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National Repository of Grey Literature 2 records found  Search took 0.01 seconds. 
Bias correction of regional climate model outputs: statistical transformations of precipitation series from the climate model ALADIN in the project PERUN
 Martínková, Marta
The outputs of regional climate models are biased. Regarding the bias correction of outputs from a climate model, the two fundamental approaches exist. First approach (e.g., Delta change method) gets the information on climate signal from comparison of the model control and future periods. Such information (change factor) is then applied to modify the observational data. Bias correction method gets the information on model bias from comparison of observational data and model outputs for the control period. The model outputs for future period are than corrected using this information on the model bias. This contribution is focused on the possibilities for bias correction of the model ALADIN (CNRM-ESM2-1) in the project PERUN and the precipitation series in daily time step for SSP5-8.5 scenario. Different statistical transformations are compared: methods based on statistical distribution, parametric transformations and non-parametric transformations (empirical quantiles method).\n
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Using Weather Generators for the Assessment of the Impact of Climate Change in Catchments
 Martínková, Marta ; Hanel, Martin (advisor) ; Máca, Petr (referee)
The main objective of this dissertation is to provide a novel approach to downscaling of outputs from regional climate models and to simulation of future climate. The resulting method consists of rain generator that operates in 6-hour time step. The generator performs well for the observational data. It consists of following steps: disaggregation of 6-hour cumulative precipitation into convective and stratiform types, fitting of first order 3-state discrete time Markov chain to the data and simulation of long time series of precipitation. Then the mixture of log-normal and Generalized Pareto distribution is fitted to stratiform events and the Generalized extreme value distribution is fitted to convective events. The impact of climate change on precipitation is evaluated by using change factors that are identified for precipitation occurrence (by comparing the transition matrices for the future and control period) and for precipitation amount (by comparing the scale and location parameters of distributions fitted for the future and control period). The observational data are then altered with obtained change factors. From evaluation of observational data it stems that the average volume of an convective event is higher for the western region than for eastern region of the Czech Republic. Additionally, statistically significant trends in number and volume of convective events were identified for the region. The relative portion of convective precipitation is the highest in summer for observational data. From analysis of RCMs simulations, it stems that even though the overall precipitation is projected to be lower in future, the proportion of convective events (versus stratiform ones) would be higher. The number of convective events is projected to be lower in the future, while the volume of a convective event to be bigger.
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See also: similar author names
3 MARTÍNKOVÁ, Markéta
7 MARTÍNKOVÁ, Michaela
1 Martinková, Magda
3 Martinková, Markéta
6 Martinková, Martina
7 Martinková, Michaela
1 Martinková, Milada
1 Martinková, Milena
9 Martinková, Monika
2 Martínková, Magdalena
3 Martínková, Marie
7 Martínková, Michaela
9 Martínková, Monika
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