THE MUST MODEL EVALUATION EXERCISE: STATISTICAL ANALYSIS OF MODELLING RESULTS

The first validation exercise of the COST action 732 lead to a substantial number of simulation results for comparison with the MUST wind tunnel experiments. Validation metrics for selected simulation results of the flow field and the concentrations are presented and compared to the state of the art. In addition mean metrics and corresponding scatter limits are computed from the individual results

THE MUST MODEL EVALUATION EXERCISE: PATTERNS IN MODEL PERFORMANCE

As part of the COST 732 action more than a dozen different research groups have modelled the MUST experiment, as simulated in a wind tunnel. The model evaluation guidance developed within COST 732 recommends \u27exploratory data analysis\u27 as one of the elements in model validation. Experience has shown that such exploratory analysis is crucial to reveal shortcomings of models that might otherwise pass unnoticed. Conditions are best for detecting common patterns and anomalies if you have a situation where several models are put into a common framework – like the case at hand. The available material provides a unique opportunity to identify and explore patterns within model performance

The must model evaluation exercise: statistical analysis of modelling results

The first validation exercise of the COST action 732 lead to a substantial number of simulation results for comparison with the MUST wind tunnel experiments. Validation metrics for selected simulation results of the flow field and the concentrations are presented and compared to the state of the art. In addition mean metrics and corresponding scatter limits are computed from the individual results

The MUST model evaluation exercise: Patterns in model performance

As part of the COST 732 action more than a dozen different research groups have modelled the MUST experiment, as simulated in a wind tunnel. The model evaluation guidance developed within COST 732 recommends 'exploratory data analysis'as one of the elements in model validation. Experience has shown that such exploratory analysis is crucial to reveal shortcomings of models that might otherwise pass unnoticed. Conditions are best for detecting common patterns and anomalies if you have a situation where several models are put into a common framework - like the case at hand. The available material provides a unique opportunity to identify and explore patterns within model performance

The must model evaluation exercise: statistical analysis of modelling results

The first validation exercise of the COST action 732 lead to a substantial number of simulation results for comparison with the MUST wind tunnel experiments. Validation metrics for selected simulation results of the flow field and the concentrations are presented and compared to the state of the art. In addition mean metrics and corresponding scatter limits are computed from the individual results

The MUST model evaluation exercise: Patterns in model performance

As part of the COST 732 action more than a dozen different research groups have modelled the MUST experiment, as simulated in a wind tunnel. The model evaluation guidance developed within COST 732 recommends 'exploratory data analysis'as one of the elements in model validation. Experience has shown that such exploratory analysis is crucial to reveal shortcomings of models that might otherwise pass unnoticed. Conditions are best for detecting common patterns and anomalies if you have a situation where several models are put into a common framework - like the case at hand. The available material provides a unique opportunity to identify and explore patterns within model performance