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In this work, a two-step algorithm to obtain water vapour profiles froma combination of Raman lidar and microwave radiometer is presented. Bothinstruments were applied during an intensive 2-month measurement campaign(HOPE) close to Jülich, western Germany, during spring 2013. To retrievereliable water vapour information from inside or above the cloud a two-stepalgorithm is applied. The first step is a Kalman filter that extends theprofiles, truncated at cloud base, to the full height range (up to 10 km) bycombining previous information and current measurement. Then the completewater vapour profile serves as input to the one-dimensional variational(1D-VAR) method, also known as optimal estimation. A forward model simulatesthe brightness temperatures which would be observed by the microwaveradiometer for the given atmospheric state. The profile is iterativelymodified according to its error bars until the modelled and the actuallymeasured brightness temperatures sufficiently agree. The functionality of theretrieval is presented in detail by means of case studies under differentconditions. A statistical analysis shows that the availability of Raman lidardata (night) improves the accuracy of the profiles even under cloudyconditions. During the day, the absence of lidar data results in largerdifferences in comparison to reference radiosondes. The data availability ofthe full-height water vapour lidar profiles of 17 % during the 2-monthcampaign is significantly enhanced to 60 % by applying the retrieval. Thebias with respect to radiosonde and the retrieved a posteriori uncertainty ofthe retrieved profiles clearly show that the application of the Kalman filterconsiderably improves the accuracy and quality of the retrieved mixing ratioprofiles

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