3,610 research outputs found
Artificial neural-network technique for precipitation nowcasting from satellite imagery
The term nowcasting reflects the need of timely and accurate predictions of risky situations related to the development of severe meteorological events. In this work the objective is the very short term prediction of the rainfall field from geostationary satellite imagery entirely based on neural network approach. The very short-time prediction (or nowcasting) process consists of two steps: first, the infrared radiance field measured from geostationary satellite (Meteosat 7) is projected ahead in time (30 min or 1 h); secondly, the projected radiances are used to estimate the rainfall field by means of a calibrated microwave-based combined algorithm. The methodology is discussed and its accuracy is quantified by means of error indicators. An application to a satellite observation of a rainfall event over Central Italy is finally shown and evaluated
Rainfall rate retrieval in presence of path attenuation using C-band polarimetric weather radars
Weather radar systems are very suitable tools for the monitoring of extreme rainfall events providing measurements with high spatial and temporal resolution over a wide geographical area. Nevertheless, radar rainfall retrieval at C-band is prone to several error sources, such as rain path attenuation which affects the accuracy of inversion algorithms. In this paper, the so-called rain profiling techniques (namely the surface reference method FV and the polarimetric method ZPHI) are applied to correct rain path attenuation and a new neural network algorithm is proposed to estimate the rain rate from the corrected measurements of reflectivity and differential reflectivity. A stochastic model, based on disdrometer measurements, is used to generate realistic range profiles of raindrop size distribution parameters while a T-matrix solution technique is adopted to compute the corresponding polarimetric variables. A sensitivity analysis is performed in order to evaluate the expected errors of these methods. It has been found that the ZPHI method is more reliable than FV, being less sensitive to calibration errors. Moreover, the proposed neural network algorithm has shown more accurate rain rate estimates than the corresponding parametric algorithm, especially in presence of calibration errors
Relation between weather radar equation and first-order backscattering theory
International audienceAim of this work is to provide a new insight into the physical basis of the meteorological-radar theory in attenuating media. Starting form the general integral form of the weather radar equation, a modified form of the classical weather radar equation at attenuating wavelength is derived. This modified radar equation includes a new parameter, called the range-bin extinction factor, taking into account the rainfall path attenuation within each range bin. It is shown that, only in the case of low-to-moderate attenuating media, the classical radar equation at attenuating wavelength can be used. These theoretical results are corroborated by using the radiative transfer theory where multiple scattering phenomena can be quantified. From a new definition of the radar reflectivity, in terms of backscattered specific intensity, a generalised radar equation is deduced. Within the assumption of first-order backscattering, the generalised radar equation is reduced to the modified radar equation, previously obtained. This analysis supports the conclusion that the description of radar observations at attenuating wavelength should include, in principle, first-order scattering effects. Numerical simulations are performed by using statistical relationships among the radar reflectivity, rain rate and specific attenuation, derived from literature. Results confirm that the effect of the range-bin extinction factor, depending on the considered frequency and range resolution, can be significant at X band for intense rain, while at Ka band and above it can become appreciable even for moderate rain. A discussion on the impact of these theoretical and numerical results is finally included
Satellite radiometric remote sensing of rainfall fields: multi-sensor retrieval techniques at geostationary scale
International audienceThe Microwave Infrared Combined Rainfall Algorithm (MICRA) consists in a statistical integration method using the satellite microwave-based rain-rate estimates, assumed to be accurate enough, to calibrate spaceborne infrared measurements on limited sub-regions and time windows. Rainfall retrieval is pursued at the space-time scale of typical geostationary observations, that is at a spatial resolution of few kilometers and a repetition period of few tens of minutes. The actual implementation is explained, although the basic concepts of MICRA are very general and the method is easy to be extended for considering innovative statistical techniques or measurements from additional space-borne platforms. In order to demonstrate the potentiality of MICRA, case studies over central Italy are also discussed. Finally, preliminary results of MICRA validation by ground based remote and in situ measurements are shown and a comparison with a Neural Network (NN) based technique is briefly illustrated
Evaluation of radiative transfer schemes for mesoscale model data assimilation: a case study
The assimilation of Special Sensor Microwave Imager (SSM/I) data into the Mesoscale Model 5 (MM5) allows for improving the weather forecast. However the results suggested an update the Radiative Transfer Equation (RTE) within the three-dimensional variational (3DVAR) algorithm which is tailored for non rainy conditions only. To this purpose, a new RTE algorithm is tested, in order to account for radiometric response in rainy regions. The new brightness temperatures (<i>T<sub>B</sub></i>) are estimated by using hydrometeor profiles from the MM5 mesoscale model, running with two different microphysical parameterizations. The goodness of the results is assessed by comparing the new <i>T<sub>B</sub></i> with those of the original RTE algorithm in the 3DVAR code and the SSM/I observed data. The results confirm a better reliability of the new RTE compared to the old one
X-band synthetic aperture radar methods
Spaceborne Synthetic Aperture Radars (SARs), operating at L-band and above, offer microwave observations of the Earth at very high spatial resolution in almost all-weather conditions. Nevertheless, precipitating clouds can significantly affect the signal backscattered from the ground surface in both amplitude and phase, especially at X band and beyond. This evidence has been assessed by numerous recent efforts analyzing data collected by COSMO-SkyMed (CSK) and TerraSAR-X (TSX) missions at X band. This sensitivity can be exploited to detect and quantify precipitations from SARs at the spatial resolution of a few hundred meters, a very appealing feature considering the current resolution of precipitation products from space. Forward models of SAR response in the presence of precipitation have been developed for analyzing SAR signature sensitivity and developing rainfall retrieval algorithms. Precipitation retrieval algorithms from SARs have also been proposed on a semi-empirical basis. This chapter will review experimental evidences, modelling approaches, retrieval methods and recent applications of X-band SAR data to rainfall estimation
Assessing radiative transfer models trained by numerical weather forecasts using sun-tracking radiometric measurements for satellite link characterization up to W band
Radio communications, and in particular Earth-to-satellite
links, are worldwide used for delivering digital services.
The bandwidth demand of such services is increasing
accordingly to the advent of more advanced applications
(e.g., multimedia services, deep-space explorations, etc.)
thus pushing the scientific community toward the
investigation of channel carriers at higher frequencies.
When using carrier frequencies above X band, the main
drawback is how to tackle the impact of tropospheric
processes (i.e., rain, cloud, water vapor). This work
assesses the joint use of weather forecast models, radiative
transfer models and Sun-tracking radiometric
measurements to explore their potential benefits in
predicting path attenuation and sky noise temperature for
slant paths at frequencies between K and W band, thus
paving the way to the optimization of satellite link-budgets
Suicidal behavior in individuals accused or convicted of child sex abuse or indecent image offenses: systematic review of prevalence and risk factors
Objective
An emerging body of research indicates that child sex abuse (CSA) offenders are at high risk of suicide when their offenses come to light and that those accused of accessing indecent images of children (IIOC) are at particular risk.
Methods
We conducted a systematic review and narrative synthesis on suicide rates and risk factors in this population of offenders. A keyword search of bibliographic databases (PsycINFO, Ovid, MEDLINE, Embase, PILOTS, SCIE, the Cochrane Central Register of Controlled Trials [CENTRAL] and CINAHL) was conducted.
Results
Eighteen articles were included in the review, with eleven studies meeting criteria for quality assessment. The risk of suicide in perpetrators of CSA and IIOC might be over 100 times that of the general population, although estimates vary widely between studies. Several complex, interlinking factors were identified as associated with risk, including shame, unique demographic characteristics of the offenders, absence of prior criminal contact, and the impact of a criminal investigation.
Conclusions
The review identified factors that may have practical, clinical, and operational implications in the prevention of suicide in CSA and IIOC perpetrators. Exploring the impact of the investigation itself on suicide risk, including potential operational strategies and clinical input to reduce risk, should be a priority
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