The Dependence of the Predictability of Mesoscale Convective Systems on the Horizontal Scale and Amplitude of Initial Errors in Idealized Simulations

Weyn, Jonathan A.; Durran, Dale R.

doi:10.1175/jas-d-17-0006.1

Cited by 44 publications

(46 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The bias can result in a systematic drift from large-scale patterns (Cha & Lee, 2009;Miguez-Macho et al, 2004;Vincent & Hahmann, 2015) due to significant distortion of large-scale baroclinic wave propagation (Guidard & Fischer, 2008;Miguez-Macho et al, 2004). In addition, the large-scale error can quickly propagate to all scales through a rapid error growth process (Weyn & Durran, 2017), causing small-scale convective systems to lose predictability within a short period (Durran & Weyn, 2016).…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Blending Scheme to Mitigate Large‐Scale Bias in Regional Models

Feng

Sun

Zhang

2020

J Adv Model Earth Syst

View full text Add to dashboard Cite

Several blending methods have been developed in dynamic downscaling and rapid cycled data assimilation. Blending the large‐scale part of the global model (GM) analysis or forecast has led to improvement in regional model (RM) simulations. However, in previous studies the blended waveband of the GM has generally been determined using a fixed, arbitrarily chosen cutoff wave number. Here we introduce a new dynamic blending (DB) scheme with a dynamic cutoff wave number computed according to the spectral characteristics of GM forecast quality and the spectral distribution of errors in the RM. The DB scheme is described and applied to eight‐day summertime and seven‐day wintertime cycled Weather Research and Forecasting Model forecasts over a regional domain in the continental United States. The scheme can determine a cutoff wave number with significant temporal variations. The temporal variation results from the error growth property of the RM and has a clear diurnal oscillation, suggesting that fewer (more) GM waves should be introduced into the RM at noon (night). The cutoff wave number difference between the two periods indicates seasonal variation of the cutoff wave number with larger day‐to‐day change in winter. Comparison among no blending experiment, two fixed wave number blending experiments, and two DB experiments with and without vertically varying cutoff wave number suggests that the DB scheme with vertically averaged but temporally varying cutoff wave number results in less model bias and less disturbance to the RM dynamic balance. By reducing the forecast background error, the DB scheme can potentially provide improved first guess for a rapid‐update‐cycle weather forecast system.

show abstract

Section: Introductionmentioning

confidence: 99%

A Dynamic Blending Scheme to Mitigate Large‐Scale Bias in Regional Models

Feng

Sun

Zhang

2020

J Adv Model Earth Syst

View full text Add to dashboard Cite

show abstract

“…In recently published papers, the predictability and various causes of the loss of predictability of other hazards related to severe storms, including tornadoes, heavy rainfall, severe convective winds, and tropical cyclones, have been quantified using ensemble simulations (Durran & Weyn, ; Emanuel & Zhang, ; Fernández‐González et al, ; Flora et al, ; Miglietta et al, , ; Munsell et al, ; Nielsen & Schumacher, ; Weyn & Durran, , ; Zhang et al, , ; Zhang & Tao, ). For example, the practical and intrinsic predictability of a tornadic supercell have been explored from the perspective of convection initiation time, which was strongly modulated by the evolution of the planetary boundary layer, and local topography (Zhang et al, , ).…”

Section: Introductionmentioning

confidence: 99%

“…Reducing the amplitude of the initial condition errors can improve predictability lead times, but this improvement diminishes with further reductions in the error amplitude. This suggests a limit to the intrinsic predictability of mesoscale convective systems, which are often thought to be particularly effective at transferring small‐scale perturbations to larger scales (Weyn & Durran, ; Zhang et al, ). In Zhang et al (), a tornadic supercell's intrinsic predictability limit was found to be 3–6 hr using an ensemble generated from small‐magnitude perturbations.…”

Section: Introductionmentioning

confidence: 99%

“…The difficulty of convective storm predictability in numerical weather forecasts is becoming increasingly problematic as the damage caused by hailstorms intensifies across the world (e.g., Allen & Tippett, 2015;Guan et al, 2015;Punge & Kunz, 2016). Considerable efforts have been made to explore the predictability of severe storms (e.g., Flora et al, 2018;Weyn & Durran, 2017). Ensemble forecasting is now an important component of operational forecasting and is increasingly used at convection-permitting or even higher spatial resolutions (Miltenberger et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Small initial condition errors can lead to vastly different intensity forecasts due to the presence of moist convection (Zhang & Sippel, 2009;Zhang & Tao, 2013), which suggests that, at least for some storms, the intensity forecasts may be of intrinsically limited predictability. Weyn and Durran (2017) discussed uncertainties, recast as the "butterfly effect" (Lorenz, 1969), and proposed that certain deterministic systems in the atmosphere have a finite range of predictability that cannot be extended by reducing the magnitude of initial condition errors to any value greater than zero. However, the system needs to be small in scale for the initial condition errors to constrain the intrinsic predictability of the atmosphere (Weyn & Durran, 2017).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sensitivity of Hail Precipitation to Ensembles of Uncertainties of Representative Initial Environmental Conditions From ECMWF

Fuqing

Zhang

et al. 2019

JGR Atmospheres

View full text Add to dashboard Cite

The sensitivity of hail precipitation from an idealized hailstorm to realistic environmental uncertainties was investigated through ensembles of cloud‐resolving simulations using the Weather Research and Forecasting model, with initial condition perturbations derived from the European Centre for Medium‐Range Weather Forecasting (ECMWF) operational ensemble. The analyses revealed that hail precipitation rate was very sensitive to small initial environmental perturbations, particularly in thermodynamic variables. The hail precipitation rate was significantly positively correlated with perturbations to the initial potential temperature below 750 hPa and to water vapor mixing ratio above 750 hPa. These small initial perturbations led to subsequent substantial differences in hail precipitation as well as in characteristics of the parent storm (e.g., updraft velocity, diabatic heating, and microphysical processes), all of which play a key role in hail growth. The larger sensitivity of hail precipitation to thermodynamic rather than kinematic environmental initial condition perturbations persisted even when the magnitude of the perturbations was reduced to 10% of the realistic uncertainties derived from the ECMWF ensemble. In the ensemble with reduced‐amplitude initial perturbations, there was still a moderately strong positive correlation between hail precipitation rate and the initial perturbations of the thermodynamic variables. However, these sensitivities were nonlinear, suggesting that the intrinsic predictability of hail precipitation rate may be limited, even when environmental uncertainties are reduced to 10%, 1.0 × 10−3, and 1.0 × 10−5 of the currently realistic magnitude of initial condition uncertainty.

show abstract

The scale dependence of initial‐condition sensitivities in simulations of convective systems over the southeastern United States

Weyn

Durran

2018

Quart J Royal Meteoro Soc

Self Cite

View full text Add to dashboard Cite

The sensitivity of ensemble simulations of deep convective events in the southeastern United States to initial-condition (IC) errors is examined by imposing idealized moisture perturbations at small and large scales. Four severe weather events are considered, ranging from a springtime frontal system to convection driven almost exclusively by daytime heating. Events with strong synoptic-scale forcing were insensitive to the scale of IC errors, but weakly forced events exhibited greater sensitivity to small-scale than large-scale IC errors. Additional ensemble simulations of idealized convective systems suggest that the greater sensitivity to small-scale IC errors of the weakly forced cases arises from their higher sensitivity to the strength and location of the first convective elements. Ensemble spread and predictability are characterized by two measures: the ratio of the perturbation kinetic energy (KE) about the ensemble mean to the background KE and the neighborhood-based fractions skill score (FSS) of hourly precipitation with respect to that in an unperturbed reference simulation. For simulations of both observed events and idealized convective systems, the FSS appears to be a more discriminating indicator of differences in predictability between different convective events. KEYWORDSconvective systems, error growth, initial conditions, numerical weather prediction, predictability, severe weather 1 et al., 2003;Selz and Craig, 2014;Sun and Zhang, 2016). Recent work has demonstrated, however, that relatively small initial-condition (IC) errors on much larger scales of (100) km can propagate rapidly downscale and subsequently reduce predictability in a similar manner to that produced by relatively large errors in the conditions on small scales, thereby making the identification of the actual initial scale of errors responsible for forecast degradation ambiguous (Durran et al., 2013;Durran and Gingrich, 2014;Durran and Weyn, 2016;Weyn and Durran, 2017). In particular, Weyn and Durran (2017, hereafter WD17) showed that intrinsic predictability in simulations of idealized mesoscale convective systems (MCSs) was independent of the horizontal scale of equal-absolute-amplitude IC errors across a range of different MCS types. This study seeks to answer the question of whether this scale independence is also present in Q J R Meteorol Soc. 2019;145 (Suppl. 1):57-74. wileyonlinelibrary.com/journal/qj How to cite this article: Weyn JA, Durran DR. The scale dependence of initial-condition sensitivities in simulations of convective systems over the southeastern United States. Q J R Meteorol Soc. 2019;145 (Suppl. 1):57-74. https://doi.

show abstract

The Dependence of the Predictability of Mesoscale Convective Systems on the Horizontal Scale and Amplitude of Initial Errors in Idealized Simulations

Cited by 44 publications

References 33 publications

A Dynamic Blending Scheme to Mitigate Large‐Scale Bias in Regional Models

A Dynamic Blending Scheme to Mitigate Large‐Scale Bias in Regional Models

Sensitivity of Hail Precipitation to Ensembles of Uncertainties of Representative Initial Environmental Conditions From ECMWF

The scale dependence of initial‐condition sensitivities in simulations of convective systems over the southeastern United States

Contact Info

Product

Resources

About