932 resultados para Geomagnetic Storm
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To improve our knowledge of the influence of land-use on solute behaviour and export rates in neotropical montane catchments we investigated total organic carbon (TOC), Ca, Mg, Na, K, NO3 and SO4 concentrations during April 2007-May 2008 at different flow conditions and over time in six forested and pasture-dominated headwaters (0.7-76 km2) in Ecuador. NO3 and SO4 concentrations decreased during the study period, with a continual decrease in NO3 and an abrupt decrease in February 2008 for SO4. We attribute this to changing weather regimes connected to a weakening La Niña event. Stream Na concentration decreased in all catchments, and Mg and Ca concentration decreased in all but the forested catchments during storm flow. Under all land-uses TOC increased at high flows. The differences in solute behaviour during storm flow might be attributed to largely shallow subsurface and surface flow paths in pasture streams on the one hand, and a predominant origin of storm flow from the organic layer in the forested streams on the other hand. Nutrient export rates in the forested streams were comparable to the values found in literature for tropical streams. They amounted to 6-8 kg/ha/y for Ca, 7-8 kg/ha/y for K, 4-5 kg/ha/y for Mg, 11-14 kg/ha/y for Na, 19-22 kg/ha/y for NO3 (i.e. 4.3-5.0 kg/ha/y NO3-N) and 17 kg/ha/y for SO4. Our data contradict the assumption that nutrient export increases with the loss of forest cover. For NO3 we observed a positive correlation of export value and percentage forest cover.
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To identify the relationship between GPS scintillation in Natal-RN (Brazil) and geomagnetic disturbances of any intensities and variations, this work made analysis of the ionospheric behavior and magnetic indexes (Dst , AE and Bz of the interplanetary magnetic field) concerning to different periods of the solar cycle between 2000 and 2014. Part of the data of this research originated at the UFRN observatory, from a GEC Plessey board connected to an ANP -C 114 antenna, modified by Cornell University’s Space group Plasma Physics in order to operate the ScintMon, a GPS monitoring program. This study, therefore, found several cases of inhibited scintillations after the main phase of magnetic storms, a fact that, along with others, corroborated with categorization of Aarons (1991) and models of disturbed dynamo (according to Bonelli, 2008) and over-shielding penetration, defended by Kelley et al. (1979) and Abdu (2011) [4]. In addition to these findings, different morphologies were noted in such disruptions in the GPS signal in accordance with previous magnetic activities. It also found a moderate relationship (R2 = 0.52) between the Dst rate (concerning to specific time) and the average of S4 through a polynomial function. This finding therefore, corroborating Ilma et al. (2012) [17], is an important evidence that the scintillation GPS are not directly controlled by magnetic induction of storms. Completing this work, this relation did show itself as a way of partial predicting of scintillations.
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Con l’avvento di Internet, il numero di utenti con un effettivo accesso alla rete e la possibilità di condividere informazioni con tutto il mondo è, negli anni, in continua crescita. Con l’introduzione dei social media, in aggiunta, gli utenti sono portati a trasferire sul web una grande quantità di informazioni personali mettendoli a disposizione delle varie aziende. Inoltre, il mondo dell’Internet Of Things, grazie al quale i sensori e le macchine risultano essere agenti sulla rete, permette di avere, per ogni utente, un numero maggiore di dispositivi, direttamente collegati tra loro e alla rete globale. Proporzionalmente a questi fattori anche la mole di dati che vengono generati e immagazzinati sta aumentando in maniera vertiginosa dando luogo alla nascita di un nuovo concetto: i Big Data. Nasce, di conseguenza, la necessità di far ricorso a nuovi strumenti che possano sfruttare la potenza di calcolo oggi offerta dalle architetture più complesse che comprendono, sotto un unico sistema, un insieme di host utili per l’analisi. A tal merito, una quantità di dati così vasta, routine se si parla di Big Data, aggiunta ad una velocità di trasmissione e trasferimento altrettanto alta, rende la memorizzazione dei dati malagevole, tanto meno se le tecniche di storage risultano essere i tradizionali DBMS. Una soluzione relazionale classica, infatti, permetterebbe di processare dati solo su richiesta, producendo ritardi, significative latenze e inevitabile perdita di frazioni di dataset. Occorre, perciò, far ricorso a nuove tecnologie e strumenti consoni a esigenze diverse dalla classica analisi batch. In particolare, è stato preso in considerazione, come argomento di questa tesi, il Data Stream Processing progettando e prototipando un sistema bastato su Apache Storm scegliendo, come campo di applicazione, la cyber security.
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Hurricane Sandy was the largest storm on historical record in the Atlantic Ocean basin with extensive coastal damage caused by large waves and high storm surge. The primary objectives of this thesis are to compare and evaluate three different spatially-varying surface wind fields of Hurricane Sandy to investigate the impact of the differences between the complex wind fields on predictions of the sea surface evolution, and to evaluate the impact of the storm on the hydrodynamics in Great South Bay (GSB) and the discharge of ocean water into the back-barrier bay from overwash over Fire Island. Three different spatially-varying surface wind fields were evaluated and compared to wind observations, including the parametric Holland (1980) model (H80), the parametric Generalized Asymmetric Holland Model (GAHM), and results from the WeatherFlow Regional Atmospheric Modelling System (WRAMS). The winds were used to drive the coupled Delft3D-SWAN hydrodynamic and ocean wave models on a regional grid. The results indicate that the WRAMS wind field produces wave model predictions in the best agreement with significant wave height observations, followed by the GAHM and H80 wind fields and that a regional atmospheric wind model is best for hindcasting hurricane waves and water levels when detailed observations are available, while a parametric vortex model is best for forecasting hurricane sea surface conditions. Using a series of four connected Delft3D-SWAN grids to achieve finer resolution over Fire Island and GSB, a higher resolution WRAMS was used to predict waves and storm surge. The results indicate that strong local winds have the largest influence on water level fluctuations in GSB. Three numerical solutions were conducted with varying extents of barrier island overwash. The simulations allowing for minor and major overwash indicated good agreement with observations in the east end of GSB and suggest that island overwash provided a significant contribution of ocean water to GSB during the storm. Limiting the overwash in the numerical model directly impacts the total discharge into GSB from the ocean through existing inlets. The results of this study indicate that barrier island overwash had a significant impact on the water levels in eastern GSB.