Design and performance of an erbium-doped silicon waveguide detector operating at 1.5 µm

A new concept for an infrared waveguide detector based on silicon is introduced. It is fabricated using silicon-on-insulator material, and consists of an erbium-doped p-n junction located in the core of a silicon ridge waveguide. The detection scheme relies on the optical absorption of 1.5-µm light by Er3+ ions in the waveguide core, followed by electron-hole pair generation by the excited Er and subsequent carrier separation by the electric field of the p-n junction. By performing optical mode calculations and including realistic doping profiles, we show that an external quantum efficiency of 10^-3 can be achieved in a 4-cm-long waveguide detector fabricated using standard silicon processing. It is found that the quantum efficiency of the detector is mainly limited by free carrier absorption in the waveguide core, and may be further enhanced by optimizing the electrical doping profiles. Preliminary photocurrent measurements on an erbium-doped Si waveguide detector at room temperature show a clear erbium related photocurrent at 1.5 µm

Advances in the space-time analysis of rainfall extremes

Statistical estimation of design rainfall is considered a consolidated topic in hydrology. However, extreme rainfalls and their consequences still constitute one of the most critical natural risks worldwide, particularly in urban environments. Additional efforts for improving the spatio-temporal analysis of extreme rainfalls are then required, particularly at the regional scale. In this work, a new set of data and techniques for improving the spatial statistical analysis of extreme rainfall is proposed. Italy is considered a challenging case study, due to its specific geographic and orographic settings, associated with recurring storm-induced disasters. At first, the rain-gauge data patchiness resulting from the evolution of the monitoring agencies and networks, is tackled with the "patched kriging" methodology. The technique, involving a sequential annual interpolation, provides complete annual maxima series consistent with the available data. This allows to extract all the information avaialble from the gauge records, considering also the information "hidden" in the shortest series, increasing the robustness of the results. Interpolation techniques, however, can only reflect the estimation variance determined by the spatial and temporal data resolution. Additional improvements can be obtained integrating the rain gauge information with remote sensing products, able to provide more details on the spatial structure of rainstorms. In this direction, a methodology aimed at maximizing the efficiency of weather radar when dealing with large rainfall intensities is developed. It consists in a quasi-real-time calibration procedure, adopting confined spatial and temporal domains for an adaptive estimation of the relation between radar reflectivity and rainfall rate. This allows one to follow the well-known spatio-temporal variability of the reflectivity-rainfall relation, making the technique suitable for a systematic operational use, regardless of the local conditions. The methodology, applied in a comprehensive case study reduces the bias and increases the accuracy of the radar-based estimations of severe rainfall intensities. The field of the satellite estimation of preciptation is then explored, by analyzing the ability of both the Tropical Rainfall Measurement Mission (TRMM) and the recently launched Global Precipitation Measurement (GPM) mission to help identifying the timing of severe rainfall events on wide spatial domains. For each considered product, the date of occurrence of the most intense annual daily records are identified and compared with the ones extracted from a global rain-gauge database. The timing information can help in tracking the pattern of deep convective systems and support the identification of localized rainfall system in poorly gauged areas. The last part of the work deals with the analysis of rainfall extremes at the country scale, with a particular focus on the most severe rainfall events occurred in Italy in the last century. Many of these events have been studied as individual case studies, due to the large recorded intensities and/or to their severe consequences, but they have been seldom expressly addressed as a definite population. To try to provide new insights in a data-drived approach, a comprehensive set of annual rainfall maxima has been compiled, collecting data from the different regional authorities in charge. The database represents the reference knowledge for extremes from 1 to 24 hours durations in Italy, and includes more than 4500 measuring points nationwide, with observation spanning the period 1916-2014. Exploratory statistical analyses for providing information on the climatology of extreme rainfall at the national scale are carried out and the stationarity in time of the highest quantiles is analysed by pooling up all the data for each duration together. The cumulative empirical distributions are explored looking for clues of the existence of a class of "super-extremes" with a peculiar statistical behavior. The analysis of the spatial the distribution of the records exceeding the 1/1000 overall empirical probability shows an interesting spatial clustering. However, once removed the influence of the uneven density of the rain gauge network in time and space, the spatial susceptibility to extraordinary events seems quite uniformly distributed at the country scale. The analyses carried out provide quantitative basis for improving the rainstorm estimation in gauged and ungauged locations, underlining the need of further research efforts for providing maps for hydrological design with uniform reliability at the various scales of technical interest

Feasibility Studies on Si-Based Biosensors

The aim of this paper is to summarize the efforts carried out so far in the fabrication of Si-based biosensors by a team of researchers in Catania, Italy. This work was born as a collaboration between the Catania section of the Microelectronic and Microsystem Institute (IMM) of the CNR, the Surfaces and Interfaces laboratory (SUPERLAB) of the Consorzio Catania Ricerche and two departments at the University of Catania: the Biomedical Science and the Biological Chemistry and Molecular Biology Departments. The first goal of our study was the definition and optimization of an immobilization protocol capable of bonding the biological sensing element on a Si-based surface via covalent chemical bonds. We chose SiO2 as the anchoring surface due to its biocompatibility and extensive presence in microelectronic devices. The immobilization protocol was tested and optimized, introducing a new step, oxide activation, using techniques compatible with microelectronic processing. The importance of the added step is described by the experimental results. We also tested different biological molecule concentrations in the immobilization solutions and the effects on the immobilized layer. Finally a MOS-like structure was designed and fabricated to test an electrical transduction mechanism. The results obtained so far and the possible evolution of the research field are described in this review paper

A global assessment of the timing of extreme rainfall from TRMM and GPM for improving hydrologic design

The tropical rainfall measuring mission (TRMM) has revolutionized the measurement of precipitation worldwide. However, TRMM significantly underestimates rainfall in deep convection systems, being therefore of little help for the analysis of extreme precipitation depths. This work evaluates the ability of both TRMM and the recently launched global precipitation measurement (GPM) mission to help in the identification of the timing of severe rainfall events. We compare the date of occurrence of the most severe daily rainfall recorded each year by a global rain gauge network with the ones estimated by TRMM. The match rate between the two is found to approach 50%, indicating significant consistency between the two data sources. This figure rises to 60% for GPM, indicating the potential for this new mission to improve the accuracy associated with TRMM. Further efforts are needed in improving the GPM conversion algorithms in order to reduce the bias affecting the estimation of intense depths. The results however show that the timing estimated from GPM can provide a solid basis for an extensive characterization of the spatio-temporal distribution of extreme rainfall in poorly gauged regions of the world

MultiRain: A GIS-based tool for multi-model estimation of regional design rainfall for scientists and practitioners

Extreme rainfall estimation is a long-standing challenge for hydrological hazard assessment and infrastructure design, particularly if considering the need to deal with climate change. Advances in statistical methods and in rainfall data availability allow for frequent updates of regional rainfall frequency analyses. These allow for new estimates that, however, cannot simply replace older ones in the risk management, due to technical, socio-economic and legislative reasons. To preserve compatibility between old and new regional estimates a multi-model approach could be used, where model uncertainties can be combined to help reach a final decision. To make this possible, one has to face the uneasy retrieval of data and results of older analyses and, quite often, non-trivial areal rainfall estimates are needed with all methods. To give an answer to these technical needs, a tool named MultiRain has been developed. The tool computes depth–duration–frequency (DDF) curves, both related to a point and integrated over an area, from multiple regional statistical analyses. The MultiRain procedure is based on Python scripting, GIS functions and web technologies, and can be performed via web-browser or in a desktop GIS environment. A demonstration version has been built using four different regional analyses proposed in a 20-years period for the North-West of Italy

Evidence for increasing rainfall extremes remains elusive at large spatial scales: the case of Italy

The widespread perception of an increase in the severity of extreme rainstorms has not found yet clear confirmation in the scientific literature, often showing vastly different results. Especially for short‐duration extremes, spatial heterogeneities can affect the outcomes of large‐scale trend analyses, providing misleading results dependent on the adopted spatial domain. Based on the availability of a renewed and comprehensive database, the present work assesses the presence of regional trends in the magnitude and frequency of annual rainfall maxima for sub‐daily durations in Italy. Versions of the Mann‐Kendall test and a record‐breaking analysis, that considers the spatial correlation, have been adopted for the scope. Significant trends do not appear at the whole‐country scale, but distinct patterns of change emerge in smaller domains having homogeneous geographical characteristics. Results of the study underline the importance of a multi‐scale approach to regional trend analysis and the need of more advanced explanations of localized trends

Radar Estimation of Intense Rainfall Rates through Adaptive Calibration of the Z-R Relation

Rainfall intensity estimation from weather radar is still significantly uncertain, due to local anomalies, radar beam attenuation, inappropriate calibration of the radar reflectivity factor (Z) to rainfall rate (R) relationship, and sampling errors. The aim of this work is to revise the use of the power-law equation commonly adopted to relate radar reflectivity and rainfall rate to increase the estimation quality in the presence of intense rainfall rates. We introduce a quasi real-time procedure for an adaptive in space and time estimation of the Z-R relation. The procedure is applied in a comprehensive case study, which includes 16 severe rainfall events in the north-west of Italy. The study demonstrates that the technique outperforms the classical estimation methods for most of the analysed events. The determination coefficient improves by up to 30% and the bias values for stratiform events decreases by up to 80% of the values obtained with the classical, non-adaptive, Z-R relations. The proposed procedure therefore shows significant potential for operational uses