Dependence of accumulated precipitation on cloud drop size distribution

Convective precipitation is the main cause of extreme rainfall events in small areas. Its primary characteristics are both large spatial and temporal variability. For this reason, the monitoring of accumulated precipitation fields (liquid and solid components) at the surface is difficult to carry out through the use of rain gauge networks or remote-sensing observations. Alternatively, numerical models seem to be the most powerful tool in simulating convective precipitation for various analyses and predictions. Due to a lack of comparisons between modelled and observed precipitation characteristics over a long period of time, we focus our research on comparisons between observations and three model samples of accumulated convective precipitation over a particular study area. We use a numerical cloud model with two model schemes involving the unified Khrgian–Mazin size distribution of cloud drops and a model scheme involving a monodisperse cloud droplet spectrum and the Marshall–Palmer size distribution for raindrops, respectively. For comparison, we have selected a study area with a sounding site. Our analysis shows that the model version with the Khrgian–Mazin size distribution exhibits a better agreement with the observed mean, median and range of extreme values of accumulated convective precipitation. Model simulations with the Khrgian–Mazin size distribution most closely match observations, with a correlation coefficient of 0.91. Use of the Marshall–Palmer size distribution, on the other hand, systemically underestimates the observed precipitation and has the lowest correlation coefficient among the methods, 0.83. Such an investigation is crucial to improve predictions of accumulated convective precipitation for various climatological and hydrological analyses and predictions.