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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/amtd-8-5467-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amtd-8-5467-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  29 May 2015

29 May 2015

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This preprint was under review for the journal AMT but the revision was not accepted.

Ground based lidar and microwave radiometry synergy for high vertically resolved thermodynamic profiling

M. Barrera-Verdejo1, S. Crewell1, U. Löhnert1, E. Orlandi1, and P. Di Girolamo2 M. Barrera-Verdejo et al.
  • 1University of Cologne Institute of Geophysics and Meteorology Pohligstr. 3, 50969 Köln, Germany
  • 2Scuola di Ingegneria Universitá degli Studi della Basilicata Campus di Macchia Romana Viale dell'Ateneo Lucano, 10, 85100 Potenza, Italy

Abstract. Continuous monitoring of atmospheric humidity and temperature profiles is important for many applications, e.g. assessment of atmospheric stability and cloud formation. While lidar measurements can provide high vertical resolution albeit with limited coverage, microwave radiometers receive information throughout the troposphere though their vertical resolution is poor. In order to overcome these specific limitations the synergy of a Microwave Radiometer (MWR) and a Raman Lidar (RL) system is presented in this work. The retrieval algorithm that combines these two instruments is an Optimal Estimation Method (OEM) that allows for a uncertainty analysis of the retrieved profiles. The OEM combines measurements and a priori information taking the uncertainty of both into account. The measurement vector consists of a set of MWR brightness temperatures and RL water vapor profiles. The method is applied for a two month field campaign around Jülich, Germany for clear sky periods. Different experiments are performed to analyse the improvements achieved via the synergy compared to the individual retrievals. When applying the combined retrieval, on average the theoretically determined absolute humidity error can be reduced by 59.8% (37.9%) with respect to the retrieval using only-MWR (only-RL) data. The analysis in terms of degrees of freedom for signal reveals that most information is gained above the usable lidar range. The retrieved profiles are further evaluated using radiosounding and GPS water vapor measurements. Within a single case study we also explore the potential of the OEM for deriving the relative humidity profile, which is especially interesting to study cloud formation in the vicinity of cloud edges. To do so temperature information is added both from RL and MWR. For temperature, it is shown that the error is reduced by 47.1% (24.6%) with respect to the only-MWR (only-RL) profile. Due to the use of MWR brightness temperatures at multiple elevation angles, the MWR provides significant information below the lidar overlap region as shown by the degrees of freedom for signal. Therefore it might be sufficient to combine RL water vapor with multi-angle, multi-wavelength MWR for the retrieval of relative humidity, however, long-term studies are necessary in the future. In general, the benefit of the sensor combination is especially strong in regions where Raman Lidar data is not available (i.e. overlap region, poor signal to noise ratio), whereas if both instruments are available, RL dominates the retrieval.

M. Barrera-Verdejo et al.

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M. Barrera-Verdejo et al.

M. Barrera-Verdejo et al.

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