National Repository of Grey Literature 5 records found  Search took 0.02 seconds. 
Stochastic Weather Generators and Regional Climate Models: Rivals or Allies?
Dubrovský, Martin ; Štěpánek, Petr ; Meitner, Jan ; Zahradníček, Pavel
The paper demonstrates 'collaboration' between the stochastic weather generator SPAGETTA (WG) and Regional Climate Models (RCM) in analysing impacts of Climate Change (CC). In the first part of the paper, the generator is compared with the ensemble of 19 RCMs in terms of their ability to reproduce 11 spatial temperature and precipitation indices in eight European regions: the indices are based on registering days and spells exhibiting spatially significant occurrence of dry, wet, hot or cold weather, or possible combination of dryor-wet and hot-or-cold conditions. The obtained results indicate that both methodologies provide weather series of comparable quality. In the second part of the paper (which was done only for the Central Europe region), the WG parameters are modified using the RCM-based CC scenarios and the synthetic weather series representing the future climate are produced. This experiment is based on a set of CC scenarios, which consist of changes in selected combinations of following characteristics: (1) mean temperature, (2) temperature variability, (3) daily average precipitation (considering only wet days), (4) probability of wet day occurrence, (5) spatial lag-0 and lag-1day correlations of temperature and precipitation series. The synthetic series generated for each version of the CC scenario are analysed in terms the above mentioned spatial validation indices, the stress was put on effect of each of the five component of the CC scenario on individual validation indices. The results of the experiment indicate that the changes in temperature means is the main contributor to the changes in the validation obviously, except for the purely precipitation-based indices. Positive changes in the lag-0 and lag-1day correlations of both temperature and precipitation are the second most significant contributor to the changes in the validation indices.
Summary report for providing meteorological forecasts for CEPS company
Farda, Aleš ; Štěpánek, Petr ; Meitner, Jan ; Zahradníček, Pavel
Global Change Research Institute CAS provides CEPS company with outputs from numerical weather prediction (NWP) models. The cooperation lasts from 2015 up to now. Selected fields of meteorological variables (namely solar radiation - global and direct, and air temperature in 2 m) are processed from grib files into suitable spatial information, like administrative districts (areal averages) or position of grid points of GFS model. NPW models used are ALADIN from Czech Hydrometeorological Institute and GFS model (from NCEP, NOAA). Outputs are provided for several days ahead, and are issued each morning.
Regional yield forecasting for improved decision making in the plant production
Trnka, Miroslav ; Hlavinka, Petr ; Kudláčková, Lucie ; Balek, Jan ; Meitner, Jan ; Možný, Martin ; Štěpánek, Petr ; Bartošová, Lenka ; Semerádová, Daniela ; Bláhová, Monika ; Lukas, Vojtěch ; Žalud, Zdeněk
The methodology describes how to predict yields of key crops, and at the same time addresses reliability of the predictions and how these can be used. The ability to predict yield levels more than 2 months prior the harvest on the level of regions (NUTS3¨) and districts (LAU1) brings also new opportunities to mitigate impacts of adverse conditions. The methodology shows that the yield forecasts and yield anomalies in particular are consistent and usable in practices. In this methodology, the results of 2018 yield forecasts are presented as an example. The yield forecasting system for the Czech Republic is fully functional and is and will be available through www.vynosy-plodin.cz.
System for monitoring and forecast of impacts of agricultural drought
Trnka, Miroslav ; Štěpánek, Petr ; Chuchma, F. ; Možný, M. ; Bartošová, Lenka ; Hlavinka, Petr ; Balek, Jan ; Zahradníček, Pavel ; Skalák, Petr ; Farda, Aleš ; Semerádová, Daniela ; Meitner, Jan ; Bláhová, M. ; Fiala, R. ; Žalud, Zdeněk
The methodology describes how to predict soil moisture and drought intensity, and at the same time addresses reliability of the predictions and how these can be used. The ability to predict soil moisture values over a period of up to 9 days is presented through using ensemble of models for numerical weather forecasts. This method brings also new opportunities to mitigate impacts during drought events by farmers using such forecasting tools. With regard to the relatively high predictability of soil moisture and drought intensity, the methodology introduces the basic procedures and provides necessary information for the users. In this methodology, the results of 2017 drought event are presented as an example. The drought forecasting system for the Czech Republic is fully functional and is and will be available through www.intersucho.cz.
Forecasting models of solar powerplants and wind farms production based on numerical weather prediction models
Farda, Aleš ; Štěpánek, Petr ; Zahradníček, Pavel ; Rajdl, Kamil ; Meitner, Jan
Performance and potential of daily based forecasts of renewable energy sources power production have been investigated for the Czech Republic. The role of individual numerical weather prediction model errors has been researched and ensemble based technique has been studied as the mean to obtain more precise and reliable results providing a benefit for end users.\n\n

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