Preliminary results of an attempt to provide soil moisture datasets in order to verify numerical weather prediction models

In the recent years, there has been a significant growth in the recognition of the soil moisture importance in large-scale hydrology and climate modelling. Soil moisture is a lower boundary condition, which rules the partitioning of energy in terms of sensible and latent heat flux. Wrong estimations of soil moisture lead to wrong simulation of the surface layer evolution and hence precipitations and cloud cover forecasts could be consequently affected. This is true for largescale medium-range weather forecasts as well as for local-scale short-range weather forecasts, particularly in those situations in which local convection is well developed. Unfortunately, despite the importance of this physical parameter there are only few soilmoisture data sets sparse in time and in space around in the world. Due to this scarcity of soil moisture observations, we developed an alternative method to provide soilmoisture datasets in order to verify numericalw eather prediction models. In this paper are presented the preliminary results of an attempt to verify soil moisture fields predicted by a mesoscale model. The data for the comparison were provided by the simulations of the diagnostic land surface scheme LSPM (Land Surface Process Model), widely used at the Piedmont Regional Weather Service for agro-meteorological purposes. To this end, LSPM was initialized and driven by Synop observations, while the surface (vegetation and soil) parameter values were initialized by ECOCLIMAP global dataset at 1km2 resolution

Overview of the first HyMeX Special Observation Period over Italy: observations and model results

Abstract. The Special Observation Period (SOP1), part of the HyMeX campaign (Hydrological cycle in the Mediterranean Experiments, 5 September–6 November 2012), was dedicated to heavy precipitation events and flash floods in the western Mediterranean, and three Italian hydro-meteorological monitoring sites were identified: Liguria–Tuscany, northeastern Italy and central Italy. The extraordinary deployment of advanced instrumentation, including instrumented aircrafts, and the use of several different operational weather forecast models, including hydrological models and marine models, allowed an unprecedented monitoring and analysis of high-impact weather events around the Italian hydro-meteorological sites. This activity has seen strong collaboration between the Italian scientific and operational communities. In this paper an overview of the Italian organization during SOP1 is provided, and selected Intensive Observation Periods (IOPs) are described. A significant event for each Italian target area is chosen for this analysis: IOP2 (12–13 September 2012) in northeastern Italy, IOP13 (15–16 October 2012) in central Italy and IOP19 (3–5 November 2012) in Liguria and Tuscany. For each IOP the meteorological characteristics, together with special observations and weather forecasts, are analyzed with the aim of highlighting strengths and weaknesses of the forecast modeling systems, including the hydrological impacts. The usefulness of having different weather forecast operational chains characterized by different numerical weather prediction models and/or different model set up or initial conditions is finally shown for one of the events (IOP19)

Mining Trajectory Data for Discovering Communities of Moving Objects

Recent advances on tracking technologies enable the collection of spatio-temporal data in the form of trajectories. The analysis of such data can convey knowledge in prominent applications, and mining groups of moving objects turns out to be a valuable mean to model their movement. Existing approaches pay particular attention in groups where objects are close and move together or follow similar trajectories by assuming that movement cannot change over time. Instead, we observe that groups can be of interest also when objects are spatially distant and have di↵erent but inter-related movements: objects can start from di↵erent places and join together to move towards a common location. To take into account inter-related movements, we have to analyze the objects jointly, follow their respective movements and consider changes of movements over time. Motivated by this, we introduce the notion of communities and propose a computational solution to discover them. The method is structured in three steps. The first step performs a feature extraction technique to elicit the inter-related movements between the objects. The second one leverages a tree-structure in order to group objects with similar inter-related movements. In the third step, these groupings are used to mine communities as groups of objects which exhibit inter-related movements over time. We evaluate our approach on real data-sets and compare it with existing algorithms

Multi-Channel Deep Feature Learning for Intrusion Detection

Networks had an increasing impact on modern life since network cybersecurity has become an important research field. Several machine learning techniques have been developed to build network intrusion detection systems for correctly detecting unforeseen cyber-attacks at the network-level. For example, deep artificial neural network architectures have recently achieved state-of-the-art results. In this paper a novel deep neural network architecture is defined, in order to learn flexible and effective intrusion detection models, by combining an unsupervised stage for multi-channel feature learning with a supervised one exploiting feature dependencies on cross channels. The aim is to investigate whether class-specific features of the network flows could be learned and added to the original ones in order to increase the model accuracy. In particular, in the unsupervised stage, two autoencoders are separately learned on normal and attack flows, respectively. As the top layer in the decoder of these autoencoders reconstructs samples in the same space as the input one, they could be used to define two new feature vectors allowing the representation of each network flow as a multi-channel sample. In the supervised stage, a multi-channel parametric convolution is adopted, in order to learn the effect of each channel on the others. In particular, as the samples belong to two different distributions (normal and attack flows), the samples labelled as normal should be more similar to the representation reconstructed with the normal autoencoder than that of the attack one, and viceversa. This expected dependency will be exploited to better disentangle the differences between normal and attack flows. The proposed neural network architecture leads to better predictive accuracy when compared to competitive intrusion detection architectures on three benchmark datasets

Development of an atmosphere-ocean coupled model and its application over the Adriatic Sea during a severe weather event of Bora wind

This study presents the results of an application to a Bora severe weather episode (January 1995) in the northern Adriatic Sea of the regional two-way atmosphere-ocean coupled model (RAMS-DieCAST), developed jointly by the Universit\ue0 di Torino and the Istituto Sperimentale Talassografico del CNR, Trieste. RAMS-DieCAST showed significantly better ability to predict the sea surface temperature (SST) and its time evolution during the above mentioned episode using a full two-way coupling as opposed to simpler one-way forcing of the ocean. In this context, we found out that even in the high-frequency variability conditions that are typical of Bora events, heat fluxes from the sea must be taken into account for a better description of air-sea interaction processes in a dynamical framework. The SST evolution has been chosen as a validation parameter, owing to its availability and relevance for the characterization of the marine environment and local weather and climate studies. The simulations carried out with RAMS-DieCAST present a small systematic error in calculating the SST evolution; however, a sensitivity analysis of the model to the preparation of initial conditions of the simulation suggested that climatological initialization could be partly responsible for this error, which might be reduced by assimilating satellite-derived SSTs into the preparation of the initial conditions of the model