Uncertainty in the area-related QPF for heavy convective precipitation

Řezáčová, Daniela; Zacharov, Petr; Sokol, Zbyněk

doi:10.1016/j.atmosres.2008.12.005

Cited by 27 publications

(17 citation statements)

References 15 publications

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“…However, as different physical behavior is apparent at different spatial scales (e.g., as shown in Roberts 2008) it is informative also to investigate how the ensemble spread varies with spatial scale, which is the subject of this paper. Whereas Rezacova et al (2009) and Zacharov and Rezacova (2009) only consider comparisons between perturbed ensemble members and the control, in this paper the FSS between all independent member-member pairs is considered. Considering all members in this manner is the best representation of total spread as it includes fully the intermember variability and does not rely on the ensemble mean, which is known to lie outside the model manifold (Ancell 2013).…”

Section: Introductionmentioning

confidence: 99%

“…This is useful for providing an overview of model performance but does not provide information regarding the spread-skill relationship of the ensemble or the spatial differences between individual pairs of ensemble members. Rezacova et al (2009) use the FSS to calculate the ensemble spread-skill relationship with the ensemble skill calculated from the FSS between ensemble memberradar comparisons and the ensemble spread from the FSS between perturbed ensemble members and the ensemble control. Following on from this, Zacharov and Rezacova (2009) determine a relationship between the FSS estimates of ensemble spread and skill and use this to predict the ensemble skill given the spread.…”

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: Introductionmentioning

confidence: 99%

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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“…Given this dependence on spatial scale, methods have also been developed to evaluate the differences between ensemble member forecasts, a measure of the ensemble spread, at different spatial scales. In particular, Johnson et al (2014) used wavelet decomposition to investigate perturbation growth, Surcel et al (2014) used spectral decomposition to investigate the filtering properties of the ensemble mean and Rezacova et al (2009), Zacharov and Rezacova (2009), Duc et al (2013) and Dey et al (2014) used the fractions skill score (FSS: Roberts and Lean, 2008;Roberts, 2008) to develop a neighbourhood-based approach to calculate the ensemble spread and skill spatially.…”

Section: Introductionmentioning

confidence: 99%

A new method for the characterization and verification of local spatial predictability for convective‐scale ensembles

Dey

Roberts

Plant

et al. 2016

Quart J Royal Meteoro Soc

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The use of kilometre‐scale ensembles in operational weather forecasting provides new challenges for forecast interpretation and evaluation to account for uncertainty on the convective scale. A new neighbourhood‐based method is presented for evaluating and characterizing the local predictability variations from convective‐scale ensembles. Spatial scales over which ensemble forecasts agree (agreement scales, SA) are calculated at each grid point ij, providing a map of the spatial agreement between forecasts. By comparing the average agreement scale obtained from ensemble member pairs (SijA(falsemm¯)) with that between members and radar observations (SijA(falsemo¯)), this approach allows the location‐dependent spatial spread–skill relationship of the ensemble to be assessed. The properties of the agreement scales are demonstrated using an idealized experiment. To demonstrate the methods in an operational context, SijA(falsemm¯) and SijA(falsemo¯) are calculated for six convective cases run with the Met Office UK Ensemble Prediction System (MOGREPS‐UK). SijA(falsemm¯) highlights predictability differences between cases, which can be linked to physical processes. Maps of SijA(falsemm¯) are found to summarize the spatial predictability in a compact and physically meaningful manner that is useful for forecasting and model interpretation. Comparison of SijA(falsemm¯) and SijA(falsemo¯) demonstrates the case‐by‐case and temporal variability of the spatial spread–skill, which can again be linked to physical processes.

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“…The results were obtained for four convective events which occurred at the territory of the Czech Republic (CR). The events, which produced heavy local rainfalls and caused local flash floods, are described in Rezacova et al (2009). The radar data fields of individual cases prove different area extents and structures.…”

Section: Introductionmentioning

confidence: 99%

The effect of radar-based QPE on the Fractions Skill Score used at the QPF verification

Zacharov

Řezáčová

2010

Adv. Geosci.

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Abstract. In this paper we show the influence of gauge adjustment technique, applied to radar-based rainfalls, on the results of QPF verification. The results were obtained for four convective events which produced heavy local rainfalls and caused local flash floods at the Czech territory. Numerical weather prediction model COSMO was run to obtain rainfall forecast and Fractions Skill Score was employed in the QPF verification. Three different radar-based quantitative precipitation estimates (QPE) were used for the verification and the verification results were compared. The QPE data sets consisted of: (a) raw radar-based rainfall values, (b) gauge corrected radar-based rainfalls with a simple domain-wide correction, and (c) radar-based rainfalls with a pixel related gauge adjustment. The results indicate small difference in area-related verification results and prove that the simple domain wide correction technique is sufficient for applying radar-based rainfalls as the verification data.

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Uncertainty in the area-related QPF for heavy convective precipitation

Cited by 27 publications

References 15 publications

A Spatial View of Ensemble Spread in Convection Permitting Ensembles

A Spatial View of Ensemble Spread in Convection Permitting Ensembles

A new method for the characterization and verification of local spatial predictability for convective‐scale ensembles

The effect of radar-based QPE on the Fractions Skill Score used at the QPF verification

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