A statistical methodology for the evaluation of long-range dispersion models

Mosca, Sonia; Graziani, Gabriela; Klug, W.; Bellasio, Roberto; Bianconi, Roberto

doi:10.1016/s1352-2310(98)00179-4

Cited by 102 publications

(65 citation statements)

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“…In this table global values of a number of parameters are given in order to compare the statistical performances of the three numerical simulations performed as described above. These statistical parameters include the normalised Graziani et al (1998) and Mosca et al (1998) mean square error (NMSE), the bias, the Pearson correlation, the figure of merit in time (FMT), the fractional bias and standard deviation, the fractions of calculated values within a factor two (FA2) and five (FA5) of the measured values, and the fraction of predictions exceeding the corresponding observations (FOEX) ranging from −50% to +50%.…”

Section: Resultsmentioning

confidence: 99%

Pollutant transport schemes integrated in a numerical weather prediction model: model description and verification results

Chenevez

Baklanov

Sørensen

2004

Meteorological Applications

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The operational version of DMI-HIRLAM using well-known basic advection schemes has been tested, showing the need for improvements for air pollution purposes. The fourth-order Bott algorithm for advection has thus been implemented in the semi-Lagrangian version of DMI-HIRLAM. Due to its practically perfect mass conservation properties and its low computational cost, this method turns out to be efficient for simulations of transport of pollutants in the atmosphere. This represents a first effort towards a fully integrated air pollution model, e.g. for forecasting purposes.

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Section: Resultsmentioning

confidence: 99%

Pollutant transport schemes integrated in a numerical weather prediction model: model description and verification results

Chenevez

Baklanov

Sørensen

2004

Meteorological Applications

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“…These typically include, the normalized mean square error (NMSE), the fraction of predictions within a factor of 2 of the observations (FAC2), the fractional bias (FB), the geometric mean bias (MG) and the geometric variance (VG) (see Appendix A for definitions). In the absence of any universally agreed performance criteria, when authors wish to compare their results to those of others, they generally cite the criteria for an 'acceptable model' proposed by Chang and Hanna (e.g., [16][17][18][19][20][21][22]), which are summarized in Table 2. The Chang and Hanna criteria were based on their experience in conducting a large number of model evaluation exercises [23].…”

Section: Performance Metricsmentioning

confidence: 99%

A Review of Methodology for Evaluating the Performance of Atmospheric Transport and Dispersion Models and Suggested Protocol for Providing More Informative Results

Herring

Huq

2018

Fluids

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Many models exist for predicting the atmospheric transport and dispersion of material following its release into the atmosphere. The purpose of these models may be to support air quality assessments and/or to predict the hazard resulting from releases of harmful materials to inform emergency response actions. In either case it is essential that the user understands the level of predictive accuracy that might be expected. However, contrary to expectation, this is not easily determined from published comparisons of model predictions against data from dispersion experiments. The paper presents and reviews the methods adopted and issues involved in comparing the predictive performance of atmospheric transport and dispersion models to experimental data, by reference to a number of experimental data sets and comparison results. It then presents an approach which is designed to make the performance of atmospheric dispersion models more transparent, through clearly defining the basis on which the comparison is made, and comparing the performance of the chosen model to that of a reference model. Such an approach establishes a clear baseline against which the accuracy of models can be evaluated and the performance benefits of more sophisticated approaches quantified. The use of a simple analytic reference model applicable to continuous ground level releases in open terrain and urban areas is shown as a proof-of-principle.

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“…To be consistent with our implementation of SAL, the spatial ash coverage is computed only for forecast ACL fields exceeding a threshold of 0.2 g m -2 . However, it is worth mentioning that a low score could also suggest two similar shapes shifted in space (Mosca et al, 1998) and, therefore, should be used 10 together with the SAL score.…”

Section: Categorical Evaluation Scoresmentioning

confidence: 99%

Volcanic ash modeling with the NMMB-MONARCH-ASH model: quantification of off-line modeling errors

Marti¹,

Folch²

2017

Preprint

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Abstract.Volcanic ash modeling systems are used to simulate the atmospheric dispersion of volcanic ash and to generate forecasts that quantify the impacts from volcanic eruptions on infrastructures, air quality, 10 aviation, and climate. The efficiency of response and mitigation actions is directly associated to the accuracy of the volcanic ash cloud detection and modeling systems. Operational forecasts build on offline coupled modeling systems where meteorological variables are updated at the specified coupling intervals. Despite the concerns from other communities regarding the accuracy of this strategy, the quantification of the systematic errors and shortcomings associated to the off-line modeling systems has 15 received no attention. This paper employs the NMMB-MONARCH-ASH model to quantify these errors by employing different quantitative and categorical evaluation scores. The skills of the off-line coupling strategy are compared against those from an on-line forecast considered to be the best estimate of the true outcome. Case studies are considered for a synthetic eruption with constant eruption source parameters and for two historical events, which suitably illustrate the severe aviation disruptive effects 20 of European (2010 Eyjafjallajökull) and South-American (2011 Cordón Caulle) volcanic eruptions. Evaluation scores indicate that systematic errors credited by off-line modeling are of the same order of magnitude as those associated to the source term uncertainties. In particular, traditional off-line forecasts employed in operational model setups can result in significant uncertainties, failing to reproduce, in the worst cases, up to 45-70% of the ash cloud of an on-line forecast. These 25 inconsistencies are anticipated to be even more relevant in scenarios where the meteorological conditions change rapidly in time. The outcome of this paper encourages operational groups responsible for real-time advisories for aviation to consider employing computationally efficient on-line dispersal models. 30Atmos. Chem. Phys. Discuss.,

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A statistical methodology for the evaluation of long-range dispersion models

Cited by 102 publications

References 1 publication

Pollutant transport schemes integrated in a numerical weather prediction model: model description and verification results

Pollutant transport schemes integrated in a numerical weather prediction model: model description and verification results

A Review of Methodology for Evaluating the Performance of Atmospheric Transport and Dispersion Models and Suggested Protocol for Providing More Informative Results

Volcanic ash modeling with the NMMB-MONARCH-ASH model: quantification of off-line modeling errors

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