<p>The representation of upper tropospheric deep convective divergent outflow (UTDCDO) is compared between ICON-simulations with convection-permitting and convection parameterised set-ups (1 and 13 km resolution) for a convective event over Germany and the Alps on June 10th-11th 2019. Three hypotheses on those UTDCDO have been formulated using idealised Large Eddy Simulations and are now tested on ICON in a convection-permitting set-up: 1. Dimensionality affects the magnitude of UTDCDO in ICON; 2. Convective aggregation and organisation affects the magnitude of those convective outflows in ICON and 3. Convective momentum transport does not affect the magnitude of UTDCDO. A moving box is used to integrate mesoscale divergence, precipitation rate and convective momentum transport. Additionally, ellipse fitting is used to make estimates of convective organisation (dimensionality, area of convective precipitation, etc.).<br />Variability in UTDCDO at a given net latent heating rate is reduced in ICON with parameterised deep convection, compared to the convection-permitting set-up. Hints, but no conclusive results are found on the effect of dimensionality on the magnitude of UT divergent deep convective outflows. An impact of convective organisation and aggregation on UTDCDO is significant in the dataset: as a consequence of outflow collisions, UTDCDO increases sub-linearly with net latent heating. We also found a statistical relation between normalised UTDCDO and normalised convective momentum transport. &#160;</p>
<p>We investigate microphysical uncertainties in hailstorms using statistical emulation in a single model framework with the objective to disentangle the relative contributions from aerosols, microphysical parameters and environmental conditions to the uncertainty in cloud-, precipitation- and hail-related parameters.</p> <p>Our selected case study is the Andreas hailstorm on 28 July 2013 in the Neckar Valley and over the Swabian Jura in Southwest Germany. We perform model simulations on cloud-resolving scale with the numerical weather prediction model ICON coupled with the aerosol module ART (ICON-ART). We use a two-moment cloud <span dir="ltr" role="presentation">microphysics</span> scheme with a representation of ice nucleation by dust aerosols.<br />We generated a perturbed parameter ensemble (PPE) to sample uncertainties in cloud-, precipitation- and hail related parameters. Six parameters from the categories aerosols, microphysics and environmental conditions were jointly perturbed, namely the cloud condensation nuclei (CCN) and ice nuclei (IN) concentrations, the riming efficiency of graupel and hail, the convective available potential energy (CAPE) and vertical wind shear. The defined parameter ranges are based on forecast analysis and literature. We used the maximin Latin hypercube algorithm to distribute the parameters well-spaced in the six-dimensional parameter uncertainty space. For these six parameters, an ensemble of 90 members was generated and in addition a smaller independent ensemble of 45 members serves for validation.</p> <p>We used the Gaussian process emulation and developed emulators for hail- and precipitation related output variables. To quantify contributions to the uncertainty in the output variables from the perturbed parameters individually as well as interactions between them, a variance-based sensitivity analysis was performed. We will present first results, which reveal the importance of the CCN concentration for controlling the number concentration of hail particles as well as the CCN concentration and environmental conditions for controlling the amount of hail and precipitation in the model. The geographical distribution of hail and precipitation shows a large variety among the ensemble members, with storm tracks shifted further to the north or south compared to the reference simulation. The path of the storm track is thereby mainly controlled by CAPE and the vertical wind shear, however, aerosol parameters seem to be important for the development of multiple storm tracks.&#160;</p>
The southern Germany and EU-nest domains are specified in Figure S1. Synoptic configurationA plot providing further information on the investigated case, based on classical 850 and 500 hPa variables, is provided in Figure S2.
Abstract. Upper-tropospheric deep convective outflows during an event on 10th–11th of June 2019 over Central Europe are analysed from simulation output of the operational numerical weather prediction model ICON. Both, a parameterised and an explicit representation of deep convective systems are studied. Near-linear response of deep convective outflow strength to net latent heating is found for parameterised convection, while coherent patterns in variability are found in convection-permitting simulations at 1 km horizontal grid spacing. Furthermore, three hypotheses on factors that may affect the magnitude of the convective outflow are tested in the convection-permitting configuration: organisation of convection through dimensionality of the systems, organisation of convection through aggregation and convective momentum transport. Convective organisation and aggregation induce a non-linear increase in the magnitude of deep convective outflows with increasing net latent heating, as shown by the confidence interval of the best fit between power transformed net latent heating and detected magnitude of outflows. However, mixed and weaker than expected signals are found in an attempt to detect the representation of dimensionality of the convection and its consequences for the divergent outflows with an ellipse fitting algorithm that describes the elongation of the intense (convective) precipitation systems. As opposed to expectations, convective momentum transport is identified to slightly increase the magnitude of divergent outflows in this case study.
<p>Forecasting high impact weather events is a major challenge for numerical weather prediction. Initial condition uncertainty plays a major role but so potentially do uncertainties arising from the representation of physical processes, e.g. cloud microphysics. In this project, we investigate the impact of these uncertainties for the forecast of cloud properties, precipitation and hail of a selected severe convective storm over South-Eastern Germany.<br>To investigate the joint impact of initial condition and parametric uncertainty a large ensemble including perturbed initial conditions and systematic variations in several cloud microphysical parameters is conducted with the ICON model (at 1 km grid-spacing). The comparison of the baseline, unperturbed simulation to satellite, radiosonde, and radar data shows that the model reproduces the key features of the storm and its evolution. In particular also substantial hail precipitation at the surface is predicted. Here, we will present first results including the simulation set-up, the evaluation of the baseline simulation, and the variability of hail forecasts from the ensemble simulation.<br>In a later stage of the project we aim to assess the relative contribution of the introduced model variations to changes in the microphysical evolution of the storm and to the fore- cast uncertainty in larger-scale meteorological conditions.</p>
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