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At the Istituto di Metodologie per l'Analisi Ambientale of the Italian National Research Council (CNR-IMAA) an
advanced observatory for the ground-based remote sensing of the atmosphere is operative. This facility is equipped with
several instruments including two multi-wavelength Raman lidars, one of which mobile, a microwave profiler, a 36 GHz
Doppler polarimetric radar, two laser ceilometers, a sun photometer, a surface radiation station and three radiosounding
stations.
CNR-IMAA atmospheric observatory (CIAO) is located in Southern Italy on the Apennine mountains (40.60N, 15.72E,
760 m a.s.l.), less than 150 km from the West, South and East coasts. The site is in a valley surrounded by low mountains
(<1100 m a.s.l.) and this location offers an optimal opportunity to study different kinds of weather and climate regimes.
CIAO represents an optimal site where testing possible synergies between active and passive techniques for improving
the profiling capabilities of several atmospheric key variables, such as aerosol, water vapour and clouds, and for the
development of an integration strategy for their long-term monitoring.
CIAO strategy aims at the combination of observations provided by active and passive sensors for providing advanced
retrievals of atmospheric parameters exploiting both the high vertical resolution of active techniques and the typical
operational capabilities of passive sensors. This combination offers a high potential for profiling atmospheric parameters
in an enlarged vertical range nearly independently on the atmospheric conditions. In this work, we describe two different
integration approaches for the improvement of water vapour profiling during cloudy condition through the combination
of Raman lidar and microwave profiler measurements. These approaches are based on the use of Kalman filtering and
Tikhonov regularization methods for the solution of the radiative transfer equation in the microwave region. The
accuracy of the retrieved water vapour profiles during cloudy conditions is improved by the use of the water vapour
Raman lidar profiles, retrieved up to a maximum height level located around the cloud base region (depending on their
optical thickness), as a constraint to the obtained solution set. The presented integration approaches allow us to provide
physically consistent solution to the inverse problem in the microwave region retrieving water vapour vertical profiles
also in presence of thick clouds. The integration of Raman lidar and microwave measurements also provides a
continuous high-resolution estimation of the water vapour content in the full troposphere and, therefore, a useful tool for
the evaluation of model capability to capture mean aspects of the water vapour field in nearly all weather conditions as
well as for the identification of possible discrepancies between observations and models.
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