Daiwen Kang Computer Science Corporation, Research Triangle Park, NC, USA

1. Evaluation of Air Quality Models Using the DISCOVER-AQ Field Measurements - Tool development and examples Daiwen Kang Computer Science Corporation, Research Triangle Park, NC, USA Wyat Appel, Pat Dolwick, Norm Possiel, Shawn Roselle, James Godowitch, Jon Pleim, and Rohit Mathur U.S. Environmental Protection Agency, Research Triangle Park, NC, USA

3. Motivations and Objectives • The Deriving Information on Surface Conditions from COlumn and VERtically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) field campaigns have collected and maintained integrated and diverse data sets • The data sets are collected from different measurement platforms with different spatial and temporal resolutions and from different research institutes and laboratories . • The files are reported in ICARTT format, however, the specific information is free-style: species names, units, unit names, location information, time information, etc. • Use these rich data sets to evaluate the air quality model performance in space and time • Bring in all the measurement data and model output data into one central venue • Integrate the various measurement data together into the same space and time with model when possible

4. Model Evaluation ProcessesTraditional Way OBS Model Output Site-cmp Post-proc for overlay Prg for overlay IOAPI file (hourly, mx8hr, etc) IOAPI file (hourly, mx8hr, etc) Paired data (hourly) Overlay plots Statistic Package Stats and plots PAVE

5. Model Evaluation ProcessesNew Development OBS (standard or non-standard) Model Output (CTM, MET, etc) R Package Stats and plots (traditional) Optional Paired data (hourly, specially formed, etc) Overlay plots Various plots (spatial-temporal overlay plots, specially formed plots, etc.)

6. Dealing with the data • Object-oriented design concept to deal with the data • Data are objects which have attributes: location (latitude, longitude, altitude), time (start-time, end-time), variables (names and units) • Pattern matching to identify different expressions of the attributes • Centralized read function to read the various data sets through attributes

7. Pairing Observation/Model Data • Bringing in the model domain information: Latitude, longitude, layer height (time-dependent) • Pairing the location and time attributes with the same model attributes • Extract the model data by the paired location and time attributes

8. Status and Features of the Development • Functions have been developed and tested to read the following observational data sets: • AQS, AIRNOW, CASTNET, IMPROVE, ICARTT • With minor modifications, other observation data formats could be easily processed • e.g. satellite data • Model outputs in different format can be easily adapted • preliminary test was done for OLAM h5 format • Functions are developed to process model output for special purposes • e.g. sub-grid analysis

9. Aircraft Vertical Profile - Ozone Examine the vertical distributions of ozone from the flight measurements and model predications for all data for a specific spiral.

10. Aircraft Vertical Profile - NOY The same can be done for all precursor species.

11. Aircraft-Model Overlay - Ozone

12. Model to Model Comparison with Overlay Plots

13. Aircraft Overlay Time Series – Ozone • Put all the time-vertical data together and pinpoint the possible performance problems at the time and layers from a broad view • When comparing two different models or model runs), easily identify where and when the differences occur

14. Comparing with Ship Measurements The model performed well over waters shown by the ship measurement

15. Ozone Sonde An overview of all the ozone sonde launches at a location over time to examine the days to examine aloft model performance.

16. Overlay Surface or Upper Layers Ship OBS At the surface, AQS over the land and ship measurements over water shown together.

17. Overlay from surface to upper layer: upper layer for P3B and UMD UMD OBS P3B OBS At upper layer (7) two aircrafts (P3B and UMD) were making measurement during this same time and in the same model layer over the same region.

18. Put data together at a cross-section by time The available observations: hourly ground measurements during the day, P3B measurements, and ozone sonde measurements (2 launches). The Mdout is matching the sonde data with the model grid cells when the sonde shifted away at above from the launching grid cell.

19. Summary • A model evaluation package using non-standard (and standard) observations is under development by bringing all the model outputs and diverse observational data together to produce various statistics and plots • Utilize the rich field campaign data in time and space to perform comprehensive model evaluations to identify potential areas for model improvement.

20. Future Plan • Since the analysis part in AMET was developed using R, this package should be easily integrated into AMET. • In addition, the data processing step (site-cmp) used in AMET may be replaced by the functions developed in this work.