A practical method to remove a priori information from lidar optimal estimation method retrievals

Abstract. Lidar retrievals of atmospheric temperature and water vapour mixing ratio profiles using the Optimal Estimation Method (OEM) typically use a retrieval grid whose number of points is larger than the number of pieces of independent information obtainable from the measurements. Consequently, retrieved geophysical quantities contain some information from their a priori, which can affect the results in the higher altitudes of the temperature and water vapour profiles due to decreasing signal-to-noise ratios. The extent of this influence can be estimated using the retrieval’s averaging kernels. The removal of formal a priori information from the retrieved profiles in the regions of prevailing a priori effects is desirable, particularly when these greatest heights are of interest for scientific studies. We demonstrate here that removal of a priori information from OEM retrievals is possible by transforming the retrieval from a fine grid to a coarser grid such that the averaging kernel is close to unity at each grid point. In this case, setting the a priori term in the OEM retrieval equation to zero minimizes the effect of the a priori for the coarse grid retrieval. We demonstrate the improvements gained by this technique for the case of a large power-aperture Rayleigh scatter lidar nighttime temperature retrieval and for a Raman scatter lidar water vapor mixing ratio retrieval during both day and night.