A new snowfall detection algorithm for high latitude regions based on a combination of active and passive sensors

Precipitation retrieval over high latitudes, particularly snowfall retrieval over ice and snow, using satellite-based passive microwave spectrometers, is currently an unsolved problem. The challenge results from the large variability of microwave emissivity spectra for snow and ice surfaces, which can mimic, to some degree, the spectral characteristics of snowfall. This work focuses on the investigation of a new snowfall detection algorithm specific for high latitude regions, based on a combination of active and passive sensors able to discriminate between snowing and non snowing areas. The space-borne Cloud Profiling Radar (on CloudSat), the Advanced Microwave Sensor units A and B (on NOAA-16) and the infrared spectrometer MODIS (on AQUA) have been co-located for 365 days, from October 1st 2006 to September 30th, 2007. CloudSat products have been used as truth to calibrate and validate all the proposed algorithms. The methodological approach followed can be summarised into two different steps. In a first step, an empirical search for a threshold, aimed at discriminating the case of no snow, was performed, following Kongoli et al. [2003]. This single-channel approach has not produced appropriate results, a more statistically sound approach was attempted. Two different techniques, which allow to compute the probability above and below a Brightness Temperature (BT) threshold, have been used on the available data. The first technique is based upon a Logistic Distribution to represent the probability of Snow given the predictors. The second technique, defined Bayesian Multivariate Binary Predictor (BMBP), is a fully Bayesian technique not requiring any hypothesis on the shape of the probabilistic model (such as for instance the Logistic), which only requires the estimation of the BT thresholds. The results obtained show that both methods proposed are able to discriminate snowing and non snowing condition over the Polar regions with a probability of correct detection larger than 0.5, highlighting the importance of a multispectral approach.

The evolution of massive clumps in star forming regions

In this thesis two related arguments are investigated: - The first stages of the process of massive star formation, investigating the physical conditions and -properties of massive clumps in different evolutionary stages, and their CO depletion; - The influence that high-mass stars have on the nearby material and on the activity of star formation. I characterise the gas and dust temperature, mass and density of a sample of massive clumps, and analyse the variation of these properties from quiescent clumps, without any sign of active star formation, to clumps likely hosting a zero-age main sequence star. I briefly discuss CO depletion and recent observations of several molecular species, tracers of Hot Cores and/or shocked gas, of a subsample of these clumps. The issue of CO depletion is addressed in more detail in a larger sample consisting of the brightest sources in the ATLASGAL survey: using a radiative tranfer code I investigate how the depletion changes from dark clouds to more evolved objects, and compare its evolution to what happens in the low-mass regime. Finally, I derive the physical properties of the molecular gas in the photon-dominated region adjacent to the HII region G353.2+0.9 in the vicinity of Pismis 24, a young, massive cluster, containing some of the most massive and hottest stars known in our Galaxy. I derive the IMF of the cluster and study the star formation activity in its surroundings. Much of the data analysis is done with a Bayesian approach. Therefore, a separate chapter is dedicated to the concepts of Bayesian statistics.

Analysis of the pig genome for the identification of genomic regions affecting production traits

The aim of this work was to identify markers associated with production traits in the pig genome using different approaches. We focused the attention on Italian Large White pig breed using Genome Wide Association Studies (GWAS) and applying a selective genotyping approach to increase the power of the analyses. Furthermore, we searched the pig genome using Next Generation Sequencing (NSG) Ion Torrent Technology to combine selective genotyping approach and deep sequencing for SNP discovery. Other two studies were carried on with a different approach. Allele frequency changes for SNPs affecting candidate genes and at Genome Wide level were analysed to identify selection signatures driven by selection program during the last 20 years. This approach confirmed that a great number of markers may affect production traits and that they are captured by the classical selection programs. GWAS revealed 123 significant or suggestively significant SNP associated with Back Fat Thickenss and 229 associated with Average Daily Gain. 16 Copy Number Variant Regions resulted more frequent in lean or fat pigs and showed that different copies of those region could have a limited impact on fat. These often appear to be involved in food intake and behavior, beside affecting genes involved in metabolic pathways and their expression. By combining NGS sequencing with selective genotyping approach, new variants where discovered and at least 54 are worth to be analysed in association studies. The study of groups of pigs undergone to stringent selection showed that allele frequency of some loci can drastically change if they are close to traits that are interesting for selection schemes. These approaches could be, in future, integrated in genomic selection plans.