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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 10 | Copyright
Atmos. Meas. Tech., 11, 5471-5488, 2018
https://doi.org/10.5194/amt-11-5471-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 05 Oct 2018

Research article | 05 Oct 2018

Retrieval of snowflake microphysical properties from multifrequency radar observations

Jussi Leinonen1,2, Matthew D. Lebsock1, Simone Tanelli1, Ousmane O. Sy1, Brenda Dolan3, Randy J. Chase4, Joseph A. Finlon4, Annakaisa von Lerber5, and Dmitri Moisseev5,6 Jussi Leinonen et al.
  • 1Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
  • 2Joint Institute for Earth System Science and Engineering, University of California, Los Angeles, California, USA
  • 3Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA
  • 4Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
  • 5Radar Science, Finnish Meteorological Institute, Helsinki, Finland
  • 6Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland

Abstract. We have developed an algorithm that retrieves the size, number concentration and density of falling snow from multifrequency radar observations. This work builds on previous studies that have indicated that three-frequency radars can provide information on snow density, potentially improving the accuracy of snow parameter estimates. The algorithm is based on a Bayesian framework, using lookup tables mapping the measurement space to the state space, which allows fast and robust retrieval. In the forward model, we calculate the radar reflectivities using recently published snow scattering databases. We demonstrate the algorithm using multifrequency airborne radar observations from the OLYMPEX–RADEX field campaign, comparing the retrieval results to hydrometeor identification using ground-based polarimetric radar and also to collocated in situ observations made using another aircraft. Using these data, we examine how the availability of multiple frequencies affects the retrieval accuracy, and we test the sensitivity of the algorithm to the prior assumptions. The results suggest that multifrequency radars are substantially better than single-frequency radars at retrieving snow microphysical properties. Meanwhile, triple-frequency radars can retrieve wider ranges of snow density than dual-frequency radars and better locate regions of high-density snow such as graupel, although these benefits are relatively modest compared to the difference in retrieval performance between dual- and single-frequency radars. We also examine the sensitivity of the retrieval results to the fixed a priori assumptions in the algorithm, showing that the multifrequency method can reliably retrieve snowflake size, while the retrieved number concentration and density are affected significantly by the assumptions.

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We developed a technique for inferring the physical properties (amount, size and density) of falling snow from radar observations made using multiple different frequencies. We tested this method using measurements from airborne radar and compared the results to direct measurements from another aircraft, as well as ground-based radar. The results demonstrate that multifrequency radars have significant advantages over those with a single frequency in determining the snow size and density.
We developed a technique for inferring the physical properties (amount, size and density) of...
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