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Design precipitation in Czech river basins
Müller, Miloslav ; Kašpar, Marek ; Hulec, Filip
Data on rainfall intensity on the territory of the Czech Republic with a horizontal resolution of 1 km2 and a time step of 10 minutes, obtained by adjusting radar estimates with data from rain gauge stations, were used to derive design areal precipitation in the Czech river basins of 1st to 4th order and in the basins of surface water bodies. When the rainfall accumulation time is doubled, the design precipitation total in individual pixels increases by about 20% on average. The magnitude of the design totals decreases with increasing catchment area, especially for shorter accumulation periods.
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Change in precipitation distribution as temperature rises expressed in diagnostic and prognostic data
Sokol, Zbyněk ; Řezáčová, Daniela
We summarize the main results that show how the distribution of precipitation changes with increasing temperature. Hourly rainfall totals from 97 rain gauge stations in the Czech Republic for the years 1997 to 2019 are used for the evaluation. Ground temperature, ground dew point temperature and temperature at the lifting condensation level are used to express the temperature change. The results show how an increase in temperature and a corresponding increase in saturation humidity (CC scaling) affects the distribution of precipitation in the study area. In general, the increase in precipitation as a function of observed temperature is clearly evident for the upper quantile values, but only for certain temperature intervals. It confirms the findings of other studies. In this paper, data from reanalyses performed by the ALADIN-CLIMAT/CZ model are treated similarly to the measured data.\n
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Evaluation of the areal extremeness of extreme weather events in Czechia in the period of 1961–2020
Kašpar, Marek ; Müller, Miloslav
Due to the multiplication of impacts of weather extremes when occurring in larger area, we proposed an areal approach of their evaluation. We evaluated six types of extreme weather events, namely, heat waves, cold waves, air temperature drops, windstorms, heavy precipitation, and heavy snowfalls. We employed the original method using the Weather Extremity Index derived from return periods of values of relevant meteorological variables in the affected area. Each event is characterized not only by the areal extremeness quantified with the index but also by the spatial extent and duration. In the studied period, heat and cold\nwaves generally reach higher index values in relation with larger affected area. The increase in the frequency and extremity of heat waves is in contrast with the decrease in the frequency of cold waves and significant heavy snowfalls. The frequency of windstorms is slightly decreasing with the most significant ones concentrated in the cold half-year. Air temperature drops are the most frequent in the warm half-year, nevertheless three of four top events occurred in January. The frequency of heavy precipitation tends to fluctuate. The obtained meteorological database of extreme events may help to estimate the parameters of future ones using climate models.
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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.
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Characteristics of convective environment in ALADIN reanalysis
Zacharov, Petr, jr. ; Vokoun, Martin
For the prediction and assessment of the potential for convective cloud formation, various characteristics of the convective environment are used. Values of CAPE, CIN, wind speed, and temperature at several standard levels are available from the ALADIN reanalysis outputs, from which wind shear and vertical temperature gradient can be calculated. Verification allows for point comparison with data from sounding measurements, for example, from Prague, Libuš.
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Precipitation by PERUN
Zacharov, Petr, jr. ; Brožková, R. ; Řezáčová, Daniela
Weather reanalyses are a powerful tool for studying historical weather both at individual points and especially over an area. The detailed reanalysis produced by the PERUN project thus makes it possible to reveal various aspects of the atmosphere to a degree that we are unable to achieve with measurements. Since it is still a model approximation, it is of course necessary to detect systematic biases by verification before its use. Climate model runs, on the other hand, can uncover the future evolution of the atmosphere. Since these calculations cannot yet be verified, it is necessary to validate a historical run of the same model and subtract the revealed systematic errors from the future projections. In the PERUN project, both the historical run and two climate runs up to 2100 have been calculated. In this work, we present a basic verification and validation of the precipitation forecasts and an evaluation of the separation of precipitation into stratiform and convective precipitation and, in addition, into\nliquid and solid precipitation.
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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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Evaluation of precipitation totals simulated by the ALADIN/PERUN atmospheric reanalysis at high spatial resolution
Bližňák, Vojtěch ; Zacharov, Petr, jr.
Atmospheric reanalyses represent powerful tools for obtaining information about the state of the atmosphere in history, which is obtained by numerical weather prediction (NWP) models whose predictions may (but may not) be improved through the assimilation of measured data. Significant developments in computer technology have recently enabled to increase their spatial resolution so that even meteorological phenomena of a local nature can be better captured. However, most NWP models compensate this capability by reducing the computational domain, which largely limits the use of these forecasts for the following meteorological, climatological and/or hydrological applications. The newly developed ALADIN/PERUN atmospheric reanalysis provides simulations of various meteorological variables at high spatial (2.3 km) and temporal (1 h) resolution over most of Europe between 1989 and 2020. Due to the high resolution of the reanalysed data, it can be expected that precipitation fields will capture local-scale processes well, and thus reproduce more faithfully, for example, heavy convective precipitation. The presented paper aims to evaluate this capability based on gauge-adjusted radar estimates of precipitation totals during warm parts of the year when strong convective but also stratiform precipitation occurs in Central Europe. The accuracy of the localization and precipitation sums will be evaluated for two different runs of the NWP model. The first one (ALADIN/Reanalysis) involves a complete assimilation of the observed data every 6 hours using a 4D-VAR assimilation scheme. The second (ALADIN/Evaluation Run) uses only the boundary conditions from the ERA-5 global reanalysis and the calculation of the forecasts is not further modified based on measured data. Comparing the two runs will provide us with information about the level of physical description in the NWP model as well as the effect of assimilation on the resulting precipitation fields. In addition, the paper is unique in that it will use detailed fields of 'observed' precipitation totals at high spatial resolution, which conventional rain gauge data cannot offer.\n
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Validation of reanalysis for Central Europe PERUN/Reanalysis
Beranová, Romana ; Rulfová, Zuzana ; Sokol, Zbyněk
PERUN/Reanalysis is based on the ALADIN numerical forecast model, which has been adapted for climatological calculations. To serve as one of the reference sets for estimating expected climate changes in the coming decades, it needs to undergo validation against station measurements and possibly against other commonly used data sets. In this conference paper, we will examine the validation of basic meteorological quantities across the computational domain of the ALADIN model. To achieve this, we will use station data from the ECA&D database, station data in the regular network (Eobs), and the ERA5 global reanalysis. Validation will be conducted for the period from 1990 to 2014. During validation, our primary focus will be on air temperature (minimum, maximum, and mean) and precipitation. We will also examine additional variables, including wind speed.\n
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