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This study explores the usage of unbiased symmetric metrics in evaluating air quality modeling, focusing on the BNMBF and ENMEF metrics. The metrics aim to address overprediction and underprediction in models and ensure impartial evaluations. Through rigorous testing with 11 models, this research seeks to enhance the accuracy and reliability of air quality assessments. For more details on this study, contact Brian K. Eder at eder@hpcc.epa.gov.
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Statistics- Definitions and Issues; Deriving “Unbiased Symmetric” Metrics Shaocai Yu*, Brian Eder*++, Robin Dennis*++, Shao-Hang Chu**, Stephen Schwartz** *Atmospheric Sciences Modeling Division, NERL ** Office of Air Quality Planning and Standards U.S. EPA, RTP, NC 27711. ***Brookhaven National Laboratory, Upton, NY 11973 ++ On assignment from Air Resources Laboratory, NOAA
CMAQ Community Multiscale Air QualityModel • Community Model • Multiscale • consistent model structures for interaction of urban through Continental scales • Multi-pollutant • ozone, speciated particulate matter, visibility, acid deposition • and air toxics
Symmetry: overprediction and underprediction are treated proportionately
BNMBF: symmetry , (Range) -∞ to +∞, + is overprediction – is underprediction
Unbiased: avoid undue influence of small numbers in denominator BNMBF:result of sum of indiv. factor bias with obs (or model) conc. as a weighting function
Test of Metrics (Continued) :11 models from IPCC (2001) (nss-SO42-)
Test of Metrics (Continued) :11 models for nss-SO42- • Model H: best; Model A: worst • Models E, G, H: acceptable • If criteria: ±25% (BNMBF), 35% (ENMEF)
Application of new Metrics for CMAQ evaluation Jan. 8 to Feb. 18, 2002
Application of new Metrics (Continued) Jan. 8 to Feb. 18, 2002
Contacts: Brian K. Eder email: eder@hpcc.epa.gov www.arl.noaa.gov/ www.epa.gov/asmdnerl