Acetylated oleuropein, a safe, biologically active semi-synthetic stable derivative of oleuropein, is proposed as an easy alternative to make oleuropein more bioavailable and suitable to be added to fatty foods.

A short-term forecast of energy consumption is affected by different factors related to the demand in residential, commercial, thermoelectric, and industrial sectors. This demand can be strongly constrained by weather variables, especially temperatures, whose forecast may be very useful to predict the balances between supply and demand, minimizing the risk of price volatility. Energy companies use the relationship between meteorological forecast output and energy request to provide an effective scheduling of national gas and power grids and reduce operational costs in critical periods. This work reports a comparison analysis for short- and medium-term daily temperature forecasts during the period 2013-2014 by using the weather model e-kmf™ (eni-kassandra meteo forecast), currently adopted in gas and power applications where meteorological output has a key role. This weather forecast system uses different models and initial data to develop probabilistic predictions from a perspective of eleven days ahead. In particular, a set of model runs with horizontal grid spacing of 5.5, 8, 13, and 18 km with the same domain size are undertaken to assess the sensitivity of temperature to horizontal resolutions. A nonlinear Kalman filter has been also applied to postprocess forecasted data in eight European cities (Milano, Roma, Torino, Napoli, Munich, Paris, Brussels, and London). Filtered forecasts over these cities have been compared to local observations taken from SYNOP (surface synoptic observations) and METAR (meteorological Aerodrome Report) stations. Skill scores of performance have been used to generally assess the forecast reliability up to day +11. In order to understand the sensitivity to the horizontal resolution, investigations have been carried out even during four specific periods of two weeks with stable and unstable weather conditions.

This paper presents a system for supporting hydropower production on mountainous areas. The system couples the outputs of a numerical weather prediction model and a snow melting and accumulation temperature-based model. Several procedures are presented for interpolating meteorological variables and calibrating and validating model parameters that can be generalized to any other mountainous area where the estimation of current and forecasted snow water equivalent and melting amount is required. The system reliability has been assessed through the validation of three components: spatial interpolation of meteorological data, mathematical modeling, and quantitative meteorological forecast. The results show that good accuracy of meteorological data spatial interpolation can be achieved when the data from snow gauges are used for assessing the precipitation lapse rate at higher altitudes, and the temperature lapse rate is computed from data at each time step. The temperature-based hydrological model proved to be effective in simulating lake inflow water volume and energy production. No clear result has been found for snow melt forecast due to the difficulties in providing reliable quantitative weather forecast in complex alpine area.

The presence of road ice has always been a key issue during winter months. A reliable forecast system capable of predicting the Land Surface Temperature (LST) and, consequently, its formation is one of the best strategies to operate towards reducing both vehicles accidents and waste of chemical solvents used for prevention which have a significant economic and environmental impact. Hence, the Meteo Expert Centre (MEC) has developed an algorithm for LST forecasts able to issue ice risk warnings as well. This algorithm operationally works every day in real-time and it is here tested, first, on a paved area of the Pedemontana Lombarda motorway and the Milano Linate airport airstrip, and, afterwards, since the LST plays a crucial role in understanding phenomena of energy exchange between soil, vegetation, and atmosphere, its knowledge and prediction becomes relevant also for other purposes such as agricultural management and irrigation system control, further experiments are carried out over two agricultural fields, one in the North and the other in the South of Italy during the SIM (Smart Irrigation Management) project. All LST analyses showed encouraging results with reasonable high values of statistical scores, in both applications on asphalted and different vegetated terrains, demonstrating that the developed algorithm has a high versatility even on completely different types of surfaces, and it can be applied as a valid tool for road ice risk warnings too.

Forecasting applications based on hourly meteorological predictions for weather variables are nowadays used in energy market operations, planning of gas and power supply, and renewable energy, among others. Available meteorological and climatological data, as well as critical thresholds of rainfall, may also have a key role in the hazard classification, related to slope instabilities of pipelines and critical infrastructures along routes. The present study concerns the performance of a weather forecast model in the framework of an early warning system (EWS) application, which supports the integrity management of oil and gas pipelines. This EWS has been applied on to a specific area: the Val dAgri basin in the Basilicata region of Southern Italy, which is extensively affected by several landslides and floods. The hourly precipitation forecasts are provided by a dedicated meteorological model, the KALM-HD, using two different horizontal resolutions, 1.25 and 5km, to analyze possible influences of the mesh grid size as well. On this area, several weather stations were specifically deployed to obtain observed data in a region where hydrogeological hazards are relevant for asset management. A comparison among observations and the KALM-HD scaled forecasts on six of these weather stations is presented to assess the model performance. Besides, precipitation, temperature, and wind speed are evaluated as well. The forecasting analysis is performed considering two years of data both on an overall and seasonal basis. Results show that the KALM-HD performs well with the 1.25km grid, particularly on temperature and wind speed variables. Since weather stations can be gathered in two main sets depending on their positions, differences arise in the forecast quality of these two groups, related to orography and thermal effects, whose detection is difficult in the typical narrow valleys characterizing the area of study. This issue prevalently influences temperatures and local winds, which, these latter, are generally underestimated, while precipitation is mainly driven by synoptic circulation and its interaction with mesoscale meteorological features.

Earth system predictions, from sub-seasonal to seasonal timescales, remain a challenging task, and the representation of predictability sources on seasonal timescales is a complex work. Nonetheless, advances in technology and science have been making continuous progress in seasonal forecasting. In a previous paper, a performance for temperature prediction by a modelling system named e-kmf^® was carried out in comparison with observations and climatology for a year of data; a low level of predictability in the sub-seasonal range, particularly in the second month, was observed over the Italian peninsula. Therefore, in this study, we focus our investigations specifically on the performance between the fifth and the eighth week of temperature forecasts over six years of simulations (2012–2018) to investigate the capability of the weather model to better reproduce the behavior of temperatures in the second month of the forecast. Although some differences in seasons are present, results have globally shown how temperature predictions have the potential to be quite skillful, with an average skill score of about 68%, with climatology used as reference; additionally, an overall anomaly correlation coefficient equal to 0.51 was shown, providing useful information for applications in planning, sales, and supply of natural energy resources.