using a combination of Raman lidar and microwave radiometer

In this work, a two-step algorithm to obtain water vapour profiles from a combination of Raman lidar and microwave radiometer is presented. Both instruments were applied during an intensive 2-month measurement campaign (HOPE) close to Julich, western Germany, during spring 2013. To retrieve reliable water vapour information from inside or above the cloud a two-step algorithm is applied. The first step is a Kalman filter that extends the profiles, truncated at cloud base, to the full height range (up to 10 km) by combining previous information and current measurement. Then the complete water vapour profile serves as input to the one-dimensional variational (1D-VAR) method, also known as optimal estimation. A forward model simulates the brightness temperatures which would be observed by the microwave radiometer for the given atmospheric state. The profile is iteratively modified according to its error bars until the modelled and the actually measured brightness temperatures sufficiently agree. The functionality of the retrieval is presented in detail by means of case studies under different conditions. A statistical analysis shows that the availability of Raman lidar data (night) improves the accuracy of the profiles even under cloudy conditions. During the day, the absence of lidar data results in larger differences in comparison to reference radiosondes. The data availability of the full-height water vapour lidar profiles of 17% during the 2-month campaign is significantly enhanced to 60% by applying the retrieval. The bias with respect to radiosonde and the retrieved a posteriori uncertainty of the retrieved profiles clearly show that the application of the Kalman filter considerably improves the accuracy and quality of the retrieved mixing ratio profiles.