Spectral properties of the two-dimensional Schrödinger Hamiltonian with various solvable confinements in the presence of a central point perturbation

We study three solvable two-dimensional systems perturbed by a point interaction centered at the origin. The unperturbed systems are the isotropic harmonic oscillator, a square pyramidal potential and a combination thereof. We study the spectrum of the perturbed systems. We show that, while most eigenvalues are not affected by the point perturbation, a few of them are strongly perturbed. We show that for some values of one parameter, these perturbed eigenvalues may take lower values than the immediately lower eigenvalue, so that level crossings occur. These level crossings are studied in some detail

Architecture of Virtual Power Plant for Ancillary Services

The increased penetration of distributed energy resources opens up issues in power system as a whole. This creates markets opportunities for ancillary services; particularly TSO deals with the issues of congestion management, reserves, reactive power control etc. Literature suggests different techniques where TSO and DSO interact with each other, and in this way, DSO can offer flexibility to TSO in terms of provision of ancillary services. The paper discusses the issues that the current power system face due to the profound effects of new generating resources, and then examines in detail the way these issues are resolved in a conventional manner. Then, the paper discusses some literature proposals for the interaction between TSO-DSO for solving the issues in an efficient manner, and finally presents the architecture where a Virtual Power Plant (VPP) is developed to facilitate DSO with a platform for the provision of ancillary services

The flash flood of the Bisagno Creek on 9th October 2014: An “unfortunate” combination of spatial and temporal scales

SummaryOn the 9th October, 2014 a strong event hit the central part of Liguria Region producing disastrous consequences to the city of Genoa where the Bisagno Creek flooded causing one death and lots of damage. The precipitation pattern responsible for the event had peculiar spatial and temporal characteristics that led to an unexpected flash flood. The temporal sequence of rainfall intensities and the particular severity of rainfall showers at small temporal scale, together with the size of the sub-basin hit by the most intense part of the rainfall were the unfortunate concurrent ingredients that led to an “almost perfect” flash flood. The peak flow was estimated to be a 100–200years order return period.The effects of the spatial and temporal scales of the precipitation pattern were investigated by coupling a rainfall downscaling model with a hydrological model setting up an experiment that follows a probabilistic approach.Supposing that the correct volume of precipitation at different spatial and temporal scales is known, the experiment provided the probability of generating events with similar effects in terms of streamflow.Furthermore, the study gives indications regarding the goodness and reliability of the forecasted rainfall field needed, not only in terms of total rainfall volume, but even in spatial and temporal pattern, to produce the observed ground effects in terms of streamflow

General calibration methodology for a combined Horton-SCS infiltration scheme in flash flood modeling

Abstract. Flood forecasting undergoes a constant evolution, becoming more and more demanding about the models used for hydrologic simulations. The advantages of developing distributed or semi-distributed models have currently been made clear. Now the importance of using continuous distributed modeling emerges. A proper schematization of the infiltration process is vital to these types of models. Many popular infiltration schemes, reliable and easy to implement, are too simplistic for the development of continuous hydrologic models. On the other hand, the unavailability of detailed and descriptive information on soil properties often limits the implementation of complete infiltration schemes. In this work, a combination between the Soil Conservation Service Curve Number method (SCS-CN) and a method derived from Horton equation is proposed in order to overcome the inherent limits of the two schemes. The SCS-CN method is easily applicable on large areas, but has structural limitations. The Horton-like methods present parameters that, though measurable to a point, are difficult to achieve a reliable estimate at catchment scale. The objective of this work is to overcome these limits by proposing a calibration procedure which maintains the large applicability of the SCS-CN method as well as the continuous description of the infiltration process given by the Horton's equation suitably modified. The estimation of the parameters of the modified Horton method is carried out using a formal analogy with the SCS-CN method under specific conditions. Some applications, at catchment scale within a distributed model, are presented

A hydrological analysis of the 4 November 2011 event in Genoa

On the 4 November 2011 a flash flood event hit the area of Genoa with dramatic consequences. Such an event represents, from the meteorological and hydrological perspective, a paradigm of flash floods in the Mediterranean environment. <br><br> The hydro-meteorological probabilistic forecasting system for small and medium size catchments in use at the Civil Protection Centre of Liguria region exhibited excellent performances for the event, by predicting, 24–48 h in advance, the potential level of risk associated with the forecast. It greatly helped the decision makers in issuing a timely and correct alert. <br><br> In this work we present the operational outputs of the system provided during the Liguria events and the post event hydrological modelling analysis that has been carried out accounting also for the crowd sourcing information and data. We discuss the benefit of the implemented probabilistic systems for decision-making under uncertainty, highlighting how, in this case, the multi-catchment approach used for predicting floods in small basins has been crucial

An Efficient Method to Take into Account Forecast Uncertainties in Large Scale Probabilistic Power Flow

The simulation of uncertainties due to renewable and load forecasts is becoming more and more important in security assessment analyses performed on large scale networks. This paper presents an efficient method to account for forecast uncertainties in probabilistic power flow (PPF) applications, based on the combination of PCA (Principal Component Analysis) and PEM (Point Estimate Method), in the context of operational planning studies applied to large scale AC grids. The benchmark against the conventional PEM method applied to large power system models shows that the proposed method assures high speed up ratios, preserving a good accuracy of the marginal distributions of the outputs

Downscaling stream flow time series from monthly to daily scales using an auto-regressive stochastic algorithm: StreamFARM

Downscaling methods are used to derive stream flow at a high temporal resolution from a data series that has a coarser time resolution. These algorithms are useful for many applications, such as water management and statistical analysis, because in many cases stream flow time series are available with coarse temporal steps (monthly), especially when considering historical data; however, in many cases, data that have a finer temporal resolution are needed (daily). In this study, we considered a simple but efficient stochastic auto-regressive model that is able to downscale the available stream flow data from monthly to daily time resolution and applied it to a large dataset that covered the entire North and Central American continent. Basins with different drainage areas and different hydro-climatic characteristics were considered, and the results show the general good ability of the analysed model to downscale monthly stream flows to daily stream flows, especially regarding the reproduction of the annual maxima. If the performance in terms of the reproduction of hydrographs and duration curves is considered, better results are obtained for those cases in which the hydrologic regime is such that the annual maxima stream flow show low or medium variability, which means that they have a low or medium coefficient of variation; however, when the variability increases, the performance of the model decreases

Pre-impact fall detection: optimal sensor positioning based on a machine learning paradigm

The aim of this study was to identify the best subset of body segments that provides for a rapid and reliable detection of the transition from steady walking to a slipping event. Fifteen healthy young subjects managed unexpected perturbations during walking. Whole-body 3D kinematics was recorded and a machine learning algorithm was developed to detect perturbation events. In particular, the linear acceleration of all the body segments was parsed by Independent Component Analysis and a Neural Network was used to classify walking from unexpected perturbations. The Mean Detection Time (MDT) was 3516123 ms with an Accuracy of 95.4%. The procedure was repeated with data related to different subsets of all body segments whose variability appeared strongly influenced by the perturbation-induced dynamic modifications. Accordingly, feet and hands accounted for most data information and the performance of the algorithm were slightly reduced using their combination. Results support the hypothesis that, in the framework of the proposed approach, the information conveyed by all the body segments is redundant to achieve effective fall detection, and suitable performance can be obtained by simply observing the kinematics of upper and lower distal extremities. Future studies are required to assess the extent to which such results can be reproduced in older adults and in different experimental conditions

Operational verification of a framework for the probabilistic nowcasting of river discharge in small and medium size basins

Forecasting river discharge is a very important issue for the prediction and monitoring of ground effects related to severe precipitation events. The meteorological forecast systems are unable to predict precipitation on small spatial (few km) and temporal (hourly) scales. For these reasons the issuing of reliable flood forecasts is not feasible in those regions where the basin's response to rainfall events is very fast and can generate flash floods. This problem can be tackled by using rainfall nowcasting techniques based on radar observations coupled with hydrological modeling. These procedures allow the forecasting of future streamflow with a few hours' notice. However, to account for the short-term uncertainties in the evolution of fine scale precipitation field, a probabilistic approach to rainfall nowcasting is needed. These uncertainties are then propagated from rainfall to runoff through a distributed hydrological model producing a set of equi-probable discharge scenarios to be used for the flood nowcasting with time horizons of a few hours. Such a hydrological nowcasting system is presented here and applied to some case studies. A first evaluation of its applicability in an operational context is provided and the opportunity of using the results quantitatively is discussed

Clutter and rainfall discrimination by means of doppler-polarimetric measurements and vertical reflectivity profile analysis

International audienceThe estimation of rainfall rate and other parameters from radar scattering volume is heavily affected by the presence of intense sea and ground clutter and echoes which appears in anomalous propagation condition. To deal with these non meteorological echoes we present a new clutter removal algorithm which combines the results of previous works. The algorithm fully exploits both the Doppler and polarimetric capabilities of the radar used and the analysis of vertical reflectivity profile in order to achieve the better identification of the meteorological and non-meteorological targets. The algorithm has been applied to the C-band radar of Monte Settepani (Savona, Italy), which runs in a high-topography environment. Preliminary results are presented