The Impact of Large-Scale Forcing on Skill of Simulated Convective Initiation and Upscale Evolution with Convection-Allowing Grid Spacings in the WRF*

Duda, Jeffrey D.; Gallus, William A.

doi:10.1175/waf-d-13-00005.1

Cited by 62 publications

(47 citation statements)

References 30 publications

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“…Recently, Duda and Gallus (2013) investigated whether there is a relationship between the accuracy of convection-allowing forecasts of convection initiation and evolution and the strength of the large-scale forcing. Their 36 cases were classified according to several measures of quasigeostrophic forcing for ascent and 700-hPa omega and 200-hPa divergence.…”

Section: A Backgroundmentioning

confidence: 99%

“…Similarly to Duda and Gallus (2013), we consider large-scale forcing to be limited to quasigeostrophic (QG) forcing for ascent [as proposed by Doswell (1987)]. According to the QG omega equation, the vertical motion is directly affected by the differential vorticity advection and by the temperature advection by the geostrophic wind (Bluestein 1992).…”

Section: B Classification Based On the Large-scale Forcing For Ascentmentioning

confidence: 99%

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The Case-to-Case Variability of the Predictability of Precipitation by a Storm-Scale Ensemble Forecasting System

Surcel

Zawadzki

Yau

2015

Monthly Weather Review

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This paper analyzes the case-to-case variability of the predictability of precipitation by a storm-scale ensemble forecasting (SSEF) system. Relationships are sought between ensemble spread and quantitative precipitation forecast (QPF) skill, and the characteristics of an event, such as the strength of the quasigeostrophic forcing for ascent, the presence of convective equilibrium, and the spatial extent of the precipitation system. It is found that most of the case-to-case variability of predictability is explained by the spatial coverage of the system. The relationship between convection and large-scale forcing seems to affect predictability mostly during the afternoon hours. While the relationships are weak for the entire dataset, two distinct types of cases are identified: widespread and diurnally forced cases. The loss of predictability at small scales, the effect of the radar data assimilation, and the comparison between forecasts from the SSEF and Lagrangian persistence forecasts are analyzed separately for these two types of cases. Despite overall predictability being better than average for the widespread cases, the loss of predictability with forecast time and spatial scale is just as rapid as for the other cases. For the diurnally forced cases, the radar data assimilation causes larger differences between the precipitation fields corresponding to the assimilating and nonassimilating members than for the widespread cases. However, the effect of radar data assimilation on QPF skill is similar for both types of cases. Also, for the diurnal cases, the models with radar data assimilation outperform very rapidly (after 2 h) the Lagrangian persistence forecasts.

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Section: A Backgroundmentioning

confidence: 99%

Section: B Classification Based On the Large-scale Forcing For Ascentmentioning

confidence: 99%

The Case-to-Case Variability of the Predictability of Precipitation by a Storm-Scale Ensemble Forecasting System

Surcel

Zawadzki

Yau

2015

Monthly Weather Review

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“…Finally, Duda and Gallus (2013) used MODE to diagnose the skill of 3-km grid-spacing WRF model simulations at predicting the convective initiation and upscale evolution of convection into MCSs.…”

Section: Introductionmentioning

confidence: 99%

Application of Object-Based Time-Domain Diagnostics for Tracking Precipitation Systems in Convection-Allowing Models

Clark

Bullock²,

Jensen³

et al. 2014

Weather and Forecasting

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Meaningful verification and evaluation of convection-allowing models requires approaches that do not rely on point-to-point matches of forecast and observed fields. In this study, one such approach-a beta version of the Method for Object-Based Diagnostic Evaluation (MODE) that incorporates the time dimension [known as MODE time-domain (MODE-TD)]-was applied to 30-h precipitation forecasts from four 4-km grid-spacing members of the 2010 Storm-Scale Ensemble Forecast system with different microphysics parameterizations. Including time in MODE-TD provides information on rainfall system evolution like lifetime, timing of initiation and dissipation, and translation.The simulations depicted the spatial distribution of time-domain precipitation objects across the United States quite well. However, all simulations overpredicted the number of objects, with the Thompson microphysics scheme overpredicting the most and the Morrison method the least. For the smallest smoothing radius and rainfall threshold used to define objects [8 km and 0.10 in. (1 in. 5 2.54 cm), respectively], the most common object duration was 3 h in both models and observations. With an increased smoothing radius and rainfall threshold, the most common duration became shorter. The simulations depicted the diurnal cycle of object frequencies well, but overpredicted object frequencies uniformly across all forecast hours. The simulations had spurious maxima in initiating objects at the beginning of the forecast and a corresponding spurious maximum in dissipating objects slightly later. Examining average object velocities, a slow bias was found in the simulations, which was most pronounced in the Thompson member. These findings should aid users and developers of convection-allowing models and motivate future work utilizing time-domain methods for verifying high-resolution forecasts.

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“…Schwartz et al (2010) consider circular neighborhoods to calculate the field of fractions at each grid point and then produce probabilistic guidance using the field of fractions as a neighborhood probability. Duda and Gallus (2013) also use the circular neighborhood approach, verifying the precipitation of mesoscale convective systems. In this paper the FSS is considered over a square neighborhood as detailed in Roberts and Lean (2008) and Roberts (2008).…”

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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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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The Impact of Large-Scale Forcing on Skill of Simulated Convective Initiation and Upscale Evolution with Convection-Allowing Grid Spacings in the WRF*

Cited by 62 publications

References 30 publications

The Case-to-Case Variability of the Predictability of Precipitation by a Storm-Scale Ensemble Forecasting System

The Case-to-Case Variability of the Predictability of Precipitation by a Storm-Scale Ensemble Forecasting System

Application of Object-Based Time-Domain Diagnostics for Tracking Precipitation Systems in Convection-Allowing Models

A Spatial View of Ensemble Spread in Convection Permitting Ensembles

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