Abstract
The Remote sensing of Electrification, Lightning, And Meso-scale/micro-scale Processes with Adaptive Ground Observations (RELAMPAGO) and the Cloud, Aerosol, and Complex Terrain Interactions Experiment Proposal (CACTI) field campaigns provided an unprecedented thirteen-disdrometer dataset in Central Argentina during the Intensive (IOP, 15 November to 15 December 2018) and Extended (EOP, 15 October 2018 to 30 April 2019) Observational Periods. The drop size distribution (DSD) parameters and their variability were analyzed across the region of interest, which was divided into three subregions characterized by the differing proximity to the Sierras de Córdoba (SDC), in order to assess the impact of complex terrain on the DSD parameters. A rigorous quality control of the data was first performed. The frequency distributions of DSD-derived parameters were analyzed, including the normalized intercept parameter (logNw), the mean volume diameter (D0), the mean mass diameter (Dm), the shape parameter (μ), the liquid water content (LWC), and the rain rate (R). The region closest to the SDC presented higher values of logNw, lower D0, and higher μ, while the opposite occurred in the farthest region, i.e., the concentration of small drops decreased while the concentration of bigger drops increased with the distance to the east of the SDC. Furthermore, the region closest to the SDC showed a bimodal distribution of D0: the lower values of D0 were associated with higher values of logNw and were found more frequently during the afternoon, while the higher D0 were associated with lower logNw and occurred more frequently during the night. The data were analyzed in comparison to the statistical analysis of Dolan et al. 2018 and sorted according to the classification proposed in the cited study. The logNw-D0 and LWC-D0 two-dimensional distributions allowed further discussion around the applicability of other mid-latitude and global precipitation classification schemes (startiform/convection) in the region of interest. Finally, three precipitation case studies were analyzed with supporting polarimetric radar data in order to relate the DSD characteristics to the precipitation type and the microphysical processes involved in each case.
Highlights
The characterization of drop size distributions (DSDs) is fundamental for both the remote sensing of precipitation and its representation in numerical models
In order to evaluate how the proximity to the complex terrain in the Sierras de Córdoba (SDC) affects the DSD parameters, the study area was subdivided into three regions, as shown in Figure 1b: Region A is the closest to the SDC and includes the disdrometers APU, FDC, CCT, 014, and 015, all located above 400 m above mean sea level (AMSL); Region B includes disdrometers 001 and 015, both between 200 m and 400 m AMSL; and Region C includes disdrometers 003, 004, 005, 007, 008, and 010, all of them below 200 m AMSL and well within the plains, approximately
The variability of the different DSD parameters was analyzed as a function of the distance to the SDC
Summary
The characterization of drop size distributions (DSDs) is fundamental for both the remote sensing of precipitation and its representation in numerical models. 2021, 13, 2026 processes in numerical models need to assume particle size distributions, generally represented by a gamma distribution with one to three prognostic parameters [5,6,7], among others. The DSD varies with the different microphysics and precipitation regimes across the world [8]. The classification of convective/stratiform precipitation from DSD parameters has been the subject of many studies, and different classification schemes have been proposed, for instance setting thresholds on variables such as the standard deviation of. Based on the PCA analysis, the dataset was divided into six different groups with similar DSD characteristics related to the dominant microphysical processes in each one of them
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