Abstract. Flash flood induced by severe convection is the hydrometeorological phenomenon that is very difficult to forecast. However, the implementation of radar measurements, especially radar-based Quantitative Precipitation Estimate (QPE) and/or radar-based quantitative Precipitation Nowcast (QPN) can improve this situation. If the radar is able to capture the development of severe convection and can produce reasonably accurate QPE in short time intervals (e.g. 10 min), then it can be used also with hydrological model.A hydrological model named Hydrog was used for investigation of simulation and possible forecasts of two flash floods that took place in the Czech Republic in 2002 and 2003. The precipitation input consisted of mean-field-biasadjusted or original radar 10-min estimates along with quantitative precipitation nowcasts up to 2 h based on COTREC method (extrapolation). Taking into account all the limited predictability of the severe convection development and the errors of the radar-based precipitation estimates, the aim of the simulations was to find out to what extend the hydrometeorological prediction system, specifically tuned for these events, was able to forecast a the flash floods. As assumed, the hydrometeorological simulations of the streamflow forecasts lagged behind the actual development but there is still some potential for successful warning, especially for areas where the flood hits lately.

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In central Europe, floods are natural disasters causing the greatest economic losses. One way to reduce partly the flood-related damage, especially the loss of lives, is a functional objective forecasting and warning system that incorporates both meteorological and hydrological models. Numerical weather prediction models operate with horizontal spatial resolution of several dozens of kilometres up to several kilometres, nevertheless, the common error in the localisation of the heavy rainfall characteristic maxima is mostly several times as large as the grid size. The distributive hydrological models for the middle sized basins (hundreds to thousands of km²) operate with the resolution of hundreds of meters. Therefore, the (in) accuracy of the meteorological forecast can heavily influence the following hydrological forecast. In general, we can say that the shorter is the duration of the given phenomenon and the smaller area it hits, the more difficult is its prediction. The time and spatial distribution of the predicted precipitation is still one of the most difficult tasks of meteorology. Hydrological forecasts are created under the conditions of great uncertainty. This paper deals with the possibilities of the current hydrology and meteorology with regard to the predictability of the flood events. The Czech Hydrometeorological Institute is responsible by law for the forecasting flood service in the Czech Republic. For the precipitation and temperature forecasts, the outputs of the numerical model of atmosphere ALADIN are used. Moreover, the meteorological community has available operational outputs of many weather prediction models, being run in several meteorological centres around the world. For the hydrological forecast, the HYDROG and AQUALOG models are utilised. The paper shows examples of the hydrological flood forecasts from the years 2002–2006 in the Dyje catchment, attention being paid to floods caused by heavy rainfalls in the summer season. The results show that it is necessary to take into account the predictability of the particular phenomenon, which can be used in the decision making process during an emergency.

Ceilometer detection can be used to determine cloud type based on cloud layer height. Satellite observations provide images of clouds’ physical properties. During the summer and winter of 2017, Satellite Application Facility on support to Nowcasting/Very Short-Range Forecasting Meteosat Second Generation (SAFNWC/MSG) cloud type was compared to cloud base layers based upon a sky condition algorithm of Vaisala CL51 ceilometer and the BL-View applied range-variant smoothing backscatter profile at the National Atmospheric Observatory in Košetice, Czech Republic. This study investigated whether the larger measurement range of CL51 improved high cloud base detection and the effect of the range-variant smoothing on cloud base detection. The comparison utilized a multi-category contingency table wherein hit rate, false alarm ratio, frequency of bias, and proportion correct were evaluated. The accuracy of low-level and high cloud type detection by satellite was almost identical in both seasons compared to that using the sky condition algorithm. The occurrence of satellite high cloud detection was greatest when the ceilometer detected high cloud base above low and/or medium cloud base. The hit rate of high cloud detection increased significantly when the BL-View-produced cloud base layer was applied as a reference. We conclude that BL-View produces more accurate high cloud base detection.

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