X-band dual-polarization radar-based hydrometeor classification for Brazilian tropical precipitation systems

Atmos. Meas. Tech., 12, 811–837, 2019 https://doi.org/10.5194/amt-12-811-2019 © Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License. X-band dual-polarization radar-based hydrometeor classification for Brazilian tropical precipitation systems Jean-François Ribaud, Luiz Augusto Toledo Machado, and Thiago Biscaro National Institute of Space Research (INPE), Center for Weather Forecast and Climate Studies (CPTEC), Rodovia Presidente Dutra, km 40, Cachoeira Paulista, SP, 12 630-000, Brazil Correspondence: Jean-François Ribaud () Received: 25 May 2018 – Discussion started: 19 September 2018 Revised: 22 December 2018 – Accepted: 14 January 2019 – Published: 6 February 2019 Abstract. The dominant hydrometeor types associated with Brazilian tropical precipitation systems are identified via research X-band dual-polarization radar deployed in the vicinity of the Manaus region (Amazonas) during both the GoAmazon2014/5 and ACRIDICON-CHUVA field experiments. The present study is based on an agglomerative hierarchical clustering (AHC) approach that makes use of dual polarimetric radar observables (reflectivity at horizontal polarization ZH , differential reflectivity ZDR , specific differential-phase KDP , and correlation coefficient ρHV ) and temperature data inferred from sounding balloons. The sensitivity of the agglomerative clustering scheme for measuring the intercluster dissimilarities (linkage criterion) is evaluated through the wet-season dataset. Both the weighted and Ward linkages exhibit better abilities to retrieve cloud microphysical species, whereas clustering outputs associated with the centroid linkage are poorly defined. The AHC method is then applied to investigate the microphysical structure of both the wet and dry seasons. The stratiform regions are composed of five hydrometeor classes: drizzle, rain, wet snow, aggregates, and ice crystals, whereas convective echoes are generally associated with light rain, moderate rain, heavy rain, graupel, aggregates, and ice crystals. The main discrepancy between the wet and dry seasons is the presence of both low- and highdensity graupel within convective regions, whereas the rainy period exhibits only one type of graupel. Finally, aggregate and ice crystal hydrometeors in the tropics are found to exhibit higher polarimetric values compared to those at midlatitudes. 1 Introduction The use of dual-polarization (DPOL) radars over several decades by national weather services as well as research laboratories has deeply changed the understanding and forecasting of many precipitation events around the world. By using a second orthogonal polarization, such weather radars enable inference of the size, shape, orientation, and phase state of different particles detected within the sampled cloud. To date, the major advances that have been made as a result of DPOL radar sensitivities are mainly related to improvement in the distinction between meteorological and non-meteorological echoes, attenuation correction, quantitative rainfall estimation, and bulk hydrometeor classification (Bringi and Chandrasekar, 2001; Bringi et al., 2007). By combining DPOL radar observables (generally, reflectivity at horizontal polarization, ZH ; differential reflectivity, ZDR ; specific differential phase, KDP ; and correlation coefficient, ρHV ) with some extra information such as temperature to locate the freezing level, the hydrometeor identification task has been the subject of many research studies. Indeed, potential benefits from this research topic are numerous such as the evaluation of microphysical parameterization in highresolution numerical weather prediction models (e.g. Augros et al., 2016; Wolfensberger and Berne, 2018), investigation of relationships between microphysics and lightning (e.g. Ribaud et al., 2016a), and improvement in weather nowcasting for high-impact meteorological events (hailstorms, flight assistance, and road safety). Three hydrometeor classification schemes have been developed since the emergence of DPOL radar in the 1980s: (1) supervised, (2) unsupervised, and (3) semi-supervised techniques (Fig. 1). Published by Copernicus Publications on behalf of the European Geosciences Union. 812 J.-F. Ribaud et al.: X-band dual-polarization radar-based hydrometeor classification Figure 1. Schematic representation of the different hydrometeor classification techniques and their principal associated benchmarks. 1. The supervised method constitutes, by far, most of the literature and is subdivided into three different techniques: the Boolean tree method, fuzzy logic, and the Bayesian approach. Here, the supervised technique refers to a priori and arbitrarily identified hydrometeor types from which DPOL radar responses have been derived from either theoretical models or empirical knowledge. Polarimetric observations are then assigned to the most suitable hydrometeor types according to their similarities. – Boolean method. This technique is the easiest way to identify dominant hydrometeor populations and has consequently been the first to be used. The algorithm relies on the beforehand definition of the ranges of DPOL radar-observable values for each hydrometeor type by the user. Then, a simple Boolean decision is applied to retrieve the dominant hydrometeor type (Seliga and Bringi, 1976; Hall et al., 1984; Bringi et al., 1986; Straka and Zrnić, 1993; Höller et al., 1994). This approach, nevertheless, does not take into account the fact that different hydrometeor types can be defined on the same range of values for the same polarimetric radar observable and, therefore, frequently leads to misclassification. – Fuzzy-logic technique (Mendel, 1995). This supervised algorithm type fixed the previous limitation by allowing a smooth transition of DPOL radarobservable ranges for all hydrometeor types. The originality of fuzzy logic is its ability to transform Atmos. Meas. Tech., 12, 811–837, 2019 sets of non-linear radar data into scalar outputs referring to different microphysical species. In this regard, each hydrometeor-type distribution is characterized by a membership function coming from either T-matrix simulations (Mishchenko and Travis, 1998) or, less frequently, aircraft in situ measurements. The hydrometeor inference is finally the result of a combination of membership functions and a set of a priori rules defined by the user (Straka, 1996; Vivekanandan et al., 1999; Liu and Chandrasekar, 2000; Marzano et al., 2006; Park et al., 2009; Dolan and Rutledge, 2009; Al-Sakka et al., 2013; Thompson et al., 2014). This method is relatively simple to implement and computationally inexpensive. A few studies, such as the Joint Polarization Experiment (Ryzhkov et al., 2005) for hail detection or even the recent use of a fuzzy-logic algorithm as an operational tool for national weather services (Al-Sakka et al., 2013), have demonstrated the robustness of this hydrometeor classification algorit (...truncated)