| Popis: |
In this study we investigate the impact of several selected sources of uncertainty on convective precipitation prediction. For this purpose, we conduct numerical simulations with the ICOsahedral Non-hydrostatic (ICON) model for two consecutive days in June, 2021, on which deep moist convection triggered by different synoptic forcing occurred over southwestern Germany. We use single- and double-moment microphysics schemes and vary the initial soil moisture, grid spacing, and cloud condensation nuclei (CCN) concentration. We compare the results with measurements conducted on the same two days during the Swabian MOSES (Modular Observation Solutions for Earth Systems) field campaign. We find that the applied dry bias (initial soil moisture in the model reduced by 25%) much better represents the actual soil moisture conditions and leads to an improved quantitative precipitation forecast when compared to radar-derived precipitation amounts. Furthermore, the model resolution impacts the precipitation amount, intensity, and the timing of convection initiation: while 1-km runs show the least root mean square error for 24-hour precipitation sums, the onset of convective precipitation in 2-km resolution runs matches better the observations. However, the overall impact of this factor is not always systematic. The comparison of several radiosounding-derived convective indices (e.g. lifted index, convective available potential energy, convective inhibition) with model data yield many non-systematic results. For instance, CCN concentrations do not seem to have any significant impact on any of the calculated indices. At the same time, runs with coarser resolution (2-km) often better depict the temporal development of CAPE but overestimate its amount. |
