Representing model uncertainty in the Met Office convection‐permitting ensemble prediction system and its impact on fog forecasting. (4th September 2016)

• Record Type: Journal Article
• Authors: McCabe, Anne; Swinbank, Richard; Tennant, Warren; Lock, Adrian
• Is Part Of: Quarterly journal of the Royal Meteorological Society. Volume 142:Number 700(2016)
• Page Start: 2897
• Page End: 2910
• Publication Date: 2016-09-04
• Subjects: ensemble prediction system -- model uncertainty -- model error -- convection‐permitting model -- stochastic physics -- fog -- low visibility -- MOGREPS; Meteorology -- Periodicals; 551.5
• DOI: 10.1002/qj.2876
• Languages: English
• ISSNs: 0035-9009

Abstract

Abstract : Representing model uncertainty in convection‐permitting ensemble prediction systems is a developing area of research. While methods for including variability to account for model uncertainty at the global scales are quite mature, it is not clear that these methods will necessarily be applicable at the convective scale. One such method is the Random Parameter (RP) scheme, where parameters from the physics parametrizations are perturbed at regular intervals throughout the forecast. In this work, we adapt the RP scheme to represent model uncertainty in the Met Office's convection‐permitting ensemble prediction system for the UK (MOGREPS‐UK). The revised version of the RP scheme is applied to a sub‐set of model parameters, chosen to target specific physical processes relevant to the UK forecast. Objective verification scores from two one‐month trials show particular improvements for visibility and surface temperature when the RP scheme is used. Application of the RP scheme results in a modest increase in the ensemble spread for all surface parameters. The results of low‐visibility case‐studies show that applying the RP scheme enables the ensemble to capture observed fog events otherwise missed by the forecast. Overall, the RP scheme has a positive effect on MOGREPS‐UK, and demonstrates the benefit of schemes that target known areas of model uncertainty.