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März 2019
Paper_180px.jpg Susanna Mohr

Barrett, A. I., Wellmann, C., Seifert, A., Hoose, C., Vogel, B. and Kunz, M., 2019: One Step at a Time: How Model Time Step Significantly Affects Convection‐Permitting Simulations. J. Adv. Model. Earth Sy., doi:10.1029/2018MS001418.

Abstract. Various fields of application, such as risk assessments of the insurance industry or the design of flood protection systems, require reliable precipitation statistics in high spatial resolution, including estimates for events with high return periods. Observations from point stations, however, lack of spatial representativeness, especially over complex terrain. Current numerical weather models are not capable of running simulations over thousands of years. This paper presents a new method for the stochastic simulation of widespread precipitation based on a linear theory describing orographic precipitation and additional functions that consider synoptically driven rainfall and embedded convection in a simplified way. The model is initialized by various statistical distribution functions describing prevailing atmospheric conditions such as wind vector, moisture content, or stability, estimated from radiosonde observations for a limited sample of observed heavy rainfall events. The model is applied for the stochastic simulation of heavy rainfall over the complex terrain of southwestern Germany. It is shown that the model provides reliable precipitation fields despite its simplicity. The differences between observed and simulated rainfall statistics are small, being of the order of only ±10 % for return periods of up to 1000 years.



Ehmele, F. and Kunz, M., 2019: Flood-related extreme precipitation in southwestern Germany: development of a two-dimensional stochastic precipitation model. Hydrol. Earth. Syst. Sci., 23, 1083-1102, doi:10.5194/hess-23-1083-2019.

Abstract. We show that there is a strong sensitivity of cloud microphysics to model time step in idealized convection‐permitting simulations using the COnsortium for Small‐scale MOdeling model. Specifically, we found a 53% reduction in precipitation when the time step is increased from 1 to 15 s, changes to the location of precipitation and hail reaching the surface, and changes to the vertical distribution of hydrometeors. The effect of cloud condensation nuclei perturbations on precipitation also changes both magnitude and sign with the changing model time step. The sensitivity arises because of the numerical implementation of processes in the model, specifically the so‐called “splitting” of the dynamics (e.g., advection and diffusion) and the parameterized physics (e.g., microphysics scheme). Calculating one step at a time (sequential‐update splitting) gives a significant time step dependence because large supersaturation with respect to liquid is generated in updraft regions, which strongly affect parameterized microphysical process rates—in particular, ice nucleation. In comparison, calculating both dynamics and microphysics using the same inputs of temperature and water vapor (hybrid parallel splitting) or adding an additional saturation adjustment within the dynamics reduces the time step sensitivity of surface precipitation by limiting the supersaturation seen by the microphysics, although sensitivity to time step remains for some processes.