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

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Flack, D. L. A., Gray, S. L. ORCID: https://orcid.org/0000-0001-8658-362X and Plant, R. S. ORCID: https://orcid.org/0000-0001-8808-0022 (2019) A simple ensemble approach for more robust process-based sensitivity analysis of case studies in convection-permitting models. Quarterly Journal of the Royal Meteorological Society, 145 (724). pp. 3089-3101. ISSN 1477-870X doi: 10.1002/qj.3606

Abstract/Summary

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.

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Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/84684
Identification Number/DOI	10.1002/qj.3606
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Uncontrolled Keywords	Convection, ensembles, MetUM, process-based sensitivity testing, CSIP, cloud droplet number concentration, case studies
Publisher	Royal Meteorological Society
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Funders:	Natural Environment Research Council Natural Environment Research Council	The sponsoring bodies who contributed funding for the creation of this item. Example: NERC Example: The Royal Society of Chemistry A pick list of funders may appear as you type in the funder's name in full or as an acronym. Select a correct match to complete the field or type in a new entry in full. For new entries, the full name is preferred.
Projects:	Exploitation of new data sources, data assimilation and ensemble techniques for storm and flood forecasting Funded by: Natural Environment Research Council (NE/E002137/1 - £525,903) Project Lead: Anthony John Illingworth 1 May 2007 - 31 July 2010 FFIR Programme Co-ordination Funded by: Natural Environment Research Council (NE/K008900/1 - £476,602) Project Lead: Sarah Louise Dance Local Lead (PI): Sarah Dance 1 June 2014 - 31 May 2019	Click Add to select your project (received at Reading) from an autocomplete list.

Deposit Details

Date Deposited:	04 Jul 2019 13:26	Date item deposited into CentAUR
Last Modified:	23 Jun 2024 04:58	Date item last modified

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