Comprehensive characterization of snowfall in terms of microphysical features, quantitative precipitation estimation and scavenging properties by in situ and remote sensing observations at an Antarctic coastal site

Snow plays a crucial role in the Earth's hydrological cycle and energy budget, making its monitoring necessary. In this context, ground-based radars and in situ instruments are essential thanks to their spatial coverage, resolution, and temporal sampling. Deep understanding and reliable measurements of snow properties are crucial over Antarctica to assess potential future changes of the surface mass balance (SMB) and define the contribution of the Antarctic ice sheet on sea-level rise. However, despite its key role, Antarctic precipitation is poorly investigated due to the continent's inaccessibility and extreme environment. In this framework, this Thesis aims to contribute to filling this gap by in-depth characterization of Antarctic precipitation at the Mario Zucchelli station from different points of view: microphysical features, quantitative precipitation estimation (QPE), vertical structure of precipitation, and scavenging properties. For this purpose, a K-band vertically pointing radar collocated with a laser disdrometer and an optical particle counter (OPC) were used. The radar probed the lowest atmospheric layers with high vertical resolution, allowing the first trusted measurement at only 105 m height. Disdrometer and OPC provided information on the particle size distribution and aerosol concentrations. An innovative snow classification methodology was designed by comparing the radar reflectivity (Ze) and disdrometer-derived reflectivity by means of DDA simulations. Results of classification were exploited in QPE through appropriate Ze-snow rate relationships. The accuracy of the resulting QPE was benchmarked against a collocated weighing gauge. Vertical radar profiles were also investigated to highlight hydrometeors' sublimation and growth processes. Finally, OPC and disdrometer data allowed providing the first-ever estimates of scavenging properties of Antarctic snowfall. Results presented in this Thesis give rise to advances in knowledge of the characteristics of snowfall in Antarctica, contributing to a better assessment of the SMB of the Antarctic ice sheet, the major player in the global sea-level rise.

Rainfall spatial predictions: a two-part model and its assessment

Spatial prediction of hourly rainfall via radar calibration is addressed. The change of support problem (COSP), arising when the spatial supports of different data sources do not coincide, is faced in a non-Gaussian setting; in fact, hourly rainfall in Emilia-Romagna region, in Italy, is characterized by abundance of zero values and right-skeweness of the distribution of positive amounts. Rain gauge direct measurements on sparsely distributed locations and hourly cumulated radar grids are provided by the ARPA-SIMC Emilia-Romagna. We propose a three-stage Bayesian hierarchical model for radar calibration, exploiting rain gauges as reference measure. Rain probability and amounts are modeled via linear relationships with radar in the log scale; spatial correlated Gaussian effects capture the residual information. We employ a probit link for rainfall probability and Gamma distribution for rainfall positive amounts; the two steps are joined via a two-part semicontinuous model. Three model specifications differently addressing COSP are presented; in particular, a stochastic weighting of all radar pixels, driven by a latent Gaussian process defined on the grid, is employed. Estimation is performed via MCMC procedures implemented in C, linked to R software. Communication and evaluation of probabilistic, point and interval predictions is investigated. A non-randomized PIT histogram is proposed for correctly assessing calibration and coverage of two-part semicontinuous models. Predictions obtained with the different model specifications are evaluated via graphical tools (Reliability Plot, Sharpness Histogram, PIT Histogram, Brier Score Plot and Quantile Decomposition Plot), proper scoring rules (Brier Score, Continuous Rank Probability Score) and consistent scoring functions (Root Mean Square Error and Mean Absolute Error addressing the predictive mean and median, respectively). Calibration is reached and the inclusion of neighbouring information slightly improves predictions. All specifications outperform a benchmark model with incorrelated effects, confirming the relevance of spatial correlation for modeling rainfall probability and accumulation.