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A simple ensemble approach for more robust process‐based sensitivity analysis of case studies in convection‐permitting models
Author(s) -
Flack David L. A.,
Gray Suzanne L.,
Plant Robert S.
Publication year - 2019
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.3606
Subject(s) - sensitivity (control systems) , convection , precipitation , environmental science , ensemble forecasting , computer science , representation (politics) , meteorology , physics , machine learning , electronic engineering , politics , law , political science , engineering
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.