Rita Pongrácz

Application of fuzzy rule-based modeling technique to regional drought

Fuzzy rule-based modeling is applied to the prediction of regional droughts (characterized by the modified Palmer index, PMDI) using two forcing inputs, El Nino/Southern Oscillation (ENSO) and large scale atmospheric circulation patterns (CPs) in a typical Great Plains state, Nebraska. Although, there is significant relationship between simultaneous monthly CP, lagged Southern Oscillation Index (SOI) and PMDI in Nebraska, the weakness of the correlations, the dependence between CP and SOI and the relatively short data set limit the applicability of statistical modeling for prediction. Due to the above difficulties, a fuzzy rule-based approach is presented to predict PMDI from monthly frequencies of daily CP types and lagged prior SOIs. The fuzzy rules are defined and calibrated using a subset called the learning set of the observed time series of premises and PMDI response. Then, another subset, the validation set is used to check how the application of fuzzy rules reproduces the observed PMDI. In all its eight climate divisions and Nebraska itself, the fuzzy rule-based technique using the joint forcing of CP and SOI, is able to learn the high variability and persistence of PMDI and results in almost perfect reproduction of the empirical frequency distributions. q 1999 Elsevier Science B.V. All rights reserved.

Validation of a high-resolution version of the regional climate model RegCM3 over the Carpathian basin

Abstract This paper presents a validation study for a high-resolution version of the Regional Climate Model version 3 (RegCM3) over the Carpathian basin and its surroundings. The horizontal grid spacing of the model is 10 km—the highest reached by RegCM3. The ability of the model to capture temporal and spatial variability of temperature and precipitation over the region of interest is evaluated using metrics spanning a wide range of temporal (daily to climatology) and spatial (inner domain average to local) scales against different observational datasets. The simulated period is 1961–90. RegCM3 shows small temperature biases but a general overestimation of precipitation, especially in winter; although, this overestimate may be artificially enhanced by uncertainties in observations. The precipitation bias over the Hungarian territory, the authors’ main area of interest, is mostly less than 20%. The model captures well the observed late twentieth-century decadal-to-interannual and interseasonal variability...

Analysis of regional climate change modelling experiments for the Carpathian Basin

In the last decade, Regional Climate Models (RCMs) nested in Global Climate Models (GCMs) have become essential tools to make climate projections with fine spatial resolution. In this paper, control runs of the RCMs RegCM and PRECIS are discussed and compared for the Central/Eastern European region. Both RCMs are three-dimensional, sigma-coordinate, primitive equation models, for the control experiments (1961-1990), they use initial and lateral boundary conditions from the European Centre for Medium-range Weather Forecast (ECMWF) reanalysis data sets (ERA-40). For the validation, monthly data sets of the Climatic Research Unit (CRU) of the University of East Anglia are used. According to the results, the model RegCM generally underestimates the temperature, while the model PRECIS overestimates it. The precipitation is generally overestimated by the RegCM simulations, and underestimated by the PRECIS simulations. In the case of PRECIS, a model experiment for the Central/Eastern European region for the 2071-2100 period is completed using the HadCM3 GCM outputs (A2 scenario) as boundary conditions. The results suggest that the significant temperature increase expected in the Carpathian Basin may considerably exceed the global warming rate. The climate of this region is expected to become wetter in winter and drier in the other seasons.

Evaluation of meteorological factors on sudden cardiovascular death

Climatic and seasonal triggering factors have received an increasing attention among risk factors of sudden cardiac death. The relationship between cold weather conditions and ischemic heart disease death is well established. In this study, there were 7450 (4967 males, 2483 females) cardiovascular death cases medico-legally autopsied between 1995 and 2004. In most of the cases (76%) cardiac death occurred at the scene, and 17% had acute ischemic heart disease. In order to examine the relationship between daily maximum, minimum and mean temperature, air humidity, air pressure, wind speed, global radiation and the daily numbers of death cases, statistical analysis were accomplished using correlation coefficients, and Box-Whisker-plot diagrams. A significant negative correlation was detected between daily mean temperature and cardiovascular mortality. A remarkable seasonal variation was found. Cold and dry weather may be an important risk factor in bringing on the onset of sudden cardiac death.

Relationship between suicidal cases and meteorological conditions

Meteorological factors are well known to modulate human health status and the rate of death cases. The suicidal rate might have been influenced by climatic and seasonal triggering factors. In this study 4918 suicidal cases (3099 male, 1819 female) in Budapest were investigated in connection with climatic data, as daily maximum, minimum temperature, and air humidity. The most frequent methods of suicide were intoxication, hanging and jumping. A mild seasonal variation was found, however, the rate of suicidal death was influenced by warm temperatures. Higher frequency of suicidal deaths was detected in warm weather with low relative humidity, which implies dominantly dry anticyclonic meteorological conditions. Our results suggest that the medico-legal investigation may help specific suicide prevention programme regarding to the climate change and meteorological conditions as potential risk factors of suicidal cases.

Fuzzy rule-based prediction of monthly precipitation

Abstract Monthly precipitation in Hungary is modeled using the Hess-Brezowsky atmospheric circulation pattern types and an ENSO index as forcing functions or inputs. The weakness of the statistical dependence between these individual inputs and precipitation prevents the use of a multivariate regression analysis for reproducing the probability distribution function of observed precipitation. In order to utilize the existing relationship between forcing functions and precipitation a fuzzy rule-based modeling technique is used. The first part of the observed input and precipitation data is used as the learning set to identify the fuzzy rules. Then, the second part of the data is used to validate the rules by comparing the frequency distributions of precipitation calculated respectively with the fuzzy rules and observed data. Example results are presented for two different climatic regions of Hungary. One of them represents a wetter climate while the other refers to the drier conditions of the Hungarian Plains. The fuzzy rule-based model reproduces the empirical frequency distributions in every season. However, as expected, the statistical prediction is better in winter, spring and fall than in the summer. The potential of the approach is important in climate change studies when the fuzzy rules obtained as described above can be used with input data stemming from GCM to predict regional/local precipitation reflecting GCM scenarios.

Long term climate deviations: An alternative approach and application on the Palmer drought severity index in Hungary

A new statistical test (Makra-test), applicable for long time series, is introduced to identify extended sub-periods, namely, ‘‘breaks’’, average of which is significantly higher or lower than the mean of the entire time series. In order to apply this test, normal distribution of the time series being examined is a sufficient condition. In another case, if the number of elements increases in the time series, its distribution is near normal and the test can be applied, as well (according to the Central Limit Theorem). The method is demonstrated on monthly Palmer drought severity index (PDSI) data sets, computed for five stations of East Hungary in 1901– 1999. Due to strongly recursive (auto-correlative) nature of PDSI every second month of the warm season (April, June, August and October) is analysed and treated as independent samples. Normality of the time series, which is a sufficient condition of the Makratest, is validated by Kolmogorov–Smirnov test and v 2 -test. Analysis of the PDSI time series indicates that separate treatment of the months is important not only to ensure the normality, but also to consider the existing slight seasonal differences in standard deviation and skewness of the index in the East-Hungarian region. The Makra-test delimits one or more (maximum 4) significant subperiods of the PDSI in every station and month (not considering the sub-intervals within the significant breaks, although most of them are also significant). Since the PDSI is based on monthly temperature and precipitation data that exhibit considerable inhomogeneities (Szentimry, 1999), the test is applied both for the original and the homogeneous time series. Effect of the inhomogeneity on long term variations of PDSI is strong: about the half of the significant breaks in the original time series disappear or become totally different from the time series based on homogenised data. All negative (dry) breaks of each month and station, however, occurred in more recent decades of the 20th century, according to both homogeneous and original series. � 2002 Elsevier Science Ltd. All rights reserved.

Fuzzy Logic in Hydrology and Water Resources

From the early application of fuzzy logic to hydrology a large amount of research has been pursued and at present, fuzzy logic has more and more become a practical tool in hydrologic analysis and water resources decision making. In this chapter the main areas of applications are highlighted. Then, one major area of hydrology, namely, hydro-climatic modeling of hydrological extremes (i.e., droughts and intensive precipitation) is selected to describe in details the methodology using fuzzy rules of inference (or in other words the fuzzy rule-based modeling technique). Results over four regions—Arizona, Nebraska, Germany and Hungary—and under three different climates—semiarid, dry and wet continental—suggest that fuzzy rule-based approach can be used successfully to predict the statistical properties of monthly precipitation and drought index from the joint forcing of macrocirculation patterns and ENSO information.

Advanced Numerical Methods for Complex Environmental Models: Needs and Availability

The understanding of lakes physical dynamics is crucial to provide scientifically credible information foron lakes ecosystem management. We show how the combination of in-situ dataobservations, remote sensing observationsdata and three15 dimensional hydrodynamic (3D) numerical simulations is capable of deliveringresolving various spatio-temporal scales involved in lakes dynamics. This combination is achieved through data assimilation (DA) and uncertainty quantification. In this study, we presentdevelop a flexible framework forby incorporating DA into lakes three-dimensional3D hydrodynamic lake models. Using an Ensemble Kalman Filter, our approach accounts for model and observational uncertainties. We demonstrate the framework by assimilating in-situ and satellite remote sensing temperature data into a three-dimensional3Dl hydrodynamic 20 model of Lake Geneva. Results show that DA effectively improves model performance over a broad range of spatio-temporal scales and physical processes. Overall, temperature errors have been reduced by 54 %. With a localization scheme, an ensemble size of 20 members is found to be sufficient to derive covariance matrices leading to satisfactory results. The entire framework has been developed for the constraintswith a goal of near real-time operational systems and near real-time operations (e.g. integration into meteolakes.ch). 25

Dynamical Downscaling of Projected 21st Century Climate for the Carpathian Basin

According to the Working Group I contributions (Solomon et al., 2007) to the Fourth Assessment Report of the Intergovermental Panel on Climate Change (IPCC), the key processes influencing the European climate include increased meridional transport of water vapour, modified atmospheric circulation, reduced winter snow cover (especially, in the northeastern regions), more frequent and more intense dry conditions of soil in summer in the Mediterranean and central European regions. Future projections of IPCC for Europe suggest that the annual mean temperature increase will likely to exceed the global warming rate in the 21st century. The largest increase is expected in winter in northern Europe (Benestad, 2005), and in summer in the Mediterranean area. Minimum temperatures in winter are very likely to increase more than the mean winter temperature in northern Europe (Hanssen-Bauer et al., 2005), while maximum temperatures in summer are likely to increase more than the mean summer temperature in southern and central Europe (Tebaldi et al., 2006). Concerning precipitation, the annual sum is very likely to increase in northern Europe (Hanssen-Bauer et al., 2005) and decrease in the Mediterranean area. On the other hand, in central Europe, which is located at the boundary of these large regions, precipitation is likely to increase in winter, while decrease in summer. In case of the summer drought events, the risk is likely to increase in central Europe and in the Mediterranean area due to projected decrease of summer precipitation and increase of spring evaporation (Pal et al., 2004; Christensen & Christensen, 2004). As a consequence of the European warming, the length of the snow season and the accumulated snow depth are very likely to decrease over the entire continent (Solomon et al., 2007). Coarse spatial resolution of global climate models (GCMs) is inappropriate to describe regional climate processes; therefore, GCM outputs of typically 100-300 km may be misleading to compose regional climate change scenarios for the 21st century (Mearns et al., 2001). In order to determine better estimations of regional climate conditions, fine resolution regional climate models (RCMs) are widely used. RCMs are limited area models nested in GCMs, i.e., the initial and the boundary conditions of RCMs are provided by the GCM outputs (Giorgi, 1990). Due to computational constrains the domain of an RCM evidently does not cover the entire globe, and sometimes not even a continent. On the other hand, their horizontal resolution may be as fine as 5-10 km. In Europe, the very first comprehensive and coordinated effort for providing RCM projections was the project PRUDENCE (Prediction of Regional scenarios and Uncertainties