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Statistics- Definitions and Issues; Deriving “Unbiased Symmetric” Metrics

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.

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Statistics- Definitions and Issues; Deriving “Unbiased Symmetric” Metrics

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  1. 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

  2. Introduction

  3. 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

  4. Symmetry: overprediction and underprediction are treated proportionately

  5. BNMBF: symmetry , (Range) -∞ to +∞, + is overprediction – is underprediction

  6. ENMEF: 0 to +∞

  7. 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

  8. Test of Metrics

  9. Test of Metrics (Continued) :11 models from IPCC (2001) (nss-SO42-)

  10. 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)

  11. Application of new Metrics for CMAQ evaluation Jan. 8 to Feb. 18, 2002

  12. Application of new Metrics (Continued) Jan. 8 to Feb. 18, 2002

  13. Contacts: Brian K. Eder email: eder@hpcc.epa.gov www.arl.noaa.gov/ www.epa.gov/asmdnerl

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