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  • Li, Haoran; Moisseev, Dmitri; von Lerber, Annakaisa (2018)
    As an ice particle grows by riming its shape is expected to change, resulting in a more spherical particle at the later stages of riming. This conceptual model is at the core of the current ice microphysical schemes and used for dual-polarization radar observation based classification of hydrometeors. A quantitative relation between riming and shapes of snowflake aggregates, however, has not been established yet. This study aims to derive this relation by using surface-based precipitation and coinciding dual-polarization radar observations. The observations were collected during four winter seasons, 49 snowstorms, at University of Helsinki measurement station in Hyytiala, Finland. Results show that relation between the differential reflectivity and reflectivity-weighted rime mass fraction is not monotonic and depends on reflectivity-weighted mean diameter. This behavior can be explained by the opposing effects of riming on dual-polarization radar observations. Riming increases particle bulk density, which leads to more pronounced dual-polarization radar signatures. As riming progresses the aspect ratio of snowflake increases slowly until the rime mass fraction value reaches a certainty value after which the aspect ratio increases more rapidly. Finally, coutilization of Z(e), Z(dr), and K-dp for inferring riming fraction is analyzed.
  • von Lerber, Annakaisa; Moisseev, Dmitri; Bliven, Larry F.; Petersen, Walter; Harri, Ari-Matti; Chandrasekar, V. (2017)
    This study uses snow events from the Biogenic Aerosols-Effects on Clouds and Climate (BAECC) 2014 campaign to investigate the connection between properties of snow and radar observations. The general hydrodynamic theory is applied to video-disdrometer measurements to retrieve masses of falling ice particles. Errors associated with the observation geometry and the measured particle size distribution (PSD) are addressed by devising a simple correction procedure. The value of the correction factor is determined by comparison of the retrieved precipitation accumulation with weighing-gauge measurements. Derived mass-dimensional relations are represented in the power-law form m = a(m)D(m)(b). It is shown that the retrieved prefactor a(m) and exponent b(m) react to changes in prevailing microphysical processes. From the derived microphysical properties, event-specific relations between the equivalent reflectivity factor Z(e) and snowfall precipitation rate S (Z(e) = a(zs)S(zs)(b)) are determined. For the studied events, the prefactor of the Z(e)-S relation varied between 53 and 782 and the exponent was in the range of 1.19-1.61. The dependence of the factors a(zs) and b(zs) on the m(D) relation and PSD are investigated. The exponent of the Z(e)-S relation mainly depends on the exponent of the m(D) relation, whereas the prefactor a(zs) depends on both the intercept parameter N-0 of the PSD and the prefactors of the m(D) and nu(D) relations. Changes in a(zs) for a given N-0 are shown to be linked to changes in liquid water path, which can be considered to be a proxy for degree of riming.
  • Tiira, Jussi; Moisseev, Dmitri (2020)
    Vertical profiles of polarimetric radar variables can be used to identify fingerprints of snow growth processes. In order to systematically study such manifestations of precipitation processes, we have developed an unsupervised classification method. The method is based on k-means clustering of vertical profiles of polarimetric radar variables, namely reflectivity, differential reflectivity and specific differential phase. For rain events, the classification is applied to radar profiles truncated at the melting layer top. For the snowfall cases, the surface air temperature is used as an additional input parameter. The proposed unsupervised classification was applied to 3.5 years of data collected by the Finnish Meteorological Institute Ikaalinen radar. The vertical profiles of radar variables were computed above the University of Helsinki Hyytiala station, located 64 km east of the radar. Using these data, we show that the profiles of radar variables can be grouped into 10 and 16 classes for rainfall and snowfall events, respectively. These classes seem to capture most important snow growth and ice cloud processes. Using this classification, the main features of the precipitation formation processes, as observed in Finland, are presented.