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Drought periods in 2014
Zahradníček, Pavel ; Trnka, Miroslav ; Štěpánek, Petr ; Semerádová, Daniela ; Farda, Aleš
The first half of year 2014 was characterized by the occurrence of extreme weather. Episode without precipitation were alternated with thunderstorms, temperature were mostly above normal and was occurred first heat wave. The year began with a very mild winter, when amount of snow was only 27% of long term average and in agricultural region South Moravia only about 10%. The winter temperature was more than 2 degrees C higher than normal. March and April were characterized by low sum of precipitation mainly again in South Moravia, both months were about 70% of long term average. March and April temperature were significantly above normal (March about more than about 4 degrees C and April more than 2 degrees C). In contrast May was cold and rainy. In the middle of the month there were persistent rainfall, which caused an increase of river levels and flooding several areas. June was again very dry. These weather conditions led to the strong dry episodes during first half of the year.
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Testing a statistical forecasting model of electric energy consumption for two regions in the Czech Republic
Rajdl, Kamil ; Farda, Aleš ; Štěpánek, Petr ; Zahradníček, Pavel
Precise forecasting of electric energy consumption is of great importance for the electric power industry. It helps system operators optimally schedule and control power systems, and even slight improvements in prediction accuracy might yield large savings or profits. For these reasons, many forecasting models based on various principles have been developed and studied. Because of energy consumption’s strong dependence on weather conditions, such models often utilize outputs from numerical weather prediction models. In this study, we present and analyse a statistical model for forecasting hourly electrical energy consumption by customers of E.ON Energie, a.s. in two regions of the Czech Republic. The aim of this model is to create hourly predictions up to several days in advance. The model uses hourly data of consumed energy from 2011–2014 and corresponding predictions of temperature and cloudiness provided by the ALADIN/ CZ model. The statistical model is based on a regression analysis applied to appropriate data samples and supplemented by several optional post-processing methods. Specifically, we use a robust linear regression algorithm to identify energy consumption’s dependence on temperature, the meteorological variable with the largest influence on consumption. Our post-processing methods focused on removing prediction bias resulting from economic situations (represented by the goss domestic product, GDP) and sudden temperature changes. We analysed the presented model from the point of view of the hourly predictions’ accuracy for 2013 and 2014. Accuracy was primarily measured by mean absolute error. It was evaluated for individual months, and the effects of individual parts of the model on accuracy value are shown. Introduction
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Köppen–Geiger climate classification by different regional climate models according to the SRES A1B scenario in the 21st century
Szabó-Takács, Beáta ; Farda, Aleš ; Zahradníček, Pavel ; Štěpánek, Petr
We investigate future climate conditions projected by six regional climate model (RCM) simulations driven by the SRES A1B emission scenario. As a diagnostic tool of climate change, we used the Köppen–Geiger climate classification as it is suitable for assessing climate change impacts on ecosystems. The analysis is based on a comparison of Köppen–Geiger climate subtypes during two future time slices (2021–2050 and 2070– 2100) with climate subtypes observed during 1961–2000. All RCMs showed expansion of the area covered by warmer climate types in the future, but the magnitude of the growth varied among RCMs. The differences stemmed from several sources, mainly boundary forcing provided by the driving global circulation models (GCMs) as well as different physical packages, resolution, and natural variability representation in individual GCMs. In general, RCMs driven by the ECHAM5-r3 GCM projected cooler climate conditions than did RCMs driven by the ARPÈGE GCM. This can be explained by two factors related to ECHAM5-r3: i) exaggerated transport of cool and moist air from the North Atlantic to Europe in summer, and ii) winter advection of cold air from the Artic owing to North Atlantic Oscillation blocking pattern alteration during solar minima as well as higher natural variability. RCM-related properties, such as physical package and spatial resolution, may also significantly affect climate predictions, although they do so to a smaller extent than does the driving GCM data.
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Heat waves over Central Europe in ALADIN-Climate/CZ regional climate model: evaluation and future projections
Lhotka, Ondřej ; Farda, A. ; Kyselý, Jan
We evaluated a simulation of Central European heat waves and analysed possible changes of their characteristics in the future climate using the ALADIN-Climate/CZ regional climate model with 25km horizontal grid spacing. Lateral boundary conditions were provided by the ARPEGE global climate model, using historical forcing and the SRES A1B scenario. Observed data were taken from the E-OBS gridded data set. Heat waves were evaluated over 1970−1999 and changes in their characteristics were assessed for 2020−2049. Their definition is based on exceedance of the 90th percentile of summer daily maximum temperature calculated separately for modelled and observed data. ALADIN-Climate/CZ simulates characteristics for the recent climate quite well, especially the overall severity of heat waves. In contrast, temperature amplitude was considerably overestimated. This model projects an increase in overall heat wave severity by a factor of 2 to 3 in the future climate, primarily driven by an increasing number of events. The study shows that ALADIN-Climate/CZ is generally capable of simulating Central European heat waves, which gives more credibility to model projections of future heat waves.
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Predikovatelnost větrných farem společnosti Amper-Market
Farda, Aleš ; Rajdl, Kamil ; Štěpánek, Petr
Tato zpráva obsahuje analýzu produkce elektrické energie větrných elektráren společnosti Amper Market a. s. z hlediska její prediktability, s hlavním cílem určení potencionální přesnosti predikcí hodinových hodnot výroby energie. Pro výpočet analyzovaných predikcí je použit regresní předpovědní model využívající rychlost a směr větru ve dvou výškových hladinách. Potřebné hodnoty těchto meteorologických prvků jsou získány ze tří numerických předpovědních modelů - Aladin, EPS Aladin a IFS. Přesnost predikce výroby je optimalizována z hlediska výběru vhodných geografických bodů pro získání výstupů numerických modelů, z hlediska kombinovaného využití těchto modelů a z hlediska individuálního nastavení regresního modelu pro jednotlivé elektrárny. Analýza byla provedena na základě dat z období leden 2013 až duben 2014.
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Projections of future water-energy-vegetation regimes at the Lysina catchment, Czech Republic
Lamačová, Anna ; Yu, X. ; Duffy, Ch. ; Krám, Pavel ; Hruška, Jakub ; Farda, Aleš
Hydrologic models represent useful tools for the understanding of forest hydrological functions. At the Lysina Critical Zone Observatory (50°03’ N, 12°40’ E; area 0.293 km2), a forest catchment in the western Czech Republic, a distributed physics-based hydrologic model, the Penn State Integrated Hydrologic Model (PIHM), was used to simulate long-term hydrological change under forest management practices, and to evaluate the comparative scenarios of the hydrological consequences under anticipated climate change. Stand age-adjusted LAI (leaf area index) curves were generated from an empirical relationship to represent changes in seasonal tree growth. By considering the age-adjusted LAI, the spatially distributed model was able to successfully simulate the integrated hydrologic response from snow melt, recharge, evapotranspiration, groundwater levels, soil moisture and streamfl ow, as well as spatial patterns of each hydrologic state and fl ux variables. Corrected climatic data from the ALADIN-Climate/CZ regional climate model with SRES A1B scenario and diff erent forest age categories (Norway spruce monoculture) were used for projection of hydrologic pattern shift at the study site in the future (2025–2050, 2071–2100). Th e model projections suggested that that the decrease in mean annual runoff would be from 422 mm (observed in 1990–2011) to 361 mm (2021–2100) and 345 mm (2071–2100) with notable changes in seasonal patterns represented by a runoff decrease in the spring and summer months.
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Quality control and homogeneity testing of daily time series of eca ECA&D
Zahradníček, Pavel ; Štěpánek, Petr ; Farda, Aleš ; Skalák, Petr
For any meaningful climate analysis, it is necessary for analysed time series to be homogeneous, which means that their variations are caused solely by variations in weather and climate (Conrad and Pollak 1950). Th us, prior to any analysis, the need to homogenize data and check their quality arises. Unfortunately, most of the climatological series that span over decades, to centuries, contain inhomogeneities caused by station relocations, exchange of observers, changes in the vicinity of the stations (e.g. urbanization), changes in instruments, observing practices (e.g. diff erent formulas for calculating daily means, diff erent observation times), etc. In this work we focused on testing the quality and homogeneity of daily data produced by ECA&D. Th is is a free available dataset of daily meteorological elements from the European Climate Assessment & Dataset (http://eca.knmi.nl/). Th is database was used to create a regular grid of EOBS points, which are oft en used to validate climate models.
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Comparison of corrected and uncorrected model simulations in the perspective of climate change in the area of the Czech Republic
Štěpánek, Petr ; Farda, Aleš ; Zahradníček, Pavel ; Skalák, Petr
In recent years, simulations from various regional climate models became available for the area of the Czech Republic, thanks to several national and international projects (e.g. the EC FP6 projects CECILIA, ENSEMBLES or the national project VaV SP/1a6/108/07). Th e simulations of all the models were performed according to the IPCC A1B emission scenario with various spatial resolutions. Since models suff er from biases, the model outputs were statistically corrected using the quantile approach of M. Déqué. Aft er correction, the RCM outputs were statistically processed and analyzed, especially for air temperature and precipitation, but also for other elements (like relative humidity, wind speed and sunshine duration). In this study, the diff erences between models outputs, as well as the corrected and uncorrected results, are presented.
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