1 / 24

Model Performance Evaluation for San Bernardino Mountains (SBM) Monitoring Sites

Model Performance Evaluation for San Bernardino Mountains (SBM) Monitoring Sites. Gail Tonnesen, Chao-Jung Chien, Zion Wang, Mohammad Omary CE-CERT, UCR April 25, 2006. Objectives.

roland
Download Presentation

Model Performance Evaluation for San Bernardino Mountains (SBM) Monitoring Sites

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Model Performance Evaluation for San Bernardino Mountains (SBM) Monitoring Sites Gail Tonnesen, Chao-Jung Chien, Zion Wang, Mohammad OmaryCE-CERT, UCRApril 25, 2006

  2. Objectives • UCR Fire Lab has long term HNO3 data in Western Riverside County that can be used in air quality model evaluations. • CE-CERT models have been previously evaluated using several ambient data networks but N species data is sparse in Riverside County. • Goal: Compare CE-CERT model predictions with Fire Lab N data.

  3. SBM monitoring data • ~11 monitoring stations being evaluated (data from UCR Fire Lab). • 2-week long sampling periods from April through October in 2002; total of 11 periods. • Gaseous HNO3 (ug/m3) data.

  4. Air Quality Modeling Results • Modeling system: CMAQ, MM5, SMOKE. • Case 1: WRAP 2002 Base C annual run: • Modeling domain: US continent; 36km grid spacing, 148 x 112 grid cells. • Case 2: Ndep 2002 base annual run: • Modeling domain: State of California; 4km grid spacing, 144 x 225 grid cells.

  5. Selected Summary Statistical Results

  6. All Days at Each Site Model to Data Comparisons

  7. All Sites and All Days Comparisons36kwrap vs. 4kndep

  8. Time series and scatter plots for all days at site: AO 36kwrap vs. 4kndep Amb. vs.36kwrap vs. 4kndep

  9. Time series and scatter plots for all days at site: BF 36kwrap vs. 4kndep Amb. vs.36kwrap vs. 4kndep

  10. Time series and scatter plots for all days at site: BP 36kwrap vs. 4kndep Amb. vs.36kwrap vs. 4kndep

  11. Time series and scatter plots for all days at site: CP 36kwrap vs. 4kndep Amb. vs.36kwrap vs. 4kndep

  12. Time series and scatter plots for all days at site: GV 36kwrap vs. 4kndep Amb. vs.36kwrap vs. 4kndep

  13. Time series and scatter plots for all days at site: HP 36kwrap vs. 4kndep Amb. vs.36kwrap vs. 4kndep

  14. Time series and scatter plots for all days at site: HV 36kwrap vs. 4kndep Amb. vs.36kwrap vs. 4kndep

  15. Time series and scatter plots for all days at site: KP 36kwrap vs. 4kndep Amb. vs.36kwrap vs. 4kndep

  16. Time series and scatter plots for all days at site: MC 36kwrap vs. 4kndep Amb. vs.36kwrap vs. 4kndep

  17. Time series and scatter plots for all days at site: OS 36kwrap vs. 4kndep Amb. vs.36kwrap vs. 4kndep

  18. Time series and scatter plots for all days at site: SP 36kwrap vs. 4kndep Amb. vs.36kwrap vs. 4kndep

  19. Spatial Overlay Plots(SBM data shown in diamonds)

  20. Summary • Model performance is reasonably good compared to other model evaluation studies: • longer term averages of model and data tend to show better performance than do hourly or daily comparisons. • Better model performance with 4km grid. • The data has more spatial variability than does the model, even at the finer 4km grid: • use of finer grid and better land use data might improve the model spatial resolution.

  21. Conclusions • The model performs reasonably well, and these model results can be used in future analyses, however, additional effort should be made to improve the accuracy and spatial resolution of the model predictions.

More Related