The TRIple-frequency and Polarimetric radar Experiment for improving process observations of winter precipitation

Abstract

Abstract. This paper describes a 2-month dataset of ground-based triple-frequency (X, Ka, and W band) Doppler radar observations during the winter season obtained at the Jülich ObservatorY for Cloud Evolution Core Facility (JOYCE-CF), Germany. All relevant post-processing steps, such as re-gridding and offset and attenuation correction, as well as quality flagging, are described. The dataset contains all necessary information required to recover data at intermediate processing steps for user-specific applications and corrections (https://doi.org/10.5281/zenodo.1341389; Dias Neto et al., 2019). The large number of ice clouds included in the dataset allows for a first statistical analysis of their multifrequency radar signatures. The reflectivity differences quantified by dual-wavelength ratios (DWRs) reveal temperature regimes where aggregation seems to be triggered. Overall, the aggregation signatures found in the triple-frequency space agree with and corroborate conclusions from previous studies. The combination of DWRs with mean Doppler velocity and linear depolarization ratio enables us to distinguish signatures of rimed particles and melting snowflakes. The riming signatures in the DWRs agree well with results found in previous triple-frequency studies. Close to the melting layer, however, we find very large DWRs (up to 20 dB), which have not been reported before. A combined analysis of these extreme DWR with mean Doppler velocity and a linear depolarization ratio allows this signature to be separated, which is most likely related to strong aggregation, from the triple-frequency characteristics of melting particles.

Estimating total attenuation using Rayleigh targets at cloud top: applications in multilayer and mixed-phase clouds observed by ground-based multifrequency radars

“…Accurate retrievals of vertical profiles of cloud and precipitation properties from space or from the ground are essential pillars for evaluating and further developing their representation in numerical models (Iguchi and Matsui, 2018). However, a recent study by Duncan and Eriksson (2018) shows, for example, that even essential columnar cloud properties, such as ice water path (IWP), show large biases between different retrieval products, which hampers their applicability to further improve model parameterizations. As mentioned by Duncan and Eriksson (2018) and others, one of the main reasons for the spread between retrieval products but also for differences in models is related to uncertainties in the underlying cloud microphysics.…”

Towards the connection between snow microphysics and melting layer: Insights from multi-frequency and dual-polarization radar observations during BAECC

“…snow when PR > 1 mm h −1 , while the reverse is observed for lighter precipitation. Despite the rather good agreement between LDR and ρ hv observations, it appears that LDR systematically reveals lower ML bottom than ρ hv , indicating that LDR can be suitable in discriminating rain and melting snow (Illingworth and Thompson, 2011;Dias Neto et al, 2019). The smaller LDR peak for rimed snow is correlated with the smaller X-band reflectivity enhancement as shown in Fig.…”

Evaluation of the reflectivity calibration of W-band radars based on observations in rain

“…Polarimetric Doppler spectra have only been sporadically used in the past, probably due to the demands regarding storage capacity and required high data quality. At centimeter wavelength, their potential has been shown for microphysical retrievals (Moisseev and Chandrasekar, 2007;Spek et al, 2008;Dufournet and Russchenberg, 2011;Pfitzenmaier et al, 2018) and efficient clutter suppression (Unal, 2009;Moisseev and Chandrasekar, 2009;Alku et al, 2015). The number of installed polarimetric Doppler cloud radars has only recently increased, with only a few studies, so far, exploring their potential for microphysical studies and retrievals (Oue et al, 2015(Oue et al, , 2018Myagkov et al, 2015Myagkov et al, , 2016b.…”

“…Drop size distributions (DSDs) observed in situ are converted to the radar reflectivity. Time series (Gage et al, 2004;Frech et al, 2017) or distributions (Kollias et al, 2019;Dias Neto et al, 2019) of calculated and observed reflectivities are then compared. Hogan et al (2003) showed a calibration verification method suitable for W-band cloud radars only.…”