Retrieval of snowflake microphysical properties from multifrequency radar observations
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Published:2018-10-05
Issue:10
Volume:11
Page:5471-5488
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ISSN:1867-8548
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Container-title:Atmospheric Measurement Techniques
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language:en
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Short-container-title:Atmos. Meas. Tech.
Author:
Leinonen JussiORCID, Lebsock Matthew D., Tanelli Simone, Sy Ousmane O., Dolan BrendaORCID, Chase Randy J., Finlon Joseph A.ORCID, von Lerber Annakaisa, Moisseev DmitriORCID
Abstract
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.
Funder
National Aeronautics and Space Administration Jet Propulsion Laboratory Horizon 2020 Framework Programme Academy of Finland
Publisher
Copernicus GmbH
Subject
Atmospheric Science
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