Ensemble forecasts of a flood‐producing storm: comparison of the influence of model‐state perturbations and parameter modifications

Leoncini, Giovanni; Plant, Robert S.; Gray, Suzanne L.; Clark, Peter A.

doi:10.1002/qj.1951

Cited by 30 publications

(34 citation statements)

References 41 publications

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“…In future discussion this will be referred to as the ''flooding'' case. Previous analysis of this case by Leoncini et al (2011) showed that the Met Office 2.2-km ensemble on this occasion gave a 30%-40% chance of a flood-producing storm within 25 km of Edinburgh; a level of significant risk.…”

Section: A Casesmentioning

confidence: 95%

“…Further investigations have been conducted into different ensemble perturbation strategies for high-resolution ensembles including initial condition perturbations (Migliorini et al 2011;Caron 2013;Kühnlein et al 2014), physics perturbations (Stensrud et al 2000;Hacker et al 2011;Gebhardt et al 2011;Vié et al 2012;Baker et al 2014), perturbation of boundary layer parameters (Martin and Xue 2006;Leoncini et al 2010;Done et al 2012), and the use of different physics schemes (Berner et al 2011;Leoncini et al 2013).…”

Section: Introductionmentioning

confidence: 99%

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A Spatial View of Ensemble Spread in Convection Permitting Ensembles

Dey

Leoncini

Roberts

et al. 2014

Monthly Weather Review

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With movement toward kilometer-scale ensembles, new techniques are needed for their characterization. A new methodology is presented for detailed spatial ensemble characterization using the fractions skill score (FSS). To evaluate spatial forecast differences, the average and standard deviation are taken of the FSS calculated over all ensemble member-member pairs at different scales and lead times. These methods were found to give important information about the ensemble behavior allowing the identification of useful spatial scales, spinup times for the model, and upscale growth of errors and forecast differences. The ensemble spread was found to be highly dependent on the spatial scales considered and the threshold applied to the field. High thresholds picked out localized and intense values that gave large temporal variability in ensemble spread: local processes and undersampling dominate for these thresholds. For lower thresholds the ensemble spread increases with time as differences between the ensemble members upscale. Two convective cases were investigated based on the Met Office United Model run at 2.2-km resolution. Different ensemble types were considered: ensembles produced using the Met Office Global and Regional Ensemble Prediction System (MOGREPS) and an ensemble produced using different model physics configurations. Comparison of the MOGREPS and multiphysics ensembles demonstrated the utility of spatial ensemble evaluation techniques for assessing the impact of different perturbation strategies and the need for assessing spread at different, believable, spatial scales.

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Section: A Casesmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

A Spatial View of Ensemble Spread in Convection Permitting Ensembles

Dey

Leoncini

Roberts

et al. 2014

Monthly Weather Review

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“…The details of the 2010 SSEF will be presented in section 3. Other successful experiments using stormscale ensemble prediction have been shown for instances of deep convection and heavy precipitation in Europe (e.g., Hohenegger et al 2008;Vi e et al 2011;Leoncini et al 2013). Many questions remain about ensemble forecasting at convection-allowing resolutions, however, including the optimal design of such an ensemble and how to balance the size of the ensemble against the computational expense of running a large number of ensemble members at high resolution (e.g., Xue et al 2011).…”

Section: B Storm-scale Ensemble Predictionmentioning

confidence: 99%

Factors Influencing the Development and Maintenance of Nocturnal Heavy-Rain-Producing Convective Systems in a Storm-Scale Ensemble

Schumacher

Clark

Xue

et al. 2013

Monthly Weather Review

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From 9 to 11 June 2010, a mesoscale convective vortex (MCV) was associated with several periods of heavy rainfall that led to flash flooding. During the overnight hours, mesoscale convective systems (MCSs) developed that moved slowly and produced heavy rainfall over small areas in south-central Texas on 9 June, north Texas on 10 June, and western Arkansas on 11 June. In this study, forecasts of this event from the Center for the Analysis and Prediction of Storms' Storm-Scale Ensemble Forecast system are examined. This ensemble, with 26 members at 4-km horizontal grid spacing, included a few members that very accurately predicted the development, maintenance, and evolution of the heavy-rain-producing MCSs, along with a majority of members that had substantial errors in their precipitation forecasts. The processes favorable for the initiation, organization, and maintenance of these heavy-rain-producing MCSs are diagnosed by comparing ensemble members with accurate and inaccurate forecasts. Even within a synoptic environment known to be conducive to extreme local rainfall, there was considerable spread in the ensemble's rainfall predictions. Because all ensemble members included an anomalously moist environment, the precipitation predictions were insensitive to the atmospheric moisture. However, the development of heavy precipitation overnight was very sensitive to the intensity and evolution of convection the previous day. Convective influences on the strength of the MCV and its associated dome of cold air at low levels determined whether subsequent deep convection was initiated and maintained. In all, this ensemble provides quantitative and qualitative information about the mesoscale processes that are most favorable (or unfavorable) for localized extreme rainfall.

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“…Furthermore, an ensemble approach is required to demonstrate the true impact of changes at this level, as individual realisations can show misleadingly high sensitivity to model changes, since cloud evolution can be highly sensitive to even very small perturbations (Leoncini et al, 2013;Morrison, 2012). However, in arriving at these simulations, we have used a number of variations in model configuration to be confident that the overall behaviour and sensitivities discussed below are at least qualitatively robust.…”

Section: Description Of Modelmentioning

confidence: 99%

The evolution of an MCS over southern England. Part 2: Model simulations and sensitivity to microphysics

Clark

Browning

Forbes

et al. 2013

Quart J Royal Meteoro Soc

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ReuseUnless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Simulations using the Met Office Unified Model at 1 km horizontal grid spacing of a Mesoscale Convective System (MCS) with a cold pool which propagated across southern England on 25 August 2005 are validated using detailed observations from the Convective Storm Initiation Project (CSIP). Early organisation of the system is not especially well treated, but the model goes on to form a system which developed qualitative and quantitative features remarkably similar to the observations.A sensitivity study suggests that the initial linear system is driven by the position of a low-level 'lid' and upper-level instability, the linear organisation being promoted by a weak rear-inflow jet forced by the upper-level warm anomaly in the cloud anvil. A weak cold pool develops in the absence of ice-phase processes, but this does not promote system propagation. Strengthening and descent of the rear-inflow jet, and acceleration of the system, is promoted by the additional heating through glaciation and cooling through snow evaporation. The surface cold pool and gust front are further strengthened by snow melting and rainfall evaporation. With ice-phase processes present, the cold pool strengthens as a result of the system development and its strength is broadly correlated with system propagation speed during the middle phase of the system's lifetime. Propagation enables the convective band to 'sweep up' any convective cells which trigger ahead of the system. The differing scales of microphysical processes means that it is difficult to form a steady-state system. The observed transition phase corresponds to an increase in the slantwise nature of the flow in the storm and in the low-level cooling by rainfall evaporation near the gust front, combined with a change in ambient conditions (advection over the sea) which eventually enable the cold pool to propagate ahead of the system. Key Words: convection; precipitation; cold pool; rear-inflow jet; cloud microphysics; mesoscale model

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Ensemble forecasts of a flood‐producing storm: comparison of the influence of model‐state perturbations and parameter modifications

Cited by 30 publications

References 41 publications

A Spatial View of Ensemble Spread in Convection Permitting Ensembles

A Spatial View of Ensemble Spread in Convection Permitting Ensembles

Factors Influencing the Development and Maintenance of Nocturnal Heavy-Rain-Producing Convective Systems in a Storm-Scale Ensemble

The evolution of an MCS over southern England. Part 2: Model simulations and sensitivity to microphysics

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