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To assess the climatology of the Czech Republic (CR) with a high spatial (1 km) resolution, this study uses radar-based precipitation data collected over the summer seasons of a 10-year period (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011). Radar reflectivity data were obtained from two C-band Doppler weather radars, integrated in time and merged with daily precipitation totals from rain gauge measurements. Using radar measurements, daily adjusted precipitation totals were later divided into 10-min precipitation totals that were continuously accumulated in a series of time windows ranging from 30 min to 24 h. This climatological analysis confirms that altitude substantially influences not only the spatial distribution of mean precipitation but also the spatial distribution of precipitation maxima as well as the diurnal cycle of precipitation in the CR. While the 24-h totals are generally maximal in mountains, mean and absolute seasonal maxima of short-term totals (up to 1 and 6 h, respectively) are detected in altitudes between 300 and 600 m a.s.l. Regarding the diurnal cycle, maximum frequency of precipitation occurs 2 h earlier in the mountains, whereas mean totals remain at the same level until 2100 UTC. The mean time during which precipitation maxima occur generally does not change with altitude. Nevertheless, a detailed regional study demonstrates that short-term precipitation maxima usually start earlier in the afternoon in and around mountainous regions. Long-term (mainly 6-h) precipitation maxima occur later than short ones but are substantially less concentrated in time, especially in the mountains. These differences between mountains and lowlands can be explained by smaller relative proportions and earlier onset of convective precipitation in mountains.

A methodology is proposed in order to combine information from radar precipitation with other observations (e.g. screen-level temperature and relative humidity) in a soil analysis scheme based on an extended Kalman filter. A preliminary study is performed over the Czech Republic for one month in July 2008 using threehour rainfall accumulations derived from two C-band radars and a land-surface scheme forced by short-range forecasts from a limited-area model. The Jacobian matrix of the observation operator is examined to make optimal choices for the estimation of the Kalman gain matrix. It is shown that the size of the perturbation for computing Jacobian matrix elements with finite differences has to be carefully chosen, since too small values lead to unphysical negative elements whereas too large values reduce the spatial variability considerably. After a log-transform of the precipitation field, the corresponding errors are more compatible with the Gaussian hypothesis of the Kalman filter. However, at locations where model rainfall is underestimated, positive soil moisture increments are much too low.