Convective-Scale Perturbation Growth across the Spectrum of Convective Regimes

Flack, David L. A.; Gray, Suzanne L.; Plant, Raymond; Lean, Humphrey; Craig, George C.

doi:10.1175/mwr-d-17-0024.1

Cited by 30 publications

(63 citation statements)

References 52 publications

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“…This diagnostic thus requires careful interpretation unless either (a) a neighbourhood is applied to the MSD (for more on neighbourhood‐based approaches see Ebert, ) or (b) the MSD is considered in components representing the common, control and perturbed parts (as in Flack et al . ). The MSD is computed here as

MSD = \frac{\sum {(P_{p} - P_{c})}^{2}}{\sum P_{c}^{2}},

where P c is the hourly accumulation of precipitation in a control forecast and P p is that in a perturbed forecast.…”

Section: Diagnosticsmentioning

confidence: 65%

“…Moreover, in studies of some more recent convective‐scale cases using this style of perturbation with newer model configurations, Flack et al . () found no problems with the specification of initial seeds.…”

Section: Modelmentioning

confidence: 97%

“…() and Flack et al . () in which more information can be found on the motivation for, and any sensitivities to, details of the implementation.…”

Section: Modelmentioning

confidence: 99%

“…() and modified by using a slightly different normalization in Flack et al . (). The form used here is the latter, specifically

F_{common} = \frac{N_{1, 2}}{N_{1} + N_{2} - N_{1, 2}},

where N x is the number of precipitating points for forecast x = 1 or 2, and with subscript “1,2” denoting the number in common in the two forecasts.…”

Section: Diagnosticsmentioning

confidence: 99%

See 3 more Smart Citations

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

Flack

Gray

Plant

2019

Quart J Royal Meteoro Soc

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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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MSD = \frac{\sum {(P_{p} - P_{c})}^{2}}{\sum P_{c}^{2}},

where P c is the hourly accumulation of precipitation in a control forecast and P p is that in a perturbed forecast.…”

Section: Diagnosticsmentioning

confidence: 65%

Section: Modelmentioning

confidence: 97%

“…() and Flack et al . () in which more information can be found on the motivation for, and any sensitivities to, details of the implementation.…”

Section: Modelmentioning

confidence: 99%

“…() and modified by using a slightly different normalization in Flack et al . (). The form used here is the latter, specifically

F_{common} = \frac{N_{1, 2}}{N_{1} + N_{2} - N_{1, 2}},

where N x is the number of precipitating points for forecast x = 1 or 2, and with subscript “1,2” denoting the number in common in the two forecasts.…”

Section: Diagnosticsmentioning

confidence: 99%

See 2 more Smart Citations

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

Flack

Gray

Plant

2019

Quart J Royal Meteoro Soc

Self Cite

View full text Add to dashboard Cite

show abstract

“…() and Flack et al . (). In section 3, we use objective verification to evaluate a month‐long trial to determine the differences between the two ensembles and assess their diurnal cycles.…”

Section: Introductionmentioning

confidence: 99%

Extreme rainfall sensitivity in convective‐scale ensemble modelling over Singapore

Porson

Hagelin

Boyd

et al. 2019

Quart J Royal Meteoro Soc

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A convective‐scale ensemble system was developed to predict the occurrence of heavy convective rainfall around Singapore with a focus on the prediction of high‐impact events. The new ensemble SINGV‐EPS has been nested within two global ensembles, MOGREPS‐G (UK Met Office) and EC‐ENS (ECMWF). Predicting the occurrence of convective rainfall in an area such as Singapore is challenging and this article discusses the use of the convection‐permitting ensemble to characterize the uncertainties in the prediction of such localized heavy rainfall. First, verification of wind, temperature, and precipitation is performed for a month‐long period to assess the relative performance of each ensemble. This reveals differences, but no robust signal to say one is better than the other. The results are not statistically significant and not all variables are consistently better with one ensemble or the other. Secondly, the precipitation characteristics of SINGV‐EPS are analysed from probabilities of precipitation and variability among the ensemble members. SINGV‐EPS is sensitive to the choice of the global ensemble providing the initial conditions and boundaries. The results suggest there is benefit, in some cases, from combining the two ensembles. Thirdly, the spread of the ensemble precipitation is analysed using the dispersion Fractions Skill Score (dFSS). We compare the impact of the initial perturbations and the perturbations in lateral boundary conditions in both nesting options. The initial perturbations dominate in the beginning of the forecasts, with influence up to T+24 h, and are associated with an upscale growth of the uncertainties. The impact of the parent ensemble and lateral boundary conditions dominate at the end of the forecast and tend to influence larger scales more.

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Stratification of the vertical spread‐skill relation by radiosonde drift in a convective‐scale ensemble

Flack

2023

Atmospheric Science Letters

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Ensemble forecasting systems provide useful insight into the uncertainty in the prediction of the atmosphere. However, most analysis considers ensembles in latitude, longitude, and time. Here, the vertical aspects of the spread‐skill relation are considered in a convective‐scale ensemble via comparisons with radiosonde ascents. The specific focus is on the impact of stratifying the spread‐skill relation by radiosonde drift. The drift acts as a proxy for the mobility of the atmosphere. The overall spread‐skill relation shows the temperature has a better relation than the dewpoint. However, the total variance comparisons between model and observations indicates that the dewpoint is underspread throughout the atmosphere, whilst the temperature is overspread through the lower atmosphere and underspread aloft. This suggests that the model bias is influencing the spread‐skill relation. Stratifying these results by the radiosonde drift indicates that the spread‐skill relation, and model bias, for both temperature and dewpoint degrades with increased mobility. For the most mobile situations, the ensemble is underspread throughout the atmosphere. These results have implications for ensemble design in terms of the role and influence of the driving ensemble in regional systems as more mobile situations will have a stronger dependence on the lateral boundary conditions. Longer term it may also imply that different strategies are required depending on the mobility of the synoptic conditions. Therefore, it argues for more consideration of “on‐demand” ensemble forecasting systems to allow a fairer representation of the uncertainty in different situations.

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Convective-Scale Perturbation Growth across the Spectrum of Convective Regimes

Cited by 30 publications

References 52 publications

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

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

Extreme rainfall sensitivity in convective‐scale ensemble modelling over Singapore

Stratification of the vertical spread‐skill relation by radiosonde drift in a convective‐scale ensemble

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