Search results for " network"
showing 10 items of 6428 documents
Assessing the suitability of American National Aeronautics and Space Administration (NASA) agro-climatology archive to predict daily meteorological v…
2017
Abstract For decades, the importance of evapotranspiration processes has been recognized in many disciplines, including hydrologic and drainage studies, irrigation systems design and management. In this research, the suitability of the Prediction Of Worldwide Energy Resource database published by the American National Aeronautics and Space Administration (POWER-NASA), to estimate daily meteorological variables and ET 0 was assessed in Sicily, Italy, for the period 2006–2014, based on ground data measured by a network of climate stations belonging to the regional Agro-meteorological Information Service (SIAS). After comparing the climate data (minimum, T min , maximum, T max , and average, T…
An overview of and issues with sky radiometer technology and SKYNET
2020
This paper is an overview of the progress in sky radiometer technology and the development of the network called SKYNET. It is found that the technology has produced useful on-site calibration methods, retrieval algorithms, and data analyses from sky radiometer observations of aerosol, cloud, water vapor, and ozone. A formula was proposed for estimating the accuracy of the sky radiometer calibration constant F0 using the improved Langley (IL) method, which was found to be a good approximation to observed monthly mean uncertainty in F0, around 0.5 % to 2.4 % at the Tokyo and Rome sites and smaller values of around 0.3 % to 0.5 % at the mountain sites at Mt. Sarasw…
A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1
2020
The hydroxyl radical (OH) plays critical roles within the troposphere, such as determining the lifetime of methane (CH4), yet is challenging to model due to its fast cycling and dependence on a multitude of sources and sinks. As a result, the reasons for variations in OH and the resulting methane lifetime (τCH4), both between models and in time, are difficult to diagnose. We apply a neural network (NN) approach to address this issue within a group of models that participated in the Chemistry-Climate Model Initiative (CCMI). Analysis of the historical specified dynamics simulations performed for CCMI indicates that the primary drivers of τCH4 differences among 10 models are the flux of UV li…
Adapting rail and road networks to weather extremes: Case studies for southern Germany and Austria
2013
Published version of an article in the journal: Natural Hazards. Also available from the publisher at: http://dx.doi.org/10.1007/s11069-013-0969-3 The assessment of the current impacts of extreme weather conditions on transport systems reveals high costs in specific locations. Prominent examples for Europe are the economic consequences of the harsh winter periods 2009/2010 and 2010/2011 and the floods in Austria, Eastern Europe, Germany and the United Kingdom in 2005 and 2007. Departing from the EC-funded project WEATHER, this paper delves into the subject of adaptation strategies by revisiting the project’s general findings on adaptation strategies and by adding two specific cases: (1) adv…
Implementation of pressure reduction valves in a dynamic water distribution numerical model to control the inequality in water supply
2013
The analysis of water distribution networks has to take into account the variability of users' water demand and the variability of network boundary conditions. In complex systems, e.g. those characterized by the presence of local private tanks and intermittent distribution, this variability suggests the use of dynamic models that are able to evaluate the rapid variability of pressures and flows in the network. The dynamic behavior of the network also affects the performance of valves that are used for controlling the network. Pressure reduction valves (PRVs) are used for controlling pressure and reducing leakages. Highly variable demands can produce significant fluctuation of the PRV set po…
EARLINET observations of the 14-22-may long-range dust transport event during SAMUM 2006: validation of results from dust transport modelling
2009
We observed a long-range transport event of mineral dust from North Africa to South Europe during the Saharan Mineral Dust Experiment (SAMUM) 2006. Geometrical and optical properties of that dust plume were determined with Sun photometer of the Aerosol Robotic Network (AERONET) and Raman lidar near the North African source region, and with Sun photometers of AERONET and lidars of the European Aerosol Research Lidar Network (EARLINET) in the far field in Europe. Extinction-to-backscatter ratios of the dust plume over Morocco and Southern Europe do not differ. Ångstr¨om exponents increase with distance from Morocco. We simulated the transport, and geometrical and optical properties of the dus…
Neural networks for analysing the relevance of input variables in the prediction of tropospheric ozone concentration
2006
Abstract This paper deals with tropospheric ozone modelling by using Artificial Neural Networks (ANNs). In this study, ambient ozone concentrations are estimated using surface meteorological variables and vehicle emission variables as predictors. The work is especially focused on analysing the importance of the input variables used by these models. This analysis is carried out in different time windows: all the time of study (April of 1997, 1999 and 2000), one month (April 1999), and finally, an hourly analysis. All the information extracted from these analyses can determine the most important factors in tropospheric ozone formation, thus achieving a qualitative model from the quantitative …
Two-days ahead prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the urban area of Palermo, Italy
2007
Abstract Artificial neural networks are functional alternative techniques in modelling the intricate vehicular exhaust emission dispersion phenomenon. Pollutant predictions are notoriously complex when using either deterministic or stochastic models, which explains why this model was developed using a neural network. Neural networks have the ability to learn about non-linear relationships between the used variables. In this paper a recurrent neural network (Elman model) based forecaster for the prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the city of Palermo is proposed. The effectiveness of the presented forecaster was tested using a time series recorded between …
Pumps as turbines (PATs) in water distribution networks affected by intermittent service
2013
A hydraulic model was developed in order to evaluate the potential energy recovery from the use of centrifugal pumps as turbines (PATs) in a water distribution network characterized by the presence of private tanks. The model integrates the Global Gradient Algorithm (GGA), with a pressure-driven model that permits a more realistic representation of the influence on the network behaviour of the private tanks filling and emptying. The model was applied to a real case study: a District Metered Area in Palermo (Italy). Three different scenarios were analysed and compared with a baseline scenario (Scenario 0 – no PAT installed) to identify the system configuration with added PATs that permits th…
Current trends in scientific research on global warming: A bibliometric analysis
2019
[EN] The objective of this paper is to contribute to a better understanding of the scientific knowledge in global warming, as well as to investigate the evolution of the research knowledge through the published papers included in Web of Science database. A bibliometric and social network analyses was performed to obtain indicators of scientific productivity, impact and collaboration between researchers, institutions and countries. A subject analysis was also carried out taking into account the key words assigned to papers and subject areas of journals. A number of 1,672 articles were analysed since 2005 until 2014. The most productive journals were Journal of Climate (n = 95) and the most f…