Relative contribution of soil moisture, boundary‐layer and microphysical perturbations on convective predictability in different weather regimes

Abstract

The relative contributions of soil moisture heterogeneities, a stochastic boundary‐layer perturbation scheme and varied aerosol concentrations representing microphysical uncertainties on the diurnal cycle of convective precipitation and its spatial variability are examined conditional on the prevailing weather regime. To achieve this, separate perturbed‐parameter ensemble simulations are performed with the Consortium for Small‐scale Modeling (COSMO) model at convection‐permitting horizontal grid spacing for 10 days during a high‐impact weather episode in 2016 in Central Europe. We consider hourly precipitation amounts and their spatial distribution, focus on ensemble mean and spread aggregated over strong and weak forcing conditions, and employ spatial evaluation techniques. The convective adjustment time‐scale diagnostic is used to distinguish the different precipitation regimes. While the total amount of daily precipitation is hardly changed by the different perturbation approaches (less than 5%), the spatial variability of precipitation exhibits clear differences. Soil moisture heterogeneity primarily introduces variability during convection initiation causing a steeper increase in normalized rainfall spread prior to the onset of afternoon precipitation. The stochastic boundary‐layer perturbations lead to the largest spatial variability impacting precipitation from initial time onwards with an amplitude comparable to the operational ensemble spread. Similarly, perturbed aerosol concentrations impact spatial precipitation variability from the model start onwards, but to a smaller degree. Soil moisture heterogeneity shows the strongest weather regime dependence, with the greatest impact on convection during weak synoptic forcing. All types of perturbation increase dispersion of precipitation while maintaining the domain‐averaged precipitation rates.

Dependence of Predictability of Precipitation in the Northwestern Mediterranean Coastal Region on the Strength of Synoptic Control

“…Moreover, the scatterplot shows that the majority of τ c values amounts to less than 3 h and a comparison with Fig. 4 in Keil et al (2019) confirms that the chosen threshold value represents a sensible classification criterion in this specific region at that time of the year.…”

“…3). This is in contrast to applications of τ c in the summer season when a threshold of 6 h is reasonable to separate mid-latitude precipitation regimes due to dynamic control (Kühnlein et al, 2014;Keil et al, 2019). However, Zimmer et al (2011) argue that the τ c diagnostic results in a continuous distribution and conclude that a value somewhere between 3 and 12 h clearly distinguishes between different regimes.…”

“…The area averaged τ c value can be used to categorically determine the weather regime of the day (e.g. Kühnlein et al, 2014;Keil et al, 2019). Firstly, Gaussian smoothed forecast fields of 3-hourly precipitation rates and most unstable CAPE are taken to calculate τ c at any gridpoint exceeding 3mm(3h) −1 in individual members given that a minimum areal coverage of this precipitation rate is reached (consistent with the thresholds applied in Kühnlein et al, 2014).…”

Comparison of Methods Accounting for Subgrid-Scale Model Error in Convective-Scale Data Assimilation

“…The effect of the PSP scheme is twofold: first, it triggers more and earlier convection in situations with weak synoptic forcing (Keil et al 2019). Second, it causes a quick error growth on convective scales if used in an ensemble forecasting setting.…”

Recent upgrades to the Met Office convective‐scale ensemble: An hourly time‐lagged 5‐day ensemble

“…The ideal perturbation strategy for convective‐scale ensembles is not yet understood and this may depend on the synoptic forcing of the events (Flack et al ., 2018; Weyn and Durran, 2018; 2019; Keil et al ., 2019). Reviewing and intercomparing the characteristics of different convective‐scale ensembles can help inform the most efficient and best performing system design for specific high‐impact weather events (Clark et al ., 2018).…”