A simple ensemble approach for more robust process‐based sensitivity analysis of case studies in convection‐permitting models

Abstract

Case studies remain an important method for meteorological parameter sensitivity process studies. However, these types of study often use just a few case studies (typically up to three) and are not tested for statistical significance. This approach can be problematic at the convective scales, since uncertainty in the representation of an event increases, and the variability in the atmosphere arising from convective‐scale noise is not routinely taken into account. Here we propose a simple ensemble method for performing more robust sensitivity analysis without the need for an operational‐style ensemble prediction system and demonstrate it using a case study from the 2005 Convective Storm Initiation Project. Boundary‐layer stochastic potential temperature perturbations with Gaussian spatial structure are used to create small ensembles to examine the impact of increasing cloud droplet number concentration (CDNC) on precipitation. Whilst there is a systematic difference between the experiments, such that increasing the CDNC reduces the precipitation, there is also an overlap between the different ensembles implying that convective‐scale variability should be taken into account in case study process‐based sensitivity studies.

Evaluation of Two Cloud-Resolving Models Using Bin or Bulk Microphysics Representation for the HyMeX-IOP7a Heavy Precipitation Event

“…So, in order to better estimate this intense precipitation event, it is important to advance our understanding of the involved microphysics processes by studying other convective systems. Moreover, a statistical approach, using various model settings such as in References [62,63], could further confirm the robustness of our simulation results. Furthermore, in future works, to make progress on the estimate of the impact of the diabatic effects of the microphysics processes on the dynamics of convective systems, it would be helpful to perform sensitivity studies using a detailed (bin) microphysics scheme coupled to the WRF model.…”

Elucidating the causes of errors in 2.2 km Met Office Unified Model simulations of a convective case over the US Great Plains

“…(2018) and Flack et al . (2019). For example, we would not be so confident about the difference seen between the two model configurations in Figure 7 if we had only run one deterministic instance of each.…”

“…Showing the ensemble mean (dot-dashed line), member 1 (dashed line) and all other members (solid lines). Averaged over the box shown in Figure 1b [Colour figure can be viewed at wileyonlinelibrary.com] This has recently been highlighted by both Ancell et al (2018) and Flack et al (2019). For example, we would not be so confident about the difference seen between the two model configurations in Figure 7 if we had only run one deterministic instance of each.…”

Confronting the Challenge of Modeling Cloud and Precipitation Microphysics

“…Bin schemes certainly provide more sophistication in representing microphysical process rates, and they have many more degrees of freedom to evolve cloud and precipitation properties; however, evidence that they actually give consistently better results when compared to available observations is lacking. Given that predictability is inherently limited at cloud and convective scales and there is large case-to-case variability in simulation quality, a large number of individual real cases and/or ensembles may be needed to evaluate microphysics schemes rigorously through comparison with observations (Flack et al, 2019;Stanford et al, 2019). In situ observations, commonly viewed as the "gold standard" for evaluation of bin 10.1029/2019MS001689 Journal of Advances in Modeling Earth Systems microphysics scheme SDs, are also lacking in terms of the number of cases, sufficient coverage spatiotemporally for any individual case, and adequate characterization of sample volumes (e.g., for drizzle-sized drops).…”