A tool to evaluate air quality model performances in regulatory applications

This paper describes the details of the DELTA Tool and Benchmarking service for air quality models, recently developed in the framework of FAIRMODE (Forum for Air Quality Modelling in Europe). One of the main objectives of the FAIRMODE activities is the development of a procedure for the evaluation and benchmarking of air quality modelling applications for regulatory purposes. The DELTA Tool is a specific software which provides summary statistics (i.e. BIAS, RMSE, correlation coefficient) as well as scatter-plots, time series plots, Taylor, Target and other diagrams providing an overview of the quality of model results with respect to monitored data. Moreover, the benchmarking service implemented in DELTA produces summary reports containing performance indicators related to a given model application in the frame of the EU Air Quality Directive (AQD, 2008). This work describes the structure of the DELTA tool and template for reporting model performances. Some examples of application are also briefly presented.

[1]  David Carruthers,et al.  Performance criteria for the benchmarking of air quality model regulatory applications: the ‘target’ approach , 2012 .

[2]  K. Taylor Summarizing multiple aspects of model performance in a single diagram , 2001 .

[3]  John Irwin,et al.  A framework for evaluating regional-scale numerical photochemical modeling systems , 2010, Environmental fluid mechanics.

[4]  Robert E. Davis,et al.  Statistics for the evaluation and comparison of models , 1985 .

[5]  Alexis Zubrow,et al.  Overview of the atmospheric model evaluation tool (AMET) v1.1 for evaluating meteorological and air quality models , 2011, Environ. Model. Softw..

[6]  R. Vautard,et al.  CityDelta: A model intercomparison study to explore the impact of emission reductions in European cities in 2010 , 2007 .

[7]  A. Russell,et al.  PM and light extinction model performance metrics, goals, and criteria for three-dimensional air quality models , 2006 .

[8]  Anthony J. Jakeman,et al.  Ten iterative steps in development and evaluation of environmental models , 2006, Environ. Model. Softw..

[9]  Gail S. Tonnesen,et al.  CMAQ/CAMx annual 2002 performance evaluation over the eastern US , 2006 .

[10]  Claudio Carnevale,et al.  Design and validation of a multiphase 3D model to simulate tropospheric pollution. , 2008 .

[11]  R. Sokhi,et al.  Evaluation of a CMAQ simulation at high resolution over the UK for the calendar year 2003 , 2010 .

[12]  Philippe Thunis,et al.  Analysis of model responses to emission-reduction scenarios within the CityDelta project , 2007 .

[13]  M. Sofiev,et al.  Ensemble dispersion forecasting—Part I: concept, approach and indicators , 2004 .

[14]  Jenise L. Swall,et al.  A procedure for inter-comparing the skill of regional-scale air quality model simulations of daily maximum 8-h ozone concentrations , 2008 .

[15]  Karl Ropkins,et al.  openair - An R package for air quality data analysis , 2012, Environ. Model. Softw..

[16]  S. Hanna,et al.  Air quality model performance evaluation , 2004 .

[17]  Luis Samaniego,et al.  Technical assessment and evaluation of environmental models and software: Letter to the Editor , 2011, Environ. Model. Softw..

[18]  C. Stow,et al.  Skill Assessment for Coupled Biological/Physical Models of Marine Systems. , 2009, Journal of marine systems : journal of the European Association of Marine Sciences and Techniques.

[19]  P. Thunis,et al.  The Impact of Meteorology on Air Quality Simulations over the Po Valley in Northern Italy , 2011 .

[20]  R Bellasio,et al.  Real-time monitoring data for real-time multi-model validation: coupling ENSEMBLE and EURDEP. , 2008, Journal of environmental radioactivity.

[21]  Silvia Curteanu,et al.  Ten steps modeling of electrolysis processes by using neural networks , 2010, Environ. Model. Softw..