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Domanska D.,University of Silesia | Wojtylak M.,Institute of Meteorology and Water Management IMGW
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

In general in the literature practitioners transform of real numbers into fuzzy numbers to the median or average, so they follow the probabilistic path. However, theoreticians do not investigate transformations of real numbers into fuzzy numbers when they analyse fuzzy numbers. They usually operate only on the fuzzy data. In the paper we describe an algorithm for transforming a sequence of real numbers into a fuzzy number. The algorithms presented are used to transform multidimensional matrices constructed from times series into fuzzy matrices. They were created for a special fuzzy number and using it as an example we show how to proceed. The algorithms were used in one of the stages of a model used to forecast pollution concentrations with the help of fuzzy numbers. The data used in the computations came from the Institute of Meteorology and Water Management (IMGW). © 2010 Springer-Verlag. Source


Domanska D.,University of Silesia | Wojtylak M.,Institute of Meteorology and Water Management IMGW
Expert Systems with Applications | Year: 2012

In the paper a model to predict the concentrations of particulate matter PM10, PM2.5, SO 2, NO, CO and O 3 for a chosen number of hours forward is proposed. The method requires historical data for a large number of points in time, particularly weather forecast data, actual weather data and pollution data. The idea is that by matching forecast data with similar forecast data in the historical data set it is possible then to obtain actual weather data and through this pollution data. To aggregate time points with similar forecast data determined by a distance function, fuzzy numbers are generated from the forecast data, covering forecast data and actual data. Again using a distance function, actual data is compared with the fuzzy number to determine how the grade of membership is. The model was prepared in such a way that all the data which is usually imprecise, chaotic, uncertain can be used. The model is used in Poland by the Institute of Meteorology and by Water Management, and by the Voivodship Inspector for Environmental Protection. It forecast selected pollution concentrations for all areas of Poland. © 2012 Elsevier Ltd. All rights reserved. Source


Domanska D.,University of Silesia | Wojtylak M.,Institute of Meteorology and Water Management IMGW | Kotarski W.,University of Silesia
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

The aim of this paper is to present a new way of multidimensional data visualization for explorative forecast built for real meteorological data coming from the Institute of Meteorology and Water Management (IMGW) in Katowice, Poland. In the earlier works two first authors of the paper proposed a method that aggregates huge amount of data based on fuzzy numbers. Explorative forecast uses similarity of data describing situations in the past to those in the future. 2D and 3D visualizations of multidimensional data can be used to carry out its analysis to find hidden information that is not visible in the raw data e.g. intervals of fuzziness, fitting real number to a fuzzy number. © 2012 Springer-Verlag Berlin Heidelberg. Source


Domanska D.,University of Silesia | Wojtylak M.,Institute of Meteorology and Water Management IMGW
Atmospheric Environment | Year: 2014

In the paper a model to predict immission concentrations of PM10, SO2, O3 for a selected number of forward time steps is proposed. The proposed model (e-APFM) is an extension of the Air Pollution Forecasting Model (APFM). APFM requires historical data for a large number of points in time, particularly weather forecast, meteorological and pollution data. e-APFM additionally requires information about the wind direction in sectors and meteorological station. This information also permits pollution at meteorological stations for which we do not have the necessary data (in particular the data about pollution) to be forecast. The experimental verification of the proposed model was conducted on the data from the Institute of Meteorology and Water Management in Poland over a period of two years (between January 2011 and December 2012). Experiments show that the e-APFM method has lower deviations between the measured and predicted concentrations compared to the APFM method for the first day and similar deviations for the next two days (for hourly values) and for the first day and mostly worse for the second and third day (for daily values). © 2014 Elsevier Ltd. Source


Spinoni J.,European Commission - Joint Research Center Ispra | Szalai S.,Szent Istvan University | Szentimrey T.,Hungarian Meteorological Service OMSZ | Lakatos M.,Hungarian Meteorological Service OMSZ | And 35 more authors.
International Journal of Climatology | Year: 2015

The Carpathians are the longest mountain range in Europe and a geographic barrier between Central Europe, Eastern Europe, and the Balkans. To investigate the climate of the area, the CARPATCLIM project members collected, quality-checked, homogenized, harmonized, and interpolated daily data for 16 meteorological variables and many derived indicators related to the period 1961-2010. The principal outcome of the project is the Climate Atlas of the Carpathian Region, hosted on a dedicated website (www.carpatclim-eu.org) and made of high-resolution daily grids (0.1° × 0.1°) of all variables and indicators at different time steps. In this article, we analyze the spatial and temporal variability of 10 variables: minimum, mean, and maximum temperature, daily temperature range, precipitation, cloud cover, relative sunshine duration, relative humidity, surface air pressure, and wind speed at 2 m. For each variable, we present the gridded climatologies for the period 1961-2010 and discuss the linear trends both on an annual and seasonal basis. Temperature was found to increase in every season, in particular in the last three decades, confirming the trends occurring in Europe; wind speed decreased in every season; cloud cover and relative humidity decreased in spring, summer, and winter, and increased in autumn, while relative sunshine duration behaved in the opposite way; precipitation and surface air pressure showed no significant trend, though they increased slightly on an annual basis. We also discuss the correlation between the variables and we highlight that in the Carpathian Region positive and negative sunshine duration anomalies are highly correlated to the corresponding temperature anomalies during the global dimming (1960s and 1970s) and brightening (1990s and 2000s) periods. © 2014 Royal Meteorological Society. Source

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