Summary and future improvement

With pyPASS, modelers can do a more comprehensive analyses outlined in MPE procedures mandated by the PROMPT approach. Modelers can holistically identify which parts of the model need more attention because pyPASS provides a variety of information based on all available data. In summary, pyPASS can help enhance the quality of MPE practices for SIP modeling:

x pyPASS creates information that is necessary for a PROMPT-like MPE that could otherwise not be available with existing tools; modelers can view information including geographical features in more integrated ways than past MPE practices.

x It improves the quality of information used in MPE for SIP modeling; the resolution of data is preserved to prevent ‘dequantification’ of graphical measures and all graphical measures are designed to convey focused information specifically for MPE.

x It reduces the resource demands to conduct MPE practices; selective data extraction permit evaluators to carry observational data and several modeling results in a personal computer. Object-oriented design of pyPASS graphics ensures good computational performance and reusability of codes. Additionally, the on-line file compression decreases the storage use more than 30 %.

x The user interface for operation offers efficient information production; users can do various comparative analyses such as observation vs. a single simulation, observation vs. multiple simulations, or a base simulation vs. multiple sensitivity runs efficiently with sets of “run-time options”.

x It utilizes observations from non-routine ground monitors; aircraft observation can be compared with model predictions and users can match the model’s data resolution with the observational data.

x It provides more guided information, as shown in our illustrative example, to clarify the priority of MPE tasks; PROMPT-like MPE emphasizes “progressive” analyses that require well-defined sequence of evaluation procedures. Because pyPASS was designed to support PROMPT-like MPE, pyPASS can help modelers implement their advanced MPE effectively.

x It ensures flexibility in improving modules; all source codes are open to public and the cost of using pyPASS is very low or free. Thus, any peer-reviewer can examine possible issues in codes and modify source code to improve pyPASS.

3.5.2 Future improvement

Even though pyPASS has many advantages over traditional MPE tools, the current pyPASS can still be improved to perform more extensive analyses. Following is the list of planned improvements for the next version of pyPASS.

First, there are increasing attention to the use of ‘probes’ such as O₃-to-NO_X for diagnostic model performance investigation (Arnold et al., 2003). These probes can provide diagnostic information more than plain concentration plots. Displaying these probes from observation and model predictions is not yet implemented in the current version of pyPASS. It would also be desirable to support various command-line computations such as calculating the differences of two model simulations. These functionalities can be realized easily since Python provides important libraries such as a parser.

Second, the current version of pyPASS has a limit in the type of high resolution measurements it can use. For example, it does not handle profiler observation and Lidar (Light Detection and Ranging). These types of observation have great potential to increase the quality of MPE by providing information aloft in detail. The most difficult issue in implementing pyPASS graphics to utilize these non-routine data are that the spatial and temporal resolutions of the observations. These vary depending on specific measurement techniques with sparse documentation on how to report the observed data properly.

Significant amount of studies may need to be conducted to use high resolution data properly for SIP MPE. For example, selecting model grid cells for visualization of vertical slices is quite challenging. Additionally, a good interpolation algorithm is needed for these high resolution data points.

Third,pyPASS is currently unable to incorporate emission information. A graphical measure for emission input inspection can be produced such as tile plots of emission

intensities from emission input files directly. Then, we can overlay transparent hodograms at monitor sites on emission plots to evaluate potentially problematic locations due to biases of predicted surface winds in the model without actually running simulations.

Fourth, the current aircraft plots lack information with regard to VOCs. A major issue in observed vs. predicted VOCs comparison is that canisters sampling times are longer than the time required by aircrafts moving across model grid cells. Solution to this issue is under investigation.

Fifth, we need an improved visualization of statistical measures so that modelers can better interpret the statistical measures with pyPASS standard graphics. Additionally, we

only estimate these statistical measures for each site at present, but sub-region analyses can provide more insights in spatial biases of model predictions.

Last, we need to develop a pyPASS module to embed texts in PNG files directly. Even thoughPNG format supports embedding of metadata in the ASCII format or some values such as statistical measures corresponding to graphs in PNG files, the direct text writing in PNG functionality is not yet implemented in pyPASS. Also, displaying those texts with proper formats will improve the amount and quality of information carried with a pyPASS plot. Users could examine and acquire graphical information, statistical information, and information metadata from a single graphic file. Currently, users may utilize external software such as ImageMagick, but it is desirable to implement the comment addition functionality in the future version of pyPASS.

Acknowledgement

This project was partially funded by the Houston Advanced Research Center through Contract H12 and partially funded by the 8-Hr Coalition through Contract HC1. We wish to thank Dr. Jim Smith at Texas Commission on Environmental Quality for sharing modeling inputs and outputs of TCEQ’s HGMCR modeling for 2000 and Dr. Daewon Byun at the University of Houston for files of the CMAQ simulation equivalent to the TCEQ HGMCR simulation. We appreciate the extraction code work supported by Dr. Robin Dennis and Mr. Todd Plessel at High Performance Computing Group at US EPA/NOAA, RTP. We also thank to Dr. Tom Tesche and Mr. Dennis McNally for sharing their Flying Data Grabber code and outputs as well as testing CAMxSubset and CMAQExtract on their platforms.

4. ALTERNATIVE PERFORMANCE EVALUATION OF THE