A multi-wavelength Raman lidar system which includes both vibrational rotational Raman and Mie scattering spectra
has been designed and described. A retrieval algorithm for water vapor and temperature has also been developed based
on the potential observations from this Raman lidar system. The performance of this retrieval method and the new lidar
system has been evaluated with a synthetic test. Using the U.S. standard atmosphere model and main parameters of this
lidar system, we have obtained signal to noise ratio(SNR)of water-vapor backscatter signals under different
circumstances of aerosol content, pulse emission energy and signal integration time. With the model calculated
backscatter signals, both atmospheric water-vapor and temperature profiles have been retrieved and their uncertainties
have been analyzed. These synthetic tests indicate that our new lidar system can obtain profiles of water-vapor and
temperature at both day and night time, but with different detection heights. The retrieval algorithm shows less than 30%
relative error for water vapor mixing ratio and good accuracy with a minimum detection of temperature less than 2 K.
This paper provides an inter-comparison study of various ground-based cloud retrieval algorithms that have been
developed to obtain cloud water content. The retrieval algorithms are classified into three types, statistical
parameterization algorithm, physical retrieval algorithm, and optimal iteration method. Analyses indicate that physical
retrieval algorithms are theoretically accurate, however, assumptions used in these methods make it challenging for them
to obtain highly reliable results. Empirical parameterization methods are simple and can be easily applied. However,
these methods are generally based on very limited cloud samples for certain types of clouds and locations, they have
much larger uncertainties. In contrast, the optimal iteration method seems to have relatively higher accuracies since the
retrieval results make the forward model simulations match observations. However, the accuracy of optimal iteration
method is highly dependent on the reliability of the forward models and the a priori information.
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