UNITED STATES OF AMERICA 

ENVIRONMENTAL PROTECTION AGENCY 

NINTH CONFERENCE ON AIR QUALITY MODELING 

_________________________________________ 

 

 

 

EPA Auditorium 

109 TW Alexander Drive 

Research Triangle Park, NC 

October 10, 2008 

 

V O L U M E 2 OF 2 

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P A G E S 1 - 317 

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The above entitled meeting was called to order by Tyler J. Fox 

PRESIDING OFFICER: 

TYLER J. FOX 

Group Leader 

Air Quality Modeling Group (C439-01) 

Office of Air Quality Planning and Standards 

EPA 

Research Triangle Park, NC 27711 
 Vol. 2, p. 2 

 A P P E A R A N C E S 

 _____________________ 

 

 Presiding: Tyler Fox, Leader, Air Quality Modeling 

 Group, EPA 

 

 ________________________________________ 

 

 
 Vol. 2, p. 3 

 T A B L E O F C O N T E N T S 

 _________________________________ 

 

 

 Topic Presenter Page No. 

 _____ _________ ________ 

 

 Opening Remarks Tyler Fox 6 

 Long Range Transport Bret Anderson 6 

 Evaluation 

 

 Questions Audience 39 

 Atmospheric Model Wyat Appel 50 

 Evaluation Tool 

 

 Comments on Evaluation Bob Paine 62 

 Procedures for Air 

 Quality and 

 Meteorological Models 

 

 AERMOD Evaluation for Roger Brode 72 

 Non-Guideline 

 Applications 

 

 Introduction to Long Bret Anderson 93 

 Transport Models 

 

 Overview of the HYSPLIT Roland Draxler 96 

 Modeling System for 

 Trajectory and 

 Dispersion Applications 

 

 Overview of the Puff- Joe Scire 118 

 Particle Model (PPM) 

 

 Development & Prakash Karamchandani 125 

 Application of Advanced 

 Plume in Grid (PiG) 

 Multi-Pollutant Models 

 

 Lunch Recess 135 

 Single Source Modeling Kirk Baker 135 

 With Photochemical 

 Models 

 

 Single Source Ozone and Ralph Morris 146 

 PM Modeling 
 Vol. 2, p. 4 

 T A B L E O F C O N T E N T S 

 _________________________________ 

 (Continued) 

 ___________ 

 

 

 Topic Presenter Page No. 

 _____ _________ ________ 

 

 Questions Audience 155 

 Comments on Behalf of Bruce Egan 158 

 American Petroleum 

 Institute 

 

 Use of CALPUFF for Doug Blewitt 170 

 Regional Oil & Gas 

 Analyses 

 

 NOAA Reanalysis Data Peter Manousos 180 

 Introduction to AWMA George Schewe 186 

 AB3 Presentations 

 

 Comments on Building Ron Petersen 188 

 And Terrain Downwash 

 Issues 

 

 Comments on Use of Joe Scire 195 

 Gridded Meteorological 

 Data 

 

 Comments on Modeling Bob Paine 198 

 PM 2.5 Emissions 

 

 Comments on AERMOD George Schewe 204 

 Comments on CALPUFF Gale Hoffnagle 208 

 Comments on Behalf of Penny Shamblin 212 

 Utility Air Regulatory 

 Group (UARG) 

 

 Regulatory Air Quality George Delic 219 

 For Next Generation 

 Computers: Prospects 

 And Challenges 

 

 General Comments on Mark Garrison 225 

 Approaches to Modeling 
 Vol. 2, p. 5 

 The following NINTH CONFERENCEE ON AIR 

 QUALITY MODELING, was held at the United States 

 Environmental Protection Agency, Building C, Auditorium 

 C-111, Research Triangle Park, North Carolina, and was 

 transcribed by, Judy D Hall, Transcriptionist, Quality Staffing, 

 Cary, NC on Thursday, October 10, 2008, commencing at 8:30 a.m. 

 ________________________________________ 
 1 Ninth Modeling Conference Vol. 2, p. 6 

 2 Tyler Fox: We got a little off schedule 

 3 yesterday afternoon at the end so we have some 

 4 revisions and catch up to do today. We’ll bypass 

 5 the summary of day 1 and jump right in of the 

 6 continuation of the CALPUFF session, but in order 

 7 to facilitate that further what we’ll do is have 

 8 Bret take his evaluation of Long Range Transport 

 9 and combine it with what he was going to do in 

10 respect to CALPUFF. So we’ll start with those 

11 two and have our Q&A sessions and go into the 

12 model evaluation session right after that. 

13 Here’s Bret. 

14 Bret Anderson: We kind of had a change in 

15 the schedule as Tyler mentioned and the 

16 presentation I was going to give yesterday 

17 afternoon was on the performance evaluation 

18 project I was working on when I came out here on 

19 detail for OAQPS. 

20 Later on in this session we were suppose to 

21 talk about the methods and metrics that were used 

22 in that. I thought it might be worthwhile rather 

23 than have it in reverse order to actually give 

24 this first so that there was a little bit of 

25 explanation of the methodology that we were 
 1 Ninth Modeling Conference Vol. 2, p. 7 

 2 employing in evaluating CALPUFF and the other 

 3 long range transport models that we were looking 

 4 at. 

 5 The evaluation paradigm for long range 

 6 transport models. LRT models play a unique role 

 7 in air quality modeling. This class of models 

 8 plays several roles. In the non regulatory 

 9 sense, we use them for emergency response 

10 modeling so we use non steady state (inaudible) 

11 puff model, particle model for these types of 

12 activities. In the regulatory community we use 

13 these for Class I increments and for what we call 

14 visibility (inaudible) modeling. As such as Joe 

15 had mentioned yesterday, the causability effects 

16 accumulative analysis he’s placed an additional 

17 level or you know replaced the requirement for 

18 additional level of skill to reflect both space 

19 and time considerations of the LRT model use. As 

20 such, we believe statistical measures should 

21 examine spatiotemporal pairing ability of LRT 

22 models. This project and I’ll get more into it 

23 when we get into the project but the over arcing 

24 goals of this project were to develop 

25 meteorological and tracer databases for 
 1 Ninth Modeling Conference Vol. 2, p. 8 

 2 evaluation of long range transport models. 

 3 As you know, there have been a number of 

 4 mesoscale tracer studies but there is no one 

 5 archive of these data sets. So the first goal 

 6 was to assemble an archive of both meteorological 

 7 and tracers for observations that we can use for 

 8 standard evaluation. Develop a consistent and 

 9 objective method for evaluating long range 

10 transport (LRT) models used by the EPA. 

11 What we’ve learned from this and I think 

12 this is one of the more important aspects of it 

13 is to reflect what we’ve learned from those 

14 evaluations and reflect that in our guidance. 

15 For example we will talk a little bit more about 

16 the update of the IWAQM and Phase 2 guidance is 

17 to use the lessons that we have learned from 

18 these evaluations to update that guidance. 

19 There were several methods I think I’m a 

20 little bit out of order here. The background 

21 evaluation on the original performance 

22 evaluations there were three or four evaluations 

23 done on these mesocale tracer studies. The two 

24 that you can find on the EPA web site are done by 

25 the Great Plains Tracer Mesocale Tracer Study and 
 1 Ninth Modeling Conference Vol. 2, p. 9 

 2 the Savannah River, and the INEL74 study in 

 3 1974 and the measures employed for these studies 

 4 I called them the Irwin methodology. They focus 

 5 on the plume center line statistics and so those 

 6 were the methods that were used for that 

 7 particular study. That was one method we used to 

 8 do the evaluation was just try to repeat what 

 9 John had done in those previous studies. 

10 In addition to the Irwin methodology, we did 

11 decide to augmented statistical measures focusing 

12 upon spatiotemporal comparisons of model13 observation pairings. This is the Irwin 

14 methodology and kind of how I have it broken out 

15 in terms of the logical how it’s organized 

16 logically. It’s broken into three segments where 

17 you see the spatial component, a temporal 

18 component, then a performance component. 

19 The spatial component consists of looking at 

20 the model’s ability to correctly predict the 

21 azimuth of plume centerline on an arc. Then it 

22 also looks at the horizontal spread of the plume 

23 to see how well how low in space it is you know 

24 the definition of the horizontal of the plume. 

25 For temporal pairing we looked at plume arrival 
 1 Ninth Modeling Conference Vol. 2, p. 10 

 2 time and transit time on an arch. For 

 3 performance we looked at things crosswind 

 4 integrated concentration and observed the fitted 

 5 maximum concentrations on that arch. That method 

 6 that John had employed to basically compute n- 

 7 hour average so depending upon however the 

 8 sampling frequency was and the duration of the 

 9 sampling on the arc was to create like a three 

10 hour or twelve hour arc concentration on that 

11 arc. Then to use trapezoidal integration program 

12 to fit an average plume on arc so these were 

13 programs that John had written ten years ago that 

14 we had the pleasure of figuring out how they 

15 operate. 

16 In addition to this, we augmented that 

17 analysis with the evaluation procedures that have 

18 been developed for the (inaudible) 2 study so 

19 these are articles that were published in the 

20 Atmospheric Environment, Mosca et al. (1998) and 

21 Draxler et al. (2001). These statistical 

22 measures are a broad set of statistical measures. 

23 They basically fall into four broad categories 

24 that are Scatter, Bias, Spatial, Cumulative 

25 distribution. I’ll show you in a minute here. 
 1 Ninth Modeling Conference Vol. 2, p. 11 

 2 This data set and these programs on the NOAA ARL 

 3 DATEM performance evaluation program. What we 

 4 did (STATMAIN) program and then augmented with 

 5 additional spatial statistics for false alarm 

 6 rates, probability of detection, and threat 

 7 scores to give us a little bit more flavor on how 

 8 the model is doing. This is just an example on 

 9 what NOAA has done in terms of trying to you know 

10 there are archived so this is kind of our goal is 

11 to have this sort of an archive so we can have 

12 those performance those data base out there to 

13 evaluate CALPUFF and the other models. 

14 These are the statistical measures and these 

15 are for Scatter. You have factor of exceedance 

16 which ranges from -50% to +50% so the lower the 

17 lower the score base the negative 50% is the you 

18 know factor towards over prediction and the 

19 positive score toward under to normalized. Then 

20 you have the factor of 2 whichever one is 

21 familiar with. The normalize mean square error 

22 and then the correlation coefficient. 

23 Cumulative distributions uses the (KSP) 

24 Kolmogorov-Smirnov Parameter and basically it 

25 looks at the maximum difference between the two 
 1 Ninth Modeling Conference Vol. 2, p. 12 

 2 distributions of the model predictions. So this 

 3 is not pairing in space and time but just looks 

 4 at the absolute distribution and the differences 

 5 in the absolute distribution of the 

 6 concentration. For Bias, we have just mean bias 

 7 (B) and the fractional bias (FB). 

 8 Then for spatial statistics the metric 

 9 that’s called the figure of merit in space (FMS) 

10 and then we’ve added additional EPA metrics, the 

11 false alarm rate (FAR), the probability of 

12 detection (POD), and the threat score (TS). Then 

13 Draxler in 2001 in the paper he wrote that is up 

14 on the NOAA webs site introduced a final metric 

15 which is basically a model success story, a model 

16 ranking which looks at one major statistic across 

17 each of those four broad categories to assign a 

18 model score to see how well it did across each of 

19 those parameters. 

20 This is just the model ranking and you can 

21 see it used the correlation coefficient 

22 fractional bias to figure the merit in space and 

23 the KS parameter and then assigns a score from 0 

24 to 4, with 0 poorest and 4 best performances. 

25 This is the unique measure that allows not only 
 1 Ninth Modeling Conference Vol. 2, p. 13 

 2 allows to give you an idea how the model performs 

 3 across all the broad categories but also allows 

 4 for direct modeling or comparison because you 

 5 have one score that is assigned to ability so 

 6 when you are comparing your four models you can 

 7 see how they compare against one another very 

 8 easily. 

 9 This is an example from a trajectory 

10 particle model that we evaluated as part of this 

11 project. This is the (inaudible) part of the 

12 model; this is a European tracer experiment and I 

13 just wanted to show you this is what the results 

14 come out from the stat name program that we were 

15 working with. As you can see, we get all the 

16 values; we get our fig of merit in space; we get 

17 our false alarm ratio; the KS parameter, the 

18 correlation of bias and the final model rating 

19 down here. 

20 This assigns an overall rank as to how well 

21 the model performed in that particular tracer 

22 study. That is the evaluation methodology used 

23 for this statistical component of it. So that’s 

24 that portion of it. We’ll get back on schedule 

25 real fast today. 
 1 Ninth Modeling Conference Vol. 2, p. 14 

 2 So this is what we were supposed to talk 

 3 about yesterday afternoon. As Tyler mentioned I 

 4 came down on rotation to OAQPS in January and my 

 5 project was to start this up basically. Back in 

 6 the 8th Modeling Conference – EPA recognized the 

 7 fact that CALPUFF model science had evolved 

 8 significantly and the IWAQM Phase 2 

 9 recommendations in many cases were clearly 

10 outdated. We had the new turbulence options; we 

11 had puffs splitting; we had all these other 

12 things that clearly be used but were not 

13 reflected in the EPA long range guidance. 

14 So we discussed the need to form a committee 

15 to prioritize or identify what the issues were 

16 and to prioritize the tasks. Then we also the 

17 need to form an updated model performance 

18 evaluation to examine new science enhancements to 

19 model which are not mentioned in the current 

20 guidance which are not reflected in current 

21 guidance. So we initiated this long range 

22 modeling project and they said we are performing 

23 five tasks for this project: The first one I 

24 mentioned earlier is to assemble a tracer and 

25 meteorological database for use with LRT model 
 1 Ninth Modeling Conference Vol. 2, p. 15 

 2 evaluations. The ultimate goal would be to have 

 3 something similar what the NOAA archive is where 

 4 we have an archive of the meteorology so we’ll 

 5 have the MM5 data that was run up there and have 

 6 all the observations within the program. Anybody 

 7 can go on the web site and get that data and do 

 8 the statistics themselves. That’s the ultimate 

 9 goal as part of this project. Unfortunately I’ve 

10 got the dog ate my lunch excuse in fact as Joe 

11 had mentioned yesterday in trying to get the data 

12 out in a timely manner has been kind of 

13 difficult. 

14 Back in June right before the Denver 

15 meeting, we had all the data assembled that I was 

16 working on and we had a hard drive failure. We 

17 lost 90% of all we had been working on and we’re 

18 in the process of trying to reconstitute those 

19 data sets and get those out there. So that was 

20 an unfortunate set back in the whole thing. 

21 That’s the ultimate goal to get those data sets 

22 assembled and get them up on the web site so that 

23 everybody can look at. You know the (inaudible) 

24 themselves similar to the datum web site and 

25 similar to what Roger has on the web site for 
 1 Ninth Modeling Conference Vol. 2, p. 16 

 2 SCRAM for the evaluation data sets for the 

 3 developmental data sets that were used for 

 4 AERMOD. 

 5 As I mentioned previously, the other goal 

 6 was to develop a comprehensive evaluation 

 7 framework (methodologies and tools) and I think 

 8 this is another point that Joe made a very good 

 9 point yesterday about you know this is a modeling 

10 system we’re talking about here. The dispersion 

11 model can only perform as well as the 

12 meteorological you supply it with. So another 

13 part of this evaluation paradigm will be and I’m 

14 not going to get into it today because we’re 

15 still wrestling with it a little bit is to look 

16 at it as a coupled system. The model’s ability 

17 is only as good as your abilities to apply it 

18 with meteorology. So that’s going to be the 

19 comprehensive evaluation framework looking at 

20 both meteorological aspects of it and the LRT 

21 model aspects of it. 

22 Then basically like I said you’re exercising 

23 and testing the meteorological LRT models for the 

24 assembled tracer database. Then like I said 

25 you’re exercising and testing meteorological and 
 1 Ninth Modeling Conference Vol. 2, p. 17 

 2 LRT models for the assembled tracer database to 

 3 provide full documentation of model evaluation 

 4 measures and results from meteorological and LRT 

 5 evaluations. And then provide the ultimate goal 

 6 to updating existing EPA LRT modeling guidance 

 7 (IWAQM Phase 2) to reflect lessons learned from 

 8 this project. 

 9 From the guidance goals basically what we said 

10 was to examine science evolution of CALPUFF 

11 modeling system to incorporate recent 

12 enhancements to model system in updated guidance 

13 but there were some overarching questions is that 

14 you can see comments that were made in the 7th and 

15 8th Modeling Conference that talk about these 

16 things. Can puff-splitting extend the effective 

17 range of CALPUFF beyond recommended distance of 

18 200-300 km? At the 7th Modeling Conference, EPA’s 

19 response comments said that they were anxiously 

20 awaiting any tracer evaluations that had been 

21 done that would do this. They said and as soon 

22 as those results were available they would put 

23 them up on the SCRAM web site. That was 2000 and 

24 now its 2008 and none of that are up there. In 

25 the absence of doing that we’re going to try to 
 1 Ninth Modeling Conference Vol. 2, p. 18 

 2 fill that void. 

 3 The next question is can guidance migrate to 

 4 recommend turbulence based dispersion (CALPUFF 

 5 and AERMOD options) over P-G? As Tyler 

 6 mentioned, back in 2006, EPA issued a Model 

 7 Clearinghouse memorandum basically in agreement 

 8 affirming that more tests needed to be done. 

 9 That’s part of this thing is to evaluate against 

10 these tracer data bases looking at both 

11 P-G and turbulence options there. Then the final 

12 one as Bill was mentioning yesterday was how best 

13 to supply meteorological data to CALPUFF? As you 

14 know, it is like any other transport model and it 

15 is very sensitive to wind field (inaudible) you 

16 know things like that. 

17 I realize this is a statement you make to 

18 see how best to apply the meteorology to it 

19 because you can’t have one set of fixed options 

20 in CALMET. Perhaps Hybrid method verses NOOB = 1 

21 or NOOB = 2. Maybe there’s a better was to do 

22 it so that’s one of the goals to evaluate the 

23 different ways in which we supply data to CALMET 

24 to see is there something or one that is better 

25 than another. 
 1 Ninth Modeling Conference Vol. 2, p. 19 

 2 The tracer experiments that we have 

 3 currently we have the Great Plains Tracer 

 4 Experiment which we are currently and will show a 

 5 lot today. Savannah River Laboratory Tracer 

 6 Experiment which was another one which had been 

 7 done. That one is underway. We had started with 

 8 the Cross-Appalachian Tracer Experiment but that 

 9 was one you know where the dog ate my lunch or 

10 ate my homework. That one suffered you know the 

11 one that was consumed in the hard drive there. 

12 Then the European Tracer Experiment which is a 

13 new one that was not considered an original one. 

14 I’ll get more into the European experiment in 

15 this presentation. 

16 Additional tracers to be included that we 

17 would like to look at more as you see in the 

18 IWAQM Phase 2 there’s talk about project MOHAVE 

19 which is one that John (inaudible) and 

20 (inaudible) from DRI had published extensively 

21 on. The other one is the VTMX where the urban 

22 2000 study in Salt Lake City. That has a very 

23 good complex terrain to it which would be useful. 

24 And then Joe Chang is here today and he published 

25 a paper about comparing CALPUFF to (inaudible) 
 1 Ninth Modeling Conference Vol. 2, p. 20 

 2 and to DLS Tract. For these two experiments here 

 3 the Dipole Pride 26, and the Overland Along-Wind 

 4 Dispersion thing we’d like to get hold of that to 

 5 include in the database. 

 6 As part of this project we are also 

 7 evaluating additional models because the question 

 8 is how well any model can do in any one of these 

 9 situations. It isn’t fair to isolate one model 

10 and say okay it either does good or does poorly. 

11 You know you have to look at it in context 

12 because what if all models are performing poorly. 

13 Then that’s not a good tracer evaluation to 

14 compare it against. It’s not fair to do it that 

15 way. You have to create a framework to 

16 understand how well can any model reasonably do 

17 with these experiments. It is important to 

18 include these other models so basically what we 

19 did was to include the two Lagrangian particle 

20 models which most maybe most of them are familiar 

21 with height split. Then the European one that is 

22 called FLEXPART that’s widely distributed 

23 throughout Europe and both of these were selected 

24 because they have are routinely used and they 

25 have widely (inaudible) in other words it is easy 
 1 Ninth Modeling Conference Vol. 2, p. 21 

 2 to take the meteorological data from MM5 and 

 3 apply to this model. 

 4 In addition we are also looking at the 

 5 transport capability of CAMx and Kirk Baker has 

 6 been working with us on this one too. Basically 

 7 it is to also create a framework to help us 

 8 understand how any model can reasonably do under 

 9 these experiments. As I mentioned before, these 

10 were the different methods the evaluation 

11 methods. 

12 Now to get into this into it a little bit here. 

13 The first one that we’re going to talk about is 

14 the Great Plains Mesoscale Tracer Experiment. 

15 This is one of the original ones that was 

16 published supporting the promulgation of CALPUFF. 

17 Briefly what is was there were two 

18 perflourocarbaon tracer releases from Norman, OK 

19 on July 8 and July 11, 1980. Basically what we 

20 had is you had two arcs of monitors that were 

21 deployed one at 100 km and another arc at 600 km. 

22 So we basically have a sampling interval was 45 

23 minutes on the 100 km arch and then the same 

24 frequency of every 3 hours on the 600 km arc. 

25 I’m trying to give you a little flavor for 
 1 Ninth Modeling Conference Vol. 2, p. 22 

 2 the (inaudible) meteorology because this 

 3 influences the performance of the model. 

 4 Basically what we had were Low Level Jets that go 

 5 over the Central Plains and you can see this is a 

 6 [ed. vertical] (inaudible) cross section from the 

 7 MM5 simulation performed with this. What you can 

 8 see is a very strong and deep Low Level Jet [ed. 

 9 Stream](inaudible) here and you know this is from 

10 750 meters up in the air and the height in the 

11 atmosphere and you can see the presence of the 

12 Jet here. 

13 This plays a major role in especially the 600 

14 km or the results for the 600 km arc. I’ll 

15 explain a little bit why in a minute. Basically, 

16 the model experimental design was to look at 

17 CALPUFF, FLEXPART and HYSPLIT and basically, what 

18 we did with CALMET meteorology we looked at 

19 (inaudible) the Hybrid mode, then NOOBS =1, then 

20 NOOBS = 2. Then at the presentation that Herman 

21 gave yesterday, we also included the MM5 CALPUFF 

22 and this is one of the data sets that we’re 

23 testing the proto type against here. 

24 Puff-Splitting was turned on for the 600 km 

25 situation or the 600 km simulation and none for 
 1 Ninth Modeling Conference Vol. 2, p. 23 

 2 the 100 km. So this was a deviation and this is 

 3 one of the areas there was a deviation from the 

 4 earlier experiment was that that one did not 

 5 consider puff splitting. But since we were 

 6 operating at 600 km we thought it was important 

 7 to test that feature. Basically like I said we 

 8 had two domains, two CALMET domains. For the 100 

 9 km arc we used the 4 km CALMET which was 

10 consistent with the previous basically what we 

11 tried to do was be as consistent with the 

12 previous CALMET and CALPUFF simulations to do 

13 this. So we had a 20 km CALMET for 600 km 

14 simulation and a 4 km CALMET for 100 km and then 

15 set those other two models there. 

16 This is the MM5 configuration and I’ll skip 

17 through this. It’s just an idea of what we’re 

18 using some of the more advanced (inaudible) in 

19 MM5 like ETA PBL and NOAH LSM. We’re not 

20 necessarily wed to EPA (inaudible) scheme but 

21 that’s one thing that would have to be evaluated 

22 as part of any publication of these results is to 

23 validate the MM5 data and that’s something we 

24 have done but haven’t looked at it as 

25 extensively. We have domain wide statistics that 
 1 Ninth Modeling Conference Vol. 2, p. 24 

 2 we haven’t went down in detail to evaluate but we 

 3 do have general performance. In general they 

 4 gave me kind of the ad hoc statistics that people 

 5 use for meteorological model evaluation. 

 6 This is the basically what I talked about 

 7 and these are from the Irwin methodology and want 

 8 to point out that this is the result of the 100 

 9 km and this was for the 600 km. As you can see 

10 CALPUFF with CALMET is doing about the same. 

11 Both put in MM5 CALPUFF within the CALMET one 

12 too. This is with the NOOBS only with this one. 

13 Then for the 600 km this is again you can see 

14 this is fairly consistent with (inaudible) we saw 

15 in the previous study which is we over predicted 

16 CALPUFF in the 100 km and unpredicted under 600 

17 km. Which was basically, the same result from 

18 the previous study from the degree of which I 

19 haven’t gotten into here as far as the absolute 

20 difference. 

21 This is one thing I think may be one take 

22 home message that I see encouragement here in 

23 terms of you know you can see the plume you know 

24 the plume is wide here. I am encouraged by the 

25 turbulence here. I think that’s one I mean 
 1 Ninth Modeling Conference Vol. 2, p. 25 

 2 obviously we need to look at this more but this 

 3 is one area if you take a look at both the 

 4 CALPUFF turbulence and the AERMOD turbulence in 

 5 this the plume signal were not exactly matching 

 6 and more in line with reality than what you would 

 7 see. I am encouraged by seeing this here. As 

 8 you can see the plume spread with P-G tends to be 

 9 larger than what we saw and maybe we need to 

10 investigate this further but clearly it s 

11 seems we can see it consistent (inaudible) over 

12 prediction of the plume width with the P-G class 

13 now. 

14 You can see here the CALMET winds did very 

15 well at the arrival time at the 100 km; it did 

16 better than the MM5 winds in terms of the arrival 

17 time at the 100 km arc. CALMET almost 

18 (inaudible) it. The MM5 had a slight delay of 

19 about an hour. So we get down here where this is 

20 where you can see you know basically depending 

21 upon which P-G or turbulence we have a little bit 

22 of variation. They are all fairly consistent 

23 either close or -1 hour, but they’re doing pretty 

24 well there. 

25 Where this created some concern for me was 
 1 Ninth Modeling Conference Vol. 2, p. 26 

 2 as you can on the 100 km arch, CALMET does very 

 3 good in terms of arrival time but also the 

 4 duration and the time that the tracer cloud 

 5 arrived on the arc where (inaudible) does a very 

 6 good job. MM5 is (inaudible) arrived late and 

 7 faster. But this is what I need to talk to Joe 

 8 more about this. We couldn’t reproduce from the 

 9 original experiment was when you look at the 600 

10 km arc we detected tracers above background 

11 concentrations for 15 hours on the arc. So what 

12 we have is in the original dating back to 1997 – 

13 1998 timeframe, they ran in CALMET and NOOBS mode 

14 and they were able to get either 13 or 14 hours 

15 on the arc. They had generally a decent 

16 agreement with the travel and the transit time on 

17 the arc. And I’ve tried it every which way and 

18 this is the one thing I’m still confused about 

19 whether I’m doing something wrong or maybe 

20 something has changed inside CALMET I don’t know. 

21 Basically, as you can see we’re basically 

22 narrowing it down to about half the travel time 

23 on the arc and show a little bit of why we’re 

24 seeing that in terms of where the wind shield was 

25 placed in the tracer cloud. What we did see here 
 1 Ninth Modeling Conference Vol. 2, p. 27 

 2 is that when we were feeding the MM5 only winds 

 3 (inaudible) CALPUFF we weren’t getting the 

 4 transit time on the arc was consistent with what 

 5 the observation was. This is where we clearly 

 6 need to go back and take another look and try to 

 7 get a better understanding of what’s going on. 

 8 This is one of the things we were not able to 

 9 replicate from the previous experiment. 

10 Now Plume Centerline, this is one of the 

11 Euro methodologies. As you can see, this is 

12 where the MM5 winds did markedly better than 

13 CALMET. CALMET was much better in terms of 

14 arrival time and the time on the arc. But the 

15 plume was a little bit displaced to the NE of 

16 where it should have been and the MM5 was like 

17 depending upon having it a bit little closer. 

18 We’re about 10 degrees off here I think we’re 

19 about 20 to 30 degrees off on this one here. So 

20 the MM5 winds were doing slightly better, but you 

21 can see the MM5 winds have it displaced more 

22 directly to the West and these are more to the 

23 East. 

24 Then on the 600 km arc the plume (inaudible) 

25 from the Euro program, you can see generally they 
 1 Ninth Modeling Conference Vol. 2, p. 28 

 2 are within range from about 25 to nearly 30 

 3 degrees as compared to 10. The displacement is 

 4 about 20 degrees off and with the NOOB we’re 

 5 getting into like right here it’s getting closer 

 6 to what the MM5 was looking at like the MM5 was a 

 7 little bit closer over here. That was an 

 8 encouraging sign for the MM5 CALPUFF. 

 9 I’m going to see if the animation works here 

10 to kind of give you an idea what we’re seeing 

11 here. Sorry about this. I’m going to break out 

12 of this if I can. Okay this is the animation 

13 from the observed and ...oh great. Sorry about all 

14 this. And as you can see, this is what we were 

15 seeing basically from the published literature 

16 dating back to 1982 – 1983. The observations 

17 were basically the plume was detected from 

18 Nebraska to Hamilton Missouri. So basically it 

19 had it sitting somewhere right here to here. It 

20 appears that the wind field was steering it l 

21 little too far to the South and East and I think 

22 that explains why we’re not seeing the terrain of 

23 the faster (inaudible) on the arc because from a 

24 meteorological perspective you don’t want to be 

25 right in the Jet coare there. Up here in 
 1 Ninth Modeling Conference Vol. 2, p. 29 

 2 Nebraska we have a fontal boundary that starting 

 3 to set up over here so what I think was happening 

 4 was that the plume came up in this area here and 

 5 encountered the frontal boundary and started to 

 6 slow down. That in fact is why you saw the 15 

 7 hour transit time because we are sliding a little 

 8 bit too far to the South and East on this one so 

 9 we’re not encountering that frontal boundary and 

10 I think that’s why it’s (inaudible). Obviously 

11 that’s what it looks like. Okay. 

12 For the MM5 CALPUFF, as you can see, it 

13 actually a lot of it has to do with the initial 

14 displacement it had the plume you can see that 

15 the plume took it a little bit further trip to 

16 the North and West than it did with the other 

17 one. It did catch the transport path a little 

18 bit closer. That’s one of the things we need to 

19 go back and look at with this tracer evaluation. 

20 It’s like why weren’t we able to replicate the 

21 CALMET wind fields from the previous one. I 

22 presumed that’s what was helping to contribute 

23 the transport differences that we were seeing 

24 from the first study to the second. Okay. 

25 This is another one. This is the European 
 1 Ninth Modeling Conference Vol. 2, p. 30 

 2 tracer experiment and basically this is probably 

 3 I call it the granddaddy of all the tracer 

 4 experiments. This is probably the most prominent 

 5 tracer experiment we have. This was Europeans 

 6 response to Chernobyl accident decided it was 

 7 necessary to test the results of the LRT models. 

 8 So the European’s tracer experiments or ETEX was 

 9 designed to validate long-range transport models 

10 used for emergency response situations and to 

11 develop a database which could be used for model 

12 evaluation purposes. 

13 They had at least 168 monitoring sites 

14 located over 17 European countries and they had 

15 two releases of perflourocarbon (PFC) tracer were 

16 made in October and November 1994 from France. 

17 They were basically 2-hour releases. It has a 

18 fairly robust network to look at here. Basically 

19 the experimental design here you can see 

20 (inaudible). This is what the synoptic features 

21 that will flavor the simulations; we have the 

22 (inaudible) over the north sea and another low 

23 developing in the Adriatic plus we have some 

24 (inaudible) passage through here and this is 

25 going to be what flavors the transport patterns 
 1 Ninth Modeling Conference Vol. 2, p. 31 

 2 that you will see. 

 3 MM5 is run again and was initialized with 

 4 NCEP Reanalysis Data and was consistent with what 

 5 was run with Great Plains with the exception we 

 6 ran a 43 vertical layer and I think I transpose 

 7 my numbers so I think it was 43 layers instead of 

 8 34 for this one. I’ll show results for this one 

 9 for these three models. I think it’s important 

10 to caveat this is an experiment that’s well we’re 

11 talking distances of 1,000 – 2,000 km here. So 

12 this is well beyond what CALPUFF what is 

13 recommended for regulatory. It’s not sitting and 

14 shows how well one model does and how bad one 

15 does. This is a good test for puff splitting 

16 because you have one arc at 600 km and now we’re 

17 at how far out can we really go with this. We 

18 felt this was a good test for puff splitting. 

19 Basically each of the models was supplied 

20 with the MM5 and there’s no CALMET in this 

21 simulation. It’s only MM5 CALPUFF so basically 

22 you have the comparison that all three models 

23 help with (inaudible) MM5. Basically we’re 

24 looking at each of the models ability with the 

25 same meteorological data. 
 1 Ninth Modeling Conference Vol. 2, p. 32 

 2 This is just a snap shot of the FLEXPART 

 3 time series at 24, 36, 48 and 60 and you’ll see 

 4 that basically this is similar to what was 

 5 observed in terms of the absolute transport 

 6 pattern if you’re just looking at the spatial 

 7 pattern. Basically what it is that within the 

 8 first 24 hours of plume as it (inaudible) along 

 9 the low country up here in to Germany? As it 

10 gets into this area up here we start with wind 

11 field (inaudible) starts (inaudible) and we get 

12 the (inaudible) in to the low up here and then we 

13 start (inaudible) low down here. At 48 hours and 

14 at 60 hours, this is basically the transport 

15 patterns would look like. 

16 This is what CALPUFF was showing here. I 

17 apologize for this I used different software 

18 (inaudible) Hysplit and CALPUFF were a lot easier 

19 to use with Surfer so this is the Surfer plot. 

20 We were able to pull in the observations so that 

21 you would have an idea what the actual 

22 observation were looking like for this. CALPUFF 

23 is doing just as well as the other models within 

24 the first 24 hours of the release. None of the 

25 models were able to get this (inaudible) extent 
 1 Ninth Modeling Conference Vol. 2, p. 33 

 2 of it. All three models CALPUFF, FLEXPART and 

 3 HYSPLIT they all had the same general convection 

 4 pattern toward the northeast and were not getting 

 5 the Westward or Eastward extent of it. By 36 

 6 hours, this is where you can see things are even 

 7 with the puff-splitting turn on we weren’t 

 8 getting caught up in the deforming wind field the 

 9 way it was. 

10 As you can see by 48 and 60 hours the 

11 simulation has pretty much broken down by that 

12 point. We are not able to do that. As I said 

13 this is well beyond the regulatory range of 

14 CALPUFF and was just an experiment to take a look 

15 and see how this puff-splitting will make a 

16 difference. I think that’s the thing here. 

17 HYSPLIT was comparable with CALPUFF in the first 

18 24 hours here and we’re not getting eastward 

19 (inaudible). By 36 hours we’re not getting the 

20 southern (inaudible) here, but HYSPLIT‘s 

21 performance improved dramatically between 48 and 

22 60 hours. By 60 hours HYSPLIT has it almost 

23 perfect in terms of the spatial pattern. 

24 So the spatial statistics...this is what we’re 

25 looking at merit in space as you can see the 
 1 Ninth Modeling Conference Vol. 2, p. 34 

 2 HYSPLIT did its best in terms of basically what 

 3 the model observed it had the best of spatial 

 4 representation. This was the coare of the 

 5 performing one here. In the end you can see 

 6 because of the way the plume was transported with 

 7 CALPUFF on that one here where the high false 

 8 alarm rate with this one which was putting the 

 9 plume in an area where nothing was being 

10 detected. So as you can see FLEXPART has a high 

11 false alarm rate as well. As you can see HYSPLIT 

12 did the better of the three models in that. 

13 In terms of the global statistics that I 

14 talked about before, as you can see, HYSPLIT was 

15 the clear winner in this one and you can see the 

16 final ranking overall. This is the Lagrangian 

17 part of the model it didn’t do much better in 

18 terms of the statistical data. It did marginally 

19 better than CALPUFF here and you know you can 

20 look at the factor of 2, the factor of 5, clearly 

21 in each case HYSPLIT was the clear winner in that 

22 one. It’s just what it is. 

23 These are some of our initial observations from 

24 that and I would like to remind everybody there 

25 are an insufficient number of tracer experiments 
 1 Ninth Modeling Conference Vol. 2, p. 35 

 2 here to draw any conclusions from current data. 

 3 As I mentioned before, there are pieces of 

 4 information we can pull out of this. I was very 

 5 encouraged with the turbulence in terms of the 

 6 plume width. It looked like it was doing better 

 7 than PG. But we obviously have a lot of work to 

 8 do and I stick to the dog ate my homework. 

 9 Basically for the Great Plains Tracer 

10 Experiment, CALPUFF/CALMET 100 km results 

11 performed well except for plume azimuth as I said 

12 it was off centered about 20 or 30 degrees. The 

13 MM5 results were better for azimuth, but worse 

14 for time of arrival and duration on 100 km arc. 

15 We were unable to replicate 600 km arc statistics 

16 from original GP80 and SRL studies conducted by 

17 EPA in 1997 despite using same raw meteorological 

18 data, horizontal, and vertical grid 

19 configurations. We are now into the Savannah 

20 River one and we’re off a little bit and unable 

21 to replicate the statistics for the Savannah 

22 River one so that’s something we need to go back 

23 and look at. 

24 The two major differences from original EPA 

25 study are updated terrain and land use from old 
 1 Ninth Modeling Conference Vol. 2, p. 36 

 2 CALPUFF 1.0 distribution and use of lambert 

 3 conformal projection for GP80 and SRL, all other 

 4 CALMET options remained constant. CALPUFF 

 5 performance varied due to variations in CALMET 

 6 options selected. As you can see, CALPUFF 

 7 results appear sensitive to manner in which 

 8 meteorology is supplied to the model. Joe 

 9 mentioned yester that CALPUFF is sensitive as to 

10 how you apply the model and that’s one of the 

11 area we need to focus on the evaluation aspect of 

12 it. I agree completely with Joe on the tone. 

13 The European Tracer Experiment and as you can 

14 see CALPUFF performs reasonably compared to 

15 particle models for first 24 hours, has more 

16 difficulty further into transport simulation, but 

17 you can see it had more difficulty as it went 

18 further into the transport simulation and we need 

19 to investigate that further. When we were 

20 looking at Puff-splitting did not change CALPUFF 

21 performance significantly. When we were looking 

22 at puff-splitting (eliminating mixing height 

23 restrictions) increased number of puffs, but did 

24 not augment model performance. We had puffs 

25 going in different directions. That’s one of the 
 1 Ninth Modeling Conference Vol. 2, p. 37 

 2 messages we need to see how we can improve the 

 3 puff-splitting in CALPUFF. 

 4 The next steps are and this is the last time 

 5 and I’m on time. Project results shown today are 

 6 work-in-progress. We have a model evaluation 

 7 protocol drafted and it describes the 

 8 meteorological metrics and the LRT metrics. The 

 9 goal is to provide the full documentation and 

10 data availability necessary. Clearly we need to 

11 engage with model developer to help us understand 

12 some of our observations. Did we go wrong in 

13 model setup? What can we do better? 

14 Has the model changed since the previous 

15 evaluations? So those are questions we have to 

16 answer. That’s my presentation. 

17 Tyler Fox: Thank you Bret. Appreciate 

18 that. We will venture into the Q&A session now. 

19 Let me just mention where are we at from the EPA 

20 perspective. As Bret indicated, we have worked 

21 diligently into trying to compile the evaluation 

22 information outlined understanding and 

23 documenting some of the issues we have found in 

24 respect to the science and implementation within 

25 the model and will fully document that. What we 
 1 Ninth Modeling Conference Vol. 2, p. 38 

 2 intend to do and we’ve made resource requests for 

 3 this is to conduct a peer review of the model and 

 4 that will follow the completion of the evaluation 

 5 and the documentation of that and release of the 

 6 information as Bret indicated. We will move 

 7 forward with that and not only take the 

 8 information we will put together but also 

 9 information the community and others want to 

10 provide either through this process, provide 

11 comments as it relates to this conference or 

12 other information that is made available that can 

13 include the evaluation Joe wants to do and others 

14 want to do and the work that AER have done. 

15 We’ll be conducting a peer review both to charge 

16 them to evaluate models and give us their opinion 

17 about the performance and the underlying science 

18 in these models and the long range transport 

19 context to meet the regulatory needs under 

20 Appendix W. 

21 And as to a future question of any 

22 recommendations or options for us to consider in 

23 terms of addressing long range transport in the 

24 future in terms of the models and their ability 

25 to meet those needs. So that is where we are 
 1 Ninth Modeling Conference Vol. 2, p. 39 

 2 just so you know and again look forward to 

 3 getting any comments or input through this 

 4 process and in the future as we move forward. 

 5 We’ll take Q&A until about 9:30 to get back on 

 6 schedule. 

 7 Bob Paine: ENSR. I have a question for Joe 

 8 Scire or EPA. There is guidance for grid spacing 

 9 in CALPUFF such as you resolve the terrain 

10 features to 5 or 10 grid elements. But recently 

11 I’ve seen some critiques that the finer you go 

12 with the grid spacing, the lower the 

13 concentrations go. Is that really true or it is 

14 really unbiased? 

15 Joe Scire: I think there are several 

16 factors that can influence how the model responds 

17 to grid spacing. One is the nature of the 

18 terrain and also the source location relative to 

19 the Class I analysis and exactly where the source 

20 is relative to that in the mean flow. What we 

21 did is....my experience is it goes both ways 

22 sometimes finer resolution produces higher 

23 impacts where the terrain may channel the flow 

24 into a Class I area. And in other cases it 

25 produces lower impacts -- maybe it’s channeled 
 1 Ninth Modeling Conference Vol. 2, p. 40 

 2 away or maybe it just takes a different 

 3 trajectory. One example is a situation where a 

 4 stack is in the valley -- with coarse resolution, 

 5 the terrain may get smoothed so much so that the 

 6 stacks are no longer below the terrain height. 

 7 Therefore it goes to the gradient flow, where in 

 8 the finer resolution, the valley floor is deeper 

 9 and the peaks are higher so maybe the stack now 

10 is within the valley and is subject to 

11 channeling. That can drastically affect the 

12 trajectory of the plume. As a test, back when we 

13 were working on the VISTAS project, we looked at 

14 the effect of terrain resolution from 90 

15 differenr source – Class I area pairs -- looking 

16 at 12 km resolution and 4 km resolution and I 

17 distributed these results to the Federal Land 

18 Managers and others. 

19 Basically what we found in 52% of the cases 

20 or whatever that works out to be 47 out of 90 -- 

21 the concentrations went up with finer resolution, 

22 not down, and in 48% of the cases, (43 of them), 

23 they went down. So I think there was pretty much 

24 a split of higher and lower terrain resolution. 

25 Christine Chambers: From Trinity 
 1 Ninth Modeling Conference Vol. 2, p. 41 

 2 Consultants. I have a follow up to that question 

 3 to that. Recently I’ve had numerous 

 4 conversations with Tim Allen that there was in a 

 5 memo distributed by EPA that specifically said 

 6 for PSD Class I increments that, in all cases, 

 7 less than 4 km grid spacing would not be accepted 

 8 for PSD Class I increments. This was from Tim 

 9 Allen in his own words based on an application in 

10 the Pacific Northwest. All projects less than 4 

11 km show a decrease in concentrations. There have 

12 been recent studies conducted by EPA to document 

13 this. I have similar studies as Joe said that 

14 depending on the case it can be up or down. Can 

15 you provide a little more insight on this memo 

16 that was supposedly issued by EPA that was 

17 submitted to the Federal Land Managers? 

18 Tyler Fox: I wish Tim was here because I’d 

19 ask him the question what memorandum he is 

20 referring to. We have not issued any memorandum 

21 to that effect. I’ve not seen any memorandum to 

22 that effect. I know Clint Bowman and others and 

23 Herman if you want to address it. Others have 

24 provided information about that. We from the 

25 program office stand point have not issued any 
 1 Ninth Modeling Conference Vol. 2, p. 42 

 2 memorandum to that effect. Herman if you want to 

 3 address that. 

 4 Herman Wong: That memorandum I wrote to the 

 5 State of Washington said that I would not accept 

 6 the 1 km grid resolution they use. The reason I 

 7 did not accept it was that we had an agreement 

 8 with the State in which a common protocol had 

 9 been developed and the State wanted to change the 

10 agreement we had. So they changed the agreement, 

11 and the State of Washington did not discuss these 

12 changes with the EPA or the FLM or the other two 

13 states. 

14 So I fired back an email saying that it was 

15 inappropriate for you to automatically decide to 

16 make a change in the current protocol to go from 

17 a 4-km grid resolution to 1-km grid resolution. 

18 I think they had even adopted a grid with a 500- 

19 meter resolution. The reason I didn’t sign off 

20 on it is was the feedback came from the Forest 

21 Service and the Fish & Wildlife and the Park 

22 Service because they wanted some demonstrations, 

23 arguments, or justification as to why should they 

24 be allowed to go down to below 4-km. We did do 

25 some-- well, Bret did some testing that came up 
 1 Ninth Modeling Conference Vol. 2, p. 43 

 2 recent results so we went to some additional 

 3 analysis from Clint [Bowman of WDOE] to justify 

 4 why he would be allowed to go to from 4-km down 

 5 to 1,000 meters and provide that to EPA, the FLMs 

 6 and the other states before we accepted it. At 

 7 this point, no, we are not accepting as Joe says 

 8 and this is the first I’ve heard of it from Joe 

 9 with an explanation as to why we should expect 

10 mixed results. Until Clint provides that 

11 information to us as we requested a couple of 

12 months ago, we are not going to change our 

13 position with respect to BART and with respect to 

14 PSD. 

15 Bret Anderson: The true story is that 

16 Clint was kind enough of to share his 

17 presentation with Roger and I back in May of this 

18 year. He said, “Hey look at these results.” and 

19 they were intriguing and what he was showing. I 

20 was working on the Great Plains Tracer Experiment 

21 at that time and at the 20 and 4 km resolution. 

22 So I created a 12 km domain and ran CALMET just 

23 running with the NOOBS only and with P-G, and 

24 sent Clint the results on that. We didn’t 

25 conduct any independent analysis on our own we 
 1 Ninth Modeling Conference Vol. 2, p. 44 

 2 just said we already had something here and maybe 

 3 you can find something useful of this. 

 4 Clint sent back a graph that he was using in 

 5 the arc statistical program and he was plotting 

 6 up the results for each time step. What he saw 

 7 was 20 big 12 kind of comparable to 20 and 4 for 

 8 the concentration was smaller. On advice from 

 9 the Park Service we said okay and what is it that 

10 the terrain causing this or the land use. So I 

11 wrote a computer program to create dummy GEO.DAT 

12 files at the same resolutions so I basically 

13 flattened the terrain so that is was 1 meter 

14 terrain for the single land use. I had all the 

15 physical properties at that same level and what 

16 we saw was when you fix all that there were no 

17 changes in concentration: 20, 12 and 4 were very 

18 comparable to one another. We didn’t draw any 

19 conclusions from that. We said clearly the 

20 terrain and land use were making a difference 

21 there. That was the extent of what we did there. 

22 What we did provide to Clint was what the Great 

23 Plains Tracer Study did and that’s probably where 

24 this thing snow balled from you know was from 

25 that where Clint did show with that resolution 
 1 Ninth Modeling Conference Vol. 2, p. 45 

 2 what the change in the resolution you did get a 

 3 fairly consistent decrease in the concentration. 

 4 So we did that one additional sensitivity test 

 5 and we saw no change. 

 6 We had no opinion whatsoever with that and 

 7 that’s the real story about this whole thing. I 

 8 think it goes back to a good point that these 

 9 decisions we make are made on the basis of 

10 science and we should have good justification one 

11 way or the other. If Herman had a protocol in 

12 place, he was justified to say if you are going 

13 to deviate from the protocol you have to have 

14 justification. I think that’s a fair 

15 explanation. But with respect to and I know 

16 there’s a lot of communication in the community 

17 that EPA has issued memos or these tests have 

18 been done. That was the extent of the testing 

19 that was done. We don’t have any information one 

20 way or another and I have never given the 

21 information to Tyler to show anything about grid 

22 resolution. That’s the reality of that 

23 discussion. 

24 Tyler Fox: We are aware of the issue and 

25 Herman did exactly what he needed to do and 
 1 Ninth Modeling Conference Vol. 2, p. 46 

 2 requiring that justification just as any 

 3 deviation of a protocol or questioning about the 

 4 underlying basis that’s being put forward. We 

 5 need to balance the process and understand things 

 6 and stay away from this EPA has demanded stuff. 

 7 The Regional Office has the authority and works 

 8 within that authority to do things. When there a 

 9 broad precedent thing they will send it through 

10 and we have the Clearinghouse and other types of 

11 mechanism in place to then get to the final 

12 interpretation of guidance or decision in a 

13 particular case. Once we have that information 

14 and once it’s brought to us we move forward in 

15 the Clearinghouse action, but nothing has been 

16 brought to us. We are aware of Clint’s 

17 presentations at the workshop and as Bret 

18 described understanding what data he’s working 

19 with in trying to help in that process. 

20 We need to ...if there are any other questions 

21 about CALPUFF people can ask those before we get 

22 into the public session. But we need to move 

23 forward to respect the schedule and the like. 

24 Especially for some of the presenters who may 

25 need to leave. I’ll just make some quick remarks 
 1 Ninth Modeling Conference Vol. 2, p. 47 

 2 here. 

 3 Going back to the 8th Modeling Conference, we 

 4 covered the first, the second and the fourth 

 5 element that we had brought up so here’s the 

 6 third element that we talked about which is 

 7 basically said we really need to focus on 

 8 appropriate evaluation methods. The focus and 

 9 the purpose of identifying areas of improvement 

10 in the modeling system understanding that 

11 emphasis on modeling systems, recognizing that 

12 the emissions meterology and underlying modeling 

13 science are all part of that system working 

14 concert. But we need to understand the influence 

15 and effect on each. 

16 Therefore with that understanding we can 

17 seek the types of improvements we need by 

18 prioritizing the research either in the community 

19 or within EPA with our Office of Research and 

20 Development and will ultimately lead to an 

21 overall improvement and understanding of the 

22 performance of these models as they are applied 

23 in the regulatory policy context. 

24 So one note is that a year and a half ago, 

25 there was an evaluation work shop and this is 
 1 Ninth Modeling Conference Vol. 2, p. 48 

 2 just an example of the framework that one can use 

 3 for model evaluation. This one refers to the 

 4 community multi scale air quality model from the 

 5 EPA (inaudible) Office of Research and 

 6 Development. Basically you’re looking at a model 

 7 and in this case CMAQ and typically what we say 

 8 is when we’re doing an operational evaluation. 

 9 So we’re looking at a base line situation 2002, 

10 2005 and we’re looking across the different 

11 chemical (inaudible) species geographically and 

12 saying are we getting the right answers? Are we 

13 predicting the level of air quality compared to 

14 observations, the predictions to our models to 

15 the observations we see? 

16 That’s a standard fair. There’s a lot of 

17 work that we put forward as EPA in doing these 

18 operational evaluations. There are ways we can 

19 improve those types of operational evaluations 

20 that get more of the spatial nuance of the 

21 (inaudible). It is critical for us to go a 

22 couple steps further and look at things such as 

23 dynamic evaluation which can start to address the 

24 questions are we capturing the changes in air 

25 quality? Over time for example a publication on 
 1 Ninth Modeling Conference Vol. 2, p. 49 

 2 the (inaudible) call we had a kind of controlled 

 3 experiment; we had a major regulation come into 

 4 play and (inaudible) country (inaudible) and we 

 5 had a time period in 2002 without it. And we had 

 6 a time period in 2004 and 2005 with it. 

 7 You can start to test the models and see how 

 8 well they replicated that change. It’s not too 

 9 often we have those types of major changes and we 

10 can observe both from the observational 

11 standpoint and the model standpoint to see 

12 whether our models are responding in the way we 

13 would expect them to. 

14 The other question is we getting the right 

15 answers for the right reasons or the wrong 

16 reasons? That’s where we need to look at the 

17 diagnostic evaluation tools and from that make 

18 sure we feed that back in to the model. This 

19 loop is important if not the ultimate goal here. 

20 These are fine and dandy but if we don’t come 

21 back and focus on improving these models we are 

22 not doing a service to the community. 

23 And lastly, we can look at probabilistic 

24 evaluation in terms of getting and understanding 

25 of the confidence of these outcomes. Here’s a 
 1 Ninth Modeling Conference Vol. 2, p. 50 

 2 framework that is being worked on; there’s no 

 3 official mandate or anything, but this is where 

 4 our Office of Research and Development (ORD) are 

 5 trying to frame this so we can work together 

 6 better as a community as we conduct evaluations 

 7 of all the models. I wanted to share that with 

 8 you. And to start off we’ll start with Wyat 

 9 Appel from our Office of Research and 

10 Development. He will present a tool as Bret 

11 mentioned its one thing to talk about methods and 

12 techniques and the like. It’s another thing to 

13 apply them. Wyat has worked with others in ORD 

14 to deliver the atmospheric model evaluation tool 

15 available through CMAQ so he’s going to walk us 

16 through that. 

17 Wyat Appel: I work in the atmospheric 

18 modeling division in ORD here at EPA. And as 

19 Tyler said we have developed an evaluation tool 

20 and I’m just going to give an overview of it. 

21 It’s really focused to the (inaudible) like CMAQ 

22 and MM5 but it can be extended to other 

23 applications as well. In that the Atmospheric 

24 Model Evaluation Tool (AMET) consists of two 

25 modules. One that focuses on meteorology in this 
 1 Ninth Modeling Conference Vol. 2, p. 51 

 2 case typically MM5 or WRF and one focuses on air 

 3 quality typically our case CMAQ but also CAMx. 

 4 It’s a combination of several Open Source 

 5 Software packages so these are all free of 

 6 charge, license free. One is a database called 

 7 MYSQL, another one is R a statistical package 

 8 that Bret mentioned that. Then Perl and all of 

 9 these are available open source and we designed 

10 on a Linux Operating System. 

11 Actually others have extended it to other 

12 platforms as well. AMET is specifically designed 

13 to compare observations against meteorological 

14 (e.g. MM5, WRF) or air quality model (e.g. CMAQ, 

15 CAMx) predictions. We’re actually not importing 

16 an entire gridded data set. We’re just using 

17 paired model observation sets which are actually 

18 a different forum for some of the applications 

19 this group will do and I’ll get into that in a 

20 second. 

21 This is a kind of a flow chart of how the 

22 system works. There is a quality side but 

23 essentially the meteorology works the same with 

24 slight differences. It starts with the 

25 observations and then model output. These are 
 1 Ninth Modeling Conference Vol. 2, p. 52 

 2 paired in space and time through software we 

 3 developed. But you can do this on your own with 

 4 other software as well if you’re not working with 

 5 these models. It is paired in space and time, we 

 6 generate database records and then those records 

 7 go into the MySQL database. In essence we are 

 8 jus populating the database with model 

 9 predictions and observations. 

10 We’ve been in to the evaluation part so when 

11 all the data and observation are in the database 

12 we use a set of [ed. Perl] (inaudible) scripts 

13 pre-generated scripts to query that database, 

14 poll the type of data you want and then create 

15 statistics or plots that we pre-generated. I 

16 will get in to some of those. For example, model 

17 performance plots; this can be normalized Mean 

18 Bias, Fraction Bias, and any number of 

19 statistical metrics. Diurnal Statistics, Time 

20 series, Spatial Statistics, Box Plots, Scatter 

21 Plots, Bar Plots, “Soccer Goal” Plots, Bugle 

22 Plots. 

23 Then often because R is open source users 

24 can develop their own scripts to do their own 

25 type of analysis. The difference with the met 
 1 Ninth Modeling Conference Vol. 2, p. 53 

 2 side is it’s a different observations and a 

 3 different set of model output. Instead here of 

 4 the MM5 or WRF and here it’s a meta data set that 

 5 is maintained by the Forecast System Lab. But 

 6 essentially from that point on it works virtually 

 7 the same. They are paired in space and time as 

 8 they do in the database. 

 9 What are the advantages of a system like 

10 this? A somewhat automated/interactive system. 

11 Data stored in relational database which is great 

12 because one it puts all your data in a single 

13 spot. If you have multiple simulations different 

14 models, it doesn’t matter; it’s all in the same 

15 database and treated the same way. The real 

16 power is it allows data queries based on many 

17 factors. For example, geographic, if you have 

18 (inaudible) information you can box down to a 

19 certain latitude or longitude. You can look at a 

20 state and if you have county information figure 

21 sites, you can do it by pretty much any met data 

22 you can query by. You can also query by the data 

23 itself. Like concentration if you want to limit 

24 to a certain concentration you can do that as 

25 well. We have pre-generated analysis scripts so 
 1 Ninth Modeling Conference Vol. 2, p. 54 

 2 this uses the same analysis for multiple 

 3 simulations for other groups. One group, in 

 4 doing their analysis, if they use this, you would 

 5 see a similar type of analysis from possibly 

 6 other groups doing it. We’re always trying to 

 7 figure out what did someone do a little 

 8 different. It kind of like using this type of 

 9 analysis among different groups. And then it’s 

10 open source pretty much free of charge. 

11 These are the types of analysis that are 

12 available on the met side and I’ll show some 

13 examples of these. There’s a met model 

14 performance summary which I have an example of 

15 and some of the plots you may be more familiar 

16 with such as Timeseries, Spatial Plots, and 

17 statistics. Bar Plots, and some specific plots 

18 to the met side includes Rawindsonde, Wind 

19 Profiler, and Aircraft Profiler. 

20 This is an example of a model performance 

21 summary. This is a plot that’s available on the 

22 met side. You see here this one is for 

23 temperature and the one on the right for wind 

24 direction. It includes a number of different 

25 plots and statistics scatter plot, model 
 1 Ninth Modeling Conference Vol. 2, p. 55 

 2 performance summary statistics, metric across 

 3 different temperature ranges and then a box plot 

 4 showing the distribution of the model. This is a 

 5 single plot so you just kind of pick this for 

 6 whatever your data set is and this is what gets 

 7 generated. And similarly on the wind direction 

 8 side in the wind direction plots where you can 

 9 see the distribution and wind speed in your data 

10 and some other summary statistics, etc. 

11 Also available are time series plots, your 

12 mixing ratio, wind speed, wind direction, but 

13 pretty much any meteorological metric you have 

14 available you will be able to apply just like 

15 this. Spatial plots are summary statistics so 

16 this is .... don’t worry about the data showed 

17 here it’s just for example. This is actually 

18 four different work simulations that are shown 

19 and these are the R (inaudible) for each of those 

20 plots. And you see color coded and then you 

21 would also be to window this down to other 

22 regions. One may say about R at least for the 

23 United States is it contains more detailed maps. 

24 If you do go down to looking at a smaller 

25 location like a state you would be able to 
 1 Ninth Modeling Conference Vol. 2, p. 56 

 2 include a county map on top of that. 

 3 This is a wind profiler comparison over time 

 4 and then (inaudible) and you see the wind speed, 

 5 a very nice plot. This would be specific where 

 6 you have wind profile information,. a nice plot. 

 7 Similarly for aircraft comparisons similar types 

 8 of plots are available with similar types of 

 9 plots available and different distributions in 

10 height. 

11 On the AQ side there is similar analysis 

12 available slightly different. Rob and I work 

13 together but we do things differently with 

14 different data. Scatter plots this includes 

15 model observation, model to model, summary 

16 statistics which usually is output as a csv text 

17 file so it’s easily imported into EXCEL. Spatial 

18 plots and box plots and these are a little more 

19 specific; stack box plots (inaudible) box plots. 

20 On the scatter plots, a basic scatter plot, 

21 it has the ability to include select statistics 

22 on it. In this case, on CMAQ, we usually prepare 

23 to admit a number of different (inaudible) so we 

24 like to keep it separated because they behave 

25 differently. But this is -- imagine if you 
 1 Ninth Modeling Conference Vol. 2, p. 57 

 2 included model to model (inaudible) single 

 3 network, multi network. And also, the ability to 

 4 temporally average this over different time 

 5 periods such as seasonally, monthly, annually, 

 6 and daily. 

 7 Spatial plots are very similar to what I 

 8 showed on the met side. Implied statistics 

 9 (inaudible) we have a number of different of 

10 statistics available. And also concentrations, 

11 model observed, the bias between the model 

12 observed and also you can sub region this out 

13 like that. Again time series plots. We’re only 

14 showing observed and (inaudible) but you could 

15 also include another model data so you could 

16 compare two model runs and see how they compare. 

17 Box plots. This is a box plot in time of day and 

18 you can ... the behavior of the data across the 

19 hours of the day. Also this is a box plot for 

20 monthly so you can see the behavior across the 

21 entire year. Stack bar plots, this is more 

22 specific to some of the data available for 

23 comparing with the model like CMAQ. But it shows 

24 the type of plots you could create that are 

25 specific. And R is very powerful as related to a 
 1 Ninth Modeling Conference Vol. 2, p. 58 

 2 lot of different plots and if someone is familiar 

 3 with R it’s generally easy to tailor it to 

 4 whatever your data or skip a type of plot you 

 5 would like to see. 

 6 Some of the other parts includes some of the 

 7 metrics are some Bugle Plots where it includes 

 8 performance criteria. These are available by 

 9 default AQ side and then the Soccer Goal Plot is 

10 a little hard to see but there are lines for the 

11 bias and a kind of outline there. One of the 

12 nice things about expanding beyond CMAQ is if you 

13 have any set of model predictions in time and 

14 space you should really be able to import that 

15 into the database and analysis just like you 

16 would any other database. Even if you are not 

17 using CMAQ or CMAx or a model like that, if you 

18 have data generally in the common (inaudible) 

19 that includes a model of and some space and time 

20 information there would be a way to get back into 

21 the database and analysis just like you would 

22 CMAQ and anything else. We just pre-generated it 

23 some scripts that will take the raw CMAQ output 

24 and bring it right into the database. 

25 Then also the analysis scripts themselves 
 1 Ninth Modeling Conference Vol. 2, p. 59 

 2 can be used outside of data met. There are 

 3 scripts so if you got data and you don’t want to 

 4 go through the hassle of putting it in the 

 5 database, take the R script and you can read it 

 6 directly in the R so that you can extend these 

 7 plots or use these plots outside of the met 

 8 system itself. 

 9 We have been working on this for a few years 

10 and early this year we released it publicly. 

11 This a script based version both the Met and AQ 

12 versions available and it includes an extensive 

13 users guide included which we have gotten good 

14 feedback. The script tit is very helpful for 

15 setting up and using. It contains most of the 

16 functionality shown here and some things 

17 developed but not included in the release but in 

18 the future we will include them. You can install 

19 the Met and AQ versions separately. Includes 

20 tutorial data and example output plots and then 

21 there’s also a Bugzilla available for AMET which 

22 you can submit any questions or problems you have 

23 with this system. 

24 For future improvements we have to build a 

25 Java interface which will be real nice since a 
 1 Ninth Modeling Conference Vol. 2, p. 60 

 2 lot of the system is picking different options, 

 3 time of day, state and region so it really lends 

 4 itself to a Java type of interface. It’s really 

 5 in the background. That would able to runs AMET 

 6 locally and accesses remote database. It would 

 7 be a little bit more user friendly than the 

 8 script database. Hopefully we can do some 

 9 additional analysis scripts similar to the ones 

10 we have build over the year. And also the ones 

11 developed externally by the user community. Then 

12 more query options. The great thing is no matter 

13 what met data you put in you can use as a query 

14 option. That’s it. Thanks. 

15 Tyler Fox: Wyat has to leave a little 

16 early so are there any specific so are there any 

17 questions about AMET. We’ll take a couple of 

18 questions. 

19 Pete Manousos: First Energy. You rolled 

20 this out publically this year you said? 

21 Wyat Appel: Yes. 

22 Pete Manousos: Okay. How far in the future 

23 do you see this being supported? 

24 Wyat Appel: That’s a good question. It’s 

25 not something we have talked about it’s still in 
 1 Ninth Modeling Conference Vol. 2, p. 61 

 2 development and I don’t know specifically that 

 3 we’ve talked about how far we will keep 

 4 supporting it. I think no matter what’s out 

 5 there it doesn’t really become defunked in any 

 6 way which is a nice thing. Even if we stopped 

 7 putting out new capabilities, that doesn’t stop 

 8 users from putting in new capabilities. But 

 9 certainly in the near future we see putting it 

10 out more scripts and then maybe putting out a 

11 Java version. 

12 Pete Manousos: And support would be through 

13 Bugzilla? 

14 Wyat Appel: Well through CMAS. It’s 

15 available through CMAS that’s the entity we go 

16 through to put it out to the public. We 

17 basically did the development internally at EPA 

18 and then go through them to get it out 

19 externally. Then we monitor the bugs that the 

20 users have. 

21 Pete Manousos: Thanks 

22 Wyat Appel: Sure. 

23 Tyler Fox: Just a side note we at EPA will 

24 be continuing to develope model evaluation tools 

25 so as long as we have the need I would imagine at 
 1 Ninth Modeling Conference Vol. 2, p. 62 

 2 least from the OAPQS standpoint I can’t speak for 

 3 ORD, we certainly will be wanting to see this 

 4 tool developed and expanded both by the user 

 5 community and internally as we move things 

 6 forward. Next on our list for the evaluation 

 7 session is Bob Paine. 

 8 Bob Paine: I’ll probably be able to go 

 9 through these very quickly because others have 

10 addressed many of the points here. I come from 

11 the point of view of the previous AERMIC 

12 committee having done a lot of the evaluation 

13 work with Roger and others on the previous 

14 versions of AERMOD. I’m going to talk about the 

15 AERMOD evaluation review, evaluation tools, and 

16 for short range modeling evaluations the somewhat 

17 dated Cox-Tikvart evaluation procedure. I will 

18 also address the BOOT/ASTM evaluation procedure 

19 and Joe Chang has been very gracious in providing 

20 some slides for this presentation. He should 

21 probably give it but I’m here anyway. 

22 I will also mention some evaluation databases 

23 that Joe has collected and should probably hand 

24 over to EPA, and also a brief comment on the 

25 gridded met evaluation. 
 1 Ninth Modeling Conference Vol. 2, p. 63 

 2 I’d like to say that Jeff Connors who is my 

 3 colleague here has done an urban evaluation and 

 4 provided that database to EPA for AERMOD. And we 

 5 used some of the evaluation tools and we will 

 6 talk about. In the future, we will be doing an 

 7 evaluation of low wind speed databases with API 

 8 funding and working with EPA on that issue as 

 9 well. 

10 There are generally two types of short-range 

11 types for evaluation of databases. One involves 

12 tracer studies and short-term intensive studies, 

13 typically with multiple rows of samplers, each 

14 with many sites where you can determine plume 

15 centerline and plume sigma-y. You can determine 

16 concentration trends with distance and maximum 

17 concentrations on tracer arcs that are used for 

18 the evaluation. You can evaluate predictions 

19 paired in time and distance in this type of 

20 evaluation. Here the limitation is the short 

21 duration of the study and you have a limited 

22 number of meteorological conditions and seasons, 

23 where the other type of database -- the long-term 

24 monitoring networks featuring year-long sampling 

25 at a few sites -- has the advantage of temporal 
 1 Ninth Modeling Conference Vol. 2, p. 64 

 2 resolution. You really have to do things in 

 3 unpaired in time and if necessary; paired in 

 4 space; so the limitation is spatial resolution 

 5 and advantage is a large number of hours in the 

 6 database. 

 7 So in the AERMOD evaluation, we have the 

 8 question: how well does AERMOD predict peak 

 9 ground-level concentrations used for compliance 

10 with air quality (AQ) standards? Is AERMOD’s 

11 performance significantly better than that of 

12 similar models? Evaluation databases were a 

13 mixture of tracer experiments and long-term 

14 studies 

15 We tended to rely on plots used extensively; 

16 they are often better than “black box” statistics 

17 like the robust highest concentration. For 

18 example, the Quantile-Quantile (Q-Q) plots will 

19 plot pairs of ranked predictions and 

20 observations, unpaired in time and can be used 

21 for both types of evaluation databases. Residual 

22 plots are plots of ratios of predicted/observed 

23 conc vs. downwind distance or wind speed, etc. 

24 They are generally used only for tracer 

25 databases. Estimates of Robust Highest 
 1 Ninth Modeling Conference Vol. 2, p. 65 

 2 Concentration, or the RHC, represent a smoothed 

 3 estimate of the highest concentrations (from Cox- 

 4 Tikvart evaluation technique). Generally, the 

 5 scatter plot (data paired in time and space) is 

 6 only used for tracer databases. 

 7 We go to the Quantile-Quantile plot which is 

 8 a ranked observation verses prediction plot and 

 9 hopefully the peak concentrations are close to 

10 the one-to-one line. Peaks on this plot here 

11 indicate where it’s closer to this model. In the 

12 range of the moderate concentrations we are a 

13 little low here. 

14 Other types of tools are the plotted model 

15 residuals, which are plots of 

16 predictions/observations as a function of an 

17 independent variable where we have group 

18 residuals according to ranges of an independent 

19 variable. You actually have a box where the 

20 midpoint is marked here. In general you see the 

21 trend is very low as a function of the 

22 independent variable. We use a box plot to 

23 indicate the distribution of the “n” points in 

24 each group. For example, the significant points 

25 for each box indicate the 2nd, 16th, 50th, 84th, and 
 1 Ninth Modeling Conference Vol. 2, p. 66 

 2 98th percentiles. A good model should have no 

 3 trend in model residuals. 

 4 This is a poor model example where you can 

 5 see this trend for the hour of the day is very 

 6 dependent on the wind speed as well. When you 

 7 see that, you see the model has some bias due to 

 8 a function that is variable. We have to 

 9 understand what is going on here because the 

10 model does have a possible problem. These are 

11 very useful tools. According to evaluation 

12 statistics that have been mentioned, the 

13 fractional bias (FB) is used in the BOOT and ASTM 

14 systems. It is basically a function of the 

15 observed and predicted concentrations where an FB 

16 of zero is a perfect model, while an FB of +/- 

17 0.67 is within a factor of 2. 

18 The major features of the older Cox-Tikvart 

19 Method would be use of the RHC statistic, re20 sampling of data used to determine confidence 

21 interval for differences in performances of 

22 models, and the composite performance measure 

23 (CPM), which combines absolute FBs for several 

24 averaging times. The model comparison measure 

25 looks at differences in CPM between models to 
 1 Ninth Modeling Conference Vol. 2, p. 67 

 2 determine the statistical significance of 

 3 differences among models and this is best suited 

 4 to long-term, sparse network evaluation 

 5 databases. 

 6 These next comparisons are borrowed from 

 7 Roger Brode actually. Several models are shown 

 8 here where we have a CPM score, and obviously, 

 9 the lower the score, the better the model. If 

10 the the model comparison measure straddles zero, 

11 then that means the models are not statistically 

12 different. In most cases here, this one is maybe 

13 just barely significantly different. Those are 

14 the features of the Cox-Tikvart method. 

15 Here we used the BOOT software, and it is 

16 used a lot in Europe. It was developed by Hanna 

17 and Chang, and is available through them. It is 

18 best suited to tracer databases and is widely 

19 distributed to (> 200) scientists in the field, 

20 mainly through the European’s Harmonisation 

21 within Atmospheric Dispersion Modelling for 

22 Regulatory Purposes – Model Validation Kit. It is 

23 generic and can be used to evaluate different 

24 kinds of models, different kinds of outputs, and 

25 different kinds of data pairings 
 1 Ninth Modeling Conference Vol. 2, p. 68 

 2 Some of the performance metrics in the BOOT 

 3 software you have seen before: factional bias, 

 4 normalized mean square error, and another way to 

 5 do the variance and bias using statistics like 

 6 geometric mean, cases within a factor of 2, and 

 7 correlation coefficient. 

 8 A way to plot the variance and bias in one 

 9 plot is here, where the X-AXIS would be the 

10 geometric mean, with over prediction on the left 

11 and under prediction on the right. The variance 

12 is on the Y-AXIS, so a perfect model is as low as 

13 you can go while keeping the bias in the middle. 

14 You can compare the two models and determine if 

15 they are significantly different. Actually, this 

16 is a plot of the various data values such that if 

17 they cross zero, they are statistically unbiased 

18 within a confidence in 95% on this case. 

19 The question comes up “What are 

20 Observations”? Observations can be measured by 

21 instruments or products of other models or 

22 analysis procedures. John Irwin three years ago 

23 was talking about the American Society for 

24 Testing Material Procedures similar to BOOT -- 

25 treating observations as snapshots of an 
 1 Ninth Modeling Conference Vol. 2, p. 69 

 2 ensemble, while model predictions often represent 

 3 ensemble averages. That’s one way you can do a 

 4 fitted observation. 

 5 The two cannot be directly compared unless 

 6 you do something with the observation. The way 

 7 you do that is group them in regimes of similar 

 8 conditions as atmospheric stability or downwind 

 9 distance. For a particular tracer arc if you 

10 have a cross wind concentration like this you 

11 would try to fit it with a best-fit Gaussian 

12 curve in order to depict an ensemble peak 

13 concentration and so on. 

14 These are again from Joe Chang and some 

15 results are sensitive to how the limited regimes 

16 are defined. You might have to idealize the 

17 experiments with concentric sampling arcs to make 

18 this work easily. To get into how the procedure 

19 should be applied to the evaluation of 3-D 

20 Eulerian air quality models, where predicted 

21 concentrations represent averages over a grid 

22 volume, but observed concentrations represent 

23 point measurements, it is difficult to figure out 

24 how you would apply this procedure. 

25 I am getting mercifully to the end now. 
 1 Ninth Modeling Conference Vol. 2, p. 70 

 2 Just want to show you a couple of slides from Joe 

 3 Chang. We have a lot of archives -- or Joe has a 

 4 lot of archived databases, but unfortunately the 

 5 budget for maintaining this is very, very slim. 

 6 The budget for collecting and analysis is a 

 7 little bit more. He would say that the more 

 8 realistic or optimistic scenario would be to have 

 9 more budget set aside for archining evaluation 

10 databases. You probably can’t see this, but you 

11 can see this on the presentations that there are 

12 over a 100 database references. For the existing 

13 data, I would like somehow to make sure with EPA 

14 that we don’t have another hard drive crash. 

15 Literally, these are about a hundred databases, 

16 so it would be nice for EPA to take ownership of 

17 these databases. 

18 I have one last comment on evaluation of 

19 gridded meteorological data. It’s almost like a 

20 new concept do we trust MM5 data instead of a 

21 meteorological tower. We need to thoroughly 

22 analysis the gridded met data. There be may be 

23 situations with poor met performance (e.g., 

24 complex terrain). Conditions of concern for 

25 dispersion modeling are how often are the winds 
 1 Ninth Modeling Conference Vol. 2, p. 71 

 2 very low from the tower verses the computed 

 3 meteorological data. 

 4 How about the Low Level Jet, which we’ve 

 5 seen before -- for example, in that Great Plains 

 6 experiment? The problem with the Low Level Jet 

 7 is that you have a sounding at 6:00 PM and 6:00 

 8 AM and the Low Level Jet happens in between. In 

 9 North Dakota, we found that the EPA model missed 

10 the Low Level Jet and underestimated the 

11 dispersion. The use of better meteorology got 

12 the plume dispersion predictions in CALPUFF 

13 better. You’ve got to have, I think, an 

14 understanding of the Low Level Jet and the wind 

15 rose profile misrepresentation, among other 

16 issues. 

17 Sources of data for testing that I would 

18 like to recommend are: we need to find tall 

19 tower data, not just surface data because a lot 

20 of the applications are for tall stacks. For 

21 example private industrial met towers for which 

22 the data has been provided to the agencies are 

23 now in the public domain. There are numerous 

24 wind energy assessment towers that are available 

25 to the public. I would recommend that these 
 1 Ninth Modeling Conference Vol. 2, p. 72 

 2 databases be used for the independent assessment 

 3 for the evaluation of the gridded met data. That 

 4 concludes my talk. 

 5 Tyler Fox: Thank you Bob. Alright, we will 

 6 finish the evaluation with Roger who will go 

 7 through some recent evaluations beyond the 

 8 typical Cox/Tixvart evaluation methods that are 

 9 appropriate in the way we use AERMOD under 

10 Appendix W [ed. for NSR and] (inaudible) PSD. 

11 But obviously as mentioned yesterday by Lee and 

12 we’re seeing more use of these types of models 

13 for exposure and other type of risks assessments 

14 which puts more stress on them from a space and 

15 time perspective. So Roger will give us some 

16 information on what we’ve learned so far on that. 

17 Roger Brode: Thank you Tyler. I 

18 appreciate the presentations that have been made. 

19 Want to mention I want to follow up in some of 

20 the work here in terms of AERMOD evaluation and 

21 some (inaudible) that has been doing to look at 

22 the model in a more robust evaluation. This is 

23 going to be more (inaudible) information that has 

24 come along recently. Very brief slide on 

25 requirements of operational Regulatory Dispersion 
 1 Ninth Modeling Conference Vol. 2, p. 73 

 2 Models vs. ER [ed. Emergency Response] Models or 

 3 other types of models that might be used. 

 4 Again some of this have already been covered 

 5 but for regulatory models need to predict the 

 6 peak of the concentration distribution, unpaired 

 7 in time and space, for comparison to AQ 

 8 standards. But in emergency response models, and 

 9 perhaps models used for risk and exposure 

10 assessments, require skill at predicting 

11 concentration distributions paired in time and 

12 space. At least understand their ability to do 

13 that. And we expect the need for that type of 

14 model performance to increase in the future and 

15 it is going to be a challenge to meet those 

16 requirements. 

17 Just some real quick examples. For 

18 regulatory model evaluation this is prairie grass 

19 one of the best databases ever collected back in 

20 the 1950’s. It is an intense tracer study as Bob 

21 Paine just mentioned so we actually had I forget 

22 how many arcs receptors densely located on a 

23 series of arcs. This is a Q-Q Plot of AERMOD 

24 evaluation in stable conditions. Sort of 

25 unpaired but sort of loosely paired in space 
 1 Ninth Modeling Conference Vol. 2, p. 74 

 2 because these are the arc (inaudible) maximum 

 3 concentration at each arc not the individual 

 4 concentrations that each receptor along the arc. 

 5 If you just unpair them in time you get a little 

 6 bit of difference a little bit more scatter plot. 

 7 But not much they are loosely paired in terms of 

 8 the arc (inaudible) maximum being applied here. 

 9 Another example is for Indianapolis that’s a 

10 tall stack or evaluation data base that was used 

11 in AERMOD performance evaluation. Again this is 

12 unpaired looks pretty good the Q-Q plot shows 

13 pretty close to one line. Then unpaired it’s a 

14 little bit messier more of a scattered plot. 

15 Just a couple of examples I’ll try to be correct. 

16 These are applications of AERMOD that have come 

17 to our attention within the agency. But someone 

18 has run AERMOD and getting results they don’t 

19 like and don’t understand so we want to share 

20 what we’ve learned. Not a real robust formalized 

21 evaluation procedure but it’s an opportunity for 

22 us to help others in their application models. 

23 But also learn ourselves how the model performs 

24 in different situation. This one sort of gets 

25 into the wind speed issue as Bob Paine mentioned 
 1 Ninth Modeling Conference Vol. 2, p. 75 

 2 and we appreciate the effort he will be 

 3 undertaking soon to evaluate model performance 

 4 under these specific conditions. We look forward 

 5 to collaborating on that. 

 6 I got permission from Lee to use this. You 

 7 heard about this the Alabama DEM study for the 

 8 Birmingham Local Area Analysis (LAA) for PM-2.5 

 9 SIP. Basically AERMOD was run initially with 

10 airport data and with the SEARCH data sets that 

11 include sonic anemometer with lower wind speed 

12 stretched so they had lots of light wind speed 

13 and the SEARCH met data. The model seemed to be 

14 over predicting. 

15 This is actually for the Wylam this is how 

16 it originally came to us and you can see a 

17 dramatic over prediction. This is actually time 

18 series plot running the model with the airport 

19 only data which that blue line down near zero and 

20 you have the SEARCH data. As you can see there’s 

21 a dramatic difference there. Won’t go into all 

22 the details here but this is the Wylam monitor 

23 which Lee presented yesterday. It’s actually 

24 pretty satisfied with the results there, it’s not 

25 perfect but at least it looks a lot better. 
 1 Ninth Modeling Conference Vol. 2, p. 76 

 2 There are a number of reasons for that. 

 3 One of which had to do with I think the 

 4 initial comparison was based on the maximum 

 5 concentration of AERMOD across the gridded 

 6 receptors (inaudible) on the monitor location to 

 7 the actual monitored concentration. It actually 

 8 had receptors in AERMOD that were either very 

 9 close to the fence on property of facility close 

10 to the fence line being compared to 

11 concentrations from the monitor. This just shows 

12 again at the airport for Birmingham that the 

13 SEARCH site pretty closes by showing the 

14 proximity but different settings. Low roughness 

15 which would be typical of a met tower at an 

16 airport. Then higher roughness at the SEARCH 

17 site. It was sited direct within a neighborhood 

18 with buildings and trees around. I suppose it is 

19 more typical of the sources. 

20 One thing that came to our attention here: 

21 This is a terrain plot and it’s not very clear 

22 here. There are some slopes involved. There’s 

23 more significant terrain features around the 

24 site. It’s not real dramatic terrain features 

25 but there are definitely slopes there. First of 
 1 Ninth Modeling Conference Vol. 2, p. 77 

 2 all this is a plot again concentrations a time 

 3 series plot based on airport data the light blue 

 4 line. Not sure about the dark line plotted 

 5 against the frequency of calms each day from the 

 6 airport. 

 7 So this is 24 hour averages and what you can 

 8 see is a pretty good correlation when the 

 9 observed concentration goes up it’s often highly 

10 correlated with high frequency calm. For example 

11 if you have 18-20 hours of calm, it indicated a 

12 lot of light wind speed, upward spike in the 

13 observed concentration. That certainly suggests 

14 an important presence of local sources of PM for 

15 that monitor. But if you look at the airport 

16 date, it actually goes down. Sort of an in birth 

17 correlation and there’s a couple of cases where 

18 you can see that trend. I think at Birmingham 

19 airport this is a case where between calm and 

20 variable winds we are looking at 25 or 30% of the 

21 data period missing either to calms or winds. 

22 So that was a sort of (inaudible) 

23 information that if you do have low level sources 

24 you will be expected high concentration under 

25 light wind conditions. There may be some 
 1 Ninth Modeling Conference Vol. 2, p. 78 

 2 question of the representative of that airport 

 3 data for that applications because you can see a 

 4 pretty clear pattern as the light wind speeds go 

 5 down, calms go up, observed concentrations go up 

 6 but the model concentration with that data 

 7 without (inaudible) the calms go down. This is 

 8 the first case where we got into the use of the 

 9 one ASOS data which we shared. 

10 The other thing I’ll point out here is this 

11 is with the SEARCH data showing a high 

12 concentration. This period stood out initially I 

13 guess a period in here the SEARCH was missing and 

14 that was one of the issues with the quality of 

15 data. Just looking at the wind direction 

16 compared with met SEARCH site and airport site to 

17 be fairly close about 5 or 6 km separation. We 

18 discovered there was an offset in the first three 

19 weeks of the year and they verified this later 

20 that the SEARCH wind directions were offset by 

21 about 120 degrees so that kind of stands out as 

22 different in some ways. 

23 This is sort of (inaudible) information. 

24 They come, we help and they go and we don’t know. 

25 Hopefully we can close the loop on a little bit 
 1 Ninth Modeling Conference Vol. 2, p. 79 

 2 better. One of the things we are looking at 

 3 more closely, we sort of realized once we 

 4 supplemented the airport with the 1-minute ASOS 

 5 we looked at what is going on under these very 

 6 light wind. For the SEARCH site you can clearly 

 7 see low wind, drainage flow, showing up under 

 8 those conditions at night. Sort of from off of 

 9 this ridge here from a northern sort of North 

10 West direction would be the typical light wind, 

11 cold air and drainage flow. At the airport it’s 

12 more from the East that direction. Once we 

13 supplemented it with 1-minute ASOS it doesn’t 

14 show up at all with this standard airport data 

15 because they’re all missing the calms. 

16 I don’t have the plot on here but from 

17 the....guess they didn’t put it in here. Here’s 

18 the SEARCH site that’s matched with the model 

19 where they had the PM 2.5 concentrations and 

20 there was actually a facility just east of the 

21 site. One of the things that is going on there 

22 is that when you use the airport data under the 

23 light winds conditions that show up at the 

24 airport when you supplement it you are getting a 

25 drainage flow towards the West basically at the 
 1 Ninth Modeling Conference Vol. 2, p. 80 

 2 monitor from the source that is the closest 

 3 source. 

 4 Whereas the SEARCH site which is right next 

 5 to the source the drainage flow is more from the 

 6 North West not directly so that the plume from 

 7 the facility would be going right at the monitor, 

 8 it would be going towards the South. That’s 

 9 contributing to what you’ve seen here as because 

10 (inaudible) offset the drainage from the SEARCH 

11 data was in the wrong direction and was basically 

12 pulling a different source. 

13 That’s one example just to see again is 

14 there a problem with the model that these light 

15 wind conditions? It’s not a clear answer one way 

16 or another but there is some concern if you use 

17 airport data and 25-30% is calm those results may 

18 be biased in the wrong directions. Whether the 

19 results are realistic or whether the problems 

20 there are sort of not clear yet. 

21 Another issue that comes up is surface 

22 roughness sensitivity and this is more recent. 

23 Example is AERMOD being applied to support 

24 exposure assessment for the Atlanta area to 

25 support current NO2 NAAQS review. Majority of NO2 
 1 Ninth Modeling Conference Vol. 2, p. 81 

 2 impacts attributed to mobile sources so major 

 3 roadways were modeled as links and minor roadways 

 4 as area sources; sort of temporally and spatially 

 5 distributed so the initial model-to-monitor 

 6 comparisons showed AERMOD concentrations 

 7 significantly exceeding monitored NO2 

 8 concentrations at 3 Atlanta monitors. An initial 

 9 assessment was that low surface roughness used to 

10 process airport data was not representative of 

11 roughness typical of source locations, and 

12 suggestion was to re-process airport data with 1m 

13 roughness to address that. 

14 We kind of suggested there are other ways. 

15 We did a broader assessment of modeling 

16 analysis, recommendation were made to acquire and 

17 process SEARCH met data as more representative of 

18 source surface characteristics of the sources. 

19 Another issue is to apply OLMGROUP option within 

20 Ozone Limiting Method to better account for NO to 

21 NO2 conversion. We suggested to apply the 

22 OLMGROUP options be applied to perhaps get a 

23 better account for the NO2 chemistry in this 

24 context. Also we looked at the source 

25 characteristics for the mobile sources and 
 1 Ninth Modeling Conference Vol. 2, p. 82 

 2 suggested some changes to better account for 

 3 vehicle induced turbulence. Especially for the 

 4 light duty vehicles they are being modeled as 

 5 basically tail pipe with the release pipe in the 

 6 small (inaudible). Those changes were made. 

 7 Just a very quick I didn’t have plots that 

 8 were on the same scale. This is sort of model to 

 9 monitor comparison at one of the NO2 monitors 

10 before. The black is the measured NO2 

11 concentration and the lighter blue is the model 

12 concentration from AERMOD. Again most of this is 

13 due to multiple sources (inaudible). Thousands 

14 of sources over the whole Atlanta area and again 

15 you’re up 300 (inaudible) and the purpose of this 

16 study is due to the exposure assessment is what 

17 the frequency of the exceedence was. There was 

18 some concern whether AERMOD could be used in this 

19 context. Once we addressed some of these issues 

20 this is the model comparison after I think the 

21 period from the previous slide was sort of in 

22 here. 

23 You can see much better (inaudible) it’s not 

24 perfect but considering all the uncertainties in 

25 the emissions and so on, we felt that was pretty 
 1 Ninth Modeling Conference Vol. 2, p. 83 

 2 good. This same kind of pattern is pretty 

 3 consistent from one month to the next at the 

 4 other monitors as well. So they seemed 

 5 encouraged by that. 

 6 Just for interest sake I went back and 

 7 modeled multiple sources again that’s majority 

 8 impacts. Again with the airport date this is the 

 9 Q-Q plot of modeled concentrations using SEARCH. 

10 These are AERMOD concentrations using the SEARCH 

11 data process with surface characteristics using 

12 AERSURFACE pretty high roughness about 0.8 meters 

13 0.7 meters verses concentration process with the 

14 airport data with the 1-minute ASOS 

15 supplementation with its roughness which is 

16 pretty low for an airport. And pretty close to 

17 the 1 to 1 line except there’s only one point I 

18 don’t know if you can see it. It’s about 2 to 1 

19 over prediction or difference between two models. 

20 But interestingly enough the met data that 

21 produced the higher concentration was the from 

22 the SEARCH site with the higher roughness. 

23 Not sure what that says but it’s an 

24 interesting result to see that the issue of 

25 surface characteristics differences between the 
 1 Ninth Modeling Conference Vol. 2, p. 84 

 2 airport and the SEARCH site didn’t seem to be 

 3 playing a very major role here. One caveat this 

 4 is right in the urban options so the urban 

 5 boundary layer enhancement is certainly helping 

 6 mitigate some of the differences you would expect 

 7 to see due to surface roughness itself. 

 8 The next one is more on the source 

 9 characterization side of this. These are issues 

10 that kind of come up. I mean all three of these 

11 issues in all three of these cases but this is a 

12 little bit more focused on that. This a model 

13 comparison and they were doing with Benzene 

14 concentrations from refineries in Texas for 

15 Residual Risk review. Actually, initial results 

16 from standard ISHD airport data showed 

17 significant under predictions and the conclusions 

18 that issue was well we need far background 

19 concentrations. We recommended using 1-minute 

20 ASOS wind data to reduce the number of calms, 

21 which contributed to under prediction. And a 

22 more detailed assessment of representativeness of 

23 met data resulted in selection of another nearby 

24 station. This was fairly close to the coast down 

25 in the Gulf Coast but not right on the coast but 
 1 Ninth Modeling Conference Vol. 2, p. 85 

 2 there are a number of sites there. 

 3 And also another non standard airport site, 

 4 the Texas (inaudible) site, I think we looked at. 

 5 This is sort of a quick end look and see if we 

 6 can learn. It’s hard to close the loop on it. 

 7 The other thing is the sensitivity of model 

 8 results to source characterization options for 

 9 storage tanks examined, with recommendations to 

10 improve characterization. So we looked at 

11 different options and there’s a lot on this 

12 slide. I think what they initially did was 

13 elevated area sources with no initial Sigma Z. 

14 That’s the very low impact that starts to come 

15 up. The monitor was kind of within 100 meters 

16 range and that could be pretty important. That 

17 might be why they were getting some un18 predictions. The other was the calms. Looking 

19 at different ways to model it there’s an area 

20 source with an initial Sigma Z or volume source 

21 but one thinks they may need to look at in terms 

22 of guidance or recommendations something from the 

23 implementation guidance is the SEARCH tank. 

24 I think a better way to do it these days 

25 with the PRIME downwash algorithms since it 
 1 Ninth Modeling Conference Vol. 2, p. 86 

 2 exclusively treats the cavity impact region is to 

 3 model tanks maybe series non buoyant point 

 4 searches around the top of the tank and input the 

 5 tank itself as a building. That is kind of the 

 6 blue curve here. So depending on where you are 

 7 you can have a whole lot of sensitivity or not 

 8 that much. But if you’re close to the sources it 

 9 can be pretty significant. 

10 These are just a range of results based on 

11 different met data and different source 

12 characterization and I think we ended up feeling 

13 that Galveston would be the most representative 

14 data and including some Sigma Z so this is 

15 putting the plume the release sight more in the 

16 middle of it and some initial Sigma Zz. There’s 

17 the 100 mile about 5.65 and here about with the 

18 Hybrid met data about 5.96 so we’re getting 

19 reasonably close. This other monitor didn’t do 

20 as well I think there were some other concerns 

21 there about whether there could be other 

22 background sources impacting that monitor. This 

23 one was pretty much downwind from one of the 

24 refineries. 

25 That’s again just some (inaudible) 
 1 Ninth Modeling Conference Vol. 2, p. 87 

 2 information. What I’d like to do is take these 

 3 opportunities to learn about the model. They are 

 4 not robust or formalized evaluations but at least 

 5 they can give us some information as to what the 

 6 limitations of the model are and the 

 7 sensitivities are. And what we need to focus on 

 8 in terms of providing better guidance. And how 

 9 to apply the model and we also want to do is 

10 build on what Bret is doing in model performance. 

11 Looking at more paired in time space basis and 

12 find out how well AERMOD does or doesn’t do with 

13 that more robust demand on its performance. 

14 Tyler Fox: Thanks Roger. So now we have 

15 any questions as it relates to the model 

16 evaluation section. 

17 Arney Srackangast: This pertains to this 

18 last evaluation that Roger was presenting related 

19 to storage tanks. I haven’t seen the study that 

20 you have but the storage tanks have been modeled 

21 quite commonly now. They’re doing maintenance, 

22 startup, shutdown permits down in the Texas area, 

23 and so they are being looked at quite closely 

24 for regulatory review. There’s a wide variation, 

25 as far as impacts go, with no clear guidance on 
 1 Ninth Modeling Conference Vol. 2, p. 88 

 2 how we should really be modeleing these. One is 

 3 almost always gravitated in the regulatory 

 4 perspective to go all the way to the highest to 

 5 be protective. But those variations close in for 

 6 receptors that are always going to show maximum 

 7 impacts on the fence line are three or four 

 8 orders of magnitude. You would almost, -- if you 

 9 picked a certain source type, -- you may not be 

10 able to permit those sources in that context. 

11 Given the need for realistic impacts what would 

12 be your suggestions for going forward with 

13 modeling storage tanks. 

14 Roger Brode: I don’t think I want to go on 

15 record as providing a recommendation here. I 

16 just want to point to something that we have been 

17 discussing is recognizing the need to provide the 

18 need to updated guidance or recommendations 

19 for...there’s a table in the ISC users guide and in 

20 the AERMOD users guide in terms of defining 

21 volume sources and that’s often been used in the 

22 past. To look at it in light of the capabilities 

23 of the model to deal with downwash that’s more 

24 directly and more completely where that may be 

25 equivalent or better ways to do some of these 
 1 Ninth Modeling Conference Vol. 2, p. 89 

 2 types of sources. 

 3 Arney Srackangast: I guess my general 

 4 question is -- I’m not familiar with all the 

 5 evaluation databases and what types of sources 

 6 have been evaluated but it seems in the grand 

 7 scheme of things, this has primarily been stacks, 

 8 elevated stacks. To what degree do we have good 

 9 confidence and in low-level fugitive sources 

10 given the PM issues we were just talking about. 

11 In Alabama, and these other source types, are 

12 woefully inadequate in evaluating the model in 

13 these other source types which drive all these 

14 analysis. 

15 Roger Brode: I think that’s a very good 

16 point especially to make in terms of Bob Paine 

17 mentioning that he’s going to be doing some 

18 evaluations looking at performance of 

19 specifically under light wind speed conditions is 

20 that’s a problem we don’t have that I’m aware of 

21 any good databases to look at especially low 

22 level fugitive type of releases under very light 

23 wind stable wind conditions. One reason for that 

24 these are small sources so the facility releasing 

25 it doesn’t have a lot of resources to go out and 
 1 Ninth Modeling Conference Vol. 2, p. 90 

 2 collect the data to show well we’re not causing 

 3 impact or not. But it’s the worst kinds of 

 4 conditions to conduct a field study. The plume 

 5 is going to meander a lot, field studies are 

 6 expensive, so you could go out and put out a lot 

 7 of monitors and spend a lot of money and miss the 

 8 plume completely because it went that way instead 

 9 of that way. Even if you do have a study like 

10 that how much confidence do we have that the 

11 metric concentration really captured the plume 

12 effectively for evaluation purposes. I think 

13 it’s an issue but maybe Joe Chang kind of build 

14 off the work he’s doing. Other databases out 

15 there can be used to inform the issue and that’s 

16 something we are trying to pursue the best we 

17 can. But for these kind of cases that come along 

18 sort of an (inaudible) case. Let’s see what we 

19 can learn from it. Is there information we can 

20 glean from that operations. It’s not very robust 

21 and it's not going to be a clear signal yes or 

22 no. But at least it gives us some information to 

23 work with. It may be the weight of evidence will 

24 start to build up one way or the other. 

25 
 1 Ninth Modeling Conference Vol. 2, p. 91 

 2 Arney Srackangast: I guess my last comment 

 3 is as you were talking about the storage tanks 

 4 being considered downwash structures. In GEP 

 5 guidance, there seems to be suggestions that you 

 6 be careful about using spherical structures as 

 7 downwash structures. There seems to be some 

 8 remarks to that effect. 

 9 Tyler Fox: Just one note. The work that 

10 Roger was showing came through the Residual Risk 

11 Program and Review there. As we mentioned 

12 yesterday, and I’ll plug the Model Clearinghouse 

13 one more time as we illustrate it to the process. 

14 To come up with the EPA guidance, I think it 

15 would be pretty presumptive of us to issue 

16 guidance with limited understanding of issues. 

17 The reason we have a clearing house process and 

18 other types of processes is to get the 

19 information from you all about these issues and 

20 be able to and either specific situations make 

21 determinations what will be appropriate at that 

22 moment. And over time as these issues come to us 

23 time and time again and we build this 

24 understanding and learning of this then we can 

25 conform guidance. Guidance to lead at a starting 
 1 Ninth Modeling Conference Vol. 2, p. 92 

 2 point is the wrong way to go about it. 

 3 We need to gather information in order to 

 4 provide you with an informed and appropriate 

 5 guidance. So we need input from you all about 

 6 these issues and situations that we may be 

 7 handling or working through and learning from 

 8 internally such as the NATS reviews and the like. 

 9 The opportunity we are taking upon ourselves to 

10 learn in order to better exercise and understand 

11 the model and the models that we deal with. We 

12 need information from you all through the 

13 processes, from the Regional Offices, through the 

14 states and local to share and gain that same 

15 experience. 

16 Again I would urge you all to be working 

17 with your state and local agencies with the 

18 Regional Offices to use the clearing house 

19 process in a way such as the program office we 

20 here can start to understand and inform and come 

21 up with the type of guidance you need. 

22 Why don’t we meet back here at 10:45 so 

23 that’s a ten minute break and we’ll start with 

24 the last session on New and Emerging Techniques. 

25 Thank you. 
 1 Ninth Modeling Conference Vol. 2, p. 93 

 2 Alright, Bret Anderson will give a brief 

 3 introductory on the Long Range Transport 

 4 component here. Then we’ll have Roland Drexler 

 5 on HYSPLIT and Joe Scire on the Puff Particle 

 6 Model and then we’ll take some Q&A soon after 

 7 that. 

 8 Bret Anderson: This is more of a 

 9 philosophical interlude as to why we’re actually 

10 having this session. In my mind, we have had in 

11 the regulatory not necessarily in the regulatory 

12 modeling community. In the modeling community as 

13 a whole in the last five to seven years, we have 

14 had two major themes that I think have kind of 

15 exposed us to some new technologies. 

16 One area is the emergency response. As you 

17 know after 9/11 a lot of the regulatory agencies 

18 had to double up on duties that provide response 

19 capabilities. So a lot of my counter parts in 

20 the EPA regions have been tasked with providing 

21 emergency response support for air modeling in 

22 case of any natural disaster or terrorist 

23 attacks. 

24 In the emergency response modeling community 

25 they have been using you know a much different 
 1 Ninth Modeling Conference Vol. 2, p. 94 

 2 class of modeling technology that is new to us. 

 3 Essentially there may be some potential 

 4 application in the future and for the regulatory 

 5 modeling community. That is one area where we 

 6 have been exposed to (interruption on phone 

 7 line). The other is you have heard a lot of talk 

 8 about the BART program which is we’ve seen a lot 

 9 of CALPUFF modeling you know we’ve also seen a 

10 number of states who have tried to use 

11 photochemical models in a more of a single source 

12 capacity. 

13 We are now seeing as I like to call it the 

14 collision of the worlds where we are seeing 

15 (inaudible) come into the near field range. So 

16 these are some new and emerging technologies that 

17 the regulatory community will have to deal with. 

18 And so we thought this session might be a good 

19 opportunity as to where the future will lie. As 

20 you know already these are just the various 

21 classes of models that the community has had to 

22 use over the years: Gaussian Plume Models (ISC, 

23 AERMOD), Gaussian Puff Models (INPUFF, CALPUFF, 

24 SCIPUFF), Lagrangian Particle Models (KSP, 

25 HYSPLIT, FLEXPART), Computational Fluid Dynamics 
 1 Ninth Modeling Conference Vol. 2, p. 95 

 2 (CFD) (FLUENT, OPENFLOW), Eulerian Models (CMAQ, 

 3 CAMx), Plume-in-Grid, Single Source Apportionment 

 4 Techniques. 

 5 As (inaudible) had mentioned earlier on the 

 6 other side is the grid models. You know we 

 7 have CMAQ and CMAx but we have new 

 8 capabilities. In these models are Plume in 

 9 Grid and single Source Apportionment technique 

10 which may have a role in the future in the 

11 regulatory realm. I just wanted to give you an 

12 example of how we’ve used particle models in 

13 Region 7 for quite a while. 

14 Just to kind of give you how we use them in 

15 a non regulatory capacity. We use them for fire 

16 forecast simulations in the Kansas Flint Hills. 

17 We have an event that goes on every spring that 

18 is (sorry) and about fire emissions model and MM5 

19 met model linked to FLEXPART. We are using it as 

20 a tool for fire forecasting. This is the 

21 Lagrangian particle model called FLEXPART. We 

22 use it for diagnostic purposes just to give us an 

23 idea of what we can reasonably expect here. 

24 These models have the capability of you know they 

25 have potential as an application in the future. 
 1 Ninth Modeling Conference Vol. 2, p. 96 

 2 This is one I like to woo the management 

 3 with as it changes colors. It doesn’t mean 

 4 anything. But it is there as are those models in 

 5 the research community and the emergency response 

 6 community that may have application in the 

 7 future. 

 8 I’m going to turn this over to Roland 

 9 Draxler and we going to talk about one of the 

10 models that is in the community. It’s called 

11 HYSPLIT. 

12 Roland Draxler: Okay can you hear me? 

13 Tyler Fox: You’re fine Roland. 

14 Roland Draxler: Okay. What was that laugh 

15 to? 

16 Tyler Fox: They were laughing at me. Don’t 

17 worry. 

18 Roland Draxler: Alright. I think I can 

19 start. 

20 Tyler Fox: Hold on a minute. Alright we’re 

21 all set. Speaking for management Bret I don’t 

22 want to see that picture again. Go right ahead 

23 Roland. 

24 Roland Draxler: Alright. I’m going to give 

25 a brief overview of HYSPLIT and the acronym is a 
 1 Ninth Modeling Conference Vol. 2, p. 97 

 2 little awkward. HYbrid Single Particle 

 3 Lagrangian Integrated Trajectory model. I try 

 4 not to use it but it’s like brand recognition and 

 5 I can’t change it anymore. But we do have a web 

 6 page and there is a lot more detailed 

 7 documentation and it goes into a lot more detail 

 8 and training materials you can go through on how 

 9 to use the model. 

10 I’m going to cover quickly the computation 

11 method; how to simulate plume dispersion, how to 

12 get air concentrations, deposition; and some 

13 examples of calculations and verification. 

14 If you go on to slide 2, you have already 

15 gotten the introduction of the variations of the 

16 lagrangian model. Basically the difference in 

17 the Eulerian approach where computing the local 

18 derivative of the concentration change which is 

19 essentially of the contribution of the advective 

20 flow and dispersion across the interface and you 

21 have to solve the entire domain. 

22 It lends itself to easily handle complex 

23 chemistry and multiple sources, but there are 

24 some issues for the computation: for problems 

25 with artificial diffusion. That also might 
 1 Ninth Modeling Conference Vol. 2, p. 98 

 2 slowly disappear as the grid size of these models 

 3 become smaller and smaller. 

 4 The lagrangian approach we’re computing the 

 5 total derivative and it’s basically the same 

 6 equation but we’re taking the advection and 

 7 putting it outside the equation and considering 

 8 that the trajectory. These kinds of approaches 

 9 are ideal for looking at single point sources. 

10 There is an implicit linearity for chemistry. 

11 That means if you have multiple point sources and 

12 want to get the concentration at a particular 

13 location you will be adding together the 

14 contribution from all sources. 

15 There are non-linear solutions available and 

16 I’ll talk about that later. And the approach is 

17 not that efficient when dealing with many 

18 sources. We’re essentially computing the same 

19 information over and over again. If you have 

20 multiple point sources, you’re doing the same 

21 calculations in the meteorology for each source. 

22 The next slide number 3. I’m going to give 

23 you a brief overview of all the features and not 

24 going into too much detail. The predictor25 corrector advection scheme; forward or backward 
 1 Ninth Modeling Conference Vol. 2, p. 99 

 2 and that means forward trajectory or dispersion. 

 3 The meteorology is external and its offline and 

 4 that means someone else provided this. The model 

 5 needs it from somewhere. Then interpolation is 

 6 linear, spatially and temporally to the 

 7 computation point. 

 8 As far as getting the meteorology from 

 9 elsewhere. We have converters available ARW, 

10 ECMWF, RAMS, MM5, NMM, GFS and so on. It’s not 

11 too hard to use one of those as a base for the 

12 other converters to get something different. The 

13 next thing is vertical mixing based upon SL 

14 (surface layer) similarity, BL, Ri, or TKE. The 

15 horizontal mixing based upon velocity 

16 deformation, SL similarity, or TKE. Mixing 

17 coefficients converted to velocity variances for 

18 dispersion. The dispersion is computed using 3D 

19 particles, puffs, or both simultaneously. 

20 Modelled particle distributions (puffs) can 

21 be either Top-Hat or Gaussian. If you are 

22 modeling air concentrations, it is from 

23 particles-in-cell or at a point from puffs. One 

24 of the features is that we can work with multiple 

25 simultaneous meteorology and concentration grids. 
 1 Ninth Modeling Conference Vol. 2, p. 100 

 2 What this means is that you might have a high 

 3 resolution or stimulation and a global lower 

 4 resolution stimulation and you can use them both 

 5 in a calculationc, when the particle is over the 

 6 high resolution terrain it would use that data 

 7 and switch to the global model. 

 8 As far as meteorology we support latitude- 

 9 longitude or conformal projections. I mentioned 

10 the meteorology. Now the non-linear chemistry 

11 modules use a hybrid Lagrangian-Eulerian 

12 exchange. It’s not part of the standard package 

13 but there’s constant rate simple transformation 

14 form one species to another. People have 

15 developed other modules for sulphur species, the 

16 ozone model, CD4, and we’ve got a mercury module. 

17 Basically the chemistry works in its hybrid 

18 approach. You release from point sources or area 

19 sources and do the computation of dispersion and 

20 transport in a lagrangian framework. The 

21 particle then contributes to the eularian 

22 concentration grid and the chemistry solution is 

23 run. The concentration change is linearly 

24 applied to the mass and the change is put back on 

25 the particle and the advection continues on. 
 1 Ninth Modeling Conference Vol. 2, p. 101 

 2 The standard graphical output in Postscript, 

 3 Shape files, or Google Earth (kml), distribution: 

 4 PC and Mac executables, and UNIX (LINUX) source. 

 5 Slide 4. I’m not going to cover all the 

 6 changes in the model. It’s not that new we 

 7 started in the early 1980. The great points I 

 8 want to highlight are the original version of the 

 9 model used was rawinsonde data with day/night 

10 (on/off) mixing. Later we basically we switched 

11 to gridded meteorological data. Based on the 

12 experiments done in the 1980, we found that we 

13 could do a better job using meteorological data 

14 instead of using observation. 

15 This is true only in the regional large 

16 scale type of situation. I’m not going to argue 

17 that a gridded meteorological model might be 

18 better when you are 5 km from power plant where 

19 you have on site meteorology. But for these 

20 large scale experiments the resolution of the 

21 rawinsonde data was really insufficient to 

22 capture regional kinds of flow patterns. We need 

23 some kind of other approach. 

24 The other thing that came later back in 

25 early 2002 we started adding a lot more options 
 1 Ninth Modeling Conference Vol. 2, p. 102 

 2 to the dispersion code because the interest 

 3 shifted away from the deterministic solution and 

 4 more probablistic solutions so we added the 

 5 ensemble, matrix, and source attribution options. 

 6 More recently we have tried to link up with the 

 7 staggered WRF grids, turbulence ensemble, urban 

 8 TKE. 

 9 The last point version 4.9 which will come 

10 out early 2009 I hope, we’re going to have rather 

11 than a plume-in-grid, we’re going to have a grid12 in-plume model. Essentially a subroutine for 

13 HYSPLIT. What that means is for very long range 

14 simulations and what we’re interested in are 

15 contributions of pollutants to the United States 

16 from China as a background contribution. If 

17 you’re running the lagrangian model for all the 

18 sources in China you’re going to need a whole lot 

19 of particles and it becomes a staggering 

20 computational problem. 

21 But for a situation like that it is very 

22 reasonable to look at an Eulerian model to 

23 provide the concentration background and combine 

24 that with Lagrangian plume model. From that 

25 stand point the way it would work would be to 
 1 Ninth Modeling Conference Vol. 2, p. 103 

 2 find the point sources all over the world and at 

 3 some predefined time the particles would be 

 4 transferred to the Eulerian model. 

 5 Let’s go on to slide 5 now. Just briefly on 

 6 how the trajectory is computed. It’s a 2 step 

 7 process (inaudible) it actually starts with 

 8 equation 2 the first-guess position. 

 9 P(t+dt) = P(t) + 0.5 [ V(P{t}) + 

10 V(P‘{t+dt}) ] dt 

11 P'(t+dt) = P(t) + V(P{t}) dt 

12 The integration time step is variable: Vmax dt < 

13 0.75. So that’s a pretty basic approach and I 

14 think all the models use some variation of that. 

15 It goes back to a 1935 meteorology book and it a 

16 pretty traditional approach. 

17 

18 Number 6 slide. Now we compute these 

19 trajectories. A single trajectory cannot 

20 properly represent the growth of a pollutant 

21 cloud when the wind field varies in space and 

22 height. This was an interesting example. Just 

23 to show you that in this case in starting a 

24 trajectory, this is Spain in case you don’t 

25 recognize the geography. Why would I run Spain? 
 1 Ninth Modeling Conference Vol. 2, p. 104 

 2 Invited to a meeting there. In any event what 

 3 we’re doing is starting a trajectory in the 

 4 illustration on the right, new trajectories are 

 5 started every 4-h at 10, 100, and 200 m AGL to 

 6 represent the boundary layer transport. It looks 

 7 like a plume because wind speed and direction 

 8 varies with height in the boundary layer. 

 9 As you can see the thing sort of spreads out 

10 and looks like a plume. But it’s just a mean 

11 wind coming out of the East (inaudible). And so 

12 you’re getting this growth in a horizontal that 

13 is a result of the wind direction shear and wind 

14 speed shear with height. And that is really 

15 driving the dispersion process. If you added any 

16 kind of turbulence on this it would have a minor 

17 effect. That is a big thing for boundary layer 

18 dispersion. 

19 In HYSPLIT we can compute the mean 

20 trajectory for each one of these. If I’m 

21 releasing thousands of particles and each one has 

22 a little bit of pollutant mass on it, that’s the 

23 3D-particle model with just the mean motion. We 

24 have to add on a turbulent component that would 

25 represent the dispersive component of the 
 1 Ninth Modeling Conference Vol. 2, p. 105 

 2 atmosphere. That’s the complete 3D-particle 

 3 approach. 

 4 Another one of the possibilities is the PUFF 

 5 approach where we’re not modeling the individual 

 6 particles, but we’re modeling how that particle 

 7 distribution changes with time. How the standard 

 8 deviation of the plume as it changes with time. 

 9 In this case it would be like a 3-D cylinder with 

10 a growing concentration distribution in the 

11 vertical and horizontal. Puffs may split if they 

12 become too large. 

13 

14 We also have a Hybrid approach where we look 

15 at the particle motion in one direction and a 

16 puff type approach in the other direction. The 

17 hybrid method always puts the particle in the 

18 vertical and puffs in the horizontal. Mainly the 

19 particle approach would give us a more accurate 

20 representation of what’s happening in the 

21 boundary layer as there is a lot more shear with 

22 height than in horizontal direction. 

23 Slide 8 shows an example of the 3D-Particles 

24 (5000). If you don’t recognize the terrain this 

25 is Fairbanks Alaska. It was in September and a 
 1 Ninth Modeling Conference Vol. 2, p. 106 

 2 very nice trip. The approach on the left 

 3 illustrates what I was saying about the 3-D 

 4 particle concentrations you can see from the 

 5 illustration what that turbulent particle 

 6 distribution looks like when added to each of 

 7 those mean particle trajectories. It’s a 

 8 lot more interesting as a vertical than a 

 9 horizontal. 

10 If you look on the right side of slide 8, 

11 this is running with 3-D Puffs and we are not 

12 really seeing any dispersion here because we’re 

13 looking at the center of the puff and that 

14 represents the mean trajectory. So about those 

15 puffs you have some distribution you just don’t 

16 see it in this plot. Everything I’m showing you 

17 on this presentation I did on my PC with the PC 

18 version of HYSPLIT. 

19 Slide 9. As far as the puff distribution, 

20 just for example, I said that there are two for 

21 modeling the distribution, it could either be a 

22 top hat or could be a Gaussian. With the top hat 

23 computation, you’re either in or out, and when 

24 you’re in you have a mean concentration and the 

25 mean concentration would be the top hat. It 
 1 Ninth Modeling Conference Vol. 2, p. 107 

 2 represents the half the mass of the Gaussian 

 3 distribution. 

 4 Slide 10. This just shows the equations 

 5 that are involved. Now some of these equations 

 6 are simplified. I dropped off some terms, so 

 7 don’t take this back and try to compute these 

 8 values. You need to go back to the original 

 9 documentation which is on the web page. But for 

10 3-D particle approach, just briefly, we’re 

11 computing a mean trajectory, but actually in this 

12 case we’re adding another term, a u-prime 

13 turbulent dispersion. That u-prime is computed 

14 from the turbulence from the previous time step, 

15 to which is added the last term here. u-double 

16 prime, which is the standard deviation of 

17 velocity component that comes out of the 

18 computer. The Gaussian random number is weighted 

19 in proportion to the turbulence that comes out of 

20 the model. That’s the particle approach. 

21 Now for the puff approach we’re using the 

22 same kind of thing, in that we’re computing the 

23 standard deviation in terms of the growth of the 

24 puff. It’s also a function of the turbulent 

25 velocity. If you would take the individual 
 1 Ninth Modeling Conference Vol. 2, p. 108 

 2 particles from the 3D calculations and compute 

 3 their deviation, the square of the deviation from 

 4 the mean position. That gives you the standard 

 5 deviation, the made as modeling the puff if you 

 6 had stationary homogeneous turbulence. You’re 

 7 supposed to get the same answer but you won’t 

 8 always get the same answer. 

 9 Slide 11 is an example of the calculation 

10 using 5000 particles or 500 puffs. In this case 

11 what’s happening at the end of the particle is 

12 spread out it becomes a noisy simulation because 

13 you don’t have enough particle density to give 

14 you a smooth plume and that’s one of the 

15 limitations with the particle approach. When you 

16 get to very long distance scales and the global 

17 scale (inaudible) for global background, it is 

18 difficult to (inaudible) to get a smooth type of 

19 simulation. That’s why we have this puff 

20 approach and especially the hybrid approach in 

21 HYSPLIT. 

22 Slide 12. Just briefly how do we compute 

23 concentrations? Well each particle if you’re 

24 running the 3D particle model the change in 

25 concentration in any grid cell will be the mass 
 1 Ninth Modeling Conference Vol. 2, p. 109 

 2 contributed by that particle divided by the grid 

 3 cell volume. If you’re using some kind of puff 

 4 approach it’s the mass of the puff divided by the 

 5 volume of the puff, basically. The approach is 

 6 the same as summing the mass dividing by the 

 7 volume. 

 8 Slide 13. This goes back and is just a 

 9 summary of why the hybrid HYSPLIT is used for the 

10 puff approach. Here’s an example on the right 

11 when you only have 500 3-D particles and you can 

12 see how they break up sooner. That’s why that 

13 500 Hybrid puff approach gives a smoother looking 

14 plume. As we saw in that vertical distribution 

15 there’s a lot more shear and lot more things 

16 going on in the vertical than in the horizontal 

17 and having the puff approach in the horizontal 

18 helps give us a smoother type of representation. 

19 Slide 14. I’m not going into much detail 

20 here but there are all kinds of deposition 

21 computations here and different ways to treat dry 

22 and wet deposition including using the resistance 

23 method which goes back to the Models-3 if you 

24 want to turn that on. Refer to the guide for 

25 this. The point I want to make is that in 
 1 Ninth Modeling Conference Vol. 2, p. 110 

 2 HYSPLIT we’re not losing particles but we are 

 3 actually depleting the mass of the particles. In 

 4 this case, we don’t want to lose particles 

 5 because there are too few in the computation so 

 6 we just lose mass. 

 7 Slide 15. I’m going to work my way down in 

 8 scale. This was the massive dust storm from 

 9 China in 2001. This was running the 3-D particle 

10 model and what you see here is the particle 

11 positions coming out of HYSPLIT just a day or two 

12 after the dust storm started. About a week later 

13 when it first started approaching the United 

14 States and the HYSPLIT particles are the black 

15 dots and the TOMS aerosol index is the color 

16 pattern underneath. We get a lot of questions 

17 from the web. People were asking how accurate 

18 was these calculations. They always try to pin 

19 you down on this and when they try to pin us down 

20 we say it’s about 20%. What they don’t believe 

21 and have a hard time believing is the longer the 

22 distance and the more dispersed the particles, 

23 the more accurate it becomes. 

24 As you can see on the right is what’s 

25 happened is the particle starts lining up with 
 1 Ninth Modeling Conference Vol. 2, p. 111 

 2 the large scale weather patterns at the frontal 

 3 boundaries and the meteorological model has 

 4 captured those features very well. You may be 

 5 off in the source location but you might be 

 6 (inaudible) as long as you are in the avenue I 

 7 should say the caveat. 

 8 Slide 16 is the same event now and it 

 9 arrived over the US over the 14th like a week 

10 later. And the following week we started 

11 measuring concentrations over the US and I just 

12 have it in the table in the middle of the graph. 

13 The numbers are in the order of 30, 40, 50 

14 micrograms per cubic meter, contributed from that 

15 event. The HYSPLIT predictions are shown in the 

16 graph and we’re actually over predicting, what 

17 might be the 100 for a low value. The timing was 

18 about the right, but concentrations a little bit 

19 high because we didn’t have deposition turned on, 

20 just standard transport and dispersion. 

21 In fact the emissions came from a dust storm 

22 module that was developed originally for looking 

23 at sand storms in Kuwait. Its self predicting 

24 what you saw in the previous slide the emissions 

25 of dust were initiated automatically, when you 
 1 Ninth Modeling Conference Vol. 2, p. 112 

 2 turn on that module, over desert land-use regions 

 3 that had a high wind velocity. 

 4 Slide 17. We’re moving down now and we’re 

 5 just covering the US. We do have an operational 

 6 wildfire smoke forecast that is running. You can 

 7 go to our web page and also the weather service 

 8 page. Our page is better than the weather 

 9 service page partly because we offer ways for 

10 verification whereas the weather page only shows 

11 the forecast. We are showing the verification 

12 every day with what was occurring yesterday as to 

13 what was observed by visible satellite imagery. 

14 You can like manipulate the times and so on. The 

15 reference is there and you can take a look at 

16 that. 

17 The last slide, 18, here is on verification 

18 down on the local scale. This is down to the 80 

19 km scale we’re looking at a tracer experiment we 

20 did in Washington DC area. This particular graph 

21 shows the monthly sampling results. The 8 hour 

22 sampling was only a few locations and was 

23 difficult. But at the monthly locations, 

24 essentially, the model didn’t show a lot of bias 

25 and we’re kind of happy with those results. 
 1 Ninth Modeling Conference Vol. 2, p. 113 

 2 Verification is the big thing and on my way 

 3 to wrapping this up and it’s important to us. 

 4 You know there has been a lot published about how 

 5 to verify models and you know for us a lot of 

 6 this you make a change or you’re trying to 

 7 improve the calculations and did it really 

 8 improve. Then you know the correlation goes up 

 9 or the bias might go down. You can always get 

10 different results and we’re trying to come up 

11 with some to know if I make these changes to my 

12 model what my overall results will be. 

13 We tried to come up with a number and this 

14 number is what we call a ranking. It is composed 

15 of 4 components such as the correlation (R) 

16 represents the scatter; the fractional bias (FB) 

17 is the mean difference between paired predictions 

18 and measurements and yields a normalized measure 

19 of the prediction bias in normalized units; the 

20 Figure-of-Merit-in-Space (FMS) is defined as the 

21 percentage of overlap between measured and 

22 predicted areas and is computed as the 

23 intersection over the union of predicted and 

24 measured concentrations; the Kolomogorov-Smirnov 

25 (KS) parameter is the maximum difference between 
 1 Ninth Modeling Conference Vol. 2, p. 114 

 2 the unpaired measured and calculated cumulative 

 3 distributions. And then these are normalized and 

 4 the perfect model would give us a rank of 4.0. 

 5 Obviously you can add other parameters if you 

 6 want. 

 7 Slide 20. One of the things we have on the 

 8 web all the tracer experiments we have been 

 9 involved with over the past 20 years. And for 

10 those tracer experiments we have run....the first 

11 question is if it’s 20 or 30 years old how can it 

12 be still relevant today? We sort of fell away 

13 from going back to these experiments because each 

14 one of them had different meteorological data 

15 available. Some of the earlier ones there was 

16 only Rawinsondes. Then we started seeing the 

17 gridded data so when we were doing later 

18 experiments. 

19 Recently NCEP completed this North American 

20 Regional Reanalysis. You can go to their web 

21 site download and convert that data so that you 

22 can use it in the model. So all of a sudden we 

23 have a consistent meteorological database that is 

24 available and we can use modeling methods and we 

25 can go back and look at the old data and see how 
 1 Ninth Modeling Conference Vol. 2, p. 115 

 2 well we are performing. This is a big thing. 

 3 And now for the first time we have statistics 

 4 that are consistent using the same meteorology. 

 5 It’s not shown here but the difference that 

 6 you find changing dispersion in the model and 

 7 changing anything you try to change when you look 

 8 at one experiment it makes a big difference. 

 9 When you start using experiment that represents 3 

10 months or 2 years worth like in METREX. So 

11 there’s lots of data. This is available on our 

12 web site. Let’s look at one briefly. Of course 

13 I’m only going to look at the best one which is 

14 ANATEX. You can see the average on the left and 

15 the paired on the right. 

16 EXPERIMENT 

17 Average 

18 Paired 

19 ACURATE 

20 3.25 

21 1.77 

22 ANATEX GGW 

23 3.48 

24 1.84 

25 ANATEX STC 
 1 Ninth Modeling Conference Vol. 2, p. 116 

 2 2.66 

 3 1.63 

 4 CAPTEX 

 5 3.24 

 6 1.63 

 7 1ETEX 

 8 2.37 

 9 1.55 

10 1INEL74 

11 1.71 

12 1.37 

13 METREX (t1) 

14 2.81 

15 1.77 

16 

17 

18 METREX (t2) 

19 2.27 

20 1.58 

21 OKC80 

22 2.50 

23 1.73 

24 

25 
 1 Ninth Modeling Conference Vol. 2, p. 117 

 2 Slide 21. On the left here it shows what the 

 3 ANATEX experiment looked like and the G over in 

 4 Montana is where the release occurred and all the 

 5 stations represent the samples that if we’re 

 6 averaging together. So when you click on that 

 7 3.48 this is the page that would come up which is 

 8 the overall statistics and you can see the 

 9 correlation is .97 which is (inaudible) probably 

10 a little bit small for you to read. But the 

11 thing is this represents a 3 month experiment so 

12 if we average each individual station by time we 

13 get a 3 months average we’re looking at the 

14 spatial distribution. The spatial performance of 

15 the model is .97 correlation coefficient and the 

16 bias was a ratio 1.37. Okay. 

17 Anyway the point I wanted to make this is 

18 available for you to look at what’s important to 

19 you. Everybody may have a different idea what is 

20 important depending on what your requirements 

21 are. 

22 Slide 22. What’s in the pipeline for 

23 version 4.9? We’ve got all these tracer 

24 experiments on the web. We want to have web 

25 interactive verification linked to DATEM. We 
 1 Ninth Modeling Conference Vol. 2, p. 118 

 2 will have the integrated global model for 

 3 background contributions. 

 4 The Chemical (CAMEO) and radiological effects 

 5 database (web) and not the PC version; GIS-like 

 6 map background layers for graphical display (pc); 

 7 model physics ensemble (pc/unix); meteorology and 

 8 turbulence already in existing version and 

 9 completely revised user’s guide with examples but 

10 not started yet. That’s the end. I hope I 

11 stayed within my time limits. 

12 Tyler Fox: Yeah that was great Roland. Are 

13 you going to stay with us during Joe’s 

14 presentation? 

15 Roland Draxler: Yeah. 

16 Tyler Fox: We have Joe Scire presenting an 

17 overview of puff particle model. 

18 Joe Scire: Okay. Last week I was asked 

19 about the particle puff model the PPM module 

20 that’s in a version of CALPUFF and I said I would 

21 be happy to write a presentation. I was 

22 traveling during that time and didn’t get back to 

23 the office so I don’t have graphics. I’ll 

24 describe the model and a little bit of history 

25 about it. 
 1 Ninth Modeling Conference Vol. 2, p. 119 

 2 This is the work of Dr. Peter de Haan as 

 3 part of his Ph.D thesis at the Swiss Federal 

 4 Institute of Technology in Zurich, Switzerland. 

 5 He spent a few months with us when I was at 

 6 (inaudible) he stayed and worked with us for a 

 7 summer. He was hard at work on his Ph.D and 

 8 there were several papers as a result of this 

 9 work. The two that I used in developing this 

10 presentation are shown on this slide. So really 

11 this is his module that was incorporated in to 

12 CALPUFF. 

13 Basically it’s a module that is an 

14 alternative or an option to treat dispersion in a 

15 more detailed way in the near field. What the 

16 purpose of the PPM the puff particle model is to 

17 try to combine the advantages of both puff and 

18 particle approaches. In one of the elements of 

19 the PPM is that it will allow you to calculate 

20 and predict the mean concentration and give 

21 (inaudible) and an averaging time. So you are 

22 computing the higher moments of the density 

23 function. 

24 Now in terms of models (inaudible) one 

25 advantage is particle models over plume models 
 1 Ninth Modeling Conference Vol. 2, p. 120 

 2 has to do with the ability of (inaudible) spatial 

 3 variably of accounting for spatial variability of 

 4 meteorological and dispersion conditions, 

 5 causality effects, low wind speed dispersion, 

 6 memory of previous hour’s emissions, spatial 

 7 variability in dispersion rates, etc. Lagrangian 

 8 stochastic particle models are state-of-the- 

 9 science approach, especially for simulation of 

10 inhomogeneous (convective) turbulence. They are 

11 computationally demanding and there is more 

12 difficult to deal with wet and dry deposition, 

13 chemistry. 

14 If you look at the Puff model types there 

15 are a couple of types within the class of puff 

16 models. One is the ensemble average puff model 

17 and CALPUFF would this type. We have a puff that 

18 consists of a center of mass and a 3-D 

19 distribution of total mass around the center. 

20 This represents the ensemble average of the 

21 concentration distribution belong to a “piece” of 

22 the pollutant release. The other type is a 

23 cluster dispersion puff model where a puff is a 

24 physical cluster of particles. Now then the 

25 concept of relative dispersion (due to turbulent 
 1 Ninth Modeling Conference Vol. 2, p. 121 

 2 eddies smaller than the puff) contribute to puff 

 3 cluster growth. Larger eddies move puffs as a 

 4 whole without changing the relative separation of 

 5 particles within the cluster (meander component). 

 6 Both of these are important. 

 7 Instantaneous puff releases require use of 

 8 relative dispersion but update frequency of flow 

 9 field is too low to resolve turbulent eddies not 

10 covered by relative dispersion concept. PPM uses 

11 a full stochastic Lagrangian particle dispersion 

12 model to determine the puff trajectory. I’ll 

13 explain this a little more in a couple of slides. 

14 Kinematic turbulent energy associated with 

15 eddies smaller than the puff size is removed 

16 since they are already accounted for the in 

17 relative dispersion. Every puff carries along 

18 its position along with the position and 

19 turbulent velocity components of the stochastic 

20 particle to which it belongs. 

21 The effect of meandering caused by turbulent 

22 eddies larger than the puff but not resolved by 

23 the flow is simulated by the puff center 

24 trajectories. Two contributions of dispersion 

25 process are the relative dispersion (small 
 1 Ninth Modeling Conference Vol. 2, p. 122 

 2 eddies) and the meander (large eddies). The 

 3 Stochastic path artificially produces the 

 4 meandering behavior, but it is necessary to 

 5 account for the spatial and temporal correlation 

 6 of turbulence. The tendency of neighboring puffs 

 7 should show similar meandering. 

 8 The way this is implemented into CALPUFF are 

 9 multiple steps. Every time there’s a newly 

10 released puff a “mirror ensemble” is attached. 

11 This mirror ensemble consists of a user-defined 

12 number of puff-particles. The time step broken 

13 into sub-steps (sampling steps) in CALPUFF. For 

14 each sub-step the mirror ensemble is advected 

15 with a PPM time step (~1-10 seconds). For every 

16 PPM time step, new particle trajectories are 

17 computed, from which the puff trajectories are 

18 derived. At the end of a sampling step, mirror 

19 ensemble’s first and second moments of mass 

20 distribution are used to compute the parent 

21 puff’s size and position and then handed back to 

22 main CALPUFF routine. 

23 CALPUFF then computes any physical process 

24 changing the puff’s mass or chemical composition 

25 (but not its size or location). At some point, 
 1 Ninth Modeling Conference Vol. 2, p. 123 

 2 the size of the particle-puffs in the mirror 

 3 ensemble will be large enough so that most of the 

 4 energy spectrum will be within the puff-particle. 

 5 Relative dispersion ~ same as absolute 

 6 dispersion. At that point, the parent puff 

 7 location and size is recomputed, the mirror 

 8 ensemble is deleted and the parent puff is 

 9 restored. Parent puff treated in normal CALPUFF 

10 way using absolute dispersion. 

11 Peter evaluated the model of several 

12 different data sets which included: 

13 •The PPM was evaluated using 

14 measurements from three tracer 

15 experiments. 

16 •Copenhagen 

17 •9 hours measurements under 

18 convective conditions 

19 •115m release height, suburban area 

20 •Lillestrom 

21 •8 observations, 15-minute 

22 averaging times 

23 •Strongly stable winter conditions 

24 •36m release height, suburban area 

25 •Kincaid 
 1 Ninth Modeling Conference Vol. 2, p. 124 

 2 •Mostly convective conditions 

 3 •171 hours of measurements 

 4 •187m power plant stack, rural 

 5 environment 

 6 Datasets are “reference datasets” developed as 

 7 part of European short-range dispersion model 

 8 harmonization workshops. 

 9 This is where I wish I had graphics but I 

10 don’t and will have to describe it to you. 

11 Copenhagen had good agreement of arcwise 

12 maximum concentrations with little overall bias 

13 and nearly all data points within factor of two 

14 of observations; some under prediction of cross15 wind integrated concentration (CIC). Very 

16 similar results to those obtained with a full 

17 Lagrangian particle dispersion model (LPFM) 

18 Lillestrom: Generally good prediction of arc19 maximum concentrations and some displacement of 

20 location of peak concentrations. 

21 Kincaid used QI=3 (highest quality) data 

22 So overall this was considered a pretty good 

23 starting point that exists in a version of 

24 CALPUFF and it’s an older version. But it’s 

25 something if there’s interest could be put in a 
 1 Ninth Modeling Conference Vol. 2, p. 125 

 2 current version of the model. You can turn the 

 3 switch on or off and you can get some experience 

 4 in determining its performance in other data 

 5 sets. That’s basically all I have. 

 6 Tyler Fox: Are there any questions for Joe or 

 7 any others. We are going to move quickly to the 

 8 next part of this which is the Single Source 

 9 Modeling. We’ll start with presentation from 

10 Prakash on Overview of CMAQ MADRID with SCICHEM. 

11 Then depending on the time we have left, we will 

12 either finish up with Kirk or Ralph or break for 

13 lunch to continue those presentations. 

14 Prakash Karamchandani: I’ll be talking about 

15 plume–in-grid modeling, which basically consists 

16 of using a plume model within a grid model to 

17 capture fine scale variability next to emissions 

18 sources. And the whole idea is that the grid 

19 models that we use typically have coarse 

20 horizontal resolution of 4 km and 12 km and 

21 cannot capture the subgrid-scale variability that 

22 we have in the emissions. So why do we use it? 

23 If you look at a grid model with a resolution of 

24 4 km or 12 km, the plume has to travel through 

25 several grid cells before it reaches the size of 
 1 Ninth Modeling Conference Vol. 2, p. 126 

 2 the grid. 

 3 That leads to unrealistic treatment of the 

 4 transport of the emissions and chemistry of the 

 5 plume. So what we’re trying to do with a plume– 

 6 in-grid model is to combine the plume model and 

 7 the grid model and carry the plume along until it 

 8 approaches a size that is comparable to the grid 

 9 size. 

10 I showed this slide yesterday and what we’re 

11 trying to do with the plume–in-grid model is to 

12 capture the first two stages, which I talked 

13 about yesterday – the early plume dispersion and 

14 the mid-range plume dispersion, and the grid 

15 model cannot predict these two stages correctly. 

16 Stage 3 is the point at which we hand over the 

17 plume to the grid model. 

18 So, like I mentioned earlier, the model 

19 consists of a reactive plume model embedded 

20 within a 3-D grid model. The plume model 

21 captures the local scale variability and the grid 

22 model provides background concentrations to the 

23 plume model. At the time we hand over the plume 

24 model to the grid model, the grid model 

25 concentrations are adjusted. There’s a two way 
 1 Ninth Modeling Conference Vol. 2, p. 127 

 2 feedback between the host grid model and the 

 3 plume model. 

 4 Plume-in-grid modeling is not new; it began in 

 5 the 1980s – one of the first models was called 

 6 PARIS - Plume-Airshed Reactive-Interacting 

 7 System. Early models were overly simplified – 

 8 simplified treatment of chemistry in some models, 

 9 no treatment of wind shear or plume overlaps, no 

10 treatment of effect of atmospheric turbulence on 

11 chemical kinetics. The development of a state12 of-the-science PiG model for ozone was initiated 

13 in 1997 under EPRI sponsorship. 

14 The embedded plume Model is SCICHEM (state-of15 the science treatment of stack plumes at the sub16 grid scale)-developed by L-3 Communications/Titan 

17 and AER. SCICHEM is based on SCIPUFF, an 

18 alternative model recommended by EPA on a case19 by-case basis for regulatory applications (also 

20 used by DTRA and referred to as HPAC). It’s a 

21 three-dimensional puff-based model, with second22 order closure approach for plume dispersion and 

23 treatment of puff splitting and merging. SCICHEM 

24 adds the full chemistry mechanism to SCIPUFF. 

25 Before CMAQ became available, SCICHEM was first 
 1 Ninth Modeling Conference Vol. 2, p. 128 

 2 embedded in MAQSIP, the precursor to the U.S. EPA 

 3 Model, CMAQ. In 2000, AER incorporated SCICHEM 

 4 into CMAQ. The model is called CMAQ-APT 

 5 (Advanced Plume Treatment). 

 6 The early applications of the model were for 

 7 ozone, where we conducted simulations for 

 8 episodes in the eastern United States with two 

 9 nested grid domains (12 and 4 km resolution) for 

10 July 1995. We also applied the model to Central 

11 California (4 km resolution) for July-August 

12 2000. The key conclusion from the eastern U.S. 

13 application: for isolated point sources, CMAQ-APT 

14 predicts lower O3 and HNO3 formation compared to 

15 the base model. 

16 We added PM and aqueous-phase chemistry 

17 treatments in 2004-2005 Two versions were 

18 developed: one including the EPA treatment of PM 

19 (CMAQ-AERO3-APT), and the second including the 

20 MADRID treatment of PM (CMAQ-MADRID-APT), 

21 developed by AER. MADRID is the Model of Aerosol 

22 Dynamics, Reaction, Ionization and Dissolution 

23 (Zhang et al., 2004, JGR) 

24 If you look at the current version we have of 

25 the plume-in-grid model, it is based on CMAQ 4.6, 
 1 Ninth Modeling Conference Vol. 2, p. 129 

 2 which is the latest available release. It was 

 3 released in 2006 and I believe 4.7 will be coming 

 4 out in a few weeks. But at the time, this is 

 5 what we had to work with. So we had the MADRID 

 6 PM treatment and the EPA PM treatment which is 

 7 AERO3. So we have two versions: CMAQ-AERO3-APT 

 8 and CMAQ-MADRID-APT. 

 9 Once we incorporated PM, we applied it to the 

10 southeastern United States. This was a study 

11 designed to supplement RPO modeling being 

12 conducted by the Visibility Improvement State and 

13 Tribal Association of the Southeast (VISTAS). 2 

14 months were simulated (January and July 2002) 

15 with Base CMAQ v 4.4 and CMAQ-APT-PM. 14 power 

16 plant plumes were explicitly simulated with the 

17 plume-in-grid approach. Model performance 

18 evaluation was conducted for Base CMAQ vs. CMAQ19 APT-PM. Power plant contributions to PM2.5 

20 components were calculated and compared for Base 

21 CMAQ and CMAQ-APT-PM. This slide shows you the 

22 modeling domain for the application and locations 

23 of 14 PiG sources 

24 This slide shows the power-plant contributions 

25 to average July PM2.5 sulfate concentrations. The 
 1 Ninth Modeling Conference Vol. 2, p. 130 

 2 left side shows you the Base CMAQ results without 

 3 plume–in-grid. The right side shows the results 

 4 of CMAQ-AERO3-APT with plume–in-grid. There is a 

 5 big difference between the contributions 

 6 especially near the source regions and even 

 7 further away from the source regions. The 

 8 maximum contributions are 4.8 μg/m3 for the grid 

 9 model and 2.4 μg/m3 for the plume-in- grid model. 

10 This slide shows the same results in a 

11 different way. It shows the change in the 

12 contribution by using the PIG treatment. You can 

13 see that the contributions drop by about 43% near 

14 the source region. Even further away it’s about 

15 1 to 5 % lower. 

16 The conclusions were that using a purely 

17 gridded approach will typically overestimate 

18 power plant contributions to PM because SO2 to 

19 sulfate and NOx to nitrate conversion rates are 

20 overestimated. Plume-in-grid PM modeling 

21 provides a better representation of the near22 source transport and chemistry of point source 

23 emissions and their contributions to PM2.5 

24 concentrations. CMAQ-AERO3-APT predicts lower 

25 power plant contributions than base CMAQ to local 
 1 Ninth Modeling Conference Vol. 2, p. 131 

 2 and regional sulfate and total nitrate, 

 3 particularly in summer. 

 4 The next improvement was the addition of 

 5 mercury in the model. The implementation of 

 6 mercury modules in CMAQ-MADRID-APT was completed 

 7 in 2006. An application of CMAQ-MADRID-APT (with 

 8 Hg) to the southeastern U.S. (12 km grid 

 9 resolution) was conducted for 2002. An 

10 application of CMAQ-MADRID-APT (with Hg) to the 

11 continental U.S. (36 km grid resolution) was 

12 conducted for 2001 (Vijayaraghavan et al., 2008, 

13 JGR). 

14 This slide shows mercury deposition with the 

15 grid model on the left hand side and the change 

16 in mercury deposition using the PIG treatment on 

17 the right hand side. What we found was the grid 

18 model overpredicted mercury deposition, 

19 especially in Pennsylvania downwind of the 

20 emissions in Ohio, and we found this 

21 overprediction was corrected by using PIG 

22 treatment. 

23 Next we looked at an issue that’s becoming 

24 important and that is population exposure to 

25 hazardous air pollutants (HAPs), which is an 
 1 Ninth Modeling Conference Vol. 2, p. 132 

 2 important health concern. Measurements show a 

 3 large spatial variability in air toxics 

 4 concentrations near roadways. Exposure levels 

 5 near roadways are factors of 10 larger than in 

 6 the background–models need to capture this 

 7 subgrid-scale variability in exposure levels. 

 8 Many of the species of interest are chemically 

 9 reactive–e.g., formaldehyde, 1,3-butadiene, 

10 acetaldehyde–models need to treat the chemistry 

11 of these species. Traditional modeling 

12 approaches are inadequate to provide both 

13 chemistry treatment and fine spatial resolution. 

14 Based on CMAQ-APT, we developed the prototype 

15 version in 2007 (Karamchandani et al., 2008, Env. 

16 Fluid Mech.). The model simulates near-source CO 

17 and benzene concentrations from roadway 

18 emissions. Chemistry is switched off for this 

19 application. Roadway emissions are treated as 

20 series of area sources along the roadway with 

21 initial size equal to the roadway width. 

22 Concentrations are calculated at discrete 

23 receptor locations by combining incremental puff 

24 concentrations with the grid-cell average 

25 background concentration. 
 1 Ninth Modeling Conference Vol. 2, p. 133 

 2 This slide shows the application for the 

 3 prototype – we looked at a busy interstate 

 4 highway in New York City (I278). This was done 

 5 for the July 11-15, 1999 period of the 

 6 NARSTO/Northeast Program. The bottom figure 

 7 shows the grid model domain. 

 8 If you look at this plot, which shows the 

 9 qualitative evaluation of CO concentrations from 

10 model results compared with CO concentration 

11 profiles measured in Los Angeles by Zhu et al. 

12 (2002), Atmos. Environ., we get good agreement. 

13 The challenge with P-in-G modeling is that it 

14 can be computationally expensive if a large 

15 number of point sources are treated with the puff 

16 model – computational requirements increase by a 

17 factor of two to three for 50 to 100 sources. 

18 Point sources have to be selected carefully to 

19 limit the number of sources treated. To obtain 

20 results in a reasonable amount of time, annual 

21 simulations are usually conducted by dividing the 

22 calendar year into quarters and simulating each 

23 quarter on different processors or machines. A 

24 parallel version of the code can address these 

25 constraints. 
 1 Ninth Modeling Conference Vol. 2, p. 134 

 2 We started the development of a parallel 

 3 version of CMAQ-MADRID-APT and completed it in 

 4 late 2007. So on a 4-processor machine, the 

 5 parallel version is about 2.5 times faster than 

 6 the single-processor version. We have an on- 

 7 going project to apply the model to the central 

 8 and eastern United States at 12 km resolution and 

 9 to evaluate it with available data. Over 150 

10 point sources are explicitly treated with APT. 

11 The simulations include annual actual and typical 

12 simulations for 2002, as well as future year 

13 emission scenarios and other emission sensitivity 

14 scenarios. 

15 This slide shows the modeling domain for the 

16 application that is currently on-going. As you 

17 can see it is a very large domain with a large 

18 number of PiG sources, and this application would 

19 not have been possible without developing the 

20 parallel version of the model. 

21 I’d like to end by acknowledging the funding 

22 from Electric Power Research Institute (EPRI), 

23 Southern Company, California Energy Commission 

24 (CEC), Atmospheric & Environmental Research, 

25 Inc.; Collaboration in Model Development: L-3 
 1 Ninth Modeling Conference Vol. 2, p. 135 

 2 COM; Parallelization Insights: David Wong, EPA; 

 3 and data sources like VISTAS; Atmospheric 

 4 Research & Analysis, Inc. (ARA) and the Georgia 

 5 Environmental Protection Division (GEPD). Thank 

 6 you. 

 7 Tyler Fox: What I’d like to do is if there 

 8 are any questions for Prakash on the CMAQ Madrid 

 9 why not ask them now. Then we can break for 

10 lunch and then start back so that Kirk and Ralph 

11 will have time to complete their presentations 

12 and we don’t have to rush. Are there any 

13 questions? Alright. We’ll see you back here at 

14 1:00 

15 We’ll all get back together. There doesn’t 

16 seem to be as many people. So as we said, we 

17 will conclude the session on New and Emerging 

18 Models with presentations by Kirk Baker and Ralph 

19 Morris on single source models and photochemical 

20 models. We’ll take some questions on that and go 

21 right in to the public session and go according 

22 to the order in the final agenda yesterday. 

23 There are a couple additions or at least one 

24 addition we can add. I’ll hand this off to Kirk. 

25 Kirk Baker: I appreciate those of you who 
 1 Ninth Modeling Conference Vol. 2, p. 136 

 2 came back after lunch. I’m going to talk a 

 3 little bit about photochemical modeling and in 

 4 general some of the features of the photochemical 

 5 models that are starting to lend itself to single 

 6 source modeling and tracking that type of thing. 

 7 I’m going to start way back at the beginning 

 8 simple (inaudible) for all types of air quality 

 9 modeling systems whether it’s dispersion, or 

10 photochemical grid model system. Essentially you 

11 will use the same emissions input, meteorological 

12 inputs and process that for the air quality 

13 model. 

14 Generally speaking the model started off as 

15 a dispersion model, simple photochemical box 

16 models that moved on to second generation 

17 photochemical models like urban REMSAD models. 

18 Those photochemical models are geared to specific 

19 type of pollutant. UAM, REMSAD for Ozone, REMSAD 

20 was primarily was developed for PM 2.5 deposition 

21 type applications. Recently in the last five or 

22 ten years, the latest generation of 

23 photochemical grid models are a one atmosphere 

24 modeling system approach where we are trying to 

25 treat all types of precursors species ozone and 
 1 Ninth Modeling Conference Vol. 2, p. 137 

 2 PM in the same modeling system. An example 

 3 would be CMAQ and CAMx. 

 4 So the One Atmosphere approach may not be 

 5 particularly meaningful to people but the way we 

 6 look at it is we put in all different types of 

 7 sources, mobile, stationary point, area sources 

 8 and all the different types of precursors, NOx, 

 9 VOC, SOx, PM and toxics and use data science 

10 chemistry and transport and meteorology inputs to 

11 predict ozone, PM acid rain, visibility and 

12 toxics, and even deposition. 

13 This was a (inaudible) slide and wasn’t 

14 going to use it but got interested in the slide 

15 on the right and how that fit into this big 

16 picture and how that fit into this big picture. 

17 I ended up interpreting this as we’re trying to 

18 prevent kids in this terrible dooms day air 

19 pollution nightmare we’re having up above. 

20 That’s what we’re trying to do here is kind of 

21 bring it back so we know why we’re doing what 

22 we’re doing. We’re trying to save these kids. 

23 Photochemical models the governing equation 

24 is at the bottom. Basically what is going on in 

25 photochemical we’re trying to make chemical 
 1 Ninth Modeling Conference Vol. 2, p. 138 

 2 transformations (Gas- & Aqueous-phase and 

 3 Heterogeneous Chemistry); advection (Horizontal & 

 4 Vertical); diffusion (Horizontal & Vertical); 

 5 removal processes (Dry & Wet Deposition). 

 6 Just in case people are not that familiar 

 7 with photochemical models. The dispersion model 

 8 shown on the left with the plume (inaudible) at a 

 9 particular source plume kind of in its own 

10 universe. On the right you have the entire 

11 universe (inaudible) into one universe model. 

12 Kind of like taking the emission sources and 

13 putting a huge set of 3-D boxes on it to solving 

14 for all these different processes going on in 

15 each grid cell. 

16 For photochemical models advantages, one of 

17 the things in using a photochemical model for 

18 single source is full state of the science gas19 phase chemistry, ability to estimate realistic 

20 ozone concentrations, no need for a constant 

21 ozone background value for PM, advanced aqueous 

22 phase chemistry provides realistic sulfate 

23 estimates; wet and dry deposition processes 

24 included, photochemical models generally have 

25 good temporal and spatial estimates of ammonia 
 1 Ninth Modeling Conference Vol. 2, p. 139 

 2 concentrations, spatial/temporal representation 

 3 of ammonia and nitric acid concentrations and 

 4 state of the science inorganic chemistry 

 5 (ISORROPIA) allow for realistic nitrate 

 6 partitioning between gas and particle phase and 

 7 Source Apportionment tools allow for tracking of 

 8 single emissions sources or groups of emissions 

 9 sources. 

10 More recently, Source Apportionment tools 

11 have been implemented in photochemical models 

12 which allows (inaudible) single or multiple 

13 emission influences. This type of technology 

14 combined with the science that is already in the 

15 grid base models is starting to lend itself to 

16 single source applications. I’ll show some 

17 examples in a minute. Source Apportionment 

18 tracks the formation and transport of PM2.5/ozone 

19 from emissions sources and allows the calculation 

20 of contributions at receptors. Chemically 

21 speciated PM2.5 contribution can be converted to 

22 light extinction for visibility applications. 

23 On the right I just plotted out how the 

24 tracking occurs for PM on the top precursor to 

25 particulate species. NOX --> NO3; SOX --> SO4; NH3 
 1 Ninth Modeling Conference Vol. 2, p. 140 

 2 --> NH4+ ; Primary OC --> POC; Primary species are 

 3 pretty self explanatory. Source Apportionment 

 4 also tracks VOC emissions too and secondary 

 5 organic aerosol, and inert species. Estimates 

 6 contributions from emissions source groups, 

 7 emissions source regions, and initial and 

 8 boundary conditions to PM2.5 by adding duplicate 

 9 model species for each contributing source. 

10 Additionally NOx and VOC emissions get tracked 

11 for their contribution to ozone if you choose 

12 that. There are also some toxics components but 

13 I wasn’t going to get into that in this 

14 presentation. 

15 So on the particulate side you see that 

16 CAMx has particulate apportionment implemented 

17 and that tracks all the chemical species: mercury 

18 and PM sulfate, nitrate, ammonium, secondary 

19 organic aerosol, and inert species. Basically, 

20 the process in which to (inaudible) for a 

21 particular source you would just include 

22 additional model species. Just put in those 

23 emissions and the models can track that with 

24 duplicate model species. And goes with the same 

25 type of atmospheric processes as all the others 
 1 Ninth Modeling Conference Vol. 2, p. 141 

 2 species do in the photochemical model. The only 

 3 difference is for non-linear processes like gas 

 4 and aqueous phase chemistry are solved for bulk 

 5 species and then apportioned to the tagged 

 6 species. 

 7 This is an example of ozone source 

 8 apportionment that has been implemented in CAMx 

 9 v4.5 (OSAT & APCA) and CMAQ v4.6 (OPTM). Tracks 

10 ozone contribution from sources similarly to PM 

11 with reactive tracers, July maximum ozone 

12 contribution from a source shown at right and 

13 OSAT is simulated separately from particulate 

14 source apportionment. 

15 This is an example of using Source 

16 Apportionment type technology. We converted the 

17 output to 1 extinction but basically at the top 

18 left is the maximum ammonium light extinction 

19 estimation from that particular source in each 

20 grid cell. You can see the hot spot over there 

21 the source would be located. The photochemical 

22 offers speciated data so it can figure out the 

23 contribution from that source to ammonium 

24 nitrate, ammonium sulfate and the primary 

25 species. So clearly this particular source has 
 1 Ninth Modeling Conference Vol. 2, p. 142 

 2 emissions dominated by sulfur dioxide. 

 3 This is the same thing I showed on the 

 4 right with the total of the maximum contribution 

 5 from a particular source over an entire year. 

 6 Just comparing that back to a very simple metric 

 7 emissions over distance to show that this type of 

 8 screening metric states they obviously agree with 

 9 each other, but there’s a lot more detail going 

10 on with the photochemical model because it’s 

11 taking a lot more processes into consideration. 

12 Issues for using PCM for Single Source 

13 Applications was touched on Photochemical models 

14 resource intensive (computational, disk space, 

15 staff) for multi-year applications, especially at 

16 grid resolutions <= 12km. Additional level of 

17 staff expertise to get people who are comfortable 

18 doing that. Existing community emissions inputs 

19 (from States, RPOs, etc) for photochemical models 

20 are actual emissions and may need to be modified 

21 if more conservative emissions estimates are 

22 necessary and useful for near-field applications. 

23 The other thing about photochemical 

24 grid models is how useful clearly it has gotten a 

25 lot of utility for long range applications but 
 1 Ninth Modeling Conference Vol. 2, p. 143 

 2 what about near-field applications? I think we 

 3 need to do some more testing and looking at the 

 4 earlier types of applications that have been done 

 5 working with near-field with photochemical 

 6 models. With the CPU getting cheaper and the 

 7 different types of extensions being added to 

 8 photochemical models like sub-cell receptor 

 9 locations, and 2-way nesting capability. And to 

10 review existing near-field applications using 

11 PCMs, evaluate tracer studies. The picture on 

12 the right was a tracer experiment we just did a 

13 preliminary test of that where we ran that 

14 through a photochemical model and that’s just an 

15 example of what the concentrations look like. 

16 Those are the types of evaluations we want to 

17 keep working on and keep looking at. 

18 Other work I will talk about briefly. 

19 The mid west RPO did some preliminary testing 

20 (not an evaluation of CAMx PSAT or CALPUFF) of 

21 single source modeling with CAMx PSAT to compare 

22 with CALPUFF visibility estimates. Several 

23 States did single source visibility modeling for 

24 sources less than 50 km from Class I areas; used 

25 sub-grid plume treatment. To make a long story 
 1 Ninth Modeling Conference Vol. 2, p. 144 

 2 short the (inaudible) modeling just try to apply 

 3 these consistently, they both use the same 

 4 meteorology output from MM5. CALPUFF was run in 

 5 a NOOBS mode. They were both processed to look 

 6 at the number of times in each grid cell that had 

 7 a 24 hour average [ed. concentration] (inaudible) 

 8 over background and they were both using actual 

 9 facility emissions not any potentials or 

10 maximuns. 

11 The other thing I want to point out 

12 before I show these result is this is not 

13 intended to be an evaluation of CAMx, PSAT or 

14 CALPUFF. We are not trying to say which is right 

15 or wrong but to find out what the differences 

16 are. This is an example for a few facilities on 

17 the top we’ve got the CALPUFF results and on the 

18 bottom are the CAMx PSAT results. One important 

19 caveat to put on this is that CALPUFF look at 

20 sulfate and nitrate impacts and CAMx just has 

21 sulfate. That could be a part of the 

22 differences, but I don’t think we expect to see a 

23 lot of visibility from nitrate. It wasn’t as 

24 common. 

25 Generally qualitatively we saw a 
 1 Ninth Modeling Conference Vol. 2, p. 145 

 2 similar type of response from both models. Not 

 3 amazing was CALPUFF had some larger extinction 

 4 (?) of the contribution. We applied CALPUFF with 

 5 the regulatory set of options which probably 

 6 closer to the most conservative types of things. 

 7 So you expect a larger contribution when you use 

 8 more conservative sets of assumptions. And with 

 9 the photochemical model really not a lot of 

10 conservative assumptions you can make because it 

11 is what it is. 

12 This is just another group of sources 

13 in the same area. Qualitatively, they are pretty 

14 similar but the extent is slightly different. 

15 Final remarks. I think the 

16 photochemical grid models provide an opportunity 

17 for credible single source modeling with Source 

18 Apportionment methodology. These models have the 

19 advantage of state of the science chemistry, but 

20 that comes with increased resource burden. These 

21 models are routinely used for other regulatory 

22 purposes like O3/PM2.5/Regional Haze State 

23 Implementation Plans so they do have regulatory 

24 history and people are more comfortable with 

25 using them in that way. 
 1 Ninth Modeling Conference Vol. 2, p. 146 

 2 Tyler Fox: Thank you Kirk. Now we 

 3 will get more details from Ralph on single source 

 4 modeling for Ozone and PM. 

 5 Ralph Morris: Thank you. I guess Kirk 

 6 set the stage pretty well giving the goal and 

 7 concept in using photo grid models for single 

 8 source or groups of sources impacts. We’re not 

 9 talking about fence line impacts, we’re talking 

10 more about the regional or further down wind a 

11 little. There’s no reason to go to a smaller 

12 grid size if you can’t use it for this. I’m 

13 going to give some examples afterwards. This is 

14 more of a slide for another group since this 

15 group knows the guidelines and the guidance. 

16 One of the emphasis for considering the 

17 photo grid models for the single source 

18 assessment are the new more stringent Ozone and 

19 PM (inaudible) standards, and to pinpoint 

20 contribution (?) (inaudible) components. We are 

21 seeing now more and more what is my source or are 

22 regional offices or states are asking: “What are 

23 the contributions of source to the Ozone and 

24 PM2.5?” 

25 New 0.075 ppm 8-hour and 35 μg/m3 24-hr 
 1 Ninth Modeling Conference Vol. 2, p. 147 

 2 PM2.5 NAAQSs will bring many more areas into 

 3 nonattainment, PM2.5 NAAQS increases importance of 

 4 secondary PM2.5. Capability needed to obtain 

 5 individual contributions to ozone and PM2.5 

 6 concentrations, deposition and visibility. 

 7 Current guideline models have no (AERMOD) or 

 8 highly simplified (CALPUFF) representation of 

 9 chemistry. Photochemical Grid Models (PGMs) have 

10 capability to correctly treat chemistry. But how 

11 can they resolve and correctly simulate near 

12 source plume chemistry and dispersion? 

13 PGMs can only resolve impacts to the 

14 grid resolution. Fine grid size is needed near 

15 the source to resolve near-source plume chemistry 

16 and dispersion. Need many grid cells to assess 

17 downwind impacts. High computer resource 

18 requirements. Must account for all emission 

19 sources. Needed to correctly simulate chemistry. 

20 Databases more costly to develop. MM5/WRF 

21 applications. SMOKE or other emissions model 

22 and more expertise needed in their application. 

23 So why are we considering this now? 

24 There has been a lot of development in modeling 

25 capability for PGM for single source but we do 
 1 Ninth Modeling Conference Vol. 2, p. 148 

 2 have two-way interactive grid nesting. Allows 

 3 fine grid over sources with coarser grid downwind 

 4 when plumes are larger. Flexi-nesting where you 

 5 can specify fine grid to resolve point source 

 6 plume chemistry and dispersion without providing 

 7 met and emission inputs and full chemistry Plume- 

 8 in-Grid Modules. Treats unique near-source 

 9 chemistry of point source plumes. Both CMAx and 

10 CMAQ have PM and Ozone Source Apportionment and 

11 allows individual source(s) assessments. Of 

12 course computational advances. Availability of 

13 PGM Databases and model set ups. RPOs, AIRPACT, 

14 SIPs, etc. and EPA has been developing stuff. 

15 I talked about the two-way interactive 

16 grid nesting and the flexi-nesting and in CAMx 

17 you have to specify the grid it interpolates. 

18 Allows specification of high resolution grid over 

19 sources with coarser grids downwind where plumes 

20 are larger. Interpolate meteorology, emissions 

21 and/or other inputs for nested fine grid from 

22 coarse grid data. Allows fine grid treatment of 

23 point source plumes. Available within the CAMx 

24 model (just specify where fine grid domains are 

25 desired in job script). Have developed tool to 
 1 Ninth Modeling Conference Vol. 2, p. 149 

 2 generate flexi-nest fine grid inputs for CMAQ 

 3 (for EPA/OAQPS) 

 4 I think I borrowed this from Prakash. 

 5 He talked about the Stage 1 and Stage 2 and the 

 6 evolution of the plume where there’s no Ozone 

 7 formed, no secondary PM formed and no stages are 

 8 very little. Whereas in a grid model you dump 

 9 those emissions and it starts forming Ozone and 

10 PM2.5 immediately. That’s one of the purposes of 

11 the Plume in Grid model. 

12 I think Kirk talked about the Ozone and 

13 PM Source Apportionment so I don’t have to talk 

14 about that. We’ll get back on time here. I’m 

15 going to talk about applications. One is down in 

16 Texas Group BART application. CAMx 36/12 km with 

17 P-in-G and PSAT. Estimation of individual 

18 contributions of 31 point sources to annual PM2.5 

19 in the eastern U.S. Individual point source 

20 contributions to 2009 annual PM2.5 concentrations. 

21 Visibility Improvements for States and Tribal 

22 Association of the Southeast (VISTAS) and 

23 Association for Integrated Planning of the 

24 Southeast (ASIP). Annual PM2.5 SIP modeling for 

25 St. Louis. Effects of local sources on PM2.5 
 1 Ninth Modeling Conference Vol. 2, p. 150 

 2 nonattainment. 

 3 I have one slide on the Texas Bart but Texas 

 4 had like 200 potential Bart eligible sources. 

 5 Rather than running each one individually we 

 6 decided to do group analysis and run them in 

 7 groups of 10. In each group Bart analysis of 10 

 8 sources at a point use PSAT to obtain PM2.5 

 9 contributions of groups of Texas BART sources for 

10 comparison with 0.5 deciview threshold. CENRAP 

11 2002 36 km modeling CAMx database. Add 12 km 

12 flexi-nest grid covering Texas and nearby Class I 

13 areas. Use IRON P-in-G for Texas BART Source. 

14 Another application is the PM2.5 Ozone ASIP 

15 model a part of VISTAS ASIP. Here’s a 36 km: 148 

16 x 112 (4 days), 12 km: 168 x 177 (10 days), 2002 

17 Annual Runs, 4 Quarters w/ ~15 day spin up, MPI 

18 w/ 6 CPUs, 19 Vertical Layers, M3Dry, CBM19 IV/AE4/SORGAM, SOA mods. In 2005 VISTAS enhanced 

20 CMAQ to include SOA from sesquiterpenes and 

21 isoprene (Morris et al., 2006). 

22 Some ASIP/VISTAS states wanted to know 

23 individual contributions of several point sources 

24 to 2009 PM2.5 levels. 31 individual point sources 

25 in 6 states identified. Contributions due to SO2 
 1 Ninth Modeling Conference Vol. 2, p. 151 

 2 and primary PM emissions requested. CALPUFF 

 3 considered for assessment. Not consistent with 

 4 CMAQ full-science chemistry. Provide 

 5 inconsistent source contributions with 2009 PM2.5 

 6 SIP projections. ASIP 36/12 km database 

 7 inappropriate for individual point source 

 8 modeling. 12 km grid cell size too coarse to 

 9 treat chemistry and dispersion of point source 

10 plumes. Use of high enough resolution to resolve 

11 point source plume would be computationally 

12 prohibitive. Would need to perform base case and 

13 31 zero-out runs to get individual source 

14 contributions. Elected to develop a new CAMx 

15 2002 database, 12/4 km domain with two-way nested 

16 grids. Plume-in-Grid to address near-source 

17 chemistry and dispersion. PM Source 

18 Apportionment Technology (PSAT) to obtain 

19 individual source contributions. 

20 

21 This is our CAMx 12/4 km domain nested 

22 within ASIP 12 km CMAQ domain (one-way nesting). 

23 CAMx 12/4 modeling using two-way interactive grid 

24 nesting. 2002 base case using standard model. 

25 2009 base case with PSAT PM2.5 source 
 1 Ninth Modeling Conference Vol. 2, p. 152 

 2 apportionment for 31 point sources. 

 3 Here’s the Huntington and Ashland and 

 4 Charleston 4 km domains. Little crosses are 

 5 point sources and circles are (inaudible) method 

 6 monitors where we are asked to get the PM2.5 

 7 impacts. You can see in some cases the sources 

 8 are located close to the grid model to the 

 9 monitor and sometimes almost (inaudible) I admit 

10 that when you are doing primary PM impacts 

11 (inaudible) for that other model CALPUFF. 

12 Something that has finer grid. So they’re pretty 

13 close there in some cases. Okay. 

14 Here’s the source apportionment. The 

15 largest contributions are the boundary 

16 conditions. The boundary conditions are outside 

17 the 12 km grid of (inaudible) and the second 

18 largest is the purple all the sources. These 

19 things here are the contributions of the 31 point 

20 sources. It doesn’t give us much information so 

21 get rid of the boundaries and other sources and 

22 have a contribution of 31 point sources. The 

23 projected 2009 design barriers at these monitors 

24 and these are the contributions. One thing we 

25 did compare (inaudible) to CAMx projections from 
 1 Ninth Modeling Conference Vol. 2, p. 153 

 2 the l2 and 4 km with the CMAx from the 12 point 

 3 grid. 

 4 For these 31 sources the contributions 

 5 are (inaudible) and those are pretty large 

 6 contributions. The largest single source 

 7 contribution is this source right near the 

 8 monitor and that’s about 2 μg which is a large 

 9 contribution source on a monitor. In this case 

10 it’s not above 15. Here’s 1 μg for this model. 

11 In St. Louis Regional 36/12 km grid and 

12 CMAQ V4.5 SOAmods. Projected 2009 and 2012 PM2.5 

13 Design Values at Granite City and East St. Louis 

14 still exceed the annual PM2.5 NAAQS. 

15 Evidence that local sources contribute 

16 to PM2.5 nonattainment at Granite City Monitor 

17 (B) and Washington St. Monitor (A). 

18 Turner and co-workers (2007a,b,c,d) 

19 have developed a Conceptual Model for PM2.5 

20 exceedences in the St. Louis area. They found 

21 that local sources contribute ~3.2 μg/m3 to PM2.5 

22 at the Granite City monitor on average. The CAMx 

23 12/4/1 km PiG modeling attributes 3.4 μg/m3 to 

24 local sources at Granite City. Recent advances 

25 in PGMs make them more suitable for assessing 
 1 Ninth Modeling Conference Vol. 2, p. 154 

 2 “single source” contributions to ozone, PM2.5, 

 3 visibility and deposition. Fine resolution 

 4 grids, two-way grid nesting, and flexi-nesting. 

 5 Full chemistry Plume-in-Grid modules. Ozone and 

 6 PM source apportionment. Full gas-phase and 

 7 aqueous-phase chemistry and aerosol thermodynamic 

 8 modules. The use PGM modeling to assess “single 

 9 source” air quality, visibility and deposition 

10 issues have become more routine. ASIP point 

11 source PM2.5 assessment. Oil and gas AQ and AQRV 

12 assessments as part of NEPA, Texas and Arkansas 

13 BART assessment. PM2.5 SIP modeling. 

14 Conclusions are that recent advances in 

15 PGMs make them more suitable for assessing 

16 “single source” contributions to ozone, PM2.5, 

17 visibility and deposition. Fine resolution 

18 grids, two-way grid nesting, and flexi-nesting. 

19 Full chemistry Plume-in-Grid modules. Ozone and 

20 PM source apportionment. Full gas-phase and 

21 aqueous-phase chemistry and aerosol thermodynamic 

22 modules. The use of PGM modeling, to assess 

23 “single source” air quality, visibility and 

24 deposition issues, has become more routine. ASIP 

25 point source PM2.5 assessment. Oil and gas AQ and 
 1 Ninth Modeling Conference Vol. 2, p. 155 

 2 AQRV assessments as part of NEPA. Texas and 

 3 Arkansas BART assessment. PM2.5 SIP modeling. 

 4 That’s all I have. 

 5 Tyler Fox: Thank you Ralph. Are there 

 6 any questions on single source? 

 7 Joe Scire: TRC. I have a question 

 8 for Ralph. When you do the (inaudible) cell 

 9 analysis do you treat terrain elevations of the 

10 receptors within the cells. The second question 

11 is do you treat any wind variability within the 

12 cell due to (inaudible)? 

13 Ralph Morris: No just using the wind 

14 that comes from the whatever you (inaudible) 

15 whether it’s a gridded wind field or (inaudible). 

16 It’s a simple application from that respect. And 

17 as far as the terrain the receptors are at the 

18 ground level so I imagine you could elevate the 

19 receptor if you like. These models are terrain 

20 (inaudible) a simple representation at this 

21 point. 

22 Joe Scire: There’s no terrain 

23 variability in the cell? That’s my question 

24 really. 

25 Ralph Morris: Yes, the terrain 
 1 Ninth Modeling Conference Vol. 2, p. 156 

 2 (inaudible) so any terrain effects are in the 

 3 wind fields that come out of MM5. 

 4 Joe Scire: That would be a resolution 

 5 of the (inaudible) the cell itself. 

 6 Ralph Morris: Yes. 

 7 Bob Paine: ENSR. I have a question 

 8 for EPA. Basically Appendix W Guidance on 

 9 modeling single source for Ozone PM2.5 seems to 

10 be sort of lacking. Are there any plans to 

11 enhance that? 

12 Tyler Fox: The purpose of this 

13 conference is to introduce these types of methods 

14 I think as we continue to evolve and as people 

15 have shown today and recognizing applications 

16 like Ralph has mentioned here. We need to begin 

17 to consider these things. As for changes from 

18 Appendix W would have to fall out of discussions 

19 both internally, with you in the community and 

20 with our policy folks in the Air Quality 

21 division. The intent here is to make us all 

22 aware and to identify that they could build an 

23 important need. As folks know with respect to 

24 the PM2.5 there may be some aspects of the 

25 implementation rules lacking in terms of 
 1 Ninth Modeling Conference Vol. 2, p. 157 

 2 accounting for secondary formation in some parts 

 3 of the country, that could be a significant 

 4 contribution. And if we are not accounting for 

 5 that in our permit programs that may not be 

 6 getting us where we need to be in terms of 

 7 attainment in those standards. Any other 

 8 questions? 

 9 That concludes this part of the 

10 conference sessions and what we have now is the 

11 public session. Let me walk through the line up 

12 for that. 

13 Peter Eckhoff: Some of you might be 

14 leaving here pretty soon. You’re welcome to keep 

15 your badges, but if you want us to recycle them 

16 for later use, I’ll put a box on the registration 

17 table. Thanks. 

18 Tyler Fox: so that everybody knows, 

19 we’ve got the schedule laid out for the 

20 presentations. We’ll start with Bruce Egan 

21 comments on behalf of API. Doug Blewitt has two 

22 presentations, and then there is a presentation 

23 for Peter Manousos and then multiple 

24 presentations on behalf of AWMA. Then we have 

25 comments on behalf of UARG from Hunton & 
 1 Ninth Modeling Conference Vol. 2, p. 158 

 2 Williams. There’s another presentation from 

 3 George Delic and another addition from Mark 

 4 Garrison from ERM and that’s the long and short 

 5 of our public presentations. Is there anybody 

 6 here who is not accounted for who plans to make a 

 7 presentation. Then I’m assuming I have 

 8 everything here. Bruce if you would like to come 

 9 on up. If you would just say your name and 

10 affiliation for the record, please recognize 

11 these will be made public. 

12 Bruce Egan: Good afternoon I’m Bruce 

13 Egan from Egan Environmental. My co authors are 

14 Steven R. Hanna, Hanna Consultants, who is 

15 talking about the same topic in Crotia at the 

16 moment and Elizabeth M. Hendrick, CCM, of Epsilon 

17 Associates Inc. We are providing comments for 

18 the API. 

19 Promulgation of more stringent ambient 

20 air standards has resulted in more non-attainment 

21 areas and the need for more complex and more 

22 regional modeling. These comments cover many 

23 issues relating to aspects of the EPA’s Guideline 

24 on Air Quality Models. Highlights are listed 

25 here and our written comments will contain 
 1 Ninth Modeling Conference Vol. 2, p. 159 

 2 details and references. We are going to provide 

 3 written documentation of this. We’ll go through 

 4 an abbreviated version of our prepared slides as 

 5 we see there are a lot of things ongoing and 

 6 there will be redundancy. 

 7 We had discussions yesterday of CALPUFF 

 8 and documentation. We would like to see that 

 9 completed and brought up to date. And there is a 

10 general concern that API has more EPA Guidance 

11 Workshops and training. Over the past two days I 

12 have seen a lot of response from EPA even before 

13 we put the comment in. It is pleasing to see 

14 much more discussion about the models and the 

15 background. 

16 One of the topics is distance limits on 

17 models especially on CALPUFF and AERMOD. As you 

18 know there is a 50 km cut off that differentiates 

19 CALPUFF and AERMOD at this time. We don’t think 

20 the distance should be arbitrary like that and 

21 should depend on the scientific issues including 

22 meteorological data and land use variations. Can 

23 you hear me? Okay. What is the minimum domain 

24 size and grid size where grid models such as CMAQ 

25 or CAMx can be used, and what is the 
 1 Ninth Modeling Conference Vol. 2, p. 160 

 2 recommendation for Plume in Grid (PinG) modeling? 

 3 Distance limits should not be arbitrary, but 

 4 should depend upon scientific issues, including 

 5 topography, wind persistence data and land use 

 6 variations. 

 7 There has been an increase in the use 

 8 of meteorological drivers (e.g., diagnostic 

 9 models such as CALMET and prognostic full-physics 

10 models such as MM5) for both steady state and 

11 time varying dispersion models (e.g., AERMOD, 

12 CALPUFF, CMAQ). Prognostic meteorological models 

13 such as MM5 and WRF (often called ‘Met models’) 

14 have been improving with advances in science and 

15 resolution. We’d like to see EPA reach out to 

16 talk to some other agencies that are working on 

17 this including DTRA and NOAA who have linked MET 

18 models with MM5 and WRF and the Puff models. 

19 We’ll come back to this issue. 

20 One of the research efforts we think is 

21 needed is to optimize use of Met model and CALMET 

22 model predictions with observations. Specific 

23 issues to clarify differences between full24 physics Met models (e.g. MM5) and CALMET; look at 

25 assessing the effects of grid size and vertical 
 1 Ninth Modeling Conference Vol. 2, p. 161 

 2 grid spacing on bias and accuracy and to develop 

 3 recommendations for optimal grid sizes for 

 4 different topographic and meteorological 

 5 settings; minimum grid size (Penn State MM5 

 6 developers recommend 4 km as a safe general rule, 

 7 although 1 km can be used in special cases; this 

 8 is due to physical assumptions in the model). 

 9 We’d like to see overall model 

10 performance of Met models coupled with dispersion 

11 models vs. field study data sets; and possible 

12 new field experiments to determine how met 

13 observations can best be used and assimilated in 

14 Met models? (e.g. note differences between NCAR 

15 and Penn State MM5 Met model data assimilation 

16 methods). We’d like to assess if CALMET (or any 

17 diagnostic model) is truly needed as an 

18 intermediate step between the Met model and the 

19 AQM. EPA should work with other agencies (DTRA, 

20 NOAA) who have operational Met model-AQM systems 

21 operating and make use of their technology where 

22 appropriate. 

23 Determine overall model performance of 

24 Met models coupled with dispersion models vs. 

25 field study data sets; possible new field 
 1 Ninth Modeling Conference Vol. 2, p. 162 

 2 experiments. Determine how met observations can 

 3 best be used and assimilated in Met models? (e.g. 

 4 note differences between NCAR and Penn State MM5 

 5 Met model data assimilation methods). Assess if 

 6 CALMET (or any diagnostic model) is truly needed 

 7 as an intermediate step between the Met model and 

 8 the AQM. 

 9 Work with other agencies (DTRA, NOAA) who have 

10 operational Met model-AQM systems operating and 

11 make use of their technology where appropriate. 

12 We’ll talk some more about data gathering in 

13 Wyoming and we’d like to see databases developed 

14 further which would provide monitoring data and 

15 emissions data inventory. 

16 We see a need for an overall model 

17 evaluations of CALPUFF using full chemistry as 

18 very limited evaluations of the model in the mode 

19 that it is being used have been conducted. 

20 Evaluation should include other models such as 

21 SCIPUFF And the ability to handle complex 

22 terrain, short term puff dispersion, chemical 

23 reactions, and other incorporated capabilities 

24 (e.g. FOG) needs to be evaluated. We recommend 

25 that EPA modify the chemistry, based on API/AER 
 1 Ninth Modeling Conference Vol. 2, p. 163 

 2 recommended revisions. 

 3 We think that documentation is incomplete, 

 4 and lack of detail causes many users to rely 

 5 heavily on default values. Need to resolve met 

 6 input questions (CALMET or Met model such as MM5 

 7 – see previous slides on Met inputs). Need to 

 8 test the use of CALPUFF for regional AQRV 

 9 analyses (NEPA studies are currently using this 

10 approach in the West). Operational use should be 

11 based on peer and stake holder review using best 

12 science approach as opposed to IWAQM mandates. 

13 We’d like to see this (field experiment) 

14 happen. Purpose: to test and improve the linkage 

15 of Met models and air quality models in 

16 mountainous terrain, such as Wyoming where there 

17 is much current mesoscale and regional modeling 

18 underway. EPA should lead the effort with 

19 invited participation of API and other industries 

20 and stakeholders. Include meteorological 

21 observations, tracer releases, and PM and 

22 visibility observations over an area of about 200 

23 km by 200 km, sufficient to test the use of Met 

24 model (e.g., MM5) direct input versus CALMET 

25 diagnostic model. 
 1 Ninth Modeling Conference Vol. 2, p. 164 

 2 I’d like to switch to model evaluation 

 3 uncertainty and these slides were written before 

 4 we knew all the things EPA is doing. Recent 

 5 improvements in regional dispersion model 

 6 performance measures have been made; EPA efforts 

 7 (in collaboration with members of an 

 8 international workgroup) are described in a 

 9 recently submitted paper by Dennis et al. I 

10 think Bob Paine has captured a lot of what we 

11 were talking about here. Rather than having 

12 different evaluation approaches and performance 

13 measures for the different model scales, a 

14 comprehensive set of performance measures should 

15 be devised for use at all model scales. I 

16 realize this differs on applications but I think 

17 we’re talking about the context of regulatory 

18 models and we understand that some of the models 

19 response is entirely dependent upon the set of 

20 priority performance methods. 

21 The bootstrap method was talked about this 

22 morning and I won’t spend much time on this. 

23 John Irwin was instrumental in the ASTM software 

24 and Joe Chang and Steve Hanna have been active 

25 with the BOOT software. We think the model 
 1 Ninth Modeling Conference Vol. 2, p. 165 

 2 acceptance criteria should be set and used in 

 3 modeling protocols and decision making. We also 

 4 believe uncertainty in model predictions (also 

 5 called “probabilistic forecasts”) should become 

 6 available to and used by regulatory decision 

 7 makers. EPA should investigate and possibly make 

 8 use of the probabilistic AQM system (Met model – 

 9 SCIPUFF) in use at DTRA. 

10 We understand the screening model, 

11 AERSCREEN, is coming out soon. We’d like to see 

12 the establishment of a peer–review panel from all 

13 segments of the community to review planned 

14 improvements and draft documents produced and EPA 

15 incorporate algorithms for near calm winds and 

16 test with appropriate field data sets; improve 

17 algorithms for use in urban areas, especially for 

18 near-ground sources in built-up downtown areas 

19 and determine science-based criteria for deciding 

20 distance limits and whether “complex terrain” is 

21 significant. 

22 Based on EPA guidance, EPA limits the 

23 influence of nearby land use in parameterizing 

24 surface roughness to a 1 km radius of ASOS 

25 anemometers generally located on airport 
 1 Ninth Modeling Conference Vol. 2, p. 166 

 2 property. For many pollutant sources this means 

 3 that the dispersion modeling domain is dominated 

 4 by surface roughness of airport property. Better 

 5 guidance is needed for translating the airport 

 6 wind observations to the land characteristics of 

 7 the pollutant source domain. For most pollutant 

 8 sources that use airport data, the dispersion 

 9 model domain is going to be entirely dominated by 

10 the surface modeling of the airport roughness. 

11 We’d like to see better guidance for translating 

12 the airport wind observation to the land 

13 characteristics of the pollutant source domain. 

14 This is the bottom line out of this. 

15 Issues on the AERMET output. AERMET Stage 3 

16 output should summarize the processed met data so 

17 the user knows during the AERMET processing steps 

18 if that year of data is suitable for regulatory 

19 modeling purposes (>90% available). We’d like to 

20 see that summarized. Currently this summary 

21 information is not provided until AERMOD is run. 

22 We are interested in the Plume Molar Volume 

23 Ratio Model (PMVRM. We like for EPA to further 

24 test this model and, if acceptable, recommend the 

25 use of this model for predicting NO2 
 1 Ninth Modeling Conference Vol. 2, p. 167 

 2 concentrations in the presence of ambient air 

 3 ozone concentrations. This should be performed 

 4 for both AERMOD and CALPUFF. 

 5 Little change of subject here. We believe 

 6 EPA has asked questions and asked for advice on 

 7 non-regulatory driven studies concerned, for 

 8 example, with health risk assessments use AQ 

 9 monitoring data combined with statistical 

10 correlations as a substitute for the use of 

11 detailed dispersion models (AERMOD, CALPUFF, or 

12 CMAQ) for estimating air quality concentrations. 

13 EPA should promote consistent and general 

14 use of dispersion models that are based on 

15 physical understanding of meteorological 

16 principles (e.g., AERMOD, CALPUFF, CMAQ, CAMx 

17 etc.) as opposed to statistical fits to site 

18 specific concentration data sets. The use of 

19 statistical models in place of more rigorous 

20 dispersion models should be reviewed by an expert 

21 panel that includes all scientific and 

22 stakeholder communities. We’d like to see EPA 

23 deal with that. I don’t know if they can do it 

24 in the context of the guidelines, but it would be 

25 good for the overall community instead of 
 1 Ninth Modeling Conference Vol. 2, p. 168 

 2 approving statistical fits. 

 3 Avoid arbitrary non-scientific criteria for 

 4 model selection (such as eliminating models with 

 5 a bias for over-prediction). Encourage 

 6 scientific peer review of all models (i.e., both 

 7 internal EPA and outside models) and of proposed 

 8 modifications to model algorithms. Model 

 9 acceptance criteria should be developed through 

10 discussions with the entire community of model 

11 developers and stakeholders. 

12 Need to update and improve model guidance 

13 and documentation. Encourage development and use 

14 of science–based models through model evaluation 

15 efforts and enhanced public involvement. Test, 

16 validate, and recommend procedures for using 

17 meteorological models to drive dispersion models. 

18 Conduct a Mesoscale/Regional collaborative model 

19 evaluation using the existing databases and/or 

20 conduct a field experiment that could be used to 

21 evaluate regional models in rural regions in the 

22 intermountain west or similar location. Thank 

23 you. 

24 Tyler Fox: Okay next we have Doug Blewitt. 

25 I did want to mention one other thing, it’s 
 1 Ninth Modeling Conference Vol. 2, p. 169 

 2 come up here and other contexts in terms of 

 3 ASIP modeling and the like. I just want to 

 4 emphasize a couple of things. One is EPA 

 5 guidance, it’s just that and sometimes it’s 

 6 interpreted as prescriptions and if you 

 7 don’t follow exactly what we say you won’t 

 8 be allowed to do something. I think we need 

 9 to recognize and I know it cuts both way. 

10 But guidance is just that, guidance. Second 

11 point is that guidance we provide is only as 

12 good as the information you provide us or 

13 the information we have. 

14 As a community and as it relates to 

15 issues here, guidance has to have a basis 

16 and has to be informed through experiences. 

17 Experiences learned not just by us but by 

18 you all. And so to the extent that in three 

19 years we would hear of your experiences. 

20 But in the interim, sharing those 

21 experiences here either through these 

22 specific applications with the state and 

23 local folks and regional folks and making 

24 sure those are understood, and will promote 

25 more communication and discussion within the 
 1 Ninth Modeling Conference Vol. 2, p. 170 

 2 region and state and local modeling. 

 3 Sharing that information through 

 4 publications; the folks in ORD in developing 

 5 CMAQ will look to the peer review literature 

 6 as we would and so to the extent these 

 7 things are published to the extent there are 

 8 other conferences. 

 9 Having more of an opportunity to get 

10 that information into our zone of awareness 

11 if you will, will definitely help that out 

12 and we can build a consensus and 

13 understanding so that we can provide the 

14 type of guidance that is needed. Providing 

15 guidance that is just complained about and 

16 not useful to you all. If we can work 

17 better together that we can provide guidance 

18 that meets your needs and has more of a long 

19 term value. I think that will be more 

20 useful to us. I just wanted to make that 

21 comment in terms of the general concept of 

22 EPA guidance. 

23 Doug Blewitt: Thank you. What I’d 

24 like to do is present issues related to air 

25 quality modeling for regional analysis for 
 1 Ninth Modeling Conference Vol. 2, p. 171 

 2 oil and gas development in the West. I’m 

 3 not presenting this in the context of an end 

 4 user and will try to present you with some 

 5 of the challenges and issues and try to 

 6 communicate to EPA some of the things we 

 7 need to work together on. I am going to 

 8 propose some long term solutions to this. 

 9 What we’re really talking about and you 

10 mentioned yesterday that we need 

11 consistency. The reason we developed 

12 guideline models was to take a model and 

13 look at it against observational data and 

14 see how it performed under a wide range of 

15 conditions. 

16 And if we got reasonable agreement with 

17 that evaluation, we could use the model in 

18 future forecasting situations without 

19 additional verifications. And what I’m 

20 going to challenge EPA here is that in the 

21 context of AQRV analysis which is what we 

22 are concerned with in terms of oil and gas 

23 development in the West. We haven’t lived 

24 up to that standard because as Bruce just 

25 said the model has not been evaluated to a 
 1 Ninth Modeling Conference Vol. 2, p. 172 

 2 large extent in a full chemistry mode. And 

 3 that’s the way we’re using the model and 

 4 there are some challenges and issues with 

 5 that. 

 6 Most of this work is being done for oil 

 7 and gas in the context of NEPA. You heard 

 8 Ralph talk about that. CALPUFF is being 

 9 used for analysis of future year regional 

10 air quality impacts under NEPA 

11 (Environmental Impact Statements) for oil 

12 and gas development in the West. A typical 

13 NEPA analysis includes up to 700 sources and 

14 impacts are projected over a 20 year period. 

15 Air quality modeling approach is: “Use 

16 the best available science to support NEPA 

17 analyses, and give greater consideration to 

18 peer-reviewed science and methodology over 

19 that which is not peer-reviewed.” (Bureau of 

20 Land Management (BLM) National Environmental 

21 Policy Act Handbook H-1790 H-1790-1 ). 

22 Visibility and deposition impacts from NOx 

23 emissions are the pollutants of concern. 

24 AQRV modeling approach is to develop a 

25 baseline emission inventory of sources not 
 1 Ninth Modeling Conference Vol. 2, p. 173 

 2 included in the monitoring data which is 

 3 then added to cumulative emissions from new 

 4 sources. 

 5 

 6 Formulation of CALPUFF chemistry. Lack 

 7 of a robust model performance evaluation in 

 8 a full chemistry mode. Indication of model 

 9 bias for NO3 impacts compared to monitored 

10 values. Outdated and prescriptive IWAQM 

11 methodology is required for model 

12 application. 

13 In the MESOPUFF II chemistry module 

14 used in CALPUFF, SO4 formation is described 

15 by 4 variables: 

16 1) Solar Radiation; 

17 2) Background Ozone (surface, user 

18 provided); 

19 3) Atmospheric Stability; and 

20 4) Relative Humidity (surrogate for 

21 aqueous-phase) 

22 NO3 formation is described by 3 

23 variables: 

24 1) Background Ozone; 

25 2) Atmospheric Stability; and 
 1 Ninth Modeling Conference Vol. 2, p. 174 

 2 3) Plume NOx Concentration 

 3 Aqueous-phase SO4 formation is 

 4 inaccurate because it is solely based on 

 5 surface relative humidity (RH). In reality, 

 6 aqueous-phase SO4 formation is not at all 

 7 affected by RH. The MESOPUFF II 

 8 transformation rates were developed using 

 9 temperatures of 86, 68 and 50°F. A 50°F 

10 minimum temperature will overstate SO4 and 

11 NO3 formation under cold conditions. – A 

12 major issue in the intermountain West. 

13 This is some work Ralph Morris did. 

14 It’s a comparison of CMAQ chemistry verses 

15 CMAQ MESO PUFF II chemistry. The blue dots 

16 are MESOPUFF II and the red dots are CMAQ 

17 and you can see there is a substantial over 

18 prediction to the MESOPUFF chemistry 

19 compared to the CMAQ chemistry. This is 

20 done for all improved sites and all CASTNET 

21 sites in the US. This is an indication that 

22 the system we’re using here is that the 

23 chemistry is not working as it should be. 

24 This is another figure that was in 

25 Prakash’s discussion yesterday. This is a 
 1 Ninth Modeling Conference Vol. 2, p. 175 

 2 different graph out of his results. There 

 3 are big differences between MESOPUFF 

 4 chemistry and RIVAD and some modified RIVAD 

 5 that API has done. We have this issue of 

 6 developing nitrate concentrations in excess 

 7 of theoretical limits and we need more 

 8 discussions on that. 

 9 Joe mentioned yesterday the SWWYTAF 

10 analysis and presented some graphs. This is 

11 really the only model verification that has 

12 been done in terms of CALPUFF. RIVAD 

13 chemistry was used. When boundary 

14 conditions were included model agreement was 

15 very good. Results were unpaired in time 

16 and space. Analysis indicated that NO3 

17 formation was limited by NH3 concentrations. 

18 This is not the way that agencies are 

19 requiring that the model should be used. 

20 The following examples present a strong 

21 indication that the as CALPUFF Model using 

22 the IWAQM protocol, has a substantial bias 

23 towards over predicting NO3 concentrations. 

24 This was the frequency distribution for 

25 Bridger CLASS I area outside of Pinedale 
 1 Ninth Modeling Conference Vol. 2, p. 176 

 2 Wyoming. An area very heavily in oil and 

 3 gas development; a lot of oil and gas wells. 

 4 The blue line is the 05 frequency 

 5 distribution site and the red line is what 

 6 CALPUFF is predicting. Now we can get into 

 7 issues of is the monitor in the right 

 8 locations and I think those are valid 

 9 questions. The issue is the source region 

10 is probably 30 to 50 km maybe even more away 

11 from the Class I areas. So you are not 

12 going to see sharp concentration gradients 

13 up there. But I’m going to challenge you 

14 with some things to think about. 

15 In this context, the model is not 

16 performing very well at all. If you look at 

17 the improved monitoring data at Bridger over 

18 the period of record, 88 through 05 there’s 

19 no change in nitrate out there. There’s 

20 been a lot of growth in NOx emissions over 

21 the time period but nitrate really hasn’t 

22 changed dramatically. 

23 I would submit if the monitor wasn’t 

24 placed in the right location, you would see 

25 some differences in these frequency 
 1 Ninth Modeling Conference Vol. 2, p. 177 

 2 distributions. If you look at the measured 

 3 concentrations the maximum measured there’s 

 4 no change. The difference in maximum 

 5 concentrations is certainly not enough to 

 6 say the monitor is in the wrong location and 

 7 that the model is performing correctly. 

 8 Relative extinction contribution for 

 9 various species for the 100 Worst Days at 

10 Bridger (Raleigh Scattering is not 

11 included). What is the composition of that 

12 material? The blue is sulfate and the red 

13 is nitrate. Nitrate isn’t playing much of a 

14 role of visibility [ed. reduction] yet the 

15 model is saying it is playing a very 

16 substantial role. In this context we’re not 

17 really doing a very good job of model 

18 accuracy. 

19 If you look at Bridger a little bit 

20 further, this is the total visibility. 

21 We’ve had growth in non emissions and 

22 visibility is not improving. This is a very 

23 different picture than what the model is 

24 saying. This has become a political model 

25 and the public is believing the model. This 
 1 Ninth Modeling Conference Vol. 2, p. 178 

 2 has become a very emotional issue in the 

 3 West. Both in Wyoming and the Four Corners 

 4 area. I think we are doing some disservice 

 5 to the science here and not looking at this 

 6 in a more complete fashion. 

 7 Another example, I did some analysis of 

 8 the Hayden Power Plant Bart analysis done by 

 9 CDPHE. And I looked at this in kind of a 

10 quick fashion but what I came away with and 

11 this is a single source area in Central 

12 Colorado, if you look at the ratio at the 

13 mountain circle as to what nitrate to 

14 sulfate in CALPUFF, it’s saying the nitrate 

15 is much larger than the sulfate. The way 

16 the model is being used is not realistic. 

17 This is another analysis and it is not 

18 clear cut. Estimated Change in NOx 

19 Emissions in Southwestern Colorado and 

20 Northern New Mexico Verses Measured Visual 

21 Range At Mesa Verde. I could argue this 

22 could be a 50 to 100,000 ton increase. The 

23 issue is as new production was run in that 

24 area, the emissions dramatically increased 

25 in that time and yet we seem to have changed 
 1 Ninth Modeling Conference Vol. 2, p. 179 

 2 in the monitoring data. 

 3 Monitoring data versus CALPUFF, 80,000 

 4 ton no change about 7,000 ton you see a 

 5 little change. Again, the model doesn’t 

 6 seem to be working well. What do we do 

 7 about this? I think there are some long and 

 8 short term solutions. In a long term 

 9 process there is a clear need for 

10 comprehensive model evaluation of CALPUFF in 

11 a full chemistry model. Without a doubt 

12 this is the most important thing that can be 

13 done with this model. 

14 There are currently data sets being 

15 developed in Wyoming, New Mexico and 

16 Colorado of emission inventory of actually 

17 of 05 and 06. It seems one of the biggest 

18 limitations in emission inventories. We’re 

19 starting to build some databases here, but 

20 it needs to be done in a public 

21 collaborative process. As Bruce mentioned, 

22 API would like to be involved in some of 

23 this work. It’s a long term thing. 

24 The conclusions and recommendations 

25 include the widespread use of meteorological 
 1 Ninth Modeling Conference Vol. 2, p. 180 

 2 model output in air quality modeling 

 3 requires: The accuracy of MM5/CALMET model 

 4 output must be tested for each dispersion 

 5 model application; EPA needs to coordinate a 

 6 stakeholder group to develop guidelines for 

 7 the use of meteorological models in air 

 8 quality analyses. 

 9 Topics that the modeling community 

10 needs to address are: Which meteorological 

11 model should be used? Grid size? How 

12 should meteorological monitoring sites be 

13 included in modeling? Model performance 

14 criteria? Meteorological model accuracy is 

15 more important than the number of years of 

16 model results used in an air quality 

17 analysis 

18 With that I’ll let you think about it. 

19 Tyler Fox: Next we have Peter Manousos 

20 for use of NOAA reanalysis data. 

21 Peter Manousos: This is going to be 

22 pretty quick it’s just 10 slides. This is 

23 sort of a mechanical experiment to see if we 

24 can use reanalysis as a source for 

25 meteorological input in AERMOD and AERMET. 
 1 Ninth Modeling Conference Vol. 2, p. 181 

 2 There are reanalysis data assets outside 

 3 that might now be suitable for use as a 

 4 meteorolgical input into AERMOD. So that’s 

 5 the goal I’ll show you what we’ve done so 

 6 far. Not to put you to sleep and I guess 

 7 I’ll answer questions after that. 

 8 Just really quickly. I’m from a 

 9 company called First Energy a really great 

10 company in Akron, Ohio and this, the borders 

11 don’t show up very well, but this Ohio, 

12 Pennsylvania and New Jersey. These are our 

13 service areas. I’ve only been there for one 

14 year and a half. I used to work for the 

15 weather service for about 15 years. That’s 

16 why I’m dealing with some of the reanalysis. 

17 If you don’t know what it is. 

18 Reanalysis data is a dynamically consistent 

19 3D analysis (“gridded snapshot”) of the 

20 atmosphere for a given point in time. It’s 

21 based off of observed data and not a 

22 prognostic product. Every so many hours 

23 NOOA cycles their models with initial data 

24 and what they’ve done they have gone back as 

25 far back as 1948 to create a reanalysis data 
 1 Ninth Modeling Conference Vol. 2, p. 182 

 2 set. More recently you heard in the HYSPLIT 

 3 discussion this morning there is a 

 4 reanalysis data set that goes back to 1979 

 5 that is available at 32 km resolution across 

 6 the US. 

 7 Who supplies it? NOAA and ECMWF. 

 8 Why the interest for AERMOD? 

 9 Potentially a source for site specific data - 

10 more representative and more complete than 

11 standard upper air and surface observations 

12 sets. Public domain (data and conversion 

13 software) and its free. Before I embarked on 

14 this study I guess or activity I went and dug 

15 around and ask some questions has anyone done 

16 this before or am I reinventing the wheel? Not 

17 much has been done. Google on AERMET and 

18 Reanalysis gives only 4 relevant hits – an end 

19 to end process has not been formally outlined. 

20 I thought I had a typo so I typed it over. 

21 This is going to be hard to see but 

22 these are your upper air sites across the CONUS 

23 (lower 48 States of the (inaudible) US) and 

24 some of Canada. This is a reanalysis data set 

25 of 2.5 degree by 2.5 degree resolution. This 
 1 Ninth Modeling Conference Vol. 2, p. 183 

 2 data set goes back to 1948 and is available at 

 3 6 hours increments now. So you can see you 

 4 might get some more site specific data but if 

 5 you use the North American Reanalysis data and 

 6 hope you can see this. 

 7 This is a 32 km grid so it’s really 

 8 attractive at least in upper air data source 

 9 for input in to AERMOD. And so being kind of a 

10 weather and technical geek, let me see if I can 

11 pull some of this data in and run it through 

12 the model. Again it was more of a mechanical 

13 exercise. I haven’t gotten to the point of 

14 creating wind roses and finding out how many 

15 calms verses what the observed data might have. 

16 I just wanted to see if it would work first. 

17 Just to give you an idea; this is an 

18 observed sounding and it has some really good 

19 vertical resolution. The red squares here give 

20 you an idea of the mandatory levels that are 

21 required by a sounding. But in our data of 

22 North American Reanalysis the blue ovals show 

23 the vertical resolution of the upper air data 

24 set and it’s in 25 mb resolution from the 

25 surface up to 700 mb and above 200 mb. Between 
 1 Ninth Modeling Conference Vol. 2, p. 184 

 2 700 mb and 300 mb, you only have 50 mb 

 3 resolution. Again it seems like you know 

 4 something that is worthy to investigate. 

 5 So I talked to Bret Anderson at a recent 

 6 conference in Boulder at the Ad Hoc conference. 

 7 You know I’ve got a method that I can extract 

 8 the gridded data and put it in a text format. 

 9 How do I test it? He said to go on the SCRAM 

10 site and use some of the cases that are there. 

11 It wasn’t as straight forward as I thought it 

12 would be so I got one case. 

13 Well it was really difficult I found some 

14 issues that some of the cases were using older 

15 versions of AERMOD that couldn’t quite run in 

16 AERMET and I couldn’t repeat. I didn’t have 

17 the older code so I had to be selective and I 

18 only could get one site so I used this case 

19 called WAVCO I don’t know what that stands for. 

20 It was just a data set for me and I used it. 

21 It uses Pittsburgh PA surface and upper air 

22 data (and on site data). Re-run with NARR (ed. 

23 North American Regional Reanalysis). Upper air 

24 data extracted from NARR grid and interpolated 

25 to a point at the location of Pittsburgh upper 
 1 Ninth Modeling Conference Vol. 2, p. 185 

 2 air site. 

 3 All other data remained consistent with the 

 4 control case. Comparison of runs (24h max 

 5 concentration for SO2). NARR run within 5% of 

 6 control for 1st high. NARR run within .07% for 

 7 2nd high. Receptor location and data of 1st and 

 8 2nd high identical in both runs. 

 9 You’re looking at a newbie I mean real 

10 newbie when it comes to running AERMET and 

11 AERMOD. I need someone to review this to see 

12 if I did the right thing, but I was encouraged 

13 to present it here. So just a real quick 

14 summary, 32km horizontal, 25 mb vertical, 3h 

15 (back to 1979) temporal resolution. Neither 

16 satisfy the hourly temporal resolution 

17 requirements of surface data for AERMOD. 

18 However, preliminary runs show NARR may be 

19 suitable as an upper air resource – need to 

20 formalize comparative testing. Mechanical 

21 process already tested Grib ==> Grid ==> Text File 

22 ==> AERMET ==> AERMOD. GEMPAK tools convert grib to 

23 grid AND list output in text format at any 

24 lat/lon input by user (via interpolation). 

25 That’s all I have. 
 1 Ninth Modeling Conference Vol. 2, p. 186 

 2 Tyler Fox: Thanks Peter. We have a host of 

 3 presentations made by AWMA and I’m going to turn 

 4 it over to George Schewe and let you manage this 

 5 assortment of presentations. 

 6 George Schewe: Thanks Tyler. Good 

 7 afternoon my name is George Schewe, an attorney 

 8 consultant. I am the current chair of the AWMA 

 9 so called AB3 Committee of meteorological and 

10 modeling. I’m going to introduce the AB3 model 

11 review group, enter comment areas, and offer 

12 comments now that we did not fit into other 

13 presentations. I think I’m the last remaining 

14 staff member from 1978 and 1979 from the model 

15 application group who are still at this meeting. 

16 Even you Peter [ed. Eckhoff]were not there yet. 

17 I was also the Project Officer of the 

18 original contract with the HG Cramer company to 

19 develop and release the Industrial Source [ed. 

20 Complex (ISC)] model. I’m not sure if that is 

21 good or bad but I’m the last one here. I’m very 

22 happy to have seen the progression to AERMOD and 

23 CALPUFF. I think Harry Cramer, rest his soul, 

24 would be pleased too. 

25 We at AB3 applaud the efforts and progress 
 1 Ninth Modeling Conference Vol. 2, p. 187 

 2 like [ed. AERMOD] (inaudible) and we offer our 

 3 comments in the spirit of cooperation with the 

 4 goal of best model performance built on best 

 5 science. That’s all I have written and wanted to 

 6 get those thoughts out. We have an illustrious 

 7 group here, myself and all of the people have 

 8 mentioned here. Some of us are going to speak 

 9 and some are not. The order of presentation is a 

10 little bit different than on your schedule and we 

11 will try and hold to 10 or 11 minutes per person 

12 except for Ron Peterson. 

13 The comment areas that we are going to 

14 emphasize are building and down wash, and 

15 meteorology inputs. I know you can read but 

16 these are the areas we will be talking about this 

17 afternoon. I’m going to make quick comments here 

18 so it didn’t fit in to any others and have been 

19 addressed over these two days. 

20 We’re a little concerned about resources you 

21 guys have to really keep the Clearinghouse and 

22 getting it really rolling. We just wanted to 

23 express that concern. We’re also little 

24 concerned about the time that is required to 

25 review the comments and vetted nationwide will 
 1 Ninth Modeling Conference Vol. 2, p. 188 

 2 take place to do that. So we just wanted to 

 3 express that concern and we want say that we’re a 

 4 little displeased or unhappy but we’d like to 

 5 have some technical input from the affected 

 6 parties such as a consultant who is working for a 

 7 company making suggestions. 

 8 We are just asking for a little more 

 9 involvement there and would make your jobs a 

10 little easier too to have us involved. Our 

11 recommendation is for affected parties involved 

12 in this process. Lastly to introduce the 

13 increase use of ozone models and I think this was 

14 brought up this morning. We are going to need 

15 some guidance on this. That’s all I have right 

16 now. Next is Ron Peterson. 

17 Ron Petersen: Thank you George. I’m Ron 

18 Petersen from CPP and I’m going to be commenting 

19 building and downwash issues. Basically the main 

20 areas of comments will be problems with BPIP. 

21 AERMOD/PRIME Problem for Short/Large Buildings. 

22 AERMOD/PRIME Underestimation For Corner Vortex 

23 and Terrain Wake Effects. 

24 As Roger mentioned yesterday some of the 

25 problems working with BPIP and working with 
 1 Ninth Modeling Conference Vol. 2, p. 189 

 2 Prime, it’s going to be hard to treat complex 

 3 geometries, may merge two structures into one 

 4 large structure, may pick the wrong dominant 

 5 building. May place the building at the wrong 

 6 location to get correct dispersion. Does not 

 7 account for lattice or cylindrical structures. 

 8 Ultimately, PRIME needs the building shape 

 9 and position that places stack in the correct 

10 Snyder/Lawson Data Base Flow Region (i.e., Data 

11 Base Used to Develop Downwash Algorithms). Other 

12 considerations are building downwash algorithms 

13 in AERMOD are designed for simple rectangular 

14 buildings. Building downwash algorithms in 

15 AERMOD only appropriate for certain building 

16 aspect ratios. Use of wind tunnel testing to 

17 determine Equivalent Building Dimensions (EBD) 

18 has been used to help solve the problem. 

19 EBD guidance provided in Tikvart July 1994 

20 Memorandum - Thus, the analysis is viewed as a 

21 source characterization study which generally has 

22 been considered under the purview of the Regional 

23 Offices. All testing to determine EBD under 

24 neutral stratification, similar to assumptions in 

25 Prime Algorithms. With AERMOD/PRIME building 
 1 Ninth Modeling Conference Vol. 2, p. 190 

 2 location is also a variable and new methods may 

 3 be appropriate and has been used on recent 

 4 studies. You may have to improve this EBD 

 5 procedure and more guidance needed. Roger 

 6 mentioned that yesterday also. 

 7 Picking the right dominant structure here’s 

 8 kind of an example a hypothetical example. You 

 9 have a power plant with a residential upwind 

10 tower, a site drawing as you might call it. Now 

11 BPIP picked this as an input so it picked the up 

12 wind residential tower to go in to the model. 

13 Maybe you need to pick something closer that’s 

14 going to be more influential. Because if you 

15 take away the residential tower this is what BPIP 

16 would put in which is really the power plant 

17 structure itself. An EBD study was done in the 

18 wind tunnel to determine the shape that would 

19 really match the dispersion for that whole 

20 complex and that’s the shape of the building that 

21 matches the dispersion. It’s much closer to the 

22 power plant structure. 

23 

24 An example of that was a Mirant Power 

25 Station study. AERMOD with BPIP predicting high 
 1 Ninth Modeling Conference Vol. 2, p. 191 

 2 concentrations at ground level and on a nearby 

 3 building. AERMOD with Equivalent Building 

 4 Dimensions gave lower concentrations and ones 

 5 that agreed better with field observations. 

 6 Here’s a new situation that has come up for 

 7 Short/Large Buildings. The wake algorithms have 

 8 only been developed/tested for limited building 

 9 aspect ratios. Short/large industrial facilities 

10 fall outside this range. 

11 Here’s a case kind of a foot print of a 

12 large industrial facility and the red square on 

13 that chart represents what the model BPIP gave 

14 for the input. That’s 17 meter high building, H 

15 = 17, L/H = 23, H/W = 0.02 very short big 

16 building. PRIME cavity and wake dimensions: W = 

17 H and L/H = 0 – 4, W = L and H/W = 1 – 3. It 

18 doesn’t really fit into what has been developed. 

19 So what was done to develop building inputs 

20 was to do a building equivalent study for this 

21 facility. Actually looking at the flow 

22 visualizations what happens is the plume 

23 essentially the wake reattaches on the roof and 

24 it’s almost like a new (inaudible) level 

25 basically. So the weight kind of falls off the 
 1 Ninth Modeling Conference Vol. 2, p. 192 

 2 end of the building. 

 3 What does it really mean as far as the 

 4 concentrations predictions. We ran five typical 

 5 sources on this facility and that’s the input for 

 6 the five sources. 1 year met data kind of a 

 7 standard AERMOD default mode. And we ran with 

 8 the BPIP inputs right here, match 24074 and when 

 9 you put the EBD in drops it to 44. So you can 

10 see we took a closer look at what was going on. 

11 We think the plume is being caught in the cavity 

12 region and being concentrated heavily. Right 

13 outside of the cavity region the concentrations 

14 drop for a factor of 3 or 4. There something in 

15 the cavity calculation that’s going [ed. wrong 

16 (?)] we think. We are still doing more research 

17 what’s happening there. These predictions are 

18 just overall maximums right in the cavities. The 

19 effect as you move further downwind becomes less. 

20 Now in the corner vortex situation which in 

21 the picture you can see that when the flow flows 

22 over a building you get two vortex almost like a 

23 tornado. Current building wake equations do not 

24 account for corner vortex. Corner vortex causes 

25 higher concentrations than currently predicted in 
 1 Ninth Modeling Conference Vol. 2, p. 193 

 2 AERMOD. 

 3 To demonstrate that I have a couple of 

 4 slides here. I’ve got 3 different building 

 5 shapes. 39 meters high, 1 to 1 and 1 to 4. The 

 6 building rotated at 45 degrees so the angle the 

 7 diagonals of wind. Now I have some input and I 

 8 ran AERMOD for these 3 cases for 1 wind speed. 

 9 You can see the worst case was this building 

10 here. That was given the highest concentrations. 

11 The lower concentrations are these two here. 

12 We actually tested these 3 shapes in the 

13 wind tunnel so I will show you what 

14 concentrations looked like in the wind tunnel. 

15 These two shapes are right here the corner of 

16 Vortex is this case right here so that the corner 

17 of vortex is increasing the concentrations by 

18 about of a factor of 2. This is all due to the 

19 downward motion created by that mini tornado off 

20 the corner. 

21 Terrain wake effects; currently the GEP 

22 stack height regulation defines nearby terrain 

23 for the purpose of limiting stack heights. Past 

24 EPA research shows that the effect of upwind 

25 terrain can be significant. Currently this effect 
 1 Ninth Modeling Conference Vol. 2, p. 194 

 2 is neglected. Recent study1) showed 

 3 concentrations increased by a nearly a factor of 

 4 two when terrain wake effect is accounted for 

 5 using Equivalent Building Dimensions in AERMOD. 

 6 A method should be developed to determine when 

 7 upwind terrain wake effects should be considered. 

 8 We’re saying that a method should be 

 9 developed to determine wind up wind terrain and 

10 wake effects might be a controlling situation. 

11 That’s just the short of some of the past 

12 summaries by Snyder and some of the group at the 

13 EPA wind tunnel where they showed these terrain 

14 application factors for different hill shapes. 

15 In that application factor just really the 

16 increase of concentration as if the hill weren’t 

17 there. 

18 So basically kind of maybe I did it within 

19 10 minutes George. Basically, continue your 

20 research on ways to improve BPIP so input 

21 dimensions match assumptions in algorithms. If 

22 needed, update guidance on use of EBD in place of 

23 BPIP for AERMOD/PRIME. Develop algorithms for 

24 the corner vortex situation. Develop method for 

25 accounting for upwind terrain wake effects. That 
 1 Ninth Modeling Conference Vol. 2, p. 195 

 2 concludes my comments here. Thanks. Our next 

 3 presenter is Joe Scire. 

 4 Joe Scire: Okay. I have just a very short 

 5 presentation about the use of gridded 

 6 meterological data on the air quality model which 

 7 we’ve talked about the last couple of days. Use 

 8 of existing tools, Two step evaluation process, 

 9 Evaluation variables, Sensitivity to prognostic 

10 model options and Metric for evaluating success. 

11 The existing tools I listened to the talks 

12 of Roger and Herman about the efforts to produce 

13 a converter for MM5 and one of the things was 

14 that resources are limited. There are some 

15 existing tools that might be helpful that could 

16 be used for this type of purpose. There are 

17 processes that are a part of the CALPUFF system 

18 but are available for use and no restriction on 

19 the use to concert MM5 data and WRF and 

20 (inaudible) (inaudible) into a standard format. 

21 What happens all these models will fit into this 

22 format so any subsequent process needs only to 

23 get to the (inaudible) files and not to the 

24 specific data sets. It’s one way of reducing the 

25 effort in the post processing by having 
 1 Ninth Modeling Conference Vol. 2, p. 196 

 2 everything to fit into a common format. I 

 3 mentioned this to Herman and thought I would 

 4 mention it while we’re here. 

 5 The other point is the processor. Do not 

 6 change the wind data. It’s exactly as it came 

 7 out of the prognostic model. Although they do 

 8 interpolate the scalars to the grid point 

 9 locations, if that isn’t what is to be done then 

10 you would have to change it or use another 

11 approach. Another item in terms of 

12 redundancy in existing tools is to mention there 

13 is going to be software produced to interface the 

14 output of MM5 converted to wind rose software. 

15 That already exists. No restrictions on use. 

16 Meteorological evaluation software is very close 

17 to be released. If you don’t have to reproduce 

18 these items, there are more resources for doing 

19 the other elements of this system. 

20 The other thing is producing met data sets 

21 for running AERMOD or CALPUFF. I think it seems 

22 appropriate to have this as a 2 step process. 

23 Evaluate gridded meteorological data performance 

24 separately from dispersion model performance. 

25 There will likely be a large sensitivity of 
 1 Ninth Modeling Conference Vol. 2, p. 197 

 2 dispersion model to met database. Separately 

 3 determine best available dataset for each 

 4 parameter. Sensitivity of prognostic model 

 5 parameters. Use of NCEP products (e.g., RUC 

 6 fields) and they are free. Sensitivity of 

 7 dispersion model to different variables. Model 

 8 parameterizations and grid resolution. 

 9 Then separately use the data sets to 

10 determine available observational datasets. 

11 Evaluate all meteorological variables. Wind 

12 speed, wind direction, Frequency of light wind 

13 speeds, etc., vertical wind and temperature 

14 structure, temperature & relative humidity, 

15 micrometeorological parameters, solar radiation, 

16 cloud cover, and ceiling height, precipitation 

17 and allow for potential use of sub-hourly 

18 prognostic data. 

19 In planning ahead I would also recommend 

20 that provision is reserved in the structure of 

21 the data set to allow sub hourly prognostic data. 

22 The reason why the MM5 simulations can’t deal 

23 with a 10 min intervals, 5 min intervals or 30 

24 min intervals. There may be applications where 

25 sub hourly data has its advantage. 
 1 Ninth Modeling Conference Vol. 2, p. 198 

 2 Then how do we determine what’s good enough? 

 3 I mean some of the results presented earlier that 

 4 the ratios were about a factor of 2 or 1.5 to 2 

 5 times higher results using prognostic data than 

 6 observation. That sounds high to me. 

 7 Consistency with results using observational 

 8 data? No under-prediction bias relative to 

 9 observed met results. Evaluate results under 

10 many different types of conditions. Coastal, 

11 flat, rolling terrain, mountainous, tracer or 

12 other observational datasets. That’s all I have. 

13 My pleasure to introduce Bob Paine who will be 

14 talking about PM.25. 

15 Bob Paine: Okay. This has been brought up 

16 in questions before and I’m going to talk more 

17 about it. This is the newest and possibly least 

18 understood criteria pollutant. My topics are: 

19 quantifying PM2.5 emissions, current and proposed 

20 regulatory requirements, challenges to PM2.5 

21 implementation, emission inventories – direct and 

22 precursors, modeling techniques – guidance, 

23 background concentrations – how to treat, and 

24 looking forward. 

25 PM2.5 is unlike other gaseous criteria 
 1 Ninth Modeling Conference Vol. 2, p. 199 

 2 pollutants, because PM2.5 generally comprises a 

 3 mixture of solid particles and liquid droplets, 

 4 some condensing from vapor – source/fuel- 

 5 specific. It is emitted directly from a source 

 6 (“primary” or “direct” emissions) and also formed 

 7 in the atmosphere (“secondary formation”) from 

 8 precursor emissions of SO2 and NOx . PM2.5 contains 

 9 filterable and condensable components that may be 

10 organic or inorganic. 

11 This slide comes from a VISTAS BART 

12 protocol, and basically, we have all the 

13 condensable side which is basically small enough 

14 to be 2.5 μg or less in size. Looking at the 

15 condensable side of this chart, the inorganic 

16 PM2.5 includes H2SO4 that adds significant 

17 measurement and quantitfication problems. The 

18 inorganic fraction could have some S04 components 

19 and then you have the organic. Looking at the 

20 filterable side, the EC is generally 2.5 μg or 

21 less and then the rest of this is shaded out -- 

22 the coarse particles which are higher than 2.5 μg 

23 are shaded out. Those are the only components of 

24 PM10 that would be excluded from PM 2.5, so it’s 

25 a fairly complicated structure. This is needed 
 1 Ninth Modeling Conference Vol. 2, p. 200 

 2 for visibility modeling because each of these 

 3 components has a different extinction efficiency 

 4 in scattering. 

 5 The measurement techniques have an 

 6 interesting history. Historically, only 

 7 filterable PM was measured and quantified. Only 

 8 filterable PM has traditionally been measured, 

 9 quantified, and modeled based on EPA Reference 

10 Method 5. Existing reference methods for 

11 condensable PM have known biases and work is 

12 underway to propose more reliable methods. EPA 

13 is well aware of limitations to existing PM2.5 

14 measurement methods – sulfates can be 

15 significantly overestimated. Uncertain emission 

16 factors exist for condensable PM – this can be a 

17 high percentage of PM2.5. 

18 So with that back drop of course we’ve been 

19 11 years with the new PM2.5 pollutant. In 1997, 

20 EPA had a PM10 surrogate policies for compliance 

21 modeling that are still in effect, Best Available 

22 Retrofit Technology implementation guidance, PM2.5 

23 NSR implementation rule, PM2.5 PSD SILs, SMCs, and 

24 increments (proposed 9/21/07; final rule 

25 pending), and the PSD increment modeling 
 1 Ninth Modeling Conference Vol. 2, p. 201 

 2 procedures (proposed 6/6/07; final rule pending). 

 3 So let’s talk about modeling Primary vs. 

 4 Secondary PM2.5. AERMOD considers primary PM2.5 

 5 only. Primary PM2.5 provides highest near-field 

 6 impacts. Secondary PM2.5 is important only at 

 7 large distances, and would probably not 

 8 contribute at location of highest primary impact. 

 9 Secondary PM2.5 could be modeled with CALPUFF. 

10 Large SO2 and NOx emission reductions may lead to 

11 PM2.5 increment expansion – does this require an 

12 unbiased model to take modeling credit? Are we 

13 ready to compile cumulative emission inventories 

14 for 3 pollutants? 

15 I’d like to address the issues with CALPUFF 

16 over predicting nitrate. If you use an 

17 inappropriate ammonia background like 10 ppb, you 

18 can get the results that over predicts by a 

19 factor of 3 verses if you use an appropriate 

20 background of the West at 0.2 ppb or even lower 

21 (and measured at 0.1 ppb in Wyoming). You will 

22 find that CALPUFF will be mostly unbiased and I 

23 think that one is one way to eliminate this 

24 problem with the perceived nitrate over 

25 predictions of CALPUFF. 
 1 Ninth Modeling Conference Vol. 2, p. 202 

 2 PM2.5 Regulations and Guidance – Unresolved 

 3 Issues. Are we okay to ignore secondary PM2.5 

 4 modeling for short-range applications? Include 

 5 secondary PM2.5 modeling for long-range 

 6 applications (e.g., Class I increment)? How to 

 7 credit precursor emission reductions? What is 

 8 the form of the 24-hour PM2.5 increment standard? 

 9 To be consistent with the NAAQS, the 24-hour 

10 increment should be the highest, 8th – highest. 

11 CALPUFF and AERMOD can provide that statistic. 

12 PM 2.5 emissions analysis. 

13 Emissions factors are available for certain 

14 source types from EPA’s AP-42, SPECIATE, and FIRE 

15 databases. Certain industry groups have also 

16 reviewed stack test data to develop emission 

17 factors. EPA demonstrates possible approach in 

18 its Interim Regulatory Impact Analysis (RIA) for 

19 the Proposed National Ambient Air Quality 

20 Standards for Particulate Matter, Appendix B – 

21 Local Scale Analysis (2005). Any of these 

22 factors are based on stack test methods known to 

23 be unreliable and have biases. 

24 

25 Example Modeling Challenge: Compute Total 
 1 Ninth Modeling Conference Vol. 2, p. 203 

 2 PM2.5 NAAQS Impact: Background + Source Impact. 

 3 Conservative approach: add peak percentile 

 4 source impact to peak percentile background, 

 5 unpaired in time. It is unlikely that these two 

 6 components happen at the same time. A refined 

 7 approach adds concurrent daily background and 

 8 source impact concentrations. If daily 

 9 background concentrations are not available, fill 

10 in missing days from higher of two bounding 

11 values 

12 To summarize: PM2.5 modeling in a regulatory 

13 context poses challenges not previously 

14 experienced for other criteria pollutants. 

15 Emissions measurement and modeling techniques 

16 need to be resolved. Background concentrations 

17 can be much higher than modeled concentrations. 

18 Due to stringent standards, there is more need 

19 for refined modeling approaches. Collaboration 

20 is necessary to implement reasonable PM2.5 impact 

21 assessment requirements. 

22 In looking ahead, unique and important 

23 issues remain unresolved for PM2.5 – little EPA 

24 guidance, PSD increments and modeling procedures. 

25 There is a role for CALPUFF (or other models) for 
 1 Ninth Modeling Conference Vol. 2, p. 204 

 2 secondary PM2.5 in long-range applications for 

 3 both increases and decreases in SO2 and NOx . 

 4 Application of local/regional background levels 

 5 in a regulatory context. That’s it. Let’s see 

 6 who’s our next one? George is next and will talk 

 7 about AERMOD. 

 8 George Schewe: Good afternoon. I’m George 

 9 Schewe and I’m with Trinity Consultants. Just a 

10 few comments on AERMOD. First of all we like 

11 AERMOD. It does things ISC3 could never do. I 

12 do want to mention a few issues. 

13 The Low wind speed issues. Modeling of 

14 roadways for NO2 and PM. Problems with modeling 

15 small urban areas. Need for post-processor to 

16 combine multiple AERMOD runs. Deposition 

17 support. Adjustments for international 

18 applications. 

19 Many investigators report that the worst-case 

20 AERMOD impacts occur for very low wind speeds, 

21 especially for low-level sources. AERMOD has 

22 limited evaluation for these conditions. 

23 ASOS use of sonic anemometer data and averaging 

24 of sub-hourly ASOS data will likely create more 

25 hours with very low wind speeds. AERMOD needs 
 1 Ninth Modeling Conference Vol. 2, p. 205 

 2 supplemental evaluation to assess the accuracy of 

 3 these “design concentration” predictions. 

 4 Roadways are characterized by enhanced 

 5 turbulence and low wind speeds generated by 

 6 traffic itself. Review of data from tracer 

 7 studies and adjustments to AERMOD modeling 

 8 procedures for roadway is an important issue for 

 9 EPA to pursue. Problems - few long-term monitors 

10 near roadways & quantification of emissions, 

11 especially PM, is questionable 

12 Nocturnal urban mixing height (Ziu) is a 

13 function of population. For small populations, 

14 Ziu can be quite low (e.g., about 200 m for a 

15 population of 50,000). This has been found to 

16 result in plume capping at night for all plumes, 

17 no matter how buoyant, leading to counter18 intuitive results. EPA should investigate this 

19 issue and correct the problem. 

20 AERMOD runs can be very long. Runs cannot 

21 be done separately and combined in postprocessor, 

22 as is done with CALPUFF. EPA should develop a 

23 system like that of the CALPUFF system, or 

24 translate AERMOD conc. files to CALPUFF-like 

25 files. TRC may have a draft code that can do 
 1 Ninth Modeling Conference Vol. 2, p. 206 

 2 this. 

 3 Dry gas deposition is not included in the 

 4 implementation guides but in the 2004 addendum – 

 5 makes for some confusion. Recommend that AERMOD 

 6 guidance provide further implementation guidance 

 7 to address use of dry gas deposition factors and 

 8 the use of ANL physical parameters for common 

 9 pollutants (Wesely, et.al, 2002). 

10 International applications have challenges 

11 due to 12Z sounding times not at sunrise. Bob 

12 Paine provided EPA (in October 2007) with several 

13 possible enhancements. Swapping of 12Z and 00Z 

14 sounding time labels. Adjustment of lower part 

15 of sounding to reflect morning minimum sfc temp. 

16 Enhanced debugging output. EPA should make these 

17 enhancements available, at least in beta test 

18 form. 

19 Issues with AERSURFACE implementation. 

20 Sensitivity of modeling to surface 

21 characteristics. Land use determination very 

22 localized - within 1 km. Greater chance of 

23 mismatch in surface type between met tower and 

24 source. For tall stack, buoyant releases, 1 km 

25 is too short of a fetch distance. Low roughness 
 1 Ninth Modeling Conference Vol. 2, p. 207 

 2 near towers increases likelihood of low u* and low 

 3 wind speed issues. Moisture assigned only on an 

 4 annual basis. 

 5 Brode et al. have written paper for A&WMA 

 6 2008 Annual Meeting on sensitivity modeling. We 

 7 recommend use of AERSCREEN with different runs 

 8 for met and application site surface 

 9 characteristics. If peak predictions are 

10 reasonably similar (say, within 10%), then assume 

11 that differences in site surface characteristics 

12 have a minor effect. 

13 A couple of comments on AERMET is that 

14 states advocating use of more recent data sets. 

15 Many more calms in recent data sets – if 

16 considered missing as suggested in GAQM, does not 

17 meet 90% capture criteria. If many calms, does 

18 CALMS preprocessor work properly? Conc 

19 artificially too low? Guidance needed on use of 

20 recent met data. If my interpretation of the 

21 Guideline on Air Quality Models is right, a calm 

22 is considered a missing data? Is that right? 

23 Roger Brode: Technically, if a site 

24 specific is considered a valid observation if the 

25 wind speed threshold is treated the same way as a 
 1 Ninth Modeling Conference Vol. 2, p. 208 

 2 missing calm. 

 3 George Schewe: Okay. That’s basically all 

 4 of my comments on AERMOD today. We’re commenting 

 5 off the off the shelf of AERMOD and not talking 

 6 all of the other things that need talking about. 

 7 Our next speaker is Gale Hofffnagle and he will 

 8 be talking about CALPUFF and the comments of the 

 9 AB3 Committee on CALPUFF. Let me see if I can 

10 find it for you. I can’t open this file. 

11 Comments from participants: The issue is you 

12 should have a new version of PP. Do you have a 

13 computer here Gale? No I don’t. Talk among 

14 yourselves. 

15 Gale Hoffnagle: These are the comments 

16 about CALPUFF and talking about CALPUFF filling 

17 your needs. About EPA concerns about CALPUFF and 

18 EPA controlling the model developing coding and 

19 using less than 50 km and use it greater 200 – 

20 300 km. Many applications in air quality 

21 modeling for the guideline purposes require air 

22 quality impact from (inaudible) stacks from long 

23 distance. 

24 We need a 3-D Lagrangian model for 

25 (inadible) will not work well for individual 
 1 Ninth Modeling Conference Vol. 2, p. 209 

 2 sources yet. We don’t believe that they sub grid 

 3 modules were single sources are up to snuff or 

 4 demonstrated. CALPUFF is a model with community 

 5 usage experience. We know how to run it and have 

 6 been running it for years. It has better 

 7 handling, low wind speed stagnation, coastal and 

 8 air issues. Complex terrain and slow reversal 

 9 and it has better handling of deposition. 

10 In general what AWMA is saying is that we 

11 need to have better models. We’ve been saying 

12 that consistently for the last 9 conferences. 

13 EPA concerns about CALPUFF are relatively 

14 unfounded. EPA’s concern about near field 

15 evaluation and CALPUFF we are going to show in 

16 our comments some 8 studies have been done and 

17 demonstrate CALPUFF in near field areas. 

18 Substantial resources from EPA will be needed to 

19 evaluate and approve the upgrades. 

20 The chemistry is fine for NOx , SO2 and PM 

21 and we need to do some other things for Ozone . 

22 EPA doesn’t have direct control of CALPUFF and 

23 there are some advantages to that. EPA does have 

24 control of the regulatory code. The developer 

25 has multiple funding sources and the resources to 
 1 Ninth Modeling Conference Vol. 2, p. 210 

 2 provide for advancement in this model. The 

 3 developer will continue to have these resources. 

 4 The developer has training classes for CALPUFF. 

 5 AWMA supports an independent work group for 

 6 advancing CALPUFF and will work to that end. EPA 

 7 doesn’t have direct control of the CALPUFF code 

 8 and there are some disadvantages. EPA has not 

 9 been able to supply any funding to provide 

10 updates that EPA wants, but the developer is 

11 willing to do this. As a result EPA says that 

12 CALPUFF lags behind in the code releases. The 

13 last users guide was released 2006. We have a 

14 new users guide for Version 6 and all we need is 

15 the EPA approval for the code. There are code 

16 changes made without EPA oversight and funding 

17 that requires EPA review. What is needed is for 

18 EPA to review the code changes that are 

19 available. We urge stronger coordination between 

20 EPA and TRC to keep the string going of improving 

21 the model. 

22 CALPUFF at less than 50 km. Why is it 50 

23 km? Bruce mentioned this as well and it should 

24 be based on the transport time. 50 km per hour 

25 is a long hour and a lot of wind speeds in most 
 1 Ninth Modeling Conference Vol. 2, p. 211 

 2 applications. I’ve been trying to find out where 

 3 the 50 km came from. I’m sure Joe Tikvart said 

 4 it one time. 

 5 Requiring equivalency demonstrations of less 

 6 than 50 km is too restrictive. We need a better 

 7 method to define precisely when complex winds 

 8 occur and require PUFF modeling. We’ll be 

 9 referring to paper I gave 3 years ago on complex 

10 modeling and a better definition of complex 

11 winds. But I think the answer is in the 

12 definition of complex winds. 

13 Adding bells and whistles to AERMOD will not 

14 make it a Lagrangian model. Another issue is 

15 that CALPUFF comparison to LRT studies have been 

16 shown relative accuracy out to 200 km. FLAG went 

17 beyond the 200 km to say 300 km. So that’s what 

18 we’re using now. Many states are using CALPUFF 

19 results and CAMx out to 600 km or more. There is 

20 no justification for going beyond 200 km in our 

21 opinion. There should be defying outer limit for 

22 more LRT field studies to be conducted. 

23 The last thing is an easier comment to make. 

24 We’re going to have an A&WMA specialty conference 

25 one year from this month. Next October we can 
 1 Ninth Modeling Conference Vol. 2, p. 212 

 2 come back to RTP and the Call for Papers will be 

 3 out soon. I look forward on the www.awma.org web 

 4 site. And for those of you are interested, AWMA 

 5 will be conducting a modeling conference in 

 6 Toronto this Spring on Canadian modeling issues. 

 7 There will be 2 modeling conferences next year. 

 8 Is that the same date as the RSL Workshop? We’ll 

 9 talk about that. That concludes the AWMA 

10 comments. 

11 Tyler Fox: The next scheduled speaker is 

12 Penny Shamblin 

13 Penny Shamblin: These will be very short. 

14 I have some sort of creeping crude that I cannot 

15 get over. My name is Penny Shamblin and I’m 

16 making this statement on behalf of the Utility 

17 Air Regulatory Group (UARG). UARG is an ad hoc 

18 group of public and private electric utility 

19 companies and their trade associations. UARG 

20 participates on behalf of its members 

21 collectively and roll makings and related 

22 proceedings under the federal Clean Air Act. 

23 We appreciate the opportunity to appear here 

24 today and make these comments. UARG has 

25 participated in all of the EPA modeling 
 1 Ninth Modeling Conference Vol. 2, p. 213 

 2 conferences to date. We have participated in the 

 3 rulemakings associated with promulgation and 

 4 revisions of Appendix W Guideline. The Modeling 

 5 Guideline is used for several purposes, including 

 6 to determine if new or modified sources that can 

 7 built and operated without causing or 

 8 contributing to a violation of the ambient 

 9 standards or the PSD increments. What is in the 

10 guideline and how EPA interprets it and applies 

11 it has a direct and important impact on UARG 

12 members and everyone else who is trying to permit 

13 facilities. 

14 EPA’s September 25th federal registry notice 

15 announcing the time and place of the conference 

16 did not provide information on specific changes 

17 that EPA is planning to make to the Modeling 

18 Guidelines to Appendix W. So our comments are 

19 preliminary and may be supplemented with more 

20 detailed comments during the 30 day public 

21 participation period. 

22 The first issue that UARG would like to 

23 raise today arises directly from the fact that 

24 the September 25 Federal Register notice provides 

25 very little information on what, if any, changes 
 1 Ninth Modeling Conference Vol. 2, p. 214 

 2 EPA is planning. If EPA wants meaningful 

 3 comments from the public concerning key questions 

 4 on the use of the air quality models, then we 

 5 need more information as to what changes will be 

 6 made. It’s not sufficient for EPA to place a 

 7 draft meeting agenda on the agency web site such 

 8 as SCRAM nor is it sufficient to publish the 

 9 conference announcement two weeks before the 

10 meeting. Rather, EPA must publish notice of 

11 these proceedings in the Federal Register at 

12 least 30 days ahead of time. And also provide 

13 the public with background information of all 

14 significant issues on which it is seeking 

15 comment. 

16 Instead of following the standard 

17 notice procedures and instead of engaging in 

18 notice-and-comment of rulemaking to change any 

19 outdated portion of the modeling guideline, EPA 

20 is moving toward using informal guidance to try 

21 and change the status quo. From discussions 

22 today, I understand guidance does not come 

23 lightly out of EPA. There are instances where it 

24 appears to do. 

25 Preceding this conference, EPA posted 
 1 Ninth Modeling Conference Vol. 2, p. 215 

 2 on its web site several guidance memoranda that 

 3 purport to make significant changes to the 

 4 procedures that affected parties have been using 

 5 to get approval for the use of non EPA preferred 

 6 models. EPA’s new procedures will uniformly make 

 7 it difficult to use anything other than the 

 8 preferred models or EPA developed models. 

 9 For example, the August 13 guidance 

10 memorandum about the regulatory status of CALPUFF 

11 for near field applications states that the use 

12 of CALPUFF must go through a more extensive 

13 review process than historically required if you 

14 want to use it for near field applications. 

15 In particular, without conducting any notice 

16 of comment, EPA has concluded in its guidance 

17 document that a modeling system like CALPUFF, 

18 will be subject to a higher burden of proof 

19 before its use will be approved in individual 

20 cases. Then, even if the permit-issuing agency 

21 permit applicants are able to do what is 

22 necessary to meet the more onerous review 

23 standards, the August 13th guidance document 

24 throws another obstacle in the way; mainly the 

25 Model Clearinghouse process. A drill that is 
 1 Ninth Modeling Conference Vol. 2, p. 216 

 2 likely to add several months or longer to the 

 3 overall new permit process which I can attest 

 4 already takes 2 or 3 years. One more layer is 

 5 not what we would like. 

 6 UARG believes that the EPA’s recently posted 

 7 guidance memoranda have placed unfair burdens on 

 8 parties trying to use CALPUFF in situations in 

 9 which that model has shown to work and function 

10 well. 

11 Appendix W allows the choice of modeling 

12 techniques in new source permitting situations to 

13 be made on a case by case basis taking into 

14 account the unique characteristics of each case. 

15 The recent guidance document, (I apologize if I 

16 get rid of the cough drop I won’t make it through 

17 this), removes the Guideline’s promise of 

18 reasonableness and flexibility and imposes what 

19 are likely to be insurmountable obstacles to the 

20 use of any models other than EPA preferred or EPA 

21 developed models even if the alternative model 

22 would make more sense in that situation. 

23 UARG believes that it is inappropriate for 

24 EPA to use informal guidance documents to make 

25 such major changes to the rules and procedures 
 1 Ninth Modeling Conference Vol. 2, p. 217 

 2 that state permitting agencies and permit 

 3 applicants have been using for years and have 

 4 worked well for years. 

 5 The second general issue that URAG would 

 6 like to raise today concerns the maintenance of 

 7 the models and the need for more timely approval 

 8 of changes to fix bugs and problems, both EPA 

 9 approved models, preferred models and 

10 alternative models. As time passes and as input 

11 to such models change, it’s almost inevitable 

12 that model users will occasionally encounter and 

13 identify problems and bugs in the model. 

14 For years EPA has done an admirable job in 

15 responding to such identified problems. In 

16 particular EPA has made timely fixes to their 

17 models. Also when developers of alternative 

18 models have reported problems and provided well 

19 founded fixes, EPA has a history of approving and 

20 promptly approving those fixes. Unfortunately 

21 during the past 2 years, URAG has seen delay in 

22 EPA review of implementation fixes for identified 

23 problems for all preferred models. 

24 In particular, URAG has seen delays for over 

25 a year in EPA’s consideration and approval of 
 1 Ninth Modeling Conference Vol. 2, p. 218 

 2 fixes that users have encountered in running 

 3 developmental models and the ones that Bob Paine 

 4 has identified in AERMOD. URAG encourages EPA to 

 5 return to its earlier approach of giving priority 

 6 to fixing the problems. 

 7 The final issue is another one that Bob 

 8 Paine spoke of dealing with PM2.5 modeling 

 9 requirements for both development of SIPs and for 

10 the evaluation of new source permitting. PM2.5 

11 ambient standards have been on the books for over 

12 a decade but we still have very little guidance 

13 and no model that does a credible job of 

14 predicting the air quality impacts of emissions 

15 for PM2.5 and PM2.5 precursors. 

16 For example, even though most PM2.5 

17 nonattainment areas are urban areas where, 

18 organics are a major component of PM2.5, existing 

19 models do a poor job of addressing the organic 

20 component. Also, for single source new 

21 permitting there’s no clear guidance on the 

22 modeling tools to use for the permit application. 

23 Until the new recent rule, this was not much of 

24 an issue because most people were using PM 10 as 

25 a surrogate. But now with the EPA delegated 
 1 Ninth Modeling Conference Vol. 2, p. 219 

 2 states, you’re required to do the PM 2.5 NAAQS 

 3 modeling and we still have no guidance on how to 

 4 do that. 

 5 So we urge EPA to take the time and 

 6 resources now to develop credible tools for PM2.5 

 7 SIP and pre-contruction permitting. That’s it. 

 8 Thank you. 

 9 Tyler Fox: Alright next is George Delic. 

10 George Delic: Thank you very much. Now for 

11 something completely different. My Ph.D was in 

12 nuclear physics which was in another life. Since 

13 coming to the US, I have focused on high 

14 performance computing and started with air 

15 quality modeling when I was a contractor in the 

16 Park for 10 years. That’s where I got to know 

17 these models. 15 years with CMAQ and 10 years 

18 with AERMOD. I’m now a private consultant. 

19 Efficiency for me is very important and that is 

20 the focus of this discussion. 

21 Here’s the layout of what we are going to 

22 talk about: 

23 1.Introduction 

24 2.Identifying the problem 

25 3.Computer hardware 
 1 Ninth Modeling Conference Vol. 2, p. 220 

 2 4.Examples of AQM performance 

 3 5.U.S. EPA AQM models: lessons learned 

 4 6.Can software and hardware help? 

 5 7.Next steps 

 6 8.Outcomes 

 7 9.Disclaimer 

 8 Regulatory Air Quality Models (AQM). They 

 9 are developed by the U.S. EPA (and contractors). 

10 Their use is mandatory for SIPs. They require 

11 long model runs. They have a dedicated user 

12 community forced to invest in support 

13 infrastructure: software, hardware, HR staff, 

14 hardware and programming environment. 

15 Revolutionary developments are here now! Other 

16 modeling disciplines report cost benefit 

17 enhancements of 50 to 100 times more 

18 Performance: HiPERiSM’s investigations with 

19 such models show: Many inefficiencies with 

20 mediocre to poor performance, mismatch to current 

21 commodity-off-the-shelf (COTS) hardware, and 

22 worse performance on next generation computers. 

23 The situation for AQM’s: the AQM community needs 

24 help and leadership. Does the U.S. EPA have a 

25 plan to face the challenges for change in COTS 
 1 Ninth Modeling Conference Vol. 2, p. 221 

 2 hardware? 

 3 What is the problem? Movement of data is 

 4 now considered to be the single most expensive 

 5 operation on commodity platforms. Don’t modern 

 6 architectures solve the problem? 

 7 They do this by inserting complex memory 

 8 hierarchies, but this challenges an application’s 

 9 ability to extract optimal performance from 

10 commodity solutions. 

11 What can be done to fix the problem? Full 

12 understanding of the memory’s architecture’s 

13 impact on application performance and then fix 

14 the problem at the source. Multi-core processors 

15 exacerbate the problem because concurrently 

16 executing threads compete for memory bandwidth 

17 the effective cache size per thread is 

18 diminished. 

19 Current generation: multi-core: 2-4 cores 

20 per CPU. Cache Level 1, 2, or 3. CPUs access 

21 memory via bus. Next generation: many-core: 8 – 

22 100’s cores per CPU. Level 1 for each core and 

23 Level 2 shared across cores. Cores access subset 

24 of L2 and memory via bus. The GPGPU revolution: 

25 Multi-processing graphics hardware with on 
 1 Ninth Modeling Conference Vol. 2, p. 222 

 2 outboard processors and programming tools for 

 3 hundreds of parallel threads. 

 4 Memory and cache: The memory hierarchy uses 

 5 cache to hide the negative effects of memory 

 6 latency. Cache space is wasted when data resides 

 7 there but is unused. Unused data in cache 

 8 consumes precious bandwidth when it was loaded 

 9 from memory. 

10 Examples of AQM performance: SOM an Ocean 

11 Model: example (a). Used as a reference. CMAQ: 

12 examples (b) and (c). Rosenbrock solver (ROS3). 

13 Euler Backward solver (EBI). AERMOD: example 

14 (d). All the above models used these HiPERiSM 

15 resources: A 64-bit (x86_64) Linux platform with 

16 a 16KB L1 data cache and 1MB L2 cache with 

17 compilers typically used by the U.S. EPA (using 

18 EPA code for CMAQ and AERMOD). SlowSpotterTM 

19 software from Acumem®, Inc. to collect 

20 performance data (for details see HiPERiSM’s Web 

21 URL). 

22 Example (a) SOM Ocean Model: Excellent 

23 cache utilization: GREEN on the right hand-side 

24 bars shows no wasted cache space – i.e. all data 

25 loaded from memory is used by the CPU. (Single 
 1 Ninth Modeling Conference Vol. 2, p. 223 

 2 CPU with one core and two cache levels). 

 3 Example (b) CMAQ ROS3 Solver: Mediocre 

 4 cache utilization: RED on the right hand-side 

 5 bars shows wasted cache space – i.e. data loaded 

 6 from memory but never used. (Single CPU with one 

 7 core and two cache levels). 

 8 Example (c) CMAQ EBI Solver: Comparing CMAQ 

 9 solvers* (EBI versus ROS3): EBI: 3x more wasted 

10 cache space. EBI: 4x worse memory prefetching 

11 performance. Linux platform with a 16KB L1 data 

12 cache and 1MB L2 cache for the mid-morning hours 

13 of a summer episode (14 August, 2006). 

14 Example (d) AERMOD. Poor cache utilization: 

15 RED on the right hand-side bars shows wasted 

16 cache space – i.e. data loaded from memory but 

17 never used. (Single CPU with one core and two 

18 cache levels). 

19 Lessons learned: Memory footprint of AQM’s: 

20 Inherent in the current state of models: 

21 inefficient use of COTS hardware, lost 

22 performance opportunities. Critical bottle-necks 

23 in memory access: cache utilization is wasteful 

24 and cost of latency leads to CPU stalls 

25 Can software or hardware help? Compilers 
 1 Ninth Modeling Conference Vol. 2, p. 224 

 2 will not solve the performance bottle-necks 

 3 because: The code lacks the right structure. 

 4 Requires too much disorganized data movement. 

 5 Next generation hardware requires data 

 6 parallelism: Needs to be expressed in the code 

 7 by the developer and it cannot be discovered by 

 8 compilers. 

 9 Next Steps: U.S. EPA needs to show 

10 leadership by: Soliciting input from the 

11 community, developing an action plan to meet the 

12 challenge, provide resources for change. 

13 Consequences of inaction include: lowered 

14 performance, and escalating support 

15 infrastructure costs. 

16 Outcomes: GREEN COMPUTING ! More 

17 efficient use of COTS computers. Lower cost of 

18 AQM support infrastructure. Higher throughput = 

19 fewer resources required. Cost benefit analysis 

20 suggests: Modification of AQM’s will yield. 

21 Boost in throughput by orders of magnitude and 

22 lower TCO (total cost of ownership). 

23 None of the work reported here has been 

24 sponsored or funded by the U.S. EPA. Further 

25 information is available at: 
 1 Ninth Modeling Conference Vol. 2, p. 225 

 2 http://www.hiperism.com and 

 3 http://www.hiclas1.com. 

 4 I recommend the transition to the modern 

 5 generation of compiler technology for AERMOD 

 6 development at the EPA and also the decision to 

 7 go with the double precision release of AERMOD. 

 8 This will remove certain problems that have 

 9 worried us. That’s it. 

10 Tyler Fox: The last presentation is from 

11 Mark Garrison from ERM. 

12 Mark Garrison: Thank you for the 

13 opportunity to say a few words this afternoon and 

14 given the hour there will be very few words 

15 spoken. I don’t have a presentation but was 

16 inspired to make these comments by the 

17 presentations yesterday. For the record, I am 

18 Mark Garrison from ERM. We service the Air 

19 Integrator for the Maryland Department of Natural 

20 Resources Power Plant Research Program. In this 

21 role we are responsible for providing technical 

22 support in the review and evaluation of air 

23 quality impacts from power plants. 

24 The analyses we are involved with range from 

25 local scale analysis using AERMOD to (inaudible) 
 1 Ninth Modeling Conference Vol. 2, p. 226 

 2 using CALPUFF to (inaudible) with CALPUFF. We’ve 

 3 done some quasi studies with CALPUFF looking at 

 4 visibility impacts, nitrate deposition impacts 

 5 and Mercury impacts. For the past couple of 

 6 years, we have been experimenting with different 

 7 ways for extracting data from MM5 and WRF file 

 8 outputs and processed through CALMET to develop 

 9 inputs for AERMOD. 

10 We have kind of settled into a preferred 

11 approach which is to extract wind profiles from 

12 prognostic models and treat them as pseudo 

13 observations and combine them with more broadly 

14 representative cloud cover and temperatures from 

15 National Weather Service Stations. Then 

16 essentially allowing AERSURFACE and AERMET to do 

17 their thing in terms of customizing the land use 

18 to (inaudible) and create inputs in to AERMOD. 

19 Now we have done some evaluations with this 

20 approach both in (inaudible) in terms of 

21 comparing the prognostic model derived wind 

22 profiles with data collected on met towers. And 

23 also an intent to do an sensitivity studies as to 

24 what kind of concentration are the result of the 

25 various approaches. And while we are somewhat 
 1 Ninth Modeling Conference Vol. 2, p. 227 

 2 limited, the evaluations I think anyway are 

 3 pretty promising in terms of coming up with an 

 4 approach, at least in my mind, that allows AERMOD 

 5 to do its thing for customizing meteorological 

 6 data on a site specific basis without relying on 

 7 land use that essentially represents airport 

 8 runways. 

 9 That’s about it. I think we are going to 

10 provide written comments and add some summaries 

11 of our evaluations. Hopefully it will be of some 

12 interest. Thank you. 

13 Tyler Fox: It is 4:30. I appreciate your 

14 time and all your input and we will be getting 

15 the transcript done and submitting that. Also, 

16 just as we have in the past, we will be compiling 

17 some of the major comments putting them together 

18 and then providing a summary or response to 

19 comments from the agency. As soon as we know 

20 what the timing will be we’ll send out a memo to 

21 everybody and let them know. Everybody have a 

22 safe trip back to your homes. 

23 

24 

25 

 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 228 

 2 KEYWORD INDEX 

 _____________ 

 3 
 4 1. AERMET 21. clearing house 

 5 2. AERMOD 22. complex 

 6 3. AERSCREEN 23. concentration 

 7 4. AERSURFACE 24. concentrations 

 8 5. air 25. convective 

 9 6. algorithms 26. data 

10 7. appendix 27. database 

11 8. ASOS 28. databases 

12 9. atmosphere 29. datum 

13 10. BART 30. default 

14 11. Birmingham 31. DEM 

15 12. boundary 32. dispersion 

16 13. calm 33. domain 

17 14. CALMET 34. downwash 

18 15. calms 35. downwind 

19 16. CALPUFF 36. EPA 

20 17. cell 37. ETA 

21 18. cells 38. Federal 

22 19. chemistry 39. fence line 

23 20. Class I 40. file 

24 

25 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 229 

 2 KEYWORD INDEX 

 _____________ 

 3 

 4 41. files 61. meteorological 

 5 42. FLEXPART 62. mixing 

 6 43. gridded 63. MM5 

 7 44. group 64. model 

 8 45. groups 65. model evaluation 

 9 46. guidance 66. modeling 

10 47. guide 67. monitor 

11 48. guideline 68. monitors 

12 49. guidelines 69. near-field 

13 50. humidity 70. NEPA 

14 51. implement 71. non regulatory 

15 52. implementation 72. NOAA 

16 53. ISC 73. NSR 

17 54. IWAQM 74. OAQPS 

18 55. Lagrangian 75. observation 

19 56. layer 76. observations 

20 57. layers 77. observed 

21 58. long range transport 78. ozone 

22 59. mesoscale 79. parameter 

23 60. met 80. parameters 

24 

25 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 230 

 2 KEYWORD INDEX 

 _____________ 

 3 

 4 81. particle 101. RUC 

 5 82. PBL 102. rule 

 6 83. Phase 2 103. run 

 7 84. photochemical 104. rural 

 8 85. PiG 105. scale 

 9 86. plume 106. SCRAM 

10 87. PRIME 107. screening 

11 88. processor 108. sensitivity 

12 89. processors 109. service 

13 90. profile 110. site 

14 91. promulgation 111. source 

15 92. protocol 112. speed 

16 93. protocols 113. stack 

17 94. PSD 114. stacks 

18 95. puff 115. statistical 

19 96. ratio 116. steady state 

20 97. ratios 117. surface 

21 98. receptor 118. surrogate 

22 99. regulatory 119. temperature 

23 100. roughness 120. terrain 

24 

25 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 231 

 2 KEYWORD INDEX 

 _____________ 

 3 

 4 121. toxics 141. 

 5 122. tracer 142. 

 6 123. turbulence 143. 

 7 124. urban 144. 

 8 125. variability 145. 

 9 126. weather 146. 

10 127. wind 147. 

11 128. wind speed 148. 

12 129. wind speeds 149. 

13 130. winds 150. 

14 131. work group 151. 

15 

16 

17 

18 

19 

20 

21 

22 

23 

24 

25 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 232 

 2 

 3 Page Ref No. Keyword = "AERMET" 

 4 ____ _______ __________________ 

 5 

 6 166 15 Issues on the AERMET output. AERMET Stage 3 

 7 166 17 the user knows during the AERMET processing steps 

 8 180 25 meteorological input in AERMOD and AERMET. 

 9 182 17 much has been done. Google on AERMET and 

10 184 16 AERMET and I couldn’t repeat. I didn’t have 

11 185 10 newbie when it comes to running AERMET and 

12 185 22 ==> AERMET ==> AERMOD. GEMPAK tools convert grib to 

13 207 13 A couple of comments on AERMET is that 

14 226 16 essentially allowing AERSURFACE and AERMET to do 

15 

16 Page Ref No. Keyword = "AERMOD" 

17 ____ _______ __________________ 

18 

19 16 4 AERMOD. 

20 18 5 and AERMOD options) over P-G? As Tyler 

21 25 4 CALPUFF turbulence and the AERMOD turbulence in 

22 62 14 versions of AERMOD. I’m going to talk about the 

23 62 15 AERMOD evaluation review, evaluation tools, and 

24 63 4 provided that database to EPA for AERMOD. And we 

25 64 7 So in the AERMOD evaluation, we have the 

26 64 8 question: how well does AERMOD predict peak 

27 64 10 with air quality (AQ) standards? Is AERMOD’s 

28 72 9 appropriate in the way we use AERMOD under 

29 72 20 the work here in terms of AERMOD evaluation and 

30 73 23 series of arcs. This is a Q-Q Plot of AERMOD 

31 74 11 in AERMOD performance evaluation. Again this is 

32 74 16 These are applications of AERMOD that have come 

33 74 18 has run AERMOD and getting results they don’t 

34 75 9 SIP. Basically AERMOD was run initially with 

35 76 5 concentration of AERMOD across the gridded 

36 76 8 had receptors in AERMOD that were either very 

37 80 23 Example is AERMOD being applied to support 

38 81 6 comparisons showed AERMOD concentrations 

39 82 12 concentration from AERMOD. Again most of this is 

40 82 18 some concern whether AERMOD could be used in this 

41 83 10 These are AERMOD concentrations using the SEARCH 

42 87 12 find out how well AERMOD does or doesn’t do with 

43 88 20 the AERMOD users guide in terms of defining 

44 94 23 AERMOD), Gaussian Puff Models (INPUFF, CALPUFF, 

45 159 17 models especially on CALPUFF and AERMOD. As you 

46 159 19 CALPUFF and AERMOD at this time. We don’t think 

47 160 11 time varying dispersion models (e.g., AERMOD, 

48 166 21 information is not provided until AERMOD is run. 

49 167 4 for both AERMOD and CALPUFF. 

50 167 16 principles (e.g., AERMOD, CALPUFF, CMAQ, CAMx 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 233 

 2 

 3 Page Ref No. Keyword = "aermod" 

 4 ____ _______ __________________ 

 5 

 6 180 25 meteorological input in AERMOD and AERMET. 

 7 181 4 meteorolgical input into AERMOD. So that’s 

 8 182 8 Why the interest for AERMOD? 

 9 183 9 for input in to AERMOD. And so being kind of a 

10 184 15 versions of AERMOD that couldn’t quite run in 

11 185 11 AERMOD. I need someone to review this to see 

12 185 17 requirements of surface data for AERMOD. 

13 185 22 ==> AERMET ==> AERMOD. GEMPAK tools convert grib to 

14 186 22 happy to have seen the progression to AERMOD and 

15 187 2 like [ed. AERMOD] (inaudible) and we offer our 

16 188 21 AERMOD/PRIME Problem for Short/Large Buildings. 

17 188 22 AERMOD/PRIME Underestimation For Corner Vortex 

18 189 13 in AERMOD are designed for simple rectangular 

19 189 15 AERMOD only appropriate for certain building 

20 189 25 Prime Algorithms. With AERMOD/PRIME building 

21 190 25 Station study. AERMOD with BPIP predicting high 

22 191 3 building. AERMOD with Equivalent Building 

23 192 7 standard AERMOD default mode. And we ran with 

24 193 2 AERMOD. 

25 193 8 ran AERMOD for these 3 cases for 1 wind speed. 

26 194 5 using Equivalent Building Dimensions in AERMOD. 

27 194 23 BPIP for AERMOD/PRIME. Develop algorithms for 

28 196 21 for running AERMOD or CALPUFF. I think it seems 

29 201 4 Secondary PM2.5. AERMOD considers primary PM2.5 

30 202 11 CALPUFF and AERMOD can provide that statistic. 

31 204 7 about AERMOD. 

32 204 10 few comments on AERMOD. First of all we like 

33 204 11 AERMOD. It does things ISC3 could never do. I 

34 204 16 combine multiple AERMOD runs. Deposition 

35 204 20 AERMOD impacts occur for very low wind speeds, 

36 204 21 especially for low-level sources. AERMOD has 

37 204 25 hours with very low wind speeds. AERMOD needs 

38 205 7 studies and adjustments to AERMOD modeling 

39 205 20 AERMOD runs can be very long. Runs cannot 

40 205 24 translate AERMOD conc. files to CALPUFF-like 

41 206 5 makes for some confusion. Recommend that AERMOD 

42 208 4 of my comments on AERMOD today. We’re commenting 

43 208 5 off the off the shelf of AERMOD and not talking 

44 211 13 Adding bells and whistles to AERMOD will not 

45 218 4 has identified in AERMOD. URAG encourages EPA to 

46 219 18 with AERMOD. I’m now a private consultant. 

47 222 13 Euler Backward solver (EBI). AERMOD: example 

48 222 18 EPA code for CMAQ and AERMOD). SlowSpotterTM 

49 223 14 Example (d) AERMOD. Poor cache utilization: 

50 225 5 generation of compiler technology for AERMOD 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 234 

 2 

 3 Page Ref No. Keyword = "aermod" 

 4 ____ _______ __________________ 

 5 

 6 225 7 go with the double precision release of AERMOD. 

 7 225 25 local scale analysis using AERMOD to (inaudible) 

 8 226 9 inputs for AERMOD. 

 9 226 18 to (inaudible) and create inputs in to AERMOD. 

10 

11 Page Ref No. Keyword = "AERSCREEN" 

12 ____ _______ _____________________ 

13 

14 165 11 AERSCREEN, is coming out soon. We’d like to see 

15 207 7 recommend use of AERSCREEN with different runs 

16 

17 Page Ref No. Keyword = "AERSURFACE" 

18 ____ _______ ______________________ 

19 

20 83 12 AERSURFACE pretty high roughness about 0.8 meters 

21 206 19 Issues with AERSURFACE implementation. 

22 226 16 essentially allowing AERSURFACE and AERMET to do 

23 

24 Page Ref No. Keyword = "air" 

25 ____ _______ _______________ 

26 

27 7 7 in air quality modeling. This class of models 

28 22 10 750 meters up in the air and the height in the 

29 48 4 community multi scale air quality model from the 

30 48 13 predicting the level of air quality compared to 

31 48 24 questions are we capturing the changes in air 

32 51 2 case typically MM5 or WRF and one focuses on air 

33 51 14 (e.g. MM5, WRF) or air quality model (e.g. CMAQ, 

34 54 19 Profiler, and Aircraft Profiler. 

35 56 7 Similarly for aircraft comparisons similar types 

36 64 10 with air quality (AQ) standards? Is AERMOD’s 

37 69 20 Eulerian air quality models, where predicted 

38 75 10 airport data and with the SEARCH data sets that 

39 75 18 series plot running the model with the airport 

40 76 12 again at the airport for Birmingham that the 

41 76 16 airport. Then higher roughness at the SEARCH 

42 77 3 series plot based on airport data the light blue 

43 77 6 airport. 

44 77 15 that monitor. But if you look at the airport 

45 77 19 airport this is a case where between calm and 

46 78 2 question of the representative of that airport 

47 78 16 compared with met SEARCH site and airport site to 

48 79 4 supplemented the airport with the 1-minute ASOS 

49 79 11 cold air and drainage flow. At the airport it’s 

50 79 14 show up at all with this standard airport data 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 235 

 2 

 3 Page Ref No. Keyword = "air" 

 4 ____ _______ _______________ 

 5 

 6 79 22 is that when you use the airport data under the 

 7 79 24 airport when you supplement it you are getting a 

 8 80 17 airport data and 25-30% is calm those results may 

 9 81 10 process airport data was not representative of 

10 81 12 suggestion was to re-process airport data with 1m 

11 83 8 impacts. Again with the airport date this is the 

12 83 14 airport data with the 1-minute ASOS 

13 83 16 pretty low for an airport. And pretty close to 

14 84 2 airport and the SEARCH site didn’t seem to be 

15 84 16 from standard ISHD airport data showed 

16 85 3 And also another non standard airport site, 

17 93 21 emergency response support for air modeling in 

18 97 12 get air concentrations, deposition; and some 

19 99 22 modeling air concentrations, it is from 

20 131 25 hazardous air pollutants (HAPs), which is an 

21 132 3 large spatial variability in air toxics 

22 136 8 simple (inaudible) for all types of air quality 

23 136 12 inputs and process that for the air quality 

24 137 18 prevent kids in this terrible dooms day air 

25 148 13 PGM Databases and model set ups. RPOs, AIRPACT, 

26 154 9 source” air quality, visibility and deposition 

27 154 23 “single source” air quality, visibility and 

28 156 20 with our policy folks in the Air Quality 

29 158 20 air standards has resulted in more non-attainment 

30 158 24 on Air Quality Models. Highlights are listed 

31 163 15 of Met models and air quality models in 

32 165 25 anemometers generally located on airport 

33 166 4 by surface roughness of airport property. Better 

34 166 5 guidance is needed for translating the airport 

35 166 8 sources that use airport data, the dispersion 

36 166 10 the surface modeling of the airport roughness. 

37 166 12 the airport wind observation to the land 

38 167 2 concentrations in the presence of ambient air 

39 167 12 CMAQ) for estimating air quality concentrations. 

40 170 24 like to do is present issues related to air 

41 172 10 air quality impacts under NEPA 

42 172 15 Air quality modeling approach is: “Use 

43 180 2 model output in air quality modeling 

44 180 7 the use of meteorological models in air 

45 180 16 model results used in an air quality 

46 182 11 standard upper air and surface observations 

47 182 22 these are your upper air sites across the CONUS 

48 183 8 attractive at least in upper air data source 

49 183 23 the vertical resolution of the upper air data 

50 184 21 It uses Pittsburgh PA surface and upper air 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 236 

 2 

 3 Page Ref No. Keyword = "air" 

 4 ____ _______ _______________ 

 5 

 6 184 23 North American Regional Reanalysis). Upper air 

 7 185 2 air site. 

 8 185 19 suitable as an upper air resource – need to 

 9 195 6 meterological data on the air quality model which 

10 202 19 the Proposed National Ambient Air Quality 

11 207 21 Guideline on Air Quality Models is right, a calm 

12 208 20 300 km. Many applications in air quality 

13 208 21 modeling for the guideline purposes require air 

14 209 8 air issues. Complex terrain and slow reversal 

15 212 17 Air Regulatory Group (UARG). UARG is an ad hoc 

16 212 22 proceedings under the federal Clean Air Act. 

17 214 4 on the use of the air quality models, then we 

18 218 14 predicting the air quality impacts of emissions 

19 219 14 performance computing and started with air 

20 220 8 Regulatory Air Quality Models (AQM). They 

21 225 18 Mark Garrison from ERM. We service the Air 

22 225 22 support in the review and evaluation of air 

23 

24 Page Ref No. Keyword = "algorithms" 

25 ____ _______ ______________________ 

26 

27 85 25 with the PRIME downwash algorithms since it 

28 165 15 incorporate algorithms for near calm winds and 

29 165 17 algorithms for use in urban areas, especially for 

30 168 8 modifications to model algorithms. Model 

31 189 11 Base Used to Develop Downwash Algorithms). Other 

32 189 12 considerations are building downwash algorithms 

33 189 14 buildings. Building downwash algorithms in 

34 189 25 Prime Algorithms. With AERMOD/PRIME building 

35 191 7 Short/Large Buildings. The wake algorithms have 

36 194 21 dimensions match assumptions in algorithms. If 

37 194 23 BPIP for AERMOD/PRIME. Develop algorithms for 

38 

39 Page Ref No. Keyword = "appendix" 

40 ____ _______ ____________________ 

41 

42 38 20 Appendix W. 

43 72 10 Appendix W [ed. for NSR and] (inaudible) PSD. 

44 156 8 for EPA. Basically Appendix W Guidance on 

45 156 18 Appendix W would have to fall out of discussions 

46 202 20 Standards for Particulate Matter, Appendix B – 

47 213 4 revisions of Appendix W Guideline. The Modeling 

48 213 18 Guidelines to Appendix W. So our comments are 

49 216 11 Appendix W allows the choice of modeling 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 237 

 2 

 3 Page Ref No. Keyword = "ASOS" 

 4 ____ _______ ________________ 

 5 

 6 78 9 one ASOS data which we shared. 

 7 79 4 supplemented the airport with the 1-minute ASOS 

 8 79 13 supplemented it with 1-minute ASOS it doesn’t 

 9 83 14 airport data with the 1-minute ASOS 

10 84 20 ASOS wind data to reduce the number of calms, 

11 165 24 surface roughness to a 1 km radius of ASOS 

12 204 23 ASOS use of sonic anemometer data and averaging 

13 204 24 of sub-hourly ASOS data will likely create more 

14 

15 Page Ref No. Keyword = "atmosphere" 

16 ____ _______ ______________________ 

17 

18 22 11 atmosphere and you can see the presence of the 

19 105 2 atmosphere. That’s the complete 3D-particle 

20 136 23 photochemical grid models are a one atmosphere 

21 137 4 So the One Atmosphere approach may not be 

22 181 20 atmosphere for a given point in time. It’s 

23 199 7 in the atmosphere (“secondary formation”) from 

24 

25 Page Ref No. Keyword = "BART" 

26 ____ _______ ________________ 

27 

28 43 13 position with respect to BART and with respect to 

29 94 8 about the BART program which is we’ve seen a lot 

30 149 16 Texas Group BART application. CAMx 36/12 km with 

31 150 3 I have one slide on the Texas Bart but Texas 

32 150 4 had like 200 potential Bart eligible sources. 

33 150 7 groups of 10. In each group Bart analysis of 10 

34 150 9 contributions of groups of Texas BART sources for 

35 150 13 areas. Use IRON P-in-G for Texas BART Source. 

36 154 13 BART assessment. PM2.5 SIP modeling. 

37 155 3 Arkansas BART assessment. PM2.5 SIP modeling. 

38 178 8 the Hayden Power Plant Bart analysis done by 

39 199 11 This slide comes from a VISTAS BART 

40 

41 Page Ref No. Keyword = "Birmingham" 

42 ____ _______ ______________________ 

43 

44 75 8 Birmingham Local Area Analysis (LAA) for PM-2.5 

45 76 12 again at the airport for Birmingham that the 

46 77 18 you can see that trend. I think at Birmingham 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 238 

 2 

 3 Page Ref No. Keyword = "boundary" 

 4 ____ _______ ____________________ 

 5 

 6 29 2 Nebraska we have a fontal boundary that starting 

 7 29 5 encountered the frontal boundary and started to 

 8 29 9 we’re not encountering that frontal boundary and 

 9 84 5 boundary layer enhancement is certainly helping 

10 104 6 represent the boundary layer transport. It looks 

11 104 8 varies with height in the boundary layer. 

12 104 17 effect. That is a big thing for boundary layer 

13 105 21 boundary layer as there is a lot more shear with 

14 140 8 boundary conditions to PM2.5 by adding duplicate 

15 152 15 largest contributions are the boundary 

16 152 16 conditions. The boundary conditions are outside 

17 175 13 chemistry was used. When boundary 

18 

19 Page Ref No. Keyword = "calm" 

20 ____ _______ ________________ 

21 

22 18 20 in CALMET. Perhaps Hybrid method verses NOOB = 1 

23 18 23 different ways in which we supply data to CALMET 

24 22 18 we did with CALMET meteorology we looked at 

25 23 8 had two domains, two CALMET domains. For the 100 

26 23 9 km arc we used the 4 km CALMET which was 

27 23 12 previous CALMET and CALPUFF simulations to do 

28 23 13 this. So we had a 20 km CALMET for 600 km 

29 23 14 simulation and a 4 km CALMET for 100 km and then 

30 24 10 CALPUFF with CALMET is doing about the same. 

31 24 11 Both put in MM5 CALPUFF within the CALMET one 

32 25 14 You can see here the CALMET winds did very 

33 25 17 time at the 100 km arc. CALMET almost 

34 26 2 as you can on the 100 km arch, CALMET does very 

35 26 13 1998 timeframe, they ran in CALMET and NOOBS mode 

36 26 20 something has changed inside CALMET I don’t know. 

37 27 13 CALMET. CALMET was much better in terms of 

38 29 21 CALMET wind fields from the previous one. I 

39 31 20 with the MM5 and there’s no CALMET in this 

40 36 4 CALMET options remained constant. CALPUFF 

41 36 5 performance varied due to variations in CALMET 

42 43 22 So I created a 12 km domain and ran CALMET just 

43 77 10 correlated with high frequency calm. For example 

44 77 11 if you have 18-20 hours of calm, it indicated a 

45 77 19 airport this is a case where between calm and 

46 80 17 airport data and 25-30% is calm those results may 

47 160 9 models such as CALMET and prognostic full-physics 

48 160 21 needed is to optimize use of Met model and CALMET 

49 160 24 physics Met models (e.g. MM5) and CALMET; look at 

50 161 16 methods). We’d like to assess if CALMET (or any 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 239 

 2 

 3 Page Ref No. Keyword = "calm" 

 4 ____ _______ ________________ 

 5 

 6 162 6 CALMET (or any diagnostic model) is truly needed 

 7 163 24 model (e.g., MM5) direct input versus CALMET 

 8 165 15 incorporate algorithms for near calm winds and 

 9 207 21 Guideline on Air Quality Models is right, a calm 

10 208 2 missing calm. 

11 226 8 outputs and processed through CALMET to develop 

12 

13 Page Ref No. Keyword = "CALMET" 

14 ____ _______ __________________ 

15 

16 18 20 in CALMET. Perhaps Hybrid method verses NOOB = 1 

17 18 23 different ways in which we supply data to CALMET 

18 22 18 we did with CALMET meteorology we looked at 

19 23 8 had two domains, two CALMET domains. For the 100 

20 23 9 km arc we used the 4 km CALMET which was 

21 23 12 previous CALMET and CALPUFF simulations to do 

22 23 13 this. So we had a 20 km CALMET for 600 km 

23 23 14 simulation and a 4 km CALMET for 100 km and then 

24 24 10 CALPUFF with CALMET is doing about the same. 

25 24 11 Both put in MM5 CALPUFF within the CALMET one 

26 25 14 You can see here the CALMET winds did very 

27 25 17 time at the 100 km arc. CALMET almost 

28 26 2 as you can on the 100 km arch, CALMET does very 

29 26 13 1998 timeframe, they ran in CALMET and NOOBS mode 

30 26 20 something has changed inside CALMET I don’t know. 

31 27 13 CALMET. CALMET was much better in terms of 

32 29 21 CALMET wind fields from the previous one. I 

33 31 20 with the MM5 and there’s no CALMET in this 

34 36 4 CALMET options remained constant. CALPUFF 

35 36 5 performance varied due to variations in CALMET 

36 43 22 So I created a 12 km domain and ran CALMET just 

37 160 9 models such as CALMET and prognostic full-physics 

38 160 21 needed is to optimize use of Met model and CALMET 

39 160 24 physics Met models (e.g. MM5) and CALMET; look at 

40 161 16 methods). We’d like to assess if CALMET (or any 

41 162 6 CALMET (or any diagnostic model) is truly needed 

42 163 24 model (e.g., MM5) direct input versus CALMET 

43 226 8 outputs and processed through CALMET to develop 

44 

45 Page Ref No. Keyword = "calms" 

46 ____ _______ _________________ 

47 

48 77 5 against the frequency of calms each day from the 

49 77 21 data period missing either to calms or winds. 

50 78 5 down, calms go up, observed concentrations go up 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 240 

 2 

 3 Page Ref No. Keyword = "calms" 

 4 ____ _______ _________________ 

 5 

 6 78 7 without (inaudible) the calms go down. This is 

 7 79 15 because they’re all missing the calms. 

 8 84 20 ASOS wind data to reduce the number of calms, 

 9 85 18 predictions. The other was the calms. Looking 

10 183 15 calms verses what the observed data might have. 

11 207 15 Many more calms in recent data sets – if 

12 207 17 meet 90% capture criteria. If many calms, does 

13 207 18 CALMS preprocessor work properly? Conc 

14 

15 Page Ref No. Keyword = "CALPUFF" 

16 ____ _______ ___________________ 

17 

18 6 6 continuation of the CALPUFF session, but in order 

19 6 10 respect to CALPUFF. So we’ll start with those 

20 7 2 employing in evaluating CALPUFF and the other 

21 11 13 evaluate CALPUFF and the other models. 

22 14 7 fact that CALPUFF model science had evolved 

23 17 10 was to examine science evolution of CALPUFF 

24 17 17 range of CALPUFF beyond recommended distance of 

25 18 13 to supply meteorological data to CALPUFF? As you 

26 19 25 a paper about comparing CALPUFF to (inaudible) 

27 21 16 published supporting the promulgation of CALPUFF. 

28 22 17 CALPUFF, FLEXPART and HYSPLIT and basically, what 

29 22 21 gave yesterday, we also included the MM5 CALPUFF 

30 23 12 previous CALMET and CALPUFF simulations to do 

31 24 10 CALPUFF with CALMET is doing about the same. 

32 24 11 Both put in MM5 CALPUFF within the CALMET one 

33 24 16 CALPUFF in the 100 km and unpredicted under 600 

34 25 4 CALPUFF turbulence and the AERMOD turbulence in 

35 27 3 (inaudible) CALPUFF we weren’t getting the 

36 28 8 encouraging sign for the MM5 CALPUFF. 

37 29 12 For the MM5 CALPUFF, as you can see, it 

38 31 12 this is well beyond what CALPUFF what is 

39 31 21 simulation. It’s only MM5 CALPUFF so basically 

40 32 16 This is what CALPUFF was showing here. I 

41 32 18 (inaudible) Hysplit and CALPUFF were a lot easier 

42 32 22 observation were looking like for this. CALPUFF 

43 33 2 of it. All three models CALPUFF, FLEXPART and 

44 33 14 CALPUFF and was just an experiment to take a look 

45 33 17 HYSPLIT was comparable with CALPUFF in the first 

46 34 7 CALPUFF on that one here where the high false 

47 34 19 better than CALPUFF here and you know you can 

48 35 10 Experiment, CALPUFF/CALMET 100 km results 

49 36 2 CALPUFF 1.0 distribution and use of lambert 

50 36 4 CALMET options remained constant. CALPUFF 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 241 

 2 

 3 Page Ref No. Keyword = "calpuff" 

 4 ____ _______ ___________________ 

 5 

 6 36 6 options selected. As you can see, CALPUFF 

 7 36 9 mentioned yester that CALPUFF is sensitive as to 

 8 36 14 see CALPUFF performs reasonably compared to 

 9 36 20 looking at Puff-splitting did not change CALPUFF 

10 37 3 puff-splitting in CALPUFF. 

11 39 9 in CALPUFF such as you resolve the terrain 

12 46 21 about CALPUFF people can ask those before we get 

13 71 12 the plume dispersion predictions in CALPUFF 

14 94 9 of CALPUFF modeling you know we’ve also seen a 

15 94 23 AERMOD), Gaussian Puff Models (INPUFF, CALPUFF, 

16 118 20 that’s in a version of CALPUFF and I said I would 

17 119 12 CALPUFF. 

18 120 17 and CALPUFF would this type. We have a puff that 

19 122 8 The way this is implemented into CALPUFF are 

20 122 13 into sub-steps (sampling steps) in CALPUFF. For 

21 122 22 main CALPUFF routine. 

22 122 23 CALPUFF then computes any physical process 

23 123 9 restored. Parent puff treated in normal CALPUFF 

24 124 24 CALPUFF and it’s an older version. But it’s 

25 143 20 (not an evaluation of CAMx PSAT or CALPUFF) of 

26 143 22 with CALPUFF visibility estimates. Several 

27 144 4 meteorology output from MM5. CALPUFF was run in 

28 144 14 CALPUFF. We are not trying to say which is right 

29 144 17 the top we’ve got the CALPUFF results and on the 

30 144 19 caveat to put on this is that CALPUFF look at 

31 145 3 amazing was CALPUFF had some larger extinction 

32 145 4 (?) of the contribution. We applied CALPUFF with 

33 151 2 and primary PM emissions requested. CALPUFF 

34 152 11 (inaudible) for that other model CALPUFF. 

35 159 7 We had discussions yesterday of CALPUFF 

36 159 17 models especially on CALPUFF and AERMOD. As you 

37 159 19 CALPUFF and AERMOD at this time. We don’t think 

38 160 12 CALPUFF, CMAQ). Prognostic meteorological models 

39 162 17 evaluations of CALPUFF using full chemistry as 

40 163 8 test the use of CALPUFF for regional AQRV 

41 167 4 for both AERMOD and CALPUFF. 

42 167 11 detailed dispersion models (AERMOD, CALPUFF, or 

43 167 16 principles (e.g., AERMOD, CALPUFF, CMAQ, CAMx 

44 172 8 Ralph talk about that. CALPUFF is being 

45 173 6 Formulation of CALPUFF chemistry. Lack 

46 173 14 used in CALPUFF, SO4 formation is described 

47 175 12 been done in terms of CALPUFF. RIVAD 

48 175 21 indication that the as CALPUFF Model using 

49 176 6 CALPUFF is predicting. Now we can get into 

50 178 14 sulfate in CALPUFF, it’s saying the nitrate 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 242 

 2 

 3 Page Ref No. Keyword = "calpuff" 

 4 ____ _______ ___________________ 

 5 

 6 179 3 Monitoring data versus CALPUFF, 80,000 

 7 179 10 comprehensive model evaluation of CALPUFF in 

 8 186 23 CALPUFF. I think Harry Cramer, rest his soul, 

 9 195 17 processes that are a part of the CALPUFF system 

10 196 21 for running AERMOD or CALPUFF. I think it seems 

11 201 9 Secondary PM2.5 could be modeled with CALPUFF. 

12 201 15 I’d like to address the issues with CALPUFF 

13 201 22 find that CALPUFF will be mostly unbiased and I 

14 201 25 predictions of CALPUFF. 

15 202 11 CALPUFF and AERMOD can provide that statistic. 

16 203 25 There is a role for CALPUFF (or other models) for 

17 205 22 as is done with CALPUFF. EPA should develop a 

18 205 23 system like that of the CALPUFF system, or 

19 205 24 translate AERMOD conc. files to CALPUFF-like 

20 208 8 be talking about CALPUFF and the comments of the 

21 208 9 AB3 Committee on CALPUFF. Let me see if I can 

22 208 16 about CALPUFF and talking about CALPUFF filling 

23 208 17 your needs. About EPA concerns about CALPUFF and 

24 209 4 demonstrated. CALPUFF is a model with community 

25 209 13 EPA concerns about CALPUFF are relatively 

26 209 15 evaluation and CALPUFF we are going to show in 

27 209 17 demonstrate CALPUFF in near field areas. 

28 209 22 EPA doesn’t have direct control of CALPUFF and 

29 210 4 The developer has training classes for CALPUFF. 

30 210 6 advancing CALPUFF and will work to that end. EPA 

31 210 7 doesn’t have direct control of the CALPUFF code 

32 210 12 CALPUFF lags behind in the code releases. The 

33 210 22 CALPUFF at less than 50 km. Why is it 50 

34 211 15 that CALPUFF comparison to LRT studies have been 

35 211 18 we’re using now. Many states are using CALPUFF 

36 215 10 memorandum about the regulatory status of CALPUFF 

37 215 12 of CALPUFF must go through a more extensive 

38 215 17 document that a modeling system like CALPUFF, 

39 216 8 parties trying to use CALPUFF in situations in 

40 226 2 using CALPUFF to (inaudible) with CALPUFF. We’ve 

41 226 3 done some quasi studies with CALPUFF looking at 

42 

43 Page Ref No. Keyword = "cell" 

44 ____ _______ ________________ 

45 

46 108 25 concentration in any grid cell will be the mass 

47 109 3 cell volume. If you’re using some kind of puff 

48 138 15 each grid cell. 

49 141 20 grid cell. You can see the hot spot over there 

50 144 6 at the number of times in each grid cell that had 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 243 

 2 

 3 Page Ref No. Keyword = "cell" 

 4 ____ _______ ________________ 

 5 

 6 151 8 modeling. 12 km grid cell size too coarse to 

 7 155 8 for Ralph. When you do the (inaudible) cell 

 8 155 12 cell due to (inaudible)? 

 9 155 23 variability in the cell? That’s my question 

10 156 5 of the (inaudible) the cell itself. 

11 

12 Page Ref No. Keyword = "cells" 

13 ____ _______ _________________ 

14 

15 125 25 several grid cells before it reaches the size of 

16 147 16 and dispersion. Need many grid cells to assess 

17 155 10 receptors within the cells. The second question 

18 

19 Page Ref No. Keyword = "chemistry" 

20 ____ _______ _____________________ 

21 

22 81 23 better account for the NO2 chemistry in this 

23 97 23 chemistry and multiple sources, but there are 

24 98 10 There is an implicit linearity for chemistry. 

25 100 10 the meteorology. Now the non-linear chemistry 

26 100 17 Basically the chemistry works in its hybrid 

27 100 22 concentration grid and the chemistry solution is 

28 120 13 chemistry. 

29 126 4 transport of the emissions and chemistry of the 

30 127 8 simplified treatment of chemistry in some models, 

31 127 24 adds the full chemistry mechanism to SCIPUFF. 

32 128 16 We added PM and aqueous-phase chemistry 

33 130 22 source transport and chemistry of point source 

34 132 10 acetaldehyde–models need to treat the chemistry 

35 132 13 chemistry treatment and fine spatial resolution. 

36 132 18 emissions. Chemistry is switched off for this 

37 137 10 chemistry and transport and meteorology inputs to 

38 138 3 Heterogeneous Chemistry); advection (Horizontal & 

39 138 19 phase chemistry, ability to estimate realistic 

40 138 22 phase chemistry provides realistic sulfate 

41 139 4 state of the science inorganic chemistry 

42 141 4 and aqueous phase chemistry are solved for bulk 

43 145 19 advantage of state of the science chemistry, but 

44 147 9 chemistry. Photochemical Grid Models (PGMs) have 

45 147 10 capability to correctly treat chemistry. But how 

46 147 12 source plume chemistry and dispersion? 

47 147 15 the source to resolve near-source plume chemistry 

48 147 19 sources. Needed to correctly simulate chemistry. 

49 148 6 plume chemistry and dispersion without providing 

50 148 7 met and emission inputs and full chemistry Plume- 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 244 

 2 

 3 Page Ref No. Keyword = "chemistry" 

 4 ____ _______ _____________________ 

 5 

 6 148 9 chemistry of point source plumes. Both CMAx and 

 7 151 4 CMAQ full-science chemistry. Provide 

 8 151 9 treat chemistry and dispersion of point source 

 9 151 17 chemistry and dispersion. PM Source 

10 154 5 Full chemistry Plume-in-Grid modules. Ozone and 

11 154 7 aqueous-phase chemistry and aerosol thermodynamic 

12 154 19 Full chemistry Plume-in-Grid modules. Ozone and 

13 154 21 aqueous-phase chemistry and aerosol thermodynamic 

14 162 17 evaluations of CALPUFF using full chemistry as 

15 162 25 that EPA modify the chemistry, based on API/AER 

16 172 2 large extent in a full chemistry mode. And 

17 173 6 Formulation of CALPUFF chemistry. Lack 

18 173 8 a full chemistry mode. Indication of model 

19 173 13 In the MESOPUFF II chemistry module 

20 174 14 It’s a comparison of CMAQ chemistry verses 

21 174 15 CMAQ MESO PUFF II chemistry. The blue dots 

22 174 18 prediction to the MESOPUFF chemistry 

23 174 19 compared to the CMAQ chemistry. This is 

24 174 23 chemistry is not working as it should be. 

25 175 4 chemistry and RIVAD and some modified RIVAD 

26 175 13 chemistry was used. When boundary 

27 179 11 a full chemistry model. Without a doubt 

28 209 20 The chemistry is fine for NOx , SO2 and PM 

29 

30 Page Ref No. Keyword = "Class I" 

31 ____ _______ ___________________ 

32 

33 7 13 these for Class I increments and for what we call 

34 39 19 the Class I analysis and exactly where the source 

35 39 24 into a Class I area. And in other cases it 

36 40 15 differenr source – Class I area pairs -- looking 

37 41 6 for PSD Class I increments that, in all cases, 

38 41 8 for PSD Class I increments. This was from Tim 

39 143 24 sources less than 50 km from Class I areas; used 

40 150 12 flexi-nest grid covering Texas and nearby Class I 

41 175 25 Bridger CLASS I area outside of Pinedale 

42 176 11 from the Class I areas. So you are not 

43 202 6 applications (e.g., Class I increment)? How to 

44 

45 Page Ref No. Keyword = "clearing house" 

46 ____ _______ __________________________ 

47 

48 91 17 The reason we have a clearing house process and 

49 92 18 Regional Offices to use the clearing house 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 245 

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 3 Page Ref No. Keyword = "complex" 

 4 ____ _______ ___________________ 

 5 

 6 19 23 good complex terrain to it which would be useful. 

 7 70 24 complex terrain). Conditions of concern for 

 8 97 22 It lends itself to easily handle complex 

 9 158 21 areas and the need for more complex and more 

10 162 21 SCIPUFF And the ability to handle complex 

11 186 20 Complex (ISC)] model. I’m not sure if that is 

12 189 2 Prime, it’s going to be hard to treat complex 

13 190 20 complex and that’s the shape of the building that 

14 209 8 air issues. Complex terrain and slow reversal 

15 211 7 method to define precisely when complex winds 

16 211 9 referring to paper I gave 3 years ago on complex 

17 211 10 modeling and a better definition of complex 

18 211 12 definition of complex winds. 

19 221 7 They do this by inserting complex memory 

20 

21 Page Ref No. Keyword = "concentration" 

22 ____ _______ _________________________ 

23 

24 10 4 integrated concentration and observed the fitted 

25 10 10 hour or twelve hour arc concentration on that 

26 12 6 concentration. For Bias, we have just mean bias 

27 44 8 the concentration was smaller. On advice from 

28 44 17 changes in concentration: 20, 12 and 4 were very 

29 45 3 fairly consistent decrease in the concentration. 

30 53 23 itself. Like concentration if you want to limit 

31 53 24 to a certain concentration you can do that as 

32 63 16 concentration trends with distance and maximum 

33 64 17 like the robust highest concentration. For 

34 65 2 Concentration, or the RHC, represent a smoothed 

35 69 10 have a cross wind concentration like this you 

36 69 13 concentration and so on. 

37 73 6 peak of the concentration distribution, unpaired 

38 73 11 concentration distributions paired in time and 

39 74 3 concentration at each arc not the individual 

40 76 5 concentration of AERMOD across the gridded 

41 76 7 the actual monitored concentration. It actually 

42 77 9 observed concentration goes up it’s often highly 

43 77 13 observed concentration. That certainly suggests 

44 77 24 you will be expected high concentration under 

45 78 6 but the model concentration with that data 

46 78 12 concentration. This period stood out initially I 

47 82 11 concentration and the lighter blue is the model 

48 82 12 concentration from AERMOD. Again most of this is 

49 83 13 0.7 meters verses concentration process with the 

50 83 21 produced the higher concentration was the from 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 246 

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 3 Page Ref No. Keyword = "concentration" 

 4 ____ _______ _________________________ 

 5 

 6 90 11 metric concentration really captured the plume 

 7 97 18 derivative of the concentration change which is 

 8 98 12 want to get the concentration at a particular 

 9 99 25 simultaneous meteorology and concentration grids. 

10 100 22 concentration grid and the chemistry solution is 

11 100 23 run. The concentration change is linearly 

12 102 23 provide the concentration background and combine 

13 105 10 a growing concentration distribution in the 

14 106 24 you’re in you have a mean concentration and the 

15 106 25 mean concentration would be the top hat. It 

16 108 25 concentration in any grid cell will be the mass 

17 119 20 and predict the mean concentration and give 

18 120 21 concentration distribution belong to a “piece” of 

19 124 15 wind integrated concentration (CIC). Very 

20 132 25 background concentration. 

21 133 10 model results compared with CO concentration 

22 144 7 a 24 hour average [ed. concentration] (inaudible) 

23 167 18 specific concentration data sets. The use of 

24 174 2 3) Plume NOx Concentration 

25 176 12 going to see sharp concentration gradients 

26 185 5 concentration for SO2). NARR run within 5% of 

27 194 16 increase of concentration as if the hill weren’t 

28 205 3 these “design concentration” predictions. 

29 226 24 what kind of concentration are the result of the 

30 

31 Page Ref No. Keyword = "concentrations" 

32 ____ _______ __________________________ 

33 

34 10 5 maximum concentrations on that arch. That method 

35 26 11 concentrations for 15 hours on the arc. So what 

36 39 13 concentrations go. Is that really true or it is 

37 40 21 the concentrations went up with finer resolution, 

38 41 11 km show a decrease in concentrations. There have 

39 57 10 statistics available. And also concentrations, 

40 63 17 concentrations on tracer arcs that are used for 

41 64 9 ground-level concentrations used for compliance 

42 65 3 estimate of the highest concentrations (from Cox43 65 9 hopefully the peak concentrations are close to 

44 65 12 range of the moderate concentrations we are a 

45 66 15 observed and predicted concentrations where an FB 

46 69 21 concentrations represent averages over a grid 

47 69 22 volume, but observed concentrations represent 

48 74 4 concentrations that each receptor along the arc. 

49 76 11 concentrations from the monitor. This just shows 

50 77 2 all this is a plot again concentrations a time 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 247 

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 3 Page Ref No. Keyword = "concentrations" 

 4 ____ _______ __________________________ 

 5 

 6 78 5 down, calms go up, observed concentrations go up 

 7 79 19 where they had the PM 2.5 concentrations and 

 8 81 6 comparisons showed AERMOD concentrations 

 9 81 8 concentrations at 3 Atlanta monitors. An initial 

10 83 9 Q-Q plot of modeled concentrations using SEARCH. 

11 83 10 These are AERMOD concentrations using the SEARCH 

12 84 14 concentrations from refineries in Texas for 

13 84 19 concentrations. We recommended using 1-minute 

14 97 12 get air concentrations, deposition; and some 

15 99 22 modeling air concentrations, it is from 

16 106 4 particle concentrations you can see from the 

17 108 23 concentrations? Well each particle if you’re 

18 111 11 measuring concentrations over the US and I just 

19 111 18 about the right, but concentrations a little bit 

20 113 24 measured concentrations; the Kolomogorov-Smirnov 

21 124 12 maximum concentrations with little overall bias 

22 124 19 maximum concentrations and some displacement of 

23 124 20 location of peak concentrations. 

24 126 22 model provides background concentrations to the 

25 126 25 concentrations are adjusted. There’s a two way 

26 129 25 to average July PM2.5 sulfate concentrations. The 

27 130 24 concentrations. CMAQ-AERO3-APT predicts lower 

28 132 4 concentrations near roadways. Exposure levels 

29 132 17 and benzene concentrations from roadway 

30 132 22 Concentrations are calculated at discrete 

31 132 24 concentrations with the grid-cell average 

32 133 9 qualitative evaluation of CO concentrations from 

33 138 20 ozone concentrations, no need for a constant 

34 139 2 concentrations, spatial/temporal representation 

35 139 3 of ammonia and nitric acid concentrations and 

36 143 15 example of what the concentrations look like. 

37 147 6 concentrations, deposition and visibility. 

38 149 20 contributions to 2009 annual PM2.5 concentrations. 

39 167 2 concentrations in the presence of ambient air 

40 167 3 ozone concentrations. This should be performed 

41 167 12 CMAQ) for estimating air quality concentrations. 

42 175 6 developing nitrate concentrations in excess 

43 175 17 formation was limited by NH3 concentrations. 

44 175 23 towards over predicting NO3 concentrations. 

45 177 3 concentrations the maximum measured there’s 

46 177 5 concentrations is certainly not enough to 

47 191 2 concentrations at ground level and on a nearby 

48 191 4 Dimensions gave lower concentrations and ones 

49 192 4 concentrations predictions. We ran five typical 

50 192 13 outside of the cavity region the concentrations 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 248 

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 3 Page Ref No. Keyword = "concentrations" 

 4 ____ _______ __________________________ 

 5 

 6 192 25 higher concentrations than currently predicted in 

 7 193 10 here. That was given the highest concentrations. 

 8 193 11 The lower concentrations are these two here. 

 9 193 14 concentrations looked like in the wind tunnel. 

10 193 17 of vortex is increasing the concentrations by 

11 194 3 concentrations increased by a nearly a factor of 

12 198 23 background concentrations – how to treat, and 

13 203 8 source impact concentrations. If daily 

14 203 9 background concentrations are not available, fill 

15 203 16 need to be resolved. Background concentrations 

16 203 17 can be much higher than modeled concentrations. 

17 

18 Page Ref No. Keyword = "convective" 

19 ____ _______ ______________________ 

20 

21 123 18 convective conditions 

22 124 2 •Mostly convective conditions 

23 

24 Page Ref No. Keyword = "data" 

25 ____ _______ ________________ 

26 

27 7 25 meteorological and tracer databases for 

28 8 5 archive of these data sets. So the first goal 

29 11 2 This data set and these programs on the NOAA ARL 

30 11 12 those performance those data base out there to 

31 14 25 meteorological database for use with LRT model 

32 15 5 have the MM5 data that was run up there and have 

33 15 7 can go on the web site and get that data and do 

34 15 11 had mentioned yesterday in trying to get the data 

35 15 15 meeting, we had all the data assembled that I was 

36 15 19 data sets and get those out there. So that was 

37 15 21 That’s the ultimate goal to get those data sets 

38 16 2 SCRAM for the evaluation data sets for the 

39 16 3 developmental data sets that were used for 

40 16 24 assembled tracer database. Then like I said 

41 17 2 LRT models for the assembled tracer database to 

42 18 10 these tracer data bases looking at both 

43 18 13 to supply meteorological data to CALPUFF? As you 

44 18 23 different ways in which we supply data to CALMET 

45 20 5 include in the database. 

46 21 2 to take the meteorological data from MM5 and 

47 22 22 and this is one of the data sets that we’re 

48 23 23 validate the MM5 data and that’s something we 

49 30 11 develop a database which could be used for model 

50 31 4 NCEP Reanalysis Data and was consistent with what 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 249 

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 3 Page Ref No. Keyword = "data" 

 4 ____ _______ ________________ 

 5 

 6 31 25 same meteorological data. 

 7 34 18 terms of the statistical data. It did marginally 

 8 35 2 here to draw any conclusions from current data. 

 9 35 18 data, horizontal, and vertical grid 

10 37 10 data availability necessary. Clearly we need to 

11 46 18 described understanding what data he’s working 

12 51 6 charge, license free. One is a database called 

13 51 16 an entire gridded data set. We’re just using 

14 52 6 generate database records and then those records 

15 52 7 go into the MySQL database. In essence we are 

16 52 8 jus populating the database with model 

17 52 11 all the data and observation are in the database 

18 52 13 pre-generated scripts to query that database, 

19 52 14 poll the type of data you want and then create 

20 53 4 the MM5 or WRF and here it’s a meta data set that 

21 53 8 they do in the database. 

22 53 11 Data stored in relational database which is great 

23 53 12 because one it puts all your data in a single 

24 53 15 database and treated the same way. The real 

25 53 16 power is it allows data queries based on many 

26 53 21 sites, you can do it by pretty much any met data 

27 53 22 you can query by. You can also query by the data 

28 55 6 whatever your data set is and this is what gets 

29 55 9 see the distribution and wind speed in your data 

30 55 16 this is .... don’t worry about the data showed 

31 56 14 different data. Scatter plots this includes 

32 57 15 also include another model data so you could 

33 57 18 you can ... the behavior of the data across the 

34 57 22 specific to some of the data available for 

35 58 4 whatever your data or skip a type of plot you 

36 58 15 into the database and analysis just like you 

37 58 16 would any other database. Even if you are not 

38 58 18 have data generally in the common (inaudible) 

39 58 21 the database and analysis just like you would 

40 58 24 and bring it right into the database. 

41 59 2 can be used outside of data met. There are 

42 59 3 scripts so if you got data and you don’t want to 

43 59 5 database, take the R script and you can read it 

44 59 20 tutorial data and example output plots and then 

45 60 6 locally and accesses remote database. It would 

46 60 8 script database. Hopefully we can do some 

47 60 13 what met data you put in you can use as a query 

48 62 22 I will also mention some evaluation databases 

49 63 4 provided that database to EPA for AERMOD. And we 

50 63 7 evaluation of low wind speed databases with API 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 250 

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 3 Page Ref No. Keyword = "data" 

 4 ____ _______ ________________ 

 5 

 6 63 11 types for evaluation of databases. One involves 

 7 63 23 where the other type of database -- the long-term 

 8 64 6 database. 

 9 64 12 similar models? Evaluation databases were a 

10 64 21 for both types of evaluation databases. Residual 

11 64 25 databases. Estimates of Robust Highest 

12 65 6 only used for tracer databases. 

13 66 20 sampling of data used to determine confidence 

14 67 5 databases. 

15 67 18 best suited to tracer databases and is widely 

16 67 25 different kinds of data pairings 

17 68 16 is a plot of the various data values such that if 

18 70 4 lot of archived databases, but unfortunately the 

19 70 10 databases. You probably can’t see this, but you 

20 70 12 over a 100 database references. For the existing 

21 70 13 data, I would like somehow to make sure with EPA 

22 70 15 Literally, these are about a hundred databases, 

23 70 17 these databases. 

24 70 19 gridded meteorological data. It’s almost like a 

25 70 20 new concept do we trust MM5 data instead of a 

26 70 22 analysis the gridded met data. There be may be 

27 71 3 meteorological data. 

28 71 17 Sources of data for testing that I would 

29 71 19 tower data, not just surface data because a lot 

30 71 22 the data has been provided to the agencies are 

31 72 2 databases be used for the independent assessment 

32 72 3 for the evaluation of the gridded met data. That 

33 73 19 one of the best databases ever collected back in 

34 74 10 tall stack or evaluation data base that was used 

35 75 10 airport data and with the SEARCH data sets that 

36 75 13 and the SEARCH met data. The model seemed to be 

37 75 19 only data which that blue line down near zero and 

38 75 20 you have the SEARCH data. As you can see there’s 

39 77 3 series plot based on airport data the light blue 

40 77 21 data period missing either to calms or winds. 

41 78 3 data for that applications because you can see a 

42 78 6 but the model concentration with that data 

43 78 9 one ASOS data which we shared. 

44 78 11 is with the SEARCH data showing a high 

45 78 15 data. Just looking at the wind direction 

46 79 14 show up at all with this standard airport data 

47 79 22 is that when you use the airport data under the 

48 80 11 data was in the wrong direction and was basically 

49 80 17 airport data and 25-30% is calm those results may 

50 81 10 process airport data was not representative of 
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 3 Page Ref No. Keyword = "data" 

 4 ____ _______ ________________ 

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 6 81 12 suggestion was to re-process airport data with 1m 

 7 81 17 process SEARCH met data as more representative of 

 8 83 11 data process with surface characteristics using 

 9 83 14 airport data with the 1-minute ASOS 

10 83 20 But interestingly enough the met data that 

11 84 16 from standard ISHD airport data showed 

12 84 20 ASOS wind data to reduce the number of calms, 

13 84 23 met data resulted in selection of another nearby 

14 86 11 different met data and different source 

15 86 14 data and including some Sigma Z so this is 

16 86 18 Hybrid met data about 5.96 so we’re getting 

17 89 5 evaluation databases and what types of sources 

18 89 21 any good databases to look at especially low 

19 90 2 collect the data to show well we’re not causing 

20 90 14 off the work he’s doing. Other databases out 

21 100 6 high resolution terrain it would use that data 

22 101 9 model used was rawinsonde data with day/night 

23 101 11 to gridded meteorological data. Based on the 

24 101 13 could do a better job using meteorological data 

25 101 21 rawinsonde data was really insufficient to 

26 114 14 one of them had different meteorological data 

27 114 17 gridded data so when we were doing later 

28 114 21 site download and convert that data so that you 

29 114 23 have a consistent meteorological database that is 

30 114 25 can go back and look at the old data and see how 

31 115 11 there’s lots of data. This is available on our 

32 118 5 database (web) and not the PC version; GIS-like 

33 123 12 different data sets which included: 

34 124 13 and nearly all data points within factor of two 

35 124 21 Kincaid used QI=3 (highest quality) data 

36 125 4 in determining its performance in other data 

37 134 9 to evaluate it with available data. Over 150 

38 135 3 and data sources like VISTAS; Atmospheric 

39 137 9 VOC, SOx, PM and toxics and use data science 

40 141 22 offers speciated data so it can figure out the 

41 147 20 Databases more costly to develop. MM5/WRF 

42 148 13 PGM Databases and model set ups. RPOs, AIRPACT, 

43 148 22 coarse grid data. Allows fine grid treatment of 

44 150 11 2002 36 km modeling CAMx database. Add 12 km 

45 151 6 SIP projections. ASIP 36/12 km database 

46 151 15 2002 database, 12/4 km domain with two-way nested 

47 159 22 meteorological data and land use variations. Can 

48 160 5 topography, wind persistence data and land use 

49 161 11 models vs. field study data sets; and possible 

50 161 15 and Penn State MM5 Met model data assimilation 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 252 

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 3 Page Ref No. Keyword = "data" 

 4 ____ _______ ________________ 

 5 

 6 161 25 field study data sets; possible new field 

 7 162 5 Met model data assimilation methods). Assess if 

 8 162 12 We’ll talk some more about data gathering in 

 9 162 13 Wyoming and we’d like to see databases developed 

10 162 14 further which would provide monitoring data and 

11 162 15 emissions data inventory. 

12 165 16 test with appropriate field data sets; improve 

13 166 8 sources that use airport data, the dispersion 

14 166 16 output should summarize the processed met data so 

15 166 18 if that year of data is suitable for regulatory 

16 167 9 monitoring data combined with statistical 

17 167 18 specific concentration data sets. The use of 

18 168 19 evaluation using the existing databases and/or 

19 171 13 look at it against observational data and 

20 173 2 included in the monitoring data which is 

21 176 17 the improved monitoring data at Bridger over 

22 179 2 in the monitoring data. 

23 179 3 Monitoring data versus CALPUFF, 80,000 

24 179 14 There are currently data sets being 

25 179 19 starting to build some databases here, but 

26 180 20 for use of NOAA reanalysis data. 

27 181 2 There are reanalysis data assets outside 

28 181 18 Reanalysis data is a dynamically consistent 

29 181 21 based off of observed data and not a 

30 181 23 NOOA cycles their models with initial data 

31 181 25 far back as 1948 to create a reanalysis data 

32 182 4 reanalysis data set that goes back to 1979 

33 182 9 Potentially a source for site specific data - 

34 182 24 some of Canada. This is a reanalysis data set 

35 183 2 data set goes back to 1948 and is available at 

36 183 4 might get some more site specific data but if 

37 183 5 you use the North American Reanalysis data and 

38 183 8 attractive at least in upper air data source 

39 183 11 pull some of this data in and run it through 

40 183 15 calms verses what the observed data might have. 

41 183 21 required by a sounding. But in our data of 

42 183 23 the vertical resolution of the upper air data 

43 184 8 the gridded data and put it in a text format. 

44 184 20 It was just a data set for me and I used it. 

45 184 22 data (and on site data). Re-run with NARR (ed. 

46 184 24 data extracted from NARR grid and interpolated 

47 185 3 All other data remained consistent with the 

48 185 7 2nd high. Receptor location and data of 1st and 

49 185 17 requirements of surface data for AERMOD. 

50 189 10 Snyder/Lawson Data Base Flow Region (i.e., Data 
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 3 Page Ref No. Keyword = "data" 

 4 ____ _______ ________________ 

 5 

 6 192 6 the five sources. 1 year met data kind of a 

 7 195 6 meterological data on the air quality model which 

 8 195 19 the use to concert MM5 data and WRF and 

 9 195 24 specific data sets. It’s one way of reducing the 

10 196 6 change the wind data. It’s exactly as it came 

11 196 20 The other thing is producing met data sets 

12 196 23 Evaluate gridded meteorological data performance 

13 197 2 dispersion model to met database. Separately 

14 197 9 Then separately use the data sets to 

15 197 18 prognostic data. 

16 197 21 the data set to allow sub hourly prognostic data. 

17 197 25 sub hourly data has its advantage. 

18 198 5 times higher results using prognostic data than 

19 198 8 data? No under-prediction bias relative to 

20 202 15 databases. Certain industry groups have also 

21 202 16 reviewed stack test data to develop emission 

22 204 23 ASOS use of sonic anemometer data and averaging 

23 204 24 of sub-hourly ASOS data will likely create more 

24 205 6 traffic itself. Review of data from tracer 

25 207 14 states advocating use of more recent data sets. 

26 207 15 Many more calms in recent data sets – if 

27 207 20 recent met data. If my interpretation of the 

28 207 22 is considered a missing data? Is that right? 

29 221 3 What is the problem? Movement of data is 

30 222 6 latency. Cache space is wasted when data resides 

31 222 7 there but is unused. Unused data in cache 

32 222 16 a 16KB L1 data cache and 1MB L2 cache with 

33 222 20 performance data (for details see HiPERiSM’s Web 

34 222 24 bars shows no wasted cache space – i.e. all data 

35 223 5 bars shows wasted cache space – i.e. data loaded 

36 223 11 performance. Linux platform with a 16KB L1 data 

37 223 16 cache space – i.e. data loaded from memory but 

38 224 4 Requires too much disorganized data movement. 

39 224 5 Next generation hardware requires data 

40 226 7 ways for extracting data from MM5 and WRF file 

41 226 22 profiles with data collected on met towers. And 

42 

43 Page Ref No. Keyword = "database" 

44 ____ _______ ____________________ 

45 

46 14 25 meteorological database for use with LRT model 

47 16 24 assembled tracer database. Then like I said 

48 17 2 LRT models for the assembled tracer database to 

49 20 5 include in the database. 

50 30 11 develop a database which could be used for model 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 254 

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 3 Page Ref No. Keyword = "database" 

 4 ____ _______ ____________________ 

 5 

 6 51 6 charge, license free. One is a database called 

 7 52 6 generate database records and then those records 

 8 52 7 go into the MySQL database. In essence we are 

 9 52 8 jus populating the database with model 

10 52 11 all the data and observation are in the database 

11 52 13 pre-generated scripts to query that database, 

12 53 8 they do in the database. 

13 53 11 Data stored in relational database which is great 

14 53 15 database and treated the same way. The real 

15 58 15 into the database and analysis just like you 

16 58 16 would any other database. Even if you are not 

17 58 21 the database and analysis just like you would 

18 58 24 and bring it right into the database. 

19 59 5 database, take the R script and you can read it 

20 60 6 locally and accesses remote database. It would 

21 60 8 script database. Hopefully we can do some 

22 63 4 provided that database to EPA for AERMOD. And we 

23 63 23 where the other type of database -- the long-term 

24 64 6 database. 

25 70 12 over a 100 database references. For the existing 

26 114 23 have a consistent meteorological database that is 

27 118 5 database (web) and not the PC version; GIS-like 

28 150 11 2002 36 km modeling CAMx database. Add 12 km 

29 151 6 SIP projections. ASIP 36/12 km database 

30 151 15 2002 database, 12/4 km domain with two-way nested 

31 197 2 dispersion model to met database. Separately 

32 

33 Page Ref No. Keyword = "databases" 

34 ____ _______ _____________________ 

35 

36 7 25 meteorological and tracer databases for 

37 62 22 I will also mention some evaluation databases 

38 63 7 evaluation of low wind speed databases with API 

39 63 11 types for evaluation of databases. One involves 

40 64 12 similar models? Evaluation databases were a 

41 64 21 for both types of evaluation databases. Residual 

42 64 25 databases. Estimates of Robust Highest 

43 65 6 only used for tracer databases. 

44 67 5 databases. 

45 67 18 best suited to tracer databases and is widely 

46 70 4 lot of archived databases, but unfortunately the 

47 70 10 databases. You probably can’t see this, but you 

48 70 15 Literally, these are about a hundred databases, 

49 70 17 these databases. 

50 72 2 databases be used for the independent assessment 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 255 

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 3 Page Ref No. Keyword = "databases" 

 4 ____ _______ _____________________ 

 5 

 6 73 19 one of the best databases ever collected back in 

 7 89 5 evaluation databases and what types of sources 

 8 89 21 any good databases to look at especially low 

 9 90 14 off the work he’s doing. Other databases out 

10 147 20 Databases more costly to develop. MM5/WRF 

11 148 13 PGM Databases and model set ups. RPOs, AIRPACT, 

12 162 13 Wyoming and we’d like to see databases developed 

13 168 19 evaluation using the existing databases and/or 

14 179 19 starting to build some databases here, but 

15 202 15 databases. Certain industry groups have also 

16 

17 Page Ref No. Keyword = "datum" 

18 ____ _______ _________________ 

19 

20 15 24 themselves similar to the datum web site and 

21 

22 Page Ref No. Keyword = "default" 

23 ____ _______ ___________________ 

24 

25 58 9 default AQ side and then the Soccer Goal Plot is 

26 163 5 heavily on default values. Need to resolve met 

27 192 7 standard AERMOD default mode. And we ran with 

28 

29 Page Ref No. Keyword = "DEM" 

30 ____ _______ _______________ 

31 

32 42 22 Service because they wanted some demonstrations, 

33 46 6 and stay away from this EPA has demanded stuff. 

34 75 7 heard about this the Alabama DEM study for the 

35 87 13 that more robust demand on its performance. 

36 120 11 computationally demanding and there is more 

37 193 3 To demonstrate that I have a couple of 

38 202 17 factors. EPA demonstrates possible approach in 

39 209 4 demonstrated. CALPUFF is a model with community 

40 209 17 demonstrate CALPUFF in near field areas. 

41 211 5 Requiring equivalency demonstrations of less 

42 

43 Page Ref No. Keyword = "dispersion" 

44 ____ _______ ______________________ 

45 

46 16 10 system we’re talking about here. The dispersion 

47 18 4 recommend turbulence based dispersion (CALPUFF 

48 20 4 Dispersion thing we’d like to get hold of that to 

49 67 21 within Atmospheric Dispersion Modelling for 

50 70 25 dispersion modeling are how often are the winds 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 256 

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 3 Page Ref No. Keyword = "dispersion" 

 4 ____ _______ ______________________ 

 5 

 6 71 11 dispersion. The use of better meteorology got 

 7 71 12 the plume dispersion predictions in CALPUFF 

 8 72 25 requirements of operational Regulatory Dispersion 

 9 97 11 method; how to simulate plume dispersion, how to 

10 97 20 flow and dispersion across the interface and you 

11 99 2 and that means forward trajectory or dispersion. 

12 99 18 dispersion. The dispersion is computed using 3D 

13 100 19 sources and do the computation of dispersion and 

14 102 2 to the dispersion code because the interest 

15 104 15 driving the dispersion process. If you added any 

16 104 18 dispersion. 

17 106 12 really seeing any dispersion here because we’re 

18 107 13 turbulent dispersion. That u-prime is computed 

19 111 20 just standard transport and dispersion. 

20 115 6 you find changing dispersion in the model and 

21 119 14 alternative or an option to treat dispersion in a 

22 120 4 meteorological and dispersion conditions, 

23 120 5 causality effects, low wind speed dispersion, 

24 120 7 variability in dispersion rates, etc. Lagrangian 

25 120 23 cluster dispersion puff model where a puff is a 

26 120 25 concept of relative dispersion (due to turbulent 

27 121 8 relative dispersion but update frequency of flow 

28 121 10 covered by relative dispersion concept. PPM uses 

29 121 11 a full stochastic Lagrangian particle dispersion 

30 121 17 relative dispersion. Every puff carries along 

31 121 24 trajectories. Two contributions of dispersion 

32 121 25 process are the relative dispersion (small 

33 123 5 Relative dispersion ~ same as absolute 

34 123 6 dispersion. At that point, the parent puff 

35 123 10 way using absolute dispersion. 

36 124 7 part of European short-range dispersion model 

37 124 17 Lagrangian particle dispersion model (LPFM) 

38 126 13 about yesterday – the early plume dispersion and 

39 126 14 the mid-range plume dispersion, and the grid 

40 127 22 order closure approach for plume dispersion and 

41 136 9 modeling systems whether it’s dispersion, or 

42 136 15 a dispersion model, simple photochemical box 

43 138 7 with photochemical models. The dispersion model 

44 147 12 source plume chemistry and dispersion? 

45 147 16 and dispersion. Need many grid cells to assess 

46 148 6 plume chemistry and dispersion without providing 

47 151 9 treat chemistry and dispersion of point source 

48 151 17 chemistry and dispersion. PM Source 

49 160 11 time varying dispersion models (e.g., AERMOD, 

50 161 10 performance of Met models coupled with dispersion 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 257 

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 3 Page Ref No. Keyword = "dispersion" 

 4 ____ _______ ______________________ 

 5 

 6 161 24 Met models coupled with dispersion models vs. 

 7 162 22 terrain, short term puff dispersion, chemical 

 8 164 5 improvements in regional dispersion model 

 9 166 3 that the dispersion modeling domain is dominated 

10 166 8 sources that use airport data, the dispersion 

11 167 11 detailed dispersion models (AERMOD, CALPUFF, or 

12 167 14 use of dispersion models that are based on 

13 167 20 dispersion models should be reviewed by an expert 

14 168 17 meteorological models to drive dispersion models. 

15 180 4 output must be tested for each dispersion 

16 189 6 location to get correct dispersion. Does not 

17 190 19 really match the dispersion for that whole 

18 190 21 matches the dispersion. It’s much closer to the 

19 196 24 separately from dispersion model performance. 

20 197 2 dispersion model to met database. Separately 

21 197 7 dispersion model to different variables. Model 

22 

23 Page Ref No. Keyword = "domain" 

24 ____ _______ __________________ 

25 

26 23 25 extensively. We have domain wide statistics that 

27 43 22 So I created a 12 km domain and ran CALMET just 

28 71 23 now in the public domain. There are numerous 

29 97 21 have to solve the entire domain. 

30 129 22 modeling domain for the application and locations 

31 133 7 shows the grid model domain. 

32 134 15 This slide shows the modeling domain for the 

33 134 17 can see it is a very large domain with a large 

34 151 15 2002 database, 12/4 km domain with two-way nested 

35 151 21 This is our CAMx 12/4 km domain nested 

36 151 22 within ASIP 12 km CMAQ domain (one-way nesting). 

37 159 23 you hear me? Okay. What is the minimum domain 

38 166 3 that the dispersion modeling domain is dominated 

39 166 7 the pollutant source domain. For most pollutant 

40 166 9 model domain is going to be entirely dominated by 

41 166 13 characteristics of the pollutant source domain. 

42 182 12 sets. Public domain (data and conversion 

43 

44 Page Ref No. Keyword = "downwash" 

45 ____ _______ ____________________ 

46 

47 85 25 with the PRIME downwash algorithms since it 

48 88 23 of the model to deal with downwash that’s more 

49 91 4 being considered downwash structures. In GEP 

50 91 7 downwash structures. There seems to be some 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 258 

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 3 Page Ref No. Keyword = "downwash" 

 4 ____ _______ ____________________ 

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 6 188 19 building and downwash issues. Basically the main 

 7 189 11 Base Used to Develop Downwash Algorithms). Other 

 8 189 12 considerations are building downwash algorithms 

 9 189 14 buildings. Building downwash algorithms in 

10 

11 Page Ref No. Keyword = "downwind" 

12 ____ _______ ____________________ 

13 

14 64 23 conc vs. downwind distance or wind speed, etc. 

15 69 8 conditions as atmospheric stability or downwind 

16 86 23 one was pretty much downwind from one of the 

17 131 19 especially in Pennsylvania downwind of the 

18 147 17 downwind impacts. High computer resource 

19 148 3 fine grid over sources with coarser grid downwind 

20 148 19 sources with coarser grids downwind where plumes 

21 192 19 effect as you move further downwind becomes less. 

22 

23 Page Ref No. Keyword = "EPA" 

24 ____ _______ _______________ 

25 

26 8 10 transport (LRT) models used by the EPA. 

27 8 24 that you can find on the EPA web site are done by 

28 12 10 and then we’ve added additional EPA metrics, the 

29 14 6 the 8th Modeling Conference – EPA recognized the 

30 14 13 reflected in the EPA long range guidance. 

31 17 6 to updating existing EPA LRT modeling guidance 

32 17 18 200-300 km? At the 7th Modeling Conference, EPA’s 

33 18 6 mentioned, back in 2006, EPA issued a Model 

34 23 20 necessarily wed to EPA (inaudible) scheme but 

35 35 17 EPA in 1997 despite using same raw meteorological 

36 35 24 The two major differences from original EPA 

37 37 19 Let me just mention where are we at from the EPA 

38 39 8 Scire or EPA. There is guidance for grid spacing 

39 41 5 memo distributed by EPA that specifically said 

40 41 12 been recent studies conducted by EPA to document 

41 41 16 that was supposedly issued by EPA that was 

42 42 12 changes with the EPA or the FLM or the other two 

43 43 5 to 1,000 meters and provide that to EPA, the FLMs 

44 45 17 that EPA has issued memos or these tests have 

45 46 6 and stay away from this EPA has demanded stuff. 

46 47 19 or within EPA with our Office of Research and 

47 48 5 EPA (inaudible) Office of Research and 

48 48 17 work that we put forward as EPA in doing these 

49 50 18 modeling division in ORD here at EPA. And as 

50 61 17 basically did the development internally at EPA 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 259 

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 3 Page Ref No. Keyword = "epa" 

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 6 61 23 Tyler Fox: Just a side note we at EPA will 

 7 62 24 over to EPA, and also a brief comment on the 

 8 63 4 provided that database to EPA for AERMOD. And we 

 9 63 8 funding and working with EPA on that issue as 

10 70 13 data, I would like somehow to make sure with EPA 

11 70 16 so it would be nice for EPA to take ownership of 

12 71 9 North Dakota, we found that the EPA model missed 

13 91 14 To come up with the EPA guidance, I think it 

14 93 20 the EPA regions have been tasked with providing 

15 127 18 alternative model recommended by EPA on a case16 128 2 embedded in MAQSIP, the precursor to the U.S. EPA 

17 128 18 developed: one including the EPA treatment of PM 

18 129 6 PM treatment and the EPA PM treatment which is 

19 135 2 COM; Parallelization Insights: David Wong, EPA; 

20 148 14 SIPs, etc. and EPA has been developing stuff. 

21 149 3 (for EPA/OAQPS) 

22 156 8 for EPA. Basically Appendix W Guidance on 

23 158 23 issues relating to aspects of the EPA’s Guideline 

24 159 10 general concern that API has more EPA Guidance 

25 159 12 have seen a lot of response from EPA even before 

26 160 15 resolution. We’d like to see EPA reach out to 

27 161 19 AQM. EPA should work with other agencies (DTRA, 

28 162 25 that EPA modify the chemistry, based on API/AER 

29 163 18 underway. EPA should lead the effort with 

30 164 4 we knew all the things EPA is doing. Recent 

31 164 6 performance measures have been made; EPA efforts 

32 165 7 makers. EPA should investigate and possibly make 

33 165 14 improvements and draft documents produced and EPA 

34 165 22 Based on EPA guidance, EPA limits the 

35 166 23 Ratio Model (PMVRM. We like for EPA to further 

36 167 6 EPA has asked questions and asked for advice on 

37 167 13 EPA should promote consistent and general 

38 167 22 stakeholder communities. We’d like to see EPA 

39 168 7 internal EPA and outside models) and of proposed 

40 169 4 emphasize a couple of things. One is EPA 

41 170 22 EPA guidance. 

42 171 6 communicate to EPA some of the things we 

43 171 20 going to challenge EPA here is that in the 

44 180 5 model application; EPA needs to coordinate a 

45 193 24 EPA research shows that the effect of upwind 

46 194 13 EPA wind tunnel where they showed these terrain 

47 200 9 quantified, and modeled based on EPA Reference 

48 200 12 underway to propose more reliable methods. EPA 

49 200 20 EPA had a PM10 surrogate policies for compliance 

50 202 14 source types from EPA’s AP-42, SPECIATE, and FIRE 
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 3 Page Ref No. Keyword = "epa" 

 4 ____ _______ _______________ 

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 6 202 17 factors. EPA demonstrates possible approach in 

 7 203 23 issues remain unresolved for PM2.5 – little EPA 

 8 205 9 EPA to pursue. Problems - few long-term monitors 

 9 205 18 intuitive results. EPA should investigate this 

10 205 22 as is done with CALPUFF. EPA should develop a 

11 206 12 Paine provided EPA (in October 2007) with several 

12 206 16 Enhanced debugging output. EPA should make these 

13 208 17 your needs. About EPA concerns about CALPUFF and 

14 208 18 EPA controlling the model developing coding and 

15 209 13 EPA concerns about CALPUFF are relatively 

16 209 14 unfounded. EPA’s concern about near field 

17 209 18 Substantial resources from EPA will be needed to 

18 209 22 EPA doesn’t have direct control of CALPUFF and 

19 209 23 there are some advantages to that. EPA does have 

20 210 6 advancing CALPUFF and will work to that end. EPA 

21 210 8 and there are some disadvantages. EPA has not 

22 210 10 updates that EPA wants, but the developer is 

23 210 11 willing to do this. As a result EPA says that 

24 210 15 the EPA approval for the code. There are code 

25 210 16 changes made without EPA oversight and funding 

26 210 17 that requires EPA review. What is needed is for 

27 210 18 EPA to review the code changes that are 

28 210 20 EPA and TRC to keep the string going of improving 

29 212 25 participated in all of the EPA modeling 

30 213 10 guideline and how EPA interprets it and applies 

31 213 14 EPA’s September 25th federal registry notice 

32 213 17 that EPA is planning to make to the Modeling 

33 214 2 EPA is planning. If EPA wants meaningful 

34 214 6 made. It’s not sufficient for EPA to place a 

35 214 10 meeting. Rather, EPA must publish notice of 

36 214 19 outdated portion of the modeling guideline, EPA 

37 214 23 lightly out of EPA. There are instances where it 

38 214 25 Preceding this conference, EPA posted 

39 215 5 to get approval for the use of non EPA preferred 

40 215 6 models. EPA’s new procedures will uniformly make 

41 215 8 preferred models or EPA developed models. 

42 215 16 of comment, EPA has concluded in its guidance 

43 216 6 UARG believes that the EPA’s recently posted 

44 216 20 use of any models other than EPA preferred or EPA 

45 216 24 EPA to use informal guidance documents to make 

46 217 8 of changes to fix bugs and problems, both EPA 

47 217 14 For years EPA has done an admirable job in 

48 217 16 particular EPA has made timely fixes to their 

49 217 19 founded fixes, EPA has a history of approving and 

50 217 22 EPA review of implementation fixes for identified 
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 5 

 6 217 25 a year in EPA’s consideration and approval of 

 7 218 4 has identified in AERMOD. URAG encourages EPA to 

 8 218 25 a surrogate. But now with the EPA delegated 

 9 219 5 So we urge EPA to take the time and 

10 220 3 5.U.S. EPA AQM models: lessons learned 

11 220 9 are developed by the U.S. EPA (and contractors). 

12 220 24 help and leadership. Does the U.S. EPA have a 

13 222 17 compilers typically used by the U.S. EPA (using 

14 222 18 EPA code for CMAQ and AERMOD). SlowSpotterTM 

15 224 9 Next Steps: U.S. EPA needs to show 

16 224 24 sponsored or funded by the U.S. EPA. Further 

17 225 6 development at the EPA and also the decision to 

18 

19 Page Ref No. Keyword = "ETA" 

20 ____ _______ _______________ 

21 

22 23 19 MM5 like ETA PBL and NOAH LSM. We’re not 

23 

24 Page Ref No. Keyword = "Federal" 

25 ____ _______ ___________________ 

26 

27 40 17 distributed these results to the Federal Land 

28 41 17 submitted to the Federal Land Managers? 

29 119 3 part of his Ph.D thesis at the Swiss Federal 

30 212 22 proceedings under the federal Clean Air Act. 

31 213 14 EPA’s September 25th federal registry notice 

32 213 24 the September 25 Federal Register notice provides 

33 214 11 these proceedings in the Federal Register at 

34 

35 Page Ref No. Keyword = "fence line" 

36 ____ _______ ______________________ 

37 

38 76 10 to the fence line being compared to 

39 88 7 impacts on the fence line are three or four 

40 146 9 talking about fence line impacts, we’re talking 

41 

42 Page Ref No. Keyword = "file" 

43 ____ _______ ________________ 

44 

45 56 17 file so it’s easily imported into EXCEL. Spatial 

46 185 21 process already tested Grib ==> Grid ==> Text File 

47 208 10 find it for you. I can’t open this file. 

48 226 7 ways for extracting data from MM5 and WRF file 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 262 

 2 

 3 Page Ref No. Keyword = "files" 

 4 ____ _______ _________________ 

 5 

 6 44 12 files at the same resolutions so I basically 

 7 101 3 Shape files, or Google Earth (kml), distribution: 

 8 195 23 get to the (inaudible) files and not to the 

 9 205 24 translate AERMOD conc. files to CALPUFF-like 

10 205 25 files. TRC may have a draft code that can do 

11 

12 Page Ref No. Keyword = "FLEXPART" 

13 ____ _______ ____________________ 

14 

15 20 22 called FLEXPART that’s widely distributed 

16 22 17 CALPUFF, FLEXPART and HYSPLIT and basically, what 

17 32 2 This is just a snap shot of the FLEXPART 

18 33 2 of it. All three models CALPUFF, FLEXPART and 

19 34 10 detected. So as you can see FLEXPART has a high 

20 94 25 HYSPLIT, FLEXPART), Computational Fluid Dynamics 

21 95 19 met model linked to FLEXPART. We are using it as 

22 95 21 Lagrangian particle model called FLEXPART. We 

23 

24 Page Ref No. Keyword = "gridded" 

25 ____ _______ ___________________ 

26 

27 51 16 an entire gridded data set. We’re just using 

28 62 25 gridded met evaluation. 

29 70 19 gridded meteorological data. It’s almost like a 

30 70 22 analysis the gridded met data. There be may be 

31 72 3 for the evaluation of the gridded met data. That 

32 76 5 concentration of AERMOD across the gridded 

33 101 11 to gridded meteorological data. Based on the 

34 101 17 that a gridded meteorological model might be 

35 114 17 gridded data so when we were doing later 

36 130 17 gridded approach will typically overestimate 

37 155 15 whether it’s a gridded wind field or (inaudible). 

38 184 8 the gridded data and put it in a text format. 

39 195 5 presentation about the use of gridded 

40 196 23 Evaluate gridded meteorological data performance 

41 

42 Page Ref No. Keyword = "group" 

43 ____ _______ _________________ 

44 

45 51 19 this group will do and I’ll get into that in a 

46 65 17 independent variable where we have group 

47 65 24 each group. For example, the significant points 

48 69 7 you do that is group them in regimes of similar 

49 145 12 This is just another group of sources 

50 146 14 more of a slide for another group since this 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 263 

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 3 Page Ref No. Keyword = "group" 

 4 ____ _______ _________________ 

 5 

 6 146 15 group knows the guidelines and the guidance. 

 7 149 16 Texas Group BART application. CAMx 36/12 km with 

 8 150 6 decided to do group analysis and run them in 

 9 180 6 stakeholder group to develop guidelines for 

10 186 11 review group, enter comment areas, and offer 

11 186 15 application group who are still at this meeting. 

12 187 7 group here, myself and all of the people have 

13 194 12 summaries by Snyder and some of the group at the 

14 210 5 AWMA supports an independent work group for 

15 212 17 Air Regulatory Group (UARG). UARG is an ad hoc 

16 212 18 group of public and private electric utility 

17 

18 Page Ref No. Keyword = "groups" 

19 ____ _______ __________________ 

20 

21 54 3 simulations for other groups. One group, in 

22 54 6 other groups doing it. We’re always trying to 

23 54 9 analysis among different groups. And then it’s 

24 139 8 single emissions sources or groups of emissions 

25 140 6 contributions from emissions source groups, 

26 146 8 source or groups of sources impacts. We’re not 

27 150 7 groups of 10. In each group Bart analysis of 10 

28 150 9 contributions of groups of Texas BART sources for 

29 202 15 databases. Certain industry groups have also 

30 

31 Page Ref No. Keyword = "guidance" 

32 ____ _______ ____________________ 

33 

34 8 14 evaluations and reflect that in our guidance. 

35 8 16 the update of the IWAQM and Phase 2 guidance is 

36 8 18 these evaluations to update that guidance. 

37 14 13 reflected in the EPA long range guidance. 

38 14 20 guidance which are not reflected in current 

39 14 21 guidance. So we initiated this long range 

40 17 6 to updating existing EPA LRT modeling guidance 

41 17 9 From the guidance goals basically what we said 

42 17 12 enhancements to model system in updated guidance 

43 18 3 The next question is can guidance migrate to 

44 39 8 Scire or EPA. There is guidance for grid spacing 

45 46 12 interpretation of guidance or decision in a 

46 85 22 of guidance or recommendations something from the 

47 85 23 implementation guidance is the SEARCH tank. 

48 87 8 in terms of providing better guidance. And how 

49 87 25 as far as impacts go, with no clear guidance on 

50 88 18 need to updated guidance or recommendations 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 264 

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 3 Page Ref No. Keyword = "guidance" 

 4 ____ _______ ____________________ 

 5 

 6 91 5 guidance, there seems to be suggestions that you 

 7 91 14 To come up with the EPA guidance, I think it 

 8 91 16 guidance with limited understanding of issues. 

 9 91 25 conform guidance. Guidance to lead at a starting 

10 92 5 guidance. So we need input from you all about 

11 92 21 up with the type of guidance you need. 

12 146 15 group knows the guidelines and the guidance. 

13 156 8 for EPA. Basically Appendix W Guidance on 

14 159 10 general concern that API has more EPA Guidance 

15 165 22 Based on EPA guidance, EPA limits the 

16 166 5 guidance is needed for translating the airport 

17 166 11 We’d like to see better guidance for translating 

18 168 12 Need to update and improve model guidance 

19 169 5 guidance, it’s just that and sometimes it’s 

20 169 10 But guidance is just that, guidance. Second 

21 169 11 point is that guidance we provide is only as 

22 169 15 issues here, guidance has to have a basis 

23 170 14 type of guidance that is needed. Providing 

24 170 15 guidance that is just complained about and 

25 170 17 better together that we can provide guidance 

26 170 22 EPA guidance. 

27 188 15 some guidance on this. That’s all I have right 

28 189 19 EBD guidance provided in Tikvart July 1994 

29 190 5 procedure and more guidance needed. Roger 

30 194 22 needed, update guidance on use of EBD in place of 

31 198 22 precursors, modeling techniques – guidance, 

32 200 22 Retrofit Technology implementation guidance, PM2.5 

33 202 2 PM2.5 Regulations and Guidance – Unresolved 

34 203 24 guidance, PSD increments and modeling procedures. 

35 206 6 guidance provide further implementation guidance 

36 207 19 artificially too low? Guidance needed on use of 

37 214 20 is moving toward using informal guidance to try 

38 214 22 today, I understand guidance does not come 

39 215 2 on its web site several guidance memoranda that 

40 215 9 For example, the August 13 guidance 

41 215 16 of comment, EPA has concluded in its guidance 

42 215 23 standards, the August 13th guidance document 

43 216 7 guidance memoranda have placed unfair burdens on 

44 216 15 The recent guidance document, (I apologize if I 

45 216 24 EPA to use informal guidance documents to make 

46 218 12 a decade but we still have very little guidance 

47 218 21 permitting there’s no clear guidance on the 

48 219 3 modeling and we still have no guidance on how to 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 265 

 2 

 3 Page Ref No. Keyword = "guide" 

 4 ____ _______ _________________ 

 5 

 6 59 13 users guide included which we have gotten good 

 7 88 19 for...there’s a table in the ISC users guide and in 

 8 88 20 the AERMOD users guide in terms of defining 

 9 109 24 want to turn that on. Refer to the guide for 

10 118 9 completely revised user’s guide with examples but 

11 146 15 group knows the guidelines and the guidance. 

12 147 7 Current guideline models have no (AERMOD) or 

13 158 23 issues relating to aspects of the EPA’s Guideline 

14 167 24 in the context of the guidelines, but it would be 

15 171 12 guideline models was to take a model and 

16 180 6 stakeholder group to develop guidelines for 

17 207 21 Guideline on Air Quality Models is right, a calm 

18 208 21 modeling for the guideline purposes require air 

19 210 13 last users guide was released 2006. We have a 

20 210 14 new users guide for Version 6 and all we need is 

21 213 4 revisions of Appendix W Guideline. The Modeling 

22 213 5 Guideline is used for several purposes, including 

23 213 10 guideline and how EPA interprets it and applies 

24 213 18 Guidelines to Appendix W. So our comments are 

25 214 19 outdated portion of the modeling guideline, EPA 

26 216 17 this), removes the Guideline’s promise of 

27 

28 Page Ref No. Keyword = "guideline" 

29 ____ _______ _____________________ 

30 

31 147 7 Current guideline models have no (AERMOD) or 

32 158 23 issues relating to aspects of the EPA’s Guideline 

33 171 12 guideline models was to take a model and 

34 207 21 Guideline on Air Quality Models is right, a calm 

35 208 21 modeling for the guideline purposes require air 

36 213 4 revisions of Appendix W Guideline. The Modeling 

37 213 5 Guideline is used for several purposes, including 

38 213 10 guideline and how EPA interprets it and applies 

39 214 19 outdated portion of the modeling guideline, EPA 

40 216 17 this), removes the Guideline’s promise of 

41 

42 Page Ref No. Keyword = "guidelines" 

43 ____ _______ ______________________ 

44 

45 146 15 group knows the guidelines and the guidance. 

46 167 24 in the context of the guidelines, but it would be 

47 180 6 stakeholder group to develop guidelines for 

48 213 18 Guidelines to Appendix W. So our comments are 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 266 

 2 

 3 Page Ref No. Keyword = "humidity" 

 4 ____ _______ ____________________ 

 5 

 6 173 20 4) Relative Humidity (surrogate for 

 7 174 5 surface relative humidity (RH). In reality, 

 8 197 14 structure, temperature & relative humidity, 

 9 

10 Page Ref No. Keyword = "implement" 

11 ____ _______ _____________________ 

12 

13 37 24 respect to the science and implementation within 

14 85 23 implementation guidance is the SEARCH tank. 

15 122 8 The way this is implemented into CALPUFF are 

16 131 5 mercury in the model. The implementation of 

17 139 11 have been implemented in photochemical models 

18 140 16 CAMx has particulate apportionment implemented 

19 141 8 apportionment that has been implemented in CAMx 

20 145 23 Implementation Plans so they do have regulatory 

21 156 25 implementation rules lacking in terms of 

22 198 21 implementation, emission inventories – direct and 

23 200 22 Retrofit Technology implementation guidance, PM2.5 

24 200 23 NSR implementation rule, PM2.5 PSD SILs, SMCs, and 

25 203 20 is necessary to implement reasonable PM2.5 impact 

26 206 4 implementation guides but in the 2004 addendum – 

27 206 6 guidance provide further implementation guidance 

28 206 19 Issues with AERSURFACE implementation. 

29 217 22 EPA review of implementation fixes for identified 

30 

31 Page Ref No. Keyword = "implementation" 

32 ____ _______ __________________________ 

33 

34 37 24 respect to the science and implementation within 

35 85 23 implementation guidance is the SEARCH tank. 

36 131 5 mercury in the model. The implementation of 

37 145 23 Implementation Plans so they do have regulatory 

38 156 25 implementation rules lacking in terms of 

39 198 21 implementation, emission inventories – direct and 

40 200 22 Retrofit Technology implementation guidance, PM2.5 

41 200 23 NSR implementation rule, PM2.5 PSD SILs, SMCs, and 

42 206 4 implementation guides but in the 2004 addendum – 

43 206 6 guidance provide further implementation guidance 

44 206 19 Issues with AERSURFACE implementation. 

45 217 22 EPA review of implementation fixes for identified 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 267 

 2 

 3 Page Ref No. Keyword = "ISC" 

 4 ____ _______ _______________ 

 5 

 6 88 19 for...there’s a table in the ISC users guide and in 

 7 204 11 AERMOD. It does things ISC3 could never do. I 

 8 

 9 Page Ref No. Keyword = "IWAQM" 

10 ____ _______ _________________ 

11 

12 8 16 the update of the IWAQM and Phase 2 guidance is 

13 14 8 significantly and the IWAQM Phase 2 

14 19 18 IWAQM Phase 2 there’s talk about project MOHAVE 

15 163 12 science approach as opposed to IWAQM mandates. 

16 173 10 values. Outdated and prescriptive IWAQM 

17 175 22 the IWAQM protocol, has a substantial bias 

18 

19 Page Ref No. Keyword = "Lagrangian" 

20 ____ _______ ______________________ 

21 

22 20 19 did was to include the two Lagrangian particle 

23 34 16 final ranking overall. This is the Lagrangian 

24 94 24 SCIPUFF), Lagrangian Particle Models (KSP, 

25 95 21 Lagrangian particle model called FLEXPART. We 

26 97 3 Lagrangian Integrated Trajectory model. I try 

27 97 16 lagrangian model. Basically the difference in 

28 98 4 The lagrangian approach we’re computing the 

29 100 11 modules use a hybrid Lagrangian-Eulerian 

30 100 20 transport in a lagrangian framework. The 

31 102 17 you’re running the lagrangian model for all the 

32 102 24 that with Lagrangian plume model. From that 

33 120 7 variability in dispersion rates, etc. Lagrangian 

34 121 11 a full stochastic Lagrangian particle dispersion 

35 124 17 Lagrangian particle dispersion model (LPFM) 

36 208 24 We need a 3-D Lagrangian model for 

37 211 14 make it a Lagrangian model. Another issue is 

38 

39 Page Ref No. Keyword = "layer" 

40 ____ _______ _________________ 

41 

42 31 6 ran a 43 vertical layer and I think I transpose 

43 84 5 boundary layer enhancement is certainly helping 

44 99 14 (surface layer) similarity, BL, Ri, or TKE. The 

45 104 6 represent the boundary layer transport. It looks 

46 104 8 varies with height in the boundary layer. 

47 104 17 effect. That is a big thing for boundary layer 

48 105 21 boundary layer as there is a lot more shear with 

49 216 4 already takes 2 or 3 years. One more layer is 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 268 

 2 

 3 Page Ref No. Keyword = "layers" 

 4 ____ _______ __________________ 

 5 

 6 31 7 my numbers so I think it was 43 layers instead of 

 7 118 6 map background layers for graphical display (pc); 

 8 150 18 w/ 6 CPUs, 19 Vertical Layers, M3Dry, CBM- 

 9 

10 Page Ref No. Keyword = "long range transport" 

11 ____ _______ ________________________________ 

12 

13 6 8 Bret take his evaluation of Long Range Transport 

14 7 3 long range transport models that we were looking 

15 8 2 evaluation of long range transport models. 

16 38 18 in these models and the long range transport 

17 38 23 terms of addressing long range transport in the 

18 93 3 introductory on the Long Range Transport 

19 

20 Page Ref No. Keyword = "mesoscale" 

21 ____ _______ _____________________ 

22 

23 8 4 mesoscale tracer studies but there is no one 

24 21 14 the Great Plains Mesoscale Tracer Experiment. 

25 163 17 is much current mesoscale and regional modeling 

26 168 18 Conduct a Mesoscale/Regional collaborative model 

27 

28 Page Ref No. Keyword = "met" 

29 ____ _______ _______________ 

30 

31 6 21 talk about the methods and metrics that were used 

32 6 25 explanation of the methodology that we were 

33 7 25 meteorological and tracer databases for 

34 8 6 was to assemble an archive of both meteorological 

35 8 9 objective method for evaluating long range 

36 8 19 There were several methods I think I’m a 

37 9 4 I called them the Irwin methodology. They focus 

38 9 6 were the methods that were used for that 

39 9 7 particular study. That was one method we used to 

40 9 10 In addition to the Irwin methodology, we did 

41 9 14 methodology and kind of how I have it broken out 

42 10 5 maximum concentrations on that arch. That method 

43 12 8 Then for spatial statistics the metric 

44 12 10 and then we’ve added additional EPA metrics, the 

45 12 14 on the NOAA webs site introduced a final metric 

46 13 22 study. That is the evaluation methodology used 

47 14 25 meteorological database for use with LRT model 

48 15 4 we have an archive of the meteorology so we’ll 

49 16 12 meteorological you supply it with. So another 

50 16 18 with meteorology. So that’s going to be the 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 269 

 2 

 3 Page Ref No. Keyword = "met" 

 4 ____ _______ _______________ 

 5 

 6 16 20 both meteorological aspects of it and the LRT 

 7 16 23 and testing the meteorological LRT models for the 

 8 16 25 you’re exercising and testing meteorological and 

 9 17 4 measures and results from meteorological and LRT 

10 18 13 to supply meteorological data to CALPUFF? As you 

11 18 18 see how best to apply the meteorology to it 

12 18 20 in CALMET. Perhaps Hybrid method verses NOOB = 1 

13 21 2 to take the meteorological data from MM5 and 

14 21 10 were the different methods the evaluation 

15 21 11 methods. 

16 22 2 the (inaudible) meteorology because this 

17 22 10 750 meters up in the air and the height in the 

18 22 18 we did with CALMET meteorology we looked at 

19 24 5 use for meteorological model evaluation. 

20 24 7 and these are from the Irwin methodology and want 

21 27 11 Euro methodologies. As you can see, this is 

22 28 24 meteorological perspective you don’t want to be 

23 31 25 same meteorological data. 

24 35 17 EPA in 1997 despite using same raw meteorological 

25 36 8 meteorology is supplied to the model. Joe 

26 37 8 meteorological metrics and the LRT metrics. The 

27 42 19 meter resolution. The reason I didn’t sign off 

28 43 5 to 1,000 meters and provide that to EPA, the FLMs 

29 44 13 flattened the terrain so that is was 1 meter 

30 47 8 appropriate evaluation methods. The focus and 

31 47 12 the emissions meterology and underlying modeling 

32 50 11 mentioned its one thing to talk about methods and 

33 50 25 modules. One that focuses on meteorology in this 

34 51 13 to compare observations against meteorological 

35 51 23 essentially the meteorology works the same with 

36 52 19 statistical metrics. Diurnal Statistics, Time 

37 52 25 type of analysis. The difference with the met 

38 53 4 the MM5 or WRF and here it’s a meta data set that 

39 53 21 sites, you can do it by pretty much any met data 

40 54 12 available on the met side and I’ll show some 

41 54 13 examples of these. There’s a met model 

42 54 18 to the met side includes Rawindsonde, Wind 

43 54 22 met side. You see here this one is for 

44 55 2 performance summary statistics, metric across 

45 55 13 pretty much any meteorological metric you have 

46 57 8 showed on the met side. Implied statistics 

47 58 7 metrics are some Bugle Plots where it includes 

48 59 2 can be used outside of data met. There are 

49 59 7 plots or use these plots outside of the met 

50 59 11 This a script based version both the Met and AQ 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 270 

 2 

 3 Page Ref No. Keyword = "met" 

 4 ____ _______ _______________ 

 5 

 6 59 19 the Met and AQ versions separately. Includes 

 7 60 13 what met data you put in you can use as a query 

 8 62 25 gridded met evaluation. 

 9 63 22 number of meteorological conditions and seasons, 

10 66 19 Method would be use of the RHC statistic, re11 67 14 the features of the Cox-Tikvart method. 

12 68 2 Some of the performance metrics in the BOOT 

13 70 19 gridded meteorological data. It’s almost like a 

14 70 21 meteorological tower. We need to thoroughly 

15 70 22 analysis the gridded met data. There be may be 

16 70 23 situations with poor met performance (e.g., 

17 71 3 meteorological data. 

18 71 11 dispersion. The use of better meteorology got 

19 71 21 example private industrial met towers for which 

20 72 3 for the evaluation of the gridded met data. That 

21 72 8 typical Cox/Tixvart evaluation methods that are 

22 75 13 and the SEARCH met data. The model seemed to be 

23 76 15 which would be typical of a met tower at an 

24 78 16 compared with met SEARCH site and airport site to 

25 81 17 process SEARCH met data as more representative of 

26 81 20 Ozone Limiting Method to better account for NO to 

27 83 12 AERSURFACE pretty high roughness about 0.8 meters 

28 83 13 0.7 meters verses concentration process with the 

29 83 20 But interestingly enough the met data that 

30 84 23 met data resulted in selection of another nearby 

31 85 15 up. The monitor was kind of within 100 meters 

32 86 11 different met data and different source 

33 86 18 Hybrid met data about 5.96 so we’re getting 

34 90 11 metric concentration really captured the plume 

35 95 19 met model linked to FLEXPART. We are using it as 

36 97 11 method; how to simulate plume dispersion, how to 

37 98 21 calculations in the meteorology for each source. 

38 99 3 The meteorology is external and its offline and 

39 99 8 As far as getting the meteorology from 

40 99 25 simultaneous meteorology and concentration grids. 

41 100 8 As far as meteorology we support latitude42 100 10 the meteorology. Now the non-linear chemistry 

43 101 11 to gridded meteorological data. Based on the 

44 101 13 could do a better job using meteorological data 

45 101 17 that a gridded meteorological model might be 

46 101 19 you have on site meteorology. But for these 

47 103 15 It goes back to a 1935 meteorology book and it a 

48 105 17 hybrid method always puts the particle in the 

49 109 23 method which goes back to the Models-3 if you 

50 111 3 boundaries and the meteorological model has 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 271 

 2 

 3 Page Ref No. Keyword = "met" 

 4 ____ _______ _______________ 

 5 

 6 111 14 micrograms per cubic meter, contributed from that 

 7 114 14 one of them had different meteorological data 

 8 114 23 have a consistent meteorological database that is 

 9 114 24 available and we can use modeling methods and we 

10 115 4 that are consistent using the same meteorology. 

11 115 10 months or 2 years worth like in METREX. So 

12 116 13 METREX (t1) 

13 116 18 METREX (t2) 

14 118 7 model physics ensemble (pc/unix); meteorology and 

15 120 4 meteorological and dispersion conditions, 

16 136 11 will use the same emissions input, meteorological 

17 137 10 chemistry and transport and meteorology inputs to 

18 142 6 Just comparing that back to a very simple metric 

19 142 8 screening metric states they obviously agree with 

20 144 4 meteorology output from MM5. CALPUFF was run in 

21 145 18 Apportionment methodology. These models have the 

22 148 7 met and emission inputs and full chemistry Plume23 148 20 are larger. Interpolate meteorology, emissions 

24 152 5 point sources and circles are (inaudible) method 

25 156 13 conference is to introduce these types of methods 

26 159 22 meteorological data and land use variations. Can 

27 160 8 of meteorological drivers (e.g., diagnostic 

28 160 12 CALPUFF, CMAQ). Prognostic meteorological models 

29 160 17 this including DTRA and NOAA who have linked MET 

30 160 21 needed is to optimize use of Met model and CALMET 

31 160 24 physics Met models (e.g. MM5) and CALMET; look at 

32 161 4 different topographic and meteorological 

33 161 10 performance of Met models coupled with dispersion 

34 161 12 new field experiments to determine how met 

35 161 14 Met models? (e.g. note differences between NCAR 

36 161 15 and Penn State MM5 Met model data assimilation 

37 161 16 methods). We’d like to assess if CALMET (or any 

38 161 18 intermediate step between the Met model and the 

39 161 20 NOAA) who have operational Met model-AQM systems 

40 161 24 Met models coupled with dispersion models vs. 

41 162 2 experiments. Determine how met observations can 

42 162 3 best be used and assimilated in Met models? (e.g. 

43 162 5 Met model data assimilation methods). Assess if 

44 162 7 as an intermediate step between the Met model and 

45 162 10 operational Met model-AQM systems operating and 

46 163 5 heavily on default values. Need to resolve met 

47 163 6 input questions (CALMET or Met model such as MM5 

48 163 7 – see previous slides on Met inputs). Need to 

49 163 15 of Met models and air quality models in 

50 163 20 and stakeholders. Include meteorological 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 272 

 2 

 3 Page Ref No. Keyword = "met" 

 4 ____ _______ _______________ 

 5 

 6 163 23 km by 200 km, sufficient to test the use of Met 

 7 164 20 priority performance methods. 

 8 164 21 The bootstrap method was talked about this 

 9 166 16 output should summarize the processed met data so 

10 167 15 physical understanding of meteorological 

11 168 17 meteorological models to drive dispersion models. 

12 172 18 peer-reviewed science and methodology over 

13 173 11 methodology is required for model 

14 179 25 include the widespread use of meteorological 

15 180 7 the use of meteorological models in air 

16 180 10 needs to address are: Which meteorological 

17 180 12 should meteorological monitoring sites be 

18 180 14 criteria? Meteorological model accuracy is 

19 180 25 meteorological input in AERMOD and AERMET. 

20 181 4 meteorolgical input into AERMOD. So that’s 

21 184 7 You know I’ve got a method that I can extract 

22 186 9 so called AB3 Committee of meteorological and 

23 187 15 meteorology inputs. I know you can read but 

24 190 2 location is also a variable and new methods may 

25 191 14 for the input. That’s 17 meter high building, H 

26 192 6 the five sources. 1 year met data kind of a 

27 193 5 shapes. 39 meters high, 1 to 1 and 1 to 4. The 

28 194 6 A method should be developed to determine when 

29 194 8 We’re saying that a method should be 

30 194 24 the corner vortex situation. Develop method for 

31 195 6 meterological data on the air quality model which 

32 195 10 model options and Metric for evaluating success. 

33 196 16 Meteorological evaluation software is very close 

34 196 20 The other thing is producing met data sets 

35 196 23 Evaluate gridded meteorological data performance 

36 197 2 dispersion model to met database. Separately 

37 197 11 Evaluate all meteorological variables. Wind 

38 198 9 observed met results. Evaluate results under 

39 200 10 Method 5. Existing reference methods for 

40 200 12 underway to propose more reliable methods. EPA 

41 200 14 measurement methods – sulfates can be 

42 202 22 factors are based on stack test methods known to 

43 206 23 mismatch in surface type between met tower and 

44 207 8 for met and application site surface 

45 207 20 recent met data. If my interpretation of the 

46 211 7 method to define precisely when complex winds 

47 226 22 profiles with data collected on met towers. And 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 273 

 2 

 3 Page Ref No. Keyword = "meteorological" 

 4 ____ _______ __________________________ 

 5 

 6 7 25 meteorological and tracer databases for 

 7 8 6 was to assemble an archive of both meteorological 

 8 14 25 meteorological database for use with LRT model 

 9 16 12 meteorological you supply it with. So another 

10 16 20 both meteorological aspects of it and the LRT 

11 16 23 and testing the meteorological LRT models for the 

12 16 25 you’re exercising and testing meteorological and 

13 17 4 measures and results from meteorological and LRT 

14 18 13 to supply meteorological data to CALPUFF? As you 

15 21 2 to take the meteorological data from MM5 and 

16 24 5 use for meteorological model evaluation. 

17 28 24 meteorological perspective you don’t want to be 

18 31 25 same meteorological data. 

19 35 17 EPA in 1997 despite using same raw meteorological 

20 37 8 meteorological metrics and the LRT metrics. The 

21 51 13 to compare observations against meteorological 

22 55 13 pretty much any meteorological metric you have 

23 63 22 number of meteorological conditions and seasons, 

24 70 19 gridded meteorological data. It’s almost like a 

25 70 21 meteorological tower. We need to thoroughly 

26 71 3 meteorological data. 

27 101 11 to gridded meteorological data. Based on the 

28 101 13 could do a better job using meteorological data 

29 101 17 that a gridded meteorological model might be 

30 111 3 boundaries and the meteorological model has 

31 114 14 one of them had different meteorological data 

32 114 23 have a consistent meteorological database that is 

33 120 4 meteorological and dispersion conditions, 

34 136 11 will use the same emissions input, meteorological 

35 159 22 meteorological data and land use variations. Can 

36 160 8 of meteorological drivers (e.g., diagnostic 

37 160 12 CALPUFF, CMAQ). Prognostic meteorological models 

38 161 4 different topographic and meteorological 

39 163 20 and stakeholders. Include meteorological 

40 167 15 physical understanding of meteorological 

41 168 17 meteorological models to drive dispersion models. 

42 179 25 include the widespread use of meteorological 

43 180 7 the use of meteorological models in air 

44 180 10 needs to address are: Which meteorological 

45 180 12 should meteorological monitoring sites be 

46 180 14 criteria? Meteorological model accuracy is 

47 180 25 meteorological input in AERMOD and AERMET. 

48 186 9 so called AB3 Committee of meteorological and 

49 196 16 Meteorological evaluation software is very close 

50 196 23 Evaluate gridded meteorological data performance 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 274 

 2 

 3 Page Ref No. Keyword = "meteorological" 

 4 ____ _______ __________________________ 

 5 

 6 197 11 Evaluate all meteorological variables. Wind 

 7 

 8 Page Ref No. Keyword = "mixing" 

 9 ____ _______ __________________ 

10 

11 36 22 at puff-splitting (eliminating mixing height 

12 55 12 mixing ratio, wind speed, wind direction, but 

13 99 13 next thing is vertical mixing based upon SL 

14 99 15 horizontal mixing based upon velocity 

15 99 16 deformation, SL similarity, or TKE. Mixing 

16 101 10 (on/off) mixing. Later we basically we switched 

17 205 12 Nocturnal urban mixing height (Ziu) is a 

18 

19 Page Ref No. Keyword = "MM5" 

20 ____ _______ _______________ 

21 

22 15 5 have the MM5 data that was run up there and have 

23 21 2 to take the meteorological data from MM5 and 

24 22 7 MM5 simulation performed with this. What you can 

25 22 21 gave yesterday, we also included the MM5 CALPUFF 

26 23 16 This is the MM5 configuration and I’ll skip 

27 23 19 MM5 like ETA PBL and NOAH LSM. We’re not 

28 23 23 validate the MM5 data and that’s something we 

29 24 11 Both put in MM5 CALPUFF within the CALMET one 

30 25 16 better than the MM5 winds in terms of the arrival 

31 25 18 (inaudible) it. The MM5 had a slight delay of 

32 26 6 good job. MM5 is (inaudible) arrived late and 

33 27 2 is that when we were feeding the MM5 only winds 

34 27 12 where the MM5 winds did markedly better than 

35 27 16 where it should have been and the MM5 was like 

36 27 20 the MM5 winds were doing slightly better, but you 

37 27 21 can see the MM5 winds have it displaced more 

38 28 6 to what the MM5 was looking at like the MM5 was a 

39 28 8 encouraging sign for the MM5 CALPUFF. 

40 29 12 For the MM5 CALPUFF, as you can see, it 

41 31 3 MM5 is run again and was initialized with 

42 31 20 with the MM5 and there’s no CALMET in this 

43 31 21 simulation. It’s only MM5 CALPUFF so basically 

44 31 23 help with (inaudible) MM5. Basically we’re 

45 35 13 MM5 results were better for azimuth, but worse 

46 50 22 and MM5 but it can be extended to other 

47 51 2 case typically MM5 or WRF and one focuses on air 

48 51 14 (e.g. MM5, WRF) or air quality model (e.g. CMAQ, 

49 53 4 the MM5 or WRF and here it’s a meta data set that 

50 70 20 new concept do we trust MM5 data instead of a 
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 6 95 18 is (sorry) and about fire emissions model and MM5 

 7 99 10 ECMWF, RAMS, MM5, NMM, GFS and so on. It’s not 

 8 144 4 meteorology output from MM5. CALPUFF was run in 

 9 147 20 Databases more costly to develop. MM5/WRF 

10 156 3 wind fields that come out of MM5. 

11 160 10 models such as MM5) for both steady state and 

12 160 13 such as MM5 and WRF (often called ‘Met models’) 

13 160 18 models with MM5 and WRF and the Puff models. 

14 160 24 physics Met models (e.g. MM5) and CALMET; look at 

15 161 5 settings; minimum grid size (Penn State MM5 

16 161 15 and Penn State MM5 Met model data assimilation 

17 162 4 note differences between NCAR and Penn State MM5 

18 163 6 input questions (CALMET or Met model such as MM5 

19 163 24 model (e.g., MM5) direct input versus CALMET 

20 180 3 requires: The accuracy of MM5/CALMET model 

21 195 13 a converter for MM5 and one of the things was 

22 195 19 the use to concert MM5 data and WRF and 

23 196 14 output of MM5 converted to wind rose software. 

24 197 22 The reason why the MM5 simulations can’t deal 

25 226 7 ways for extracting data from MM5 and WRF file 

26 

27 Page Ref No. Keyword = "model" 

28 ____ _______ _________________ 

29 

30 6 12 model evaluation session right after that. 

31 7 10 modeling so we use non steady state (inaudible) 

32 7 11 puff model, particle model for these types of 

33 7 14 visibility (inaudible) modeling. As such as Joe 

34 7 19 and time considerations of the LRT model use. As 

35 9 12 upon spatiotemporal comparisons of model36 9 20 the model’s ability to correctly predict the 

37 11 8 the model is doing. This is just an example on 

38 12 2 distributions of the model predictions. So this 

39 12 15 which is basically a model success story, a model 

40 12 18 model score to see how well it did across each of 

41 12 20 This is just the model ranking and you can 

42 13 2 allows to give you an idea how the model performs 

43 13 4 for direct modeling or comparison because you 

44 13 10 particle model that we evaluated as part of this 

45 13 12 model; this is a European tracer experiment and I 

46 13 18 correlation of bias and the final model rating 

47 13 21 the model performed in that particular tracer 

48 14 6 the 8th Modeling Conference – EPA recognized the 

49 14 7 fact that CALPUFF model science had evolved 

50 14 17 need to form an updated model performance 
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 6 14 19 model which are not mentioned in the current 

 7 14 22 modeling project and they said we are performing 

 8 14 25 meteorological database for use with LRT model 

 9 16 9 point yesterday about you know this is a modeling 

10 16 11 model can only perform as well as the 

11 16 16 at it as a coupled system. The model’s ability 

12 16 21 model aspects of it. 

13 17 3 provide full documentation of model evaluation 

14 17 6 to updating existing EPA LRT modeling guidance 

15 17 11 modeling system to incorporate recent 

16 17 12 enhancements to model system in updated guidance 

17 17 15 8th Modeling Conference that talk about these 

18 17 18 200-300 km? At the 7th Modeling Conference, EPA’s 

19 18 6 mentioned, back in 2006, EPA issued a Model 

20 18 14 know, it is like any other transport model and it 

21 20 8 is how well any model can do in any one of these 

22 20 9 situations. It isn’t fair to isolate one model 

23 20 16 understand how well can any model reasonably do 

24 21 3 apply to this model. 

25 21 8 understand how any model can reasonably do under 

26 22 3 influences the performance of the model. 

27 22 16 the model experimental design was to look at 

28 24 5 use for meteorological model evaluation. 

29 30 11 develop a database which could be used for model 

30 31 14 shows how well one model does and how bad one 

31 34 3 the model observed it had the best of spatial 

32 34 17 part of the model it didn’t do much better in 

33 36 8 meteorology is supplied to the model. Joe 

34 36 10 how you apply the model and that’s one of the 

35 36 24 not augment model performance. We had puffs 

36 37 6 work-in-progress. We have a model evaluation 

37 37 11 engage with model developer to help us understand 

38 37 13 model setup? What can we do better? 

39 37 14 Has the model changed since the previous 

40 37 25 the model and will fully document that. What we 

41 38 3 this is to conduct a peer review of the model and 

42 39 16 factors that can influence how the model responds 

43 47 3 Going back to the 8th Modeling Conference, we 

44 47 10 in the modeling system understanding that 

45 47 11 emphasis on modeling systems, recognizing that 

46 47 12 the emissions meterology and underlying modeling 

47 48 3 for model evaluation. This one refers to the 

48 48 4 community multi scale air quality model from the 

49 48 6 Development. Basically you’re looking at a model 

50 49 11 standpoint and the model standpoint to see 
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 6 49 18 sure we feed that back in to the model. This 

 7 50 14 to deliver the atmospheric model evaluation tool 

 8 50 18 modeling division in ORD here at EPA. And as 

 9 50 24 Model Evaluation Tool (AMET) consists of two 

10 51 14 (e.g. MM5, WRF) or air quality model (e.g. CMAQ, 

11 51 17 paired model observation sets which are actually 

12 51 25 observations and then model output. These are 

13 52 8 jus populating the database with model 

14 52 16 will get in to some of those. For example, model 

15 53 3 different set of model output. Instead here of 

16 54 13 examples of these. There’s a met model 

17 54 20 This is an example of a model performance 

18 54 25 plots and statistics scatter plot, model 

19 55 4 showing the distribution of the model. This is a 

20 56 15 model observation, model to model, summary 

21 57 2 included model to model (inaudible) single 

22 57 11 model observed, the bias between the model 

23 57 15 also include another model data so you could 

24 57 16 compare two model runs and see how they compare. 

25 57 23 comparing with the model like CMAQ. But it shows 

26 58 13 have any set of model predictions in time and 

27 58 17 using CMAQ or CMAx or a model like that, if you 

28 58 19 that includes a model of and some space and time 

29 61 24 be continuing to develope model evaluation tools 

30 62 16 for short range modeling evaluations the somewhat 

31 65 11 indicate where it’s closer to this model. In the 

32 65 14 Other types of tools are the plotted model 

33 66 2 98th percentiles. A good model should have no 

34 66 3 trend in model residuals. 

35 66 4 This is a poor model example where you can 

36 66 7 see that, you see the model has some bias due to 

37 66 10 model does have a possible problem. These are 

38 66 16 of zero is a perfect model, while an FB of +/- 

39 66 24 averaging times. The model comparison measure 

40 67 9 the lower the score, the better the model. If 

41 67 10 the the model comparison measure straddles zero, 

42 67 21 within Atmospheric Dispersion Modelling for 

43 67 22 Regulatory Purposes – Model Validation Kit. It is 

44 68 12 is on the Y-AXIS, so a perfect model is as low as 

45 69 2 ensemble, while model predictions often represent 

46 70 25 dispersion modeling are how often are the winds 

47 71 9 North Dakota, we found that the EPA model missed 

48 72 22 the model in a more robust evaluation. This is 

49 73 14 model performance to increase in the future and 

50 73 18 regulatory model evaluation this is prairie grass 
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 6 74 23 But also learn ourselves how the model performs 

 7 75 3 undertaking soon to evaluate model performance 

 8 75 13 and the SEARCH met data. The model seemed to be 

 9 75 18 series plot running the model with the airport 

10 78 6 but the model concentration with that data 

11 79 18 the SEARCH site that’s matched with the model 

12 80 14 there a problem with the model that these light 

13 81 3 roadways were modeled as links and minor roadways 

14 81 5 distributed so the initial model-to-monitor 

15 81 15 We did a broader assessment of modeling 

16 82 4 light duty vehicles they are being modeled as 

17 82 8 were on the same scale. This is sort of model to 

18 82 11 concentration and the lighter blue is the model 

19 82 20 this is the model comparison after I think the 

20 83 7 modeled multiple sources again that’s majority 

21 83 9 Q-Q plot of modeled concentrations using SEARCH. 

22 84 12 little bit more focused on that. This a model 

23 85 7 The other thing is the sensitivity of model 

24 85 19 at different ways to model it there’s an area 

25 86 3 model tanks maybe series non buoyant point 

26 87 3 opportunities to learn about the model. They are 

27 87 6 limitations of the model are and the 

28 87 9 to apply the model and we also want to do is 

29 87 10 build on what Bret is doing in model performance. 

30 87 15 any questions as it relates to the model 

31 87 20 you have but the storage tanks have been modeled 

32 88 2 how we should really be modeleing these. One is 

33 88 13 modeling storage tanks. 

34 88 23 of the model to deal with downwash that’s more 

35 89 12 woefully inadequate in evaluating the model in 

36 91 12 yesterday, and I’ll plug the Model Clearinghouse 

37 93 6 Model and then we’ll take some Q&A soon after 

38 93 12 modeling community. In the modeling community as 

39 93 21 emergency response support for air modeling in 

40 93 24 In the emergency response modeling community 

41 94 2 class of modeling technology that is new to us. 

42 94 5 modeling community. That is one area where we 

43 94 9 of CALPUFF modeling you know we’ve also seen a 

44 95 18 is (sorry) and about fire emissions model and MM5 

45 95 19 met model linked to FLEXPART. We are using it as 

46 95 21 Lagrangian particle model called FLEXPART. We 

47 97 3 Lagrangian Integrated Trajectory model. I try 

48 97 9 to use the model. 

49 97 16 lagrangian model. Basically the difference in 

50 99 4 that means someone else provided this. The model 
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 6 99 20 Modelled particle distributions (puffs) can 

 7 99 22 modeling air concentrations, it is from 

 8 100 7 and switch to the global model. 

 9 100 16 ozone model, CD4, and we’ve got a mercury module. 

10 101 6 changes in the model. It’s not that new we 

11 101 9 model used was rawinsonde data with day/night 

12 101 17 that a gridded meteorological model might be 

13 102 12 in-plume model. Essentially a subroutine for 

14 102 17 you’re running the lagrangian model for all the 

15 102 22 reasonable to look at an Eulerian model to 

16 102 24 that with Lagrangian plume model. From that 

17 103 4 transferred to the Eulerian model. 

18 104 23 3D-particle model with just the mean motion. We 

19 105 5 approach where we’re not modeling the individual 

20 105 6 particles, but we’re modeling how that particle 

21 106 21 modeling the distribution, it could either be a 

22 107 20 the model. That’s the particle approach. 

23 108 5 deviation, the made as modeling the puff if you 

24 108 24 running the 3D particle model the change in 

25 110 10 model and what you see here is the particle 

26 111 3 boundaries and the meteorological model has 

27 112 24 essentially, the model didn’t show a lot of bias 

28 113 12 model what my overall results will be. 

29 114 4 the perfect model would give us a rank of 4.0. 

30 114 22 can use it in the model. So all of a sudden we 

31 114 24 available and we can use modeling methods and we 

32 115 6 you find changing dispersion in the model and 

33 117 15 the model is .97 correlation coefficient and the 

34 118 2 will have the integrated global model for 

35 118 7 model physics ensemble (pc/unix); meteorology and 

36 118 17 overview of puff particle model. 

37 118 19 about the particle puff model the PPM module 

38 118 24 describe the model and a little bit of history 

39 119 16 purpose of the PPM the puff particle model is to 

40 120 14 If you look at the Puff model types there 

41 120 23 cluster dispersion puff model where a puff is a 

42 121 12 model to determine the puff trajectory. I’ll 

43 123 11 Peter evaluated the model of several 

44 124 7 part of European short-range dispersion model 

45 124 17 Lagrangian particle dispersion model (LPFM) 

46 125 2 current version of the model. You can turn the 

47 125 9 Modeling. We’ll start with presentation from 

48 125 15 plume–in-grid modeling, which basically consists 

49 125 16 of using a plume model within a grid model to 

50 125 23 If you look at a grid model with a resolution of 
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 6 126 6 in-grid model is to combine the plume model and 

 7 126 7 the grid model and carry the plume along until it 

 8 126 11 trying to do with the plume–in-grid model is to 

 9 126 15 model cannot predict these two stages correctly. 

10 126 17 plume to the grid model. 

11 126 18 So, like I mentioned earlier, the model 

12 126 19 consists of a reactive plume model embedded 

13 126 20 within a 3-D grid model. The plume model 

14 126 22 model provides background concentrations to the 

15 126 23 plume model. At the time we hand over the plume 

16 126 24 model to the grid model, the grid model 

17 127 2 feedback between the host grid model and the 

18 127 3 plume model. 

19 127 4 Plume-in-grid modeling is not new; it began in 

20 127 12 of-the-science PiG model for ozone was initiated 

21 127 14 The embedded plume Model is SCICHEM (state-of22 127 18 alternative model recommended by EPA on a case23 127 21 three-dimensional puff-based model, with second24 128 3 Model, CMAQ. In 2000, AER incorporated SCICHEM 

25 128 4 into CMAQ. The model is called CMAQ-APT 

26 128 6 The early applications of the model were for 

27 128 10 July 1995. We also applied the model to Central 

28 128 15 the base model. 

29 128 21 developed by AER. MADRID is the Model of Aerosol 

30 128 25 the plume-in-grid model, it is based on CMAQ 4.6, 

31 129 11 designed to supplement RPO modeling being 

32 129 17 plume-in-grid approach. Model performance 

33 129 22 modeling domain for the application and locations 

34 130 9 model and 2.4 μg/m3 for the plume-in- grid model. 

35 130 20 overestimated. Plume-in-grid PM modeling 

36 131 5 mercury in the model. The implementation of 

37 131 15 grid model on the left hand side and the change 

38 131 18 model overpredicted mercury deposition, 

39 132 11 of these species. Traditional modeling 

40 132 16 Fluid Mech.). The model simulates near-source CO 

41 133 7 shows the grid model domain. 

42 133 10 model results compared with CO concentration 

43 133 13 The challenge with P-in-G modeling is that it 

44 133 16 model – computational requirements increase by a 

45 134 7 going project to apply the model to the central 

46 134 15 This slide shows the modeling domain for the 

47 134 20 parallel version of the model. 

48 134 25 Inc.; Collaboration in Model Development: L-3 

49 136 3 little bit about photochemical modeling and in 

50 136 6 source modeling and tracking that type of thing. 
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 6 136 9 modeling systems whether it’s dispersion, or 

 7 136 10 photochemical grid model system. Essentially you 

 8 136 13 model. 

 9 136 14 Generally speaking the model started off as 

10 136 15 a dispersion model, simple photochemical box 

11 136 24 modeling system approach where we are trying to 

12 137 2 PM in the same modeling system. An example 

13 138 11 universe (inaudible) into one universe model. 

14 138 17 the things in using a photochemical model for 

15 140 9 model species for each contributing source. 

16 140 22 additional model species. Just put in those 

17 140 24 duplicate model species. And goes with the same 

18 141 2 species do in the photochemical model. The only 

19 142 10 on with the photochemical model because it’s 

20 143 14 through a photochemical model and that’s just an 

21 143 21 single source modeling with CAMx PSAT to compare 

22 143 23 States did single source visibility modeling for 

23 144 2 short the (inaudible) modeling just try to apply 

24 145 9 the photochemical model really not a lot of 

25 145 17 for credible single source modeling with Source 

26 146 4 modeling for Ozone and PM. 

27 147 21 applications. SMOKE or other emissions model 

28 147 24 There has been a lot of development in modeling 

29 148 13 PGM Databases and model set ups. RPOs, AIRPACT, 

30 148 24 model (just specify where fine grid domains are 

31 149 8 very little. Whereas in a grid model you dump 

32 149 11 the Plume in Grid model. 

33 149 24 Southeast (ASIP). Annual PM2.5 SIP modeling for 

34 150 11 2002 36 km modeling CAMx database. Add 12 km 

35 150 15 model a part of VISTAS ASIP. Here’s a 36 km: 148 

36 151 8 modeling. 12 km grid cell size too coarse to 

37 151 23 CAMx 12/4 modeling using two-way interactive grid 

38 151 24 nesting. 2002 base case using standard model. 

39 152 8 are located close to the grid model to the 

40 152 11 (inaudible) for that other model CALPUFF. 

41 153 10 it’s not above 15. Here’s 1 μg for this model. 

42 153 19 have developed a Conceptual Model for PM2.5 

43 153 23 12/4/1 km PiG modeling attributes 3.4 μg/m3 to 

44 154 8 modules. The use PGM modeling to assess “single 

45 154 13 BART assessment. PM2.5 SIP modeling. 

46 154 22 modules. The use of PGM modeling, to assess 

47 155 3 Arkansas BART assessment. PM2.5 SIP modeling. 

48 156 9 modeling single source for Ozone PM2.5 seems to 

49 158 22 regional modeling. These comments cover many 

50 160 2 recommendation for Plume in Grid (PinG) modeling? 
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 6 160 21 needed is to optimize use of Met model and CALMET 

 7 160 22 model predictions with observations. Specific 

 8 161 8 is due to physical assumptions in the model). 

 9 161 9 We’d like to see overall model 

10 161 15 and Penn State MM5 Met model data assimilation 

11 161 17 diagnostic model) is truly needed as an 

12 161 18 intermediate step between the Met model and the 

13 161 20 NOAA) who have operational Met model-AQM systems 

14 161 23 Determine overall model performance of 

15 162 5 Met model data assimilation methods). Assess if 

16 162 6 CALMET (or any diagnostic model) is truly needed 

17 162 7 as an intermediate step between the Met model and 

18 162 10 operational Met model-AQM systems operating and 

19 162 16 We see a need for an overall model 

20 162 18 very limited evaluations of the model in the mode 

21 163 6 input questions (CALMET or Met model such as MM5 

22 163 17 is much current mesoscale and regional modeling 

23 163 24 model (e.g., MM5) direct input versus CALMET 

24 163 25 diagnostic model. 

25 164 2 I’d like to switch to model evaluation 

26 164 5 improvements in regional dispersion model 

27 164 13 measures for the different model scales, a 

28 164 15 be devised for use at all model scales. I 

29 164 25 with the BOOT software. We think the model 

30 165 3 modeling protocols and decision making. We also 

31 165 4 believe uncertainty in model predictions (also 

32 165 8 use of the probabilistic AQM system (Met model – 

33 165 10 We understand the screening model, 

34 166 3 that the dispersion modeling domain is dominated 

35 166 9 model domain is going to be entirely dominated by 

36 166 10 the surface modeling of the airport roughness. 

37 166 19 modeling purposes (>90% available). We’d like to 

38 166 23 Ratio Model (PMVRM. We like for EPA to further 

39 166 24 test this model and, if acceptable, recommend the 

40 166 25 use of this model for predicting NO2 

41 168 8 modifications to model algorithms. Model 

42 168 10 discussions with the entire community of model 

43 168 12 Need to update and improve model guidance 

44 168 18 Conduct a Mesoscale/Regional collaborative model 

45 169 3 ASIP modeling and the like. I just want to 

46 170 2 region and state and local modeling. 

47 170 25 quality modeling for regional analysis for 

48 171 17 that evaluation, we could use the model in 

49 171 25 said the model has not been evaluated to a 

50 172 3 that’s the way we’re using the model and 
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 6 172 15 Air quality modeling approach is: “Use 

 7 172 24 AQRV modeling approach is to develop a 

 8 173 7 of a robust model performance evaluation in 

 9 173 8 a full chemistry mode. Indication of model 

10 173 11 methodology is required for model 

11 175 11 really the only model verification that has 

12 175 14 conditions were included model agreement was 

13 175 19 requiring that the model should be used. 

14 175 21 indication that the as CALPUFF Model using 

15 176 15 In this context, the model is not 

16 177 7 that the model is performing correctly. 

17 177 15 model is saying it is playing a very 

18 177 17 really doing a very good job of model 

19 177 23 different picture than what the model is 

20 177 24 saying. This has become a political model 

21 177 25 and the public is believing the model. This 

22 178 16 the model is being used is not realistic. 

23 179 5 little change. Again, the model doesn’t 

24 179 10 comprehensive model evaluation of CALPUFF in 

25 179 11 a full chemistry model. Without a doubt 

26 179 13 done with this model. 

27 180 2 model output in air quality modeling 

28 180 3 requires: The accuracy of MM5/CALMET model 

29 180 5 model application; EPA needs to coordinate a 

30 180 9 Topics that the modeling community 

31 180 11 model should be used? Grid size? How 

32 180 13 included in modeling? Model performance 

33 180 14 criteria? Meteorological model accuracy is 

34 180 16 model results used in an air quality 

35 183 12 the model. Again it was more of a mechanical 

36 186 10 modeling. I’m going to introduce the AB3 model 

37 186 14 staff member from 1978 and 1979 from the model 

38 186 20 Complex (ISC)] model. I’m not sure if that is 

39 187 4 goal of best model performance built on best 

40 190 12 wind residential tower to go in to the model. 

41 191 13 that chart represents what the model BPIP gave 

42 195 6 meterological data on the air quality model which 

43 195 10 model options and Metric for evaluating success. 

44 196 7 out of the prognostic model. Although they do 

45 196 24 separately from dispersion model performance. 

46 197 2 dispersion model to met database. Separately 

47 197 4 parameter. Sensitivity of prognostic model 

48 197 7 dispersion model to different variables. Model 

49 198 22 precursors, modeling techniques – guidance, 

50 200 2 for visibility modeling because each of these 
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 6 200 9 quantified, and modeled based on EPA Reference 

 7 200 21 modeling that are still in effect, Best Available 

 8 200 25 pending), and the PSD increment modeling 

 9 201 3 So let’s talk about modeling Primary vs. 

10 201 9 Secondary PM2.5 could be modeled with CALPUFF. 

11 201 12 unbiased model to take modeling credit? Are we 

12 202 4 modeling for short-range applications? Include 

13 202 5 secondary PM2.5 modeling for long-range 

14 202 25 Example Modeling Challenge: Compute Total 

15 203 12 To summarize: PM2.5 modeling in a regulatory 

16 203 15 Emissions measurement and modeling techniques 

17 203 17 can be much higher than modeled concentrations. 

18 203 19 for refined modeling approaches. Collaboration 

19 203 24 guidance, PSD increments and modeling procedures. 

20 204 13 The Low wind speed issues. Modeling of 

21 204 14 roadways for NO2 and PM. Problems with modeling 

22 205 7 studies and adjustments to AERMOD modeling 

23 206 20 Sensitivity of modeling to surface 

24 207 6 2008 Annual Meeting on sensitivity modeling. We 

25 208 18 EPA controlling the model developing coding and 

26 208 21 modeling for the guideline purposes require air 

27 208 24 We need a 3-D Lagrangian model for 

28 209 4 demonstrated. CALPUFF is a model with community 

29 210 2 provide for advancement in this model. The 

30 210 21 the model. 

31 211 8 occur and require PUFF modeling. We’ll be 

32 211 10 modeling and a better definition of complex 

33 211 14 make it a Lagrangian model. Another issue is 

34 212 5 will be conducting a modeling conference in 

35 212 6 Toronto this Spring on Canadian modeling issues. 

36 212 7 There will be 2 modeling conferences next year. 

37 212 25 participated in all of the EPA modeling 

38 213 4 revisions of Appendix W Guideline. The Modeling 

39 213 17 that EPA is planning to make to the Modeling 

40 214 19 outdated portion of the modeling guideline, EPA 

41 215 17 document that a modeling system like CALPUFF, 

42 215 25 Model Clearinghouse process. A drill that is 

43 216 9 which that model has shown to work and function 

44 216 11 Appendix W allows the choice of modeling 

45 217 12 that model users will occasionally encounter and 

46 217 13 identify problems and bugs in the model. 

47 218 8 Paine spoke of dealing with PM2.5 modeling 

48 218 13 and no model that does a credible job of 

49 218 22 modeling tools to use for the permit application. 

50 219 3 modeling and we still have no guidance on how to 
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 6 219 15 quality modeling when I was a contractor in the 

 7 220 11 long model runs. They have a dedicated user 

 8 220 16 modeling disciplines report cost benefit 

 9 222 11 Model: example (a). Used as a reference. CMAQ: 

10 222 22 Example (a) SOM Ocean Model: Excellent 

11 226 21 comparing the prognostic model derived wind 

12 

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16 6 12 model evaluation session right after that. 

17 17 3 provide full documentation of model evaluation 

18 24 5 use for meteorological model evaluation. 

19 37 6 work-in-progress. We have a model evaluation 

20 48 3 for model evaluation. This one refers to the 

21 50 14 to deliver the atmospheric model evaluation tool 

22 50 24 Model Evaluation Tool (AMET) consists of two 

23 61 24 be continuing to develope model evaluation tools 

24 73 18 regulatory model evaluation this is prairie grass 

25 164 2 I’d like to switch to model evaluation 

26 168 14 of science–based models through model evaluation 

27 179 10 comprehensive model evaluation of CALPUFF in 

28 

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32 7 7 in air quality modeling. This class of models 

33 7 10 modeling so we use non steady state (inaudible) 

34 7 14 visibility (inaudible) modeling. As such as Joe 

35 13 4 for direct modeling or comparison because you 

36 14 6 the 8th Modeling Conference – EPA recognized the 

37 14 22 modeling project and they said we are performing 

38 16 9 point yesterday about you know this is a modeling 

39 17 6 to updating existing EPA LRT modeling guidance 

40 17 11 modeling system to incorporate recent 

41 17 15 8th Modeling Conference that talk about these 

42 17 18 200-300 km? At the 7th Modeling Conference, EPA’s 

43 47 3 Going back to the 8th Modeling Conference, we 

44 47 10 in the modeling system understanding that 

45 47 11 emphasis on modeling systems, recognizing that 

46 47 12 the emissions meterology and underlying modeling 

47 50 18 modeling division in ORD here at EPA. And as 

48 62 16 for short range modeling evaluations the somewhat 

49 70 25 dispersion modeling are how often are the winds 

50 81 15 We did a broader assessment of modeling 
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 6 88 13 modeling storage tanks. 

 7 93 12 modeling community. In the modeling community as 

 8 93 21 emergency response support for air modeling in 

 9 93 24 In the emergency response modeling community 

10 94 2 class of modeling technology that is new to us. 

11 94 5 modeling community. That is one area where we 

12 94 9 of CALPUFF modeling you know we’ve also seen a 

13 99 22 modeling air concentrations, it is from 

14 105 5 approach where we’re not modeling the individual 

15 105 6 particles, but we’re modeling how that particle 

16 106 21 modeling the distribution, it could either be a 

17 108 5 deviation, the made as modeling the puff if you 

18 114 24 available and we can use modeling methods and we 

19 125 9 Modeling. We’ll start with presentation from 

20 125 15 plume–in-grid modeling, which basically consists 

21 127 4 Plume-in-grid modeling is not new; it began in 

22 129 11 designed to supplement RPO modeling being 

23 129 22 modeling domain for the application and locations 

24 130 20 overestimated. Plume-in-grid PM modeling 

25 132 11 of these species. Traditional modeling 

26 133 13 The challenge with P-in-G modeling is that it 

27 134 15 This slide shows the modeling domain for the 

28 136 3 little bit about photochemical modeling and in 

29 136 6 source modeling and tracking that type of thing. 

30 136 9 modeling systems whether it’s dispersion, or 

31 136 24 modeling system approach where we are trying to 

32 137 2 PM in the same modeling system. An example 

33 143 21 single source modeling with CAMx PSAT to compare 

34 143 23 States did single source visibility modeling for 

35 144 2 short the (inaudible) modeling just try to apply 

36 145 17 for credible single source modeling with Source 

37 146 4 modeling for Ozone and PM. 

38 147 24 There has been a lot of development in modeling 

39 149 24 Southeast (ASIP). Annual PM2.5 SIP modeling for 

40 150 11 2002 36 km modeling CAMx database. Add 12 km 

41 151 8 modeling. 12 km grid cell size too coarse to 

42 151 23 CAMx 12/4 modeling using two-way interactive grid 

43 153 23 12/4/1 km PiG modeling attributes 3.4 μg/m3 to 

44 154 8 modules. The use PGM modeling to assess “single 

45 154 13 BART assessment. PM2.5 SIP modeling. 

46 154 22 modules. The use of PGM modeling, to assess 

47 155 3 Arkansas BART assessment. PM2.5 SIP modeling. 

48 156 9 modeling single source for Ozone PM2.5 seems to 

49 158 22 regional modeling. These comments cover many 

50 160 2 recommendation for Plume in Grid (PinG) modeling? 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 287 

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 3 Page Ref No. Keyword = "modeling" 

 4 ____ _______ ____________________ 

 5 

 6 163 17 is much current mesoscale and regional modeling 

 7 165 3 modeling protocols and decision making. We also 

 8 166 3 that the dispersion modeling domain is dominated 

 9 166 10 the surface modeling of the airport roughness. 

10 166 19 modeling purposes (>90% available). We’d like to 

11 169 3 ASIP modeling and the like. I just want to 

12 170 2 region and state and local modeling. 

13 170 25 quality modeling for regional analysis for 

14 172 15 Air quality modeling approach is: “Use 

15 172 24 AQRV modeling approach is to develop a 

16 180 2 model output in air quality modeling 

17 180 9 Topics that the modeling community 

18 180 13 included in modeling? Model performance 

19 186 10 modeling. I’m going to introduce the AB3 model 

20 198 22 precursors, modeling techniques – guidance, 

21 200 2 for visibility modeling because each of these 

22 200 21 modeling that are still in effect, Best Available 

23 200 25 pending), and the PSD increment modeling 

24 201 3 So let’s talk about modeling Primary vs. 

25 201 12 unbiased model to take modeling credit? Are we 

26 202 4 modeling for short-range applications? Include 

27 202 5 secondary PM2.5 modeling for long-range 

28 202 25 Example Modeling Challenge: Compute Total 

29 203 12 To summarize: PM2.5 modeling in a regulatory 

30 203 15 Emissions measurement and modeling techniques 

31 203 19 for refined modeling approaches. Collaboration 

32 203 24 guidance, PSD increments and modeling procedures. 

33 204 13 The Low wind speed issues. Modeling of 

34 204 14 roadways for NO2 and PM. Problems with modeling 

35 205 7 studies and adjustments to AERMOD modeling 

36 206 20 Sensitivity of modeling to surface 

37 207 6 2008 Annual Meeting on sensitivity modeling. We 

38 208 21 modeling for the guideline purposes require air 

39 211 8 occur and require PUFF modeling. We’ll be 

40 211 10 modeling and a better definition of complex 

41 212 5 will be conducting a modeling conference in 

42 212 6 Toronto this Spring on Canadian modeling issues. 

43 212 7 There will be 2 modeling conferences next year. 

44 212 25 participated in all of the EPA modeling 

45 213 4 revisions of Appendix W Guideline. The Modeling 

46 213 17 that EPA is planning to make to the Modeling 

47 214 19 outdated portion of the modeling guideline, EPA 

48 215 17 document that a modeling system like CALPUFF, 

49 216 11 Appendix W allows the choice of modeling 

50 218 8 Paine spoke of dealing with PM2.5 modeling 
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 3 Page Ref No. Keyword = "modeling" 

 4 ____ _______ ____________________ 

 5 

 6 218 22 modeling tools to use for the permit application. 

 7 219 3 modeling and we still have no guidance on how to 

 8 219 15 quality modeling when I was a contractor in the 

 9 220 16 modeling disciplines report cost benefit 

10 

11 Page Ref No. Keyword = "monitor" 

12 ____ _______ ___________________ 

13 

14 30 13 They had at least 168 monitoring sites 

15 61 19 externally. Then we monitor the bugs that the 

16 63 24 monitoring networks featuring year-long sampling 

17 75 22 the details here but this is the Wylam monitor 

18 76 6 receptors (inaudible) on the monitor location to 

19 76 7 the actual monitored concentration. It actually 

20 76 11 concentrations from the monitor. This just shows 

21 77 15 that monitor. But if you look at the airport 

22 80 2 monitor from the source that is the closest 

23 80 7 the facility would be going right at the monitor, 

24 81 7 significantly exceeding monitored NO2 

25 85 15 up. The monitor was kind of within 100 meters 

26 86 19 reasonably close. This other monitor didn’t do 

27 86 22 background sources impacting that monitor. This 

28 152 9 monitor and sometimes almost (inaudible) I admit 

29 153 8 monitor and that’s about 2 μg which is a large 

30 153 9 contribution source on a monitor. In this case 

31 153 16 to PM2.5 nonattainment at Granite City Monitor 

32 153 17 (B) and Washington St. Monitor (A). 

33 153 22 at the Granite City monitor on average. The CAMx 

34 162 14 further which would provide monitoring data and 

35 167 9 monitoring data combined with statistical 

36 173 2 included in the monitoring data which is 

37 173 9 bias for NO3 impacts compared to monitored 

38 176 7 issues of is the monitor in the right 

39 176 17 the improved monitoring data at Bridger over 

40 176 23 I would submit if the monitor wasn’t 

41 177 6 say the monitor is in the wrong location and 

42 179 2 in the monitoring data. 

43 179 3 Monitoring data versus CALPUFF, 80,000 

44 180 12 should meteorological monitoring sites be 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 289 

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 3 Page Ref No. Keyword = "monitors" 

 4 ____ _______ ____________________ 

 5 

 6 21 20 had is you had two arcs of monitors that were 

 7 81 8 concentrations at 3 Atlanta monitors. An initial 

 8 82 9 monitor comparison at one of the NO2 monitors 

 9 83 4 other monitors as well. So they seemed 

10 90 7 of monitors and spend a lot of money and miss the 

11 152 6 monitors where we are asked to get the PM2.5 

12 152 23 projected 2009 design barriers at these monitors 

13 205 9 EPA to pursue. Problems - few long-term monitors 

14 

15 Page Ref No. Keyword = "near-field" 

16 ____ _______ ______________________ 

17 

18 142 22 necessary and useful for near-field applications. 

19 143 2 what about near-field applications? I think we 

20 143 5 working with near-field with photochemical 

21 143 10 review existing near-field applications using 

22 201 5 only. Primary PM2.5 provides highest near-field 

23 

24 Page Ref No. Keyword = "NEPA" 

25 ____ _______ ________________ 

26 

27 154 12 assessments as part of NEPA, Texas and Arkansas 

28 155 2 AQRV assessments as part of NEPA. Texas and 

29 172 7 and gas in the context of NEPA. You heard 

30 172 10 air quality impacts under NEPA 

31 172 13 NEPA analysis includes up to 700 sources and 

32 172 16 the best available science to support NEPA 

33 

34 Page Ref No. Keyword = "non regulatory" 

35 ____ _______ __________________________ 

36 

37 7 8 plays several roles. In the non regulatory 

38 95 15 a non regulatory capacity. We use them for fire 

39 

40 Page Ref No. Keyword = "NOAA" 

41 ____ _______ ________________ 

42 

43 11 2 This data set and these programs on the NOAA ARL 

44 11 9 what NOAA has done in terms of trying to you know 

45 12 14 on the NOAA webs site introduced a final metric 

46 15 3 something similar what the NOAA archive is where 

47 160 17 this including DTRA and NOAA who have linked MET 

48 161 20 NOAA) who have operational Met model-AQM systems 

49 162 9 Work with other agencies (DTRA, NOAA) who have 

50 180 20 for use of NOAA reanalysis data. 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 290 

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 3 Page Ref No. Keyword = "noaa" 

 4 ____ _______ ________________ 

 5 

 6 182 7 Who supplies it? NOAA and ECMWF. 

 7 

 8 Page Ref No. Keyword = "NSR" 

 9 ____ _______ _______________ 

10 

11 72 10 Appendix W [ed. for NSR and] (inaudible) PSD. 

12 200 23 NSR implementation rule, PM2.5 PSD SILs, SMCs, and 

13 

14 Page Ref No. Keyword = "OAQPS" 

15 ____ _______ _________________ 

16 

17 6 19 detail for OAQPS. 

18 14 4 came down on rotation to OAQPS in January and my 

19 

20 Page Ref No. Keyword = "observation" 

21 ____ _______ _______________________ 

22 

23 9 13 observation pairings. This is the Irwin 

24 27 5 the observation was. This is where we clearly 

25 32 22 observation were looking like for this. CALPUFF 

26 49 10 can observe both from the observational 

27 51 17 paired model observation sets which are actually 

28 52 11 all the data and observation are in the database 

29 56 15 model observation, model to model, summary 

30 65 8 a ranked observation verses prediction plot and 

31 69 4 fitted observation. 

32 69 6 you do something with the observation. The way 

33 101 14 instead of using observation. 

34 166 12 the airport wind observation to the land 

35 171 13 look at it against observational data and 

36 197 10 determine available observational datasets. 

37 198 6 observation. That sounds high to me. 

38 198 7 Consistency with results using observational 

39 198 12 other observational datasets. That’s all I have. 

40 207 24 specific is considered a valid observation if the 

41 

42 Page Ref No. Keyword = "observations" 

43 ____ _______ ________________________ 

44 

45 8 7 and tracers for observations that we can use for 

46 15 6 all the observations within the program. Anybody 

47 28 16 dating back to 1982 – 1983. The observations 

48 32 20 We were able to pull in the observations so that 

49 34 23 These are some of our initial observations from 

50 37 12 some of our observations. Did we go wrong in 
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 3 Page Ref No. Keyword = "observations" 

 4 ____ _______ ________________________ 

 5 

 6 48 14 observations, the predictions to our models to 

 7 48 15 the observations we see? 

 8 51 13 to compare observations against meteorological 

 9 51 25 observations and then model output. These are 

10 52 9 predictions and observations. 

11 53 2 side is it’s a different observations and a 

12 64 20 observations, unpaired in time and can be used 

13 68 20 Observations”? Observations can be measured by 

14 68 25 treating observations as snapshots of an 

15 123 21 •8 observations, 15-minute 

16 124 14 of observations; some under prediction of cross17 160 22 model predictions with observations. Specific 

18 161 13 observations can best be used and assimilated in 

19 162 2 experiments. Determine how met observations can 

20 163 21 observations, tracer releases, and PM and 

21 163 22 visibility observations over an area of about 200 

22 166 6 wind observations to the land characteristics of 

23 182 11 standard upper air and surface observations 

24 191 5 that agreed better with field observations. 

25 226 13 observations and combine them with more broadly 

26 

27 Page Ref No. Keyword = "observed" 

28 ____ _______ ____________________ 

29 

30 10 4 integrated concentration and observed the fitted 

31 28 13 from the observed and ...oh great. Sorry about all 

32 32 5 observed in terms of the absolute transport 

33 34 3 the model observed it had the best of spatial 

34 57 11 model observed, the bias between the model 

35 57 12 observed and also you can sub region this out 

36 57 14 showing observed and (inaudible) but you could 

37 66 15 observed and predicted concentrations where an FB 

38 69 22 volume, but observed concentrations represent 

39 77 9 observed concentration goes up it’s often highly 

40 77 13 observed concentration. That certainly suggests 

41 78 5 down, calms go up, observed concentrations go up 

42 112 13 what was observed by visible satellite imagery. 

43 181 21 based off of observed data and not a 

44 183 15 calms verses what the observed data might have. 

45 183 18 observed sounding and it has some really good 

46 198 9 observed met results. Evaluate results under 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 292 

 2 

 3 Page Ref No. Keyword = "ozone" 

 4 ____ _______ _________________ 

 5 

 6 81 20 Ozone Limiting Method to better account for NO to 

 7 100 16 ozone model, CD4, and we’ve got a mercury module. 

 8 127 12 of-the-science PiG model for ozone was initiated 

 9 128 7 ozone, where we conducted simulations for 

10 136 19 type of pollutant. UAM, REMSAD for Ozone, REMSAD 

11 136 25 treat all types of precursors species ozone and 

12 137 11 predict ozone, PM acid rain, visibility and 

13 138 20 ozone concentrations, no need for a constant 

14 138 21 ozone background value for PM, advanced aqueous 

15 140 11 for their contribution to ozone if you choose 

16 141 7 This is an example of ozone source 

17 141 10 ozone contribution from sources similarly to PM 

18 141 11 with reactive tracers, July maximum ozone 

19 146 4 modeling for Ozone and PM. 

20 146 18 assessment are the new more stringent Ozone and 

21 146 23 the contributions of source to the Ozone and 

22 147 5 individual contributions to ozone and PM2.5 

23 148 10 CMAQ have PM and Ozone Source Apportionment and 

24 149 6 evolution of the plume where there’s no Ozone 

25 149 9 those emissions and it starts forming Ozone and 

26 149 12 I think Kirk talked about the Ozone and 

27 150 14 Another application is the PM2.5 Ozone ASIP 

28 154 2 “single source” contributions to ozone, PM2.5, 

29 154 5 Full chemistry Plume-in-Grid modules. Ozone and 

30 154 16 “single source” contributions to ozone, PM2.5, 

31 154 19 Full chemistry Plume-in-Grid modules. Ozone and 

32 156 9 modeling single source for Ozone PM2.5 seems to 

33 167 3 ozone concentrations. This should be performed 

34 173 17 2) Background Ozone (surface, user 

35 173 24 1) Background Ozone; 

36 188 13 increase use of ozone models and I think this was 

37 209 21 and we need to do some other things for Ozone . 

38 

39 Page Ref No. Keyword = "parameter" 

40 ____ _______ _____________________ 

41 

42 11 24 Kolmogorov-Smirnov Parameter and basically it 

43 12 23 the KS parameter and then assigns a score from 0 

44 13 17 our false alarm ratio; the KS parameter, the 

45 113 25 (KS) parameter is the maximum difference between 

46 165 23 influence of nearby land use in parameterizing 

47 197 4 parameter. Sensitivity of prognostic model 

48 197 8 parameterizations and grid resolution. 
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 3 Page Ref No. Keyword = "parameters" 

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 5 

 6 12 19 those parameters. 

 7 114 5 Obviously you can add other parameters if you 

 8 197 5 parameters. Use of NCEP products (e.g., RUC 

 9 197 15 micrometeorological parameters, solar radiation, 

10 206 8 the use of ANL physical parameters for common 

11 

12 Page Ref No. Keyword = "particle" 

13 ____ _______ ____________________ 

14 

15 7 11 puff model, particle model for these types of 

16 13 10 particle model that we evaluated as part of this 

17 20 19 did was to include the two Lagrangian particle 

18 36 15 particle models for first 24 hours, has more 

19 93 5 on HYSPLIT and Joe Scire on the Puff Particle 

20 94 24 SCIPUFF), Lagrangian Particle Models (KSP, 

21 95 12 example of how we’ve used particle models in 

22 95 21 Lagrangian particle model called FLEXPART. We 

23 97 2 little awkward. HYbrid Single Particle 

24 99 20 Modelled particle distributions (puffs) can 

25 100 5 in a calculationc, when the particle is over the 

26 100 21 particle then contributes to the eularian 

27 100 25 the particle and the advection continues on. 

28 105 15 at the particle motion in one direction and a 

29 105 17 hybrid method always puts the particle in the 

30 105 19 particle approach would give us a more accurate 

31 106 4 particle concentrations you can see from the 

32 106 5 illustration what that turbulent particle 

33 106 7 those mean particle trajectories. It’s a 

34 107 10 3-D particle approach, just briefly, we’re 

35 107 20 the model. That’s the particle approach. 

36 108 11 what’s happening at the end of the particle is 

37 108 13 you don’t have enough particle density to give 

38 108 15 limitations with the particle approach. When you 

39 108 23 concentrations? Well each particle if you’re 

40 108 24 running the 3D particle model the change in 

41 109 2 contributed by that particle divided by the grid 

42 110 9 China in 2001. This was running the 3-D particle 

43 110 10 model and what you see here is the particle 

44 110 25 happened is the particle starts lining up with 

45 118 17 overview of puff particle model. 

46 118 19 about the particle puff model the PPM module 

47 119 16 purpose of the PPM the puff particle model is to 

48 119 18 particle approaches. In one of the elements of 

49 119 25 advantage is particle models over plume models 

50 120 8 stochastic particle models are state-of-the- 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 294 

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 3 Page Ref No. Keyword = "particle" 

 4 ____ _______ ____________________ 

 5 

 6 121 11 a full stochastic Lagrangian particle dispersion 

 7 121 20 particle to which it belongs. 

 8 122 16 PPM time step, new particle trajectories are 

 9 123 2 the size of the particle-puffs in the mirror 

10 124 17 Lagrangian particle dispersion model (LPFM) 

11 139 6 partitioning between gas and particle phase and 

12 

13 Page Ref No. Keyword = "PBL" 

14 ____ _______ _______________ 

15 

16 23 19 MM5 like ETA PBL and NOAH LSM. We’re not 

17 

18 Page Ref No. Keyword = "Phase 2" 

19 ____ _______ ___________________ 

20 

21 8 16 the update of the IWAQM and Phase 2 guidance is 

22 14 8 significantly and the IWAQM Phase 2 

23 17 7 (IWAQM Phase 2) to reflect lessons learned from 

24 19 18 IWAQM Phase 2 there’s talk about project MOHAVE 

25 

26 Page Ref No. Keyword = "photochemical" 

27 ____ _______ _________________________ 

28 

29 94 11 photochemical models in a more of a single source 

30 135 19 Morris on single source models and photochemical 

31 136 3 little bit about photochemical modeling and in 

32 136 4 general some of the features of the photochemical 

33 136 10 photochemical grid model system. Essentially you 

34 136 15 a dispersion model, simple photochemical box 

35 136 17 photochemical models like urban REMSAD models. 

36 136 18 Those photochemical models are geared to specific 

37 136 23 photochemical grid models are a one atmosphere 

38 137 23 Photochemical models the governing equation 

39 137 25 photochemical we’re trying to make chemical 

40 138 7 with photochemical models. The dispersion model 

41 138 16 For photochemical models advantages, one of 

42 138 17 the things in using a photochemical model for 

43 138 24 included, photochemical models generally have 

44 139 11 have been implemented in photochemical models 

45 141 2 species do in the photochemical model. The only 

46 141 21 the source would be located. The photochemical 

47 142 10 on with the photochemical model because it’s 

48 142 13 Applications was touched on Photochemical models 

49 142 19 (from States, RPOs, etc) for photochemical models 

50 142 23 The other thing about photochemical 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 295 

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 3 Page Ref No. Keyword = "photochemical" 

 4 ____ _______ _________________________ 

 5 

 6 143 5 working with near-field with photochemical 

 7 143 8 photochemical models like sub-cell receptor 

 8 143 14 through a photochemical model and that’s just an 

 9 145 9 the photochemical model really not a lot of 

10 145 16 photochemical grid models provide an opportunity 

11 147 9 chemistry. Photochemical Grid Models (PGMs) have 

12 

13 Page Ref No. Keyword = "PiG" 

14 ____ _______ _______________ 

15 

16 127 12 of-the-science PiG model for ozone was initiated 

17 129 23 of 14 PiG sources 

18 130 12 contribution by using the PIG treatment. You can 

19 131 16 in mercury deposition using the PIG treatment on 

20 131 21 overprediction was corrected by using PIG 

21 134 18 number of PiG sources, and this application would 

22 153 23 12/4/1 km PiG modeling attributes 3.4 μg/m3 to 

23 

24 Page Ref No. Keyword = "plume" 

25 ____ _______ _________________ 

26 

27 9 5 on the plume center line statistics and so those 

28 9 21 azimuth of plume centerline on an arc. Then it 

29 9 22 also looks at the horizontal spread of the plume 

30 9 24 the definition of the horizontal of the plume. 

31 9 25 For temporal pairing we looked at plume arrival 

32 10 12 to fit an average plume on arc so these were 

33 24 23 terms of you know you can see the plume you know 

34 24 24 the plume is wide here. I am encouraged by the 

35 25 5 this the plume signal were not exactly matching 

36 25 8 you can see the plume spread with P-G tends to be 

37 25 12 prediction of the plume width with the P-G class 

38 27 10 Now Plume Centerline, this is one of the 

39 27 15 plume was a little bit displaced to the NE of 

40 27 24 Then on the 600 km arc the plume (inaudible) 

41 28 17 were basically the plume was detected from 

42 29 4 was that the plume came up in this area here and 

43 29 14 displacement it had the plume you can see that 

44 29 15 the plume took it a little bit further trip to 

45 32 8 first 24 hours of plume as it (inaudible) along 

46 34 6 because of the way the plume was transported with 

47 34 9 plume in an area where nothing was being 

48 35 6 plume width. It looked like it was doing better 

49 35 11 performed well except for plume azimuth as I said 

50 40 12 trajectory of the plume. As a test, back when we 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 296 

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 3 Page Ref No. Keyword = "plume" 

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 5 

 6 63 14 with many sites where you can determine plume 

 7 63 15 centerline and plume sigma-y. You can determine 

 8 71 12 the plume dispersion predictions in CALPUFF 

 9 80 6 North West not directly so that the plume from 

10 86 15 putting the plume the release sight more in the 

11 90 4 conditions to conduct a field study. The plume 

12 90 8 plume completely because it went that way instead 

13 90 11 metric concentration really captured the plume 

14 94 22 use over the years: Gaussian Plume Models (ISC, 

15 95 3 CAMx), Plume-in-Grid, Single Source Apportionment 

16 95 8 capabilities. In these models are Plume in 

17 97 11 method; how to simulate plume dispersion, how to 

18 102 11 than a plume-in-grid, we’re going to have a grid19 102 24 that with Lagrangian plume model. From that 

20 104 7 like a plume because wind speed and direction 

21 104 10 and looks like a plume. But it’s just a mean 

22 105 8 deviation of the plume as it changes with time. 

23 108 14 you a smooth plume and that’s one of the 

24 109 14 plume. As we saw in that vertical distribution 

25 119 25 advantage is particle models over plume models 

26 125 15 plume–in-grid modeling, which basically consists 

27 125 16 of using a plume model within a grid model to 

28 125 24 4 km or 12 km, the plume has to travel through 

29 126 5 plume. So what we’re trying to do with a plume– 

30 126 6 in-grid model is to combine the plume model and 

31 126 7 the grid model and carry the plume along until it 

32 126 11 trying to do with the plume–in-grid model is to 

33 126 13 about yesterday – the early plume dispersion and 

34 126 14 the mid-range plume dispersion, and the grid 

35 126 17 plume to the grid model. 

36 126 19 consists of a reactive plume model embedded 

37 126 20 within a 3-D grid model. The plume model 

38 126 23 plume model. At the time we hand over the plume 

39 127 3 plume model. 

40 127 4 Plume-in-grid modeling is not new; it began in 

41 127 6 PARIS - Plume-Airshed Reactive-Interacting 

42 127 9 no treatment of wind shear or plume overlaps, no 

43 127 14 The embedded plume Model is SCICHEM (state-of44 127 22 order closure approach for plume dispersion and 

45 128 5 (Advanced Plume Treatment). 

46 128 25 the plume-in-grid model, it is based on CMAQ 4.6, 

47 129 17 plume-in-grid approach. Model performance 

48 130 3 plume–in-grid. The right side shows the results 

49 130 4 of CMAQ-AERO3-APT with plume–in-grid. There is a 

50 130 9 model and 2.4 μg/m3 for the plume-in- grid model. 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 297 

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 3 Page Ref No. Keyword = "plume" 

 4 ____ _______ _________________ 

 5 

 6 130 20 overestimated. Plume-in-grid PM modeling 

 7 138 8 shown on the left with the plume (inaudible) at a 

 8 138 9 particular source plume kind of in its own 

 9 143 25 sub-grid plume treatment. To make a long story 

10 147 12 source plume chemistry and dispersion? 

11 147 15 the source to resolve near-source plume chemistry 

12 148 6 plume chemistry and dispersion without providing 

13 148 7 met and emission inputs and full chemistry Plume14 149 6 evolution of the plume where there’s no Ozone 

15 149 11 the Plume in Grid model. 

16 151 11 point source plume would be computationally 

17 151 16 grids. Plume-in-Grid to address near-source 

18 154 5 Full chemistry Plume-in-Grid modules. Ozone and 

19 154 19 Full chemistry Plume-in-Grid modules. Ozone and 

20 160 2 recommendation for Plume in Grid (PinG) modeling? 

21 166 22 We are interested in the Plume Molar Volume 

22 174 2 3) Plume NOx Concentration 

23 191 22 visualizations what happens is the plume 

24 192 11 We think the plume is being caught in the cavity 

25 

26 Page Ref No. Keyword = "PRIME" 

27 ____ _______ _________________ 

28 

29 85 25 with the PRIME downwash algorithms since it 

30 107 16 prime, which is the standard deviation of 

31 189 2 Prime, it’s going to be hard to treat complex 

32 189 8 Ultimately, PRIME needs the building shape 

33 189 25 Prime Algorithms. With AERMOD/PRIME building 

34 191 16 building. PRIME cavity and wake dimensions: W = 

35 

36 Page Ref No. Keyword = "processor" 

37 ____ _______ _____________________ 

38 

39 196 5 The other point is the processor. Do not 

40 

41 Page Ref No. Keyword = "processors" 

42 ____ _______ ______________________ 

43 

44 133 23 quarter on different processors or machines. A 

45 221 14 the problem at the source. Multi-core processors 

46 222 2 outboard processors and programming tools for 
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 3 Page Ref No. Keyword = "profile" 

 4 ____ _______ ___________________ 

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 6 54 19 Profiler, and Aircraft Profiler. 

 7 56 3 This is a wind profiler comparison over time 

 8 56 6 you have wind profile information,. a nice plot. 

 9 71 15 rose profile misrepresentation, among other 

10 

11 Page Ref No. Keyword = "promulgation" 

12 ____ _______ ________________________ 

13 

14 21 16 published supporting the promulgation of CALPUFF. 

15 158 19 Promulgation of more stringent ambient 

16 213 3 rulemakings associated with promulgation and 

17 

18 Page Ref No. Keyword = "protocol" 

19 ____ _______ ____________________ 

20 

21 37 7 protocol drafted and it describes the 

22 42 8 with the State in which a common protocol had 

23 42 16 make a change in the current protocol to go from 

24 45 11 way or the other. If Herman had a protocol in 

25 45 13 to deviate from the protocol you have to have 

26 46 3 deviation of a protocol or questioning about the 

27 175 22 the IWAQM protocol, has a substantial bias 

28 199 12 protocol, and basically, we have all the 

29 

30 Page Ref No. Keyword = "protocols" 

31 ____ _______ _____________________ 

32 

33 165 3 modeling protocols and decision making. We also 

34 

35 Page Ref No. Keyword = "PSD" 

36 ____ _______ _______________ 

37 

38 41 6 for PSD Class I increments that, in all cases, 

39 41 8 for PSD Class I increments. This was from Tim 

40 43 14 PSD. 

41 72 10 Appendix W [ed. for NSR and] (inaudible) PSD. 

42 200 23 NSR implementation rule, PM2.5 PSD SILs, SMCs, and 

43 200 25 pending), and the PSD increment modeling 

44 203 24 guidance, PSD increments and modeling procedures. 

45 213 9 standards or the PSD increments. What is in the 
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 3 Page Ref No. Keyword = "puff" 

 4 ____ _______ ________________ 

 5 

 6 7 11 puff model, particle model for these types of 

 7 17 16 things. Can puff-splitting extend the effective 

 8 22 24 Puff-Splitting was turned on for the 600 km 

 9 23 5 consider puff splitting. But since we were 

10 31 15 does. This is a good test for puff splitting 

11 31 18 felt this was a good test for puff splitting. 

12 33 7 with the puff-splitting turn on we weren’t 

13 33 15 and see how this puff-splitting will make a 

14 36 20 looking at Puff-splitting did not change CALPUFF 

15 36 22 at puff-splitting (eliminating mixing height 

16 37 3 puff-splitting in CALPUFF. 

17 93 5 on HYSPLIT and Joe Scire on the Puff Particle 

18 94 23 AERMOD), Gaussian Puff Models (INPUFF, CALPUFF, 

19 105 4 Another one of the possibilities is the PUFF 

20 105 16 puff type approach in the other direction. The 

21 106 13 looking at the center of the puff and that 

22 106 19 Slide 9. As far as the puff distribution, 

23 107 21 Now for the puff approach we’re using the 

24 107 24 puff. It’s also a function of the turbulent 

25 108 5 deviation, the made as modeling the puff if you 

26 108 19 simulation. That’s why we have this puff 

27 109 3 cell volume. If you’re using some kind of puff 

28 109 4 approach it’s the mass of the puff divided by the 

29 109 5 volume of the puff, basically. The approach is 

30 109 10 puff approach. Here’s an example on the right 

31 109 13 500 Hybrid puff approach gives a smoother looking 

32 109 17 and having the puff approach in the horizontal 

33 118 17 overview of puff particle model. 

34 118 19 about the particle puff model the PPM module 

35 119 16 purpose of the PPM the puff particle model is to 

36 119 17 try to combine the advantages of both puff and 

37 120 14 If you look at the Puff model types there 

38 120 15 are a couple of types within the class of puff 

39 120 16 models. One is the ensemble average puff model 

40 120 17 and CALPUFF would this type. We have a puff that 

41 120 23 cluster dispersion puff model where a puff is a 

42 121 2 eddies smaller than the puff) contribute to puff 

43 121 7 Instantaneous puff releases require use of 

44 121 12 model to determine the puff trajectory. I’ll 

45 121 15 eddies smaller than the puff size is removed 

46 121 17 relative dispersion. Every puff carries along 

47 121 22 eddies larger than the puff but not resolved by 

48 121 23 the flow is simulated by the puff center 

49 122 10 released puff a “mirror ensemble” is attached. 

50 122 12 number of puff-particles. The time step broken 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 300 

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 3 Page Ref No. Keyword = "puff" 

 4 ____ _______ ________________ 

 5 

 6 122 17 computed, from which the puff trajectories are 

 7 122 21 puff’s size and position and then handed back to 

 8 122 24 changing the puff’s mass or chemical composition 

 9 123 4 energy spectrum will be within the puff-particle. 

10 123 6 dispersion. At that point, the parent puff 

11 123 8 ensemble is deleted and the parent puff is 

12 123 9 restored. Parent puff treated in normal CALPUFF 

13 127 21 three-dimensional puff-based model, with second14 127 23 treatment of puff splitting and merging. SCICHEM 

15 132 23 receptor locations by combining incremental puff 

16 133 15 number of point sources are treated with the puff 

17 160 18 models with MM5 and WRF and the Puff models. 

18 162 22 terrain, short term puff dispersion, chemical 

19 174 15 CMAQ MESO PUFF II chemistry. The blue dots 

20 211 8 occur and require PUFF modeling. We’ll be 

21 

22 Page Ref No. Keyword = "ratio" 

23 ____ _______ _________________ 

24 

25 13 17 our false alarm ratio; the KS parameter, the 

26 55 12 mixing ratio, wind speed, wind direction, but 

27 117 16 bias was a ratio 1.37. Okay. 

28 166 23 Ratio Model (PMVRM. We like for EPA to further 

29 178 12 Colorado, if you look at the ratio at the 

30 

31 Page Ref No. Keyword = "ratios" 

32 ____ _______ __________________ 

33 

34 64 22 plots are plots of ratios of predicted/observed 

35 189 16 aspect ratios. Use of wind tunnel testing to 

36 191 9 aspect ratios. Short/large industrial facilities 

37 198 4 the ratios were about a factor of 2 or 1.5 to 2 

38 

39 Page Ref No. Keyword = "receptor" 

40 ____ _______ ____________________ 

41 

42 74 4 concentrations that each receptor along the arc. 

43 132 23 receptor locations by combining incremental puff 

44 143 8 photochemical models like sub-cell receptor 

45 155 19 receptor if you like. These models are terrain 

46 185 7 2nd high. Receptor location and data of 1st and 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 301 

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 3 Page Ref No. Keyword = "regulatory" 

 4 ____ _______ ______________________ 

 5 

 6 7 8 plays several roles. In the non regulatory 

 7 7 12 activities. In the regulatory community we use 

 8 31 13 recommended for regulatory. It’s not sitting and 

 9 33 13 this is well beyond the regulatory range of 

10 38 19 context to meet the regulatory needs under 

11 47 23 in the regulatory policy context. 

12 67 22 Regulatory Purposes – Model Validation Kit. It is 

13 72 25 requirements of operational Regulatory Dispersion 

14 73 5 but for regulatory models need to predict the 

15 73 18 regulatory model evaluation this is prairie grass 

16 87 24 for regulatory review. There’s a wide variation, 

17 88 3 almost always gravitated in the regulatory 

18 93 11 the regulatory not necessarily in the regulatory 

19 93 17 know after 9/11 a lot of the regulatory agencies 

20 94 4 application in the future and for the regulatory 

21 94 17 the regulatory community will have to deal with. 

22 95 11 regulatory realm. I just wanted to give you an 

23 95 15 a non regulatory capacity. We use them for fire 

24 127 19 by-case basis for regulatory applications (also 

25 145 5 the regulatory set of options which probably 

26 145 21 models are routinely used for other regulatory 

27 145 23 Implementation Plans so they do have regulatory 

28 164 17 we’re talking about the context of regulatory 

29 165 6 available to and used by regulatory decision 

30 166 18 if that year of data is suitable for regulatory 

31 198 20 regulatory requirements, challenges to PM2.5 

32 202 18 its Interim Regulatory Impact Analysis (RIA) for 

33 203 12 To summarize: PM2.5 modeling in a regulatory 

34 204 5 in a regulatory context. That’s it. Let’s see 

35 209 24 control of the regulatory code. The developer 

36 212 17 Air Regulatory Group (UARG). UARG is an ad hoc 

37 215 10 memorandum about the regulatory status of CALPUFF 

38 220 8 Regulatory Air Quality Models (AQM). They 

39 

40 Page Ref No. Keyword = "roughness" 

41 ____ _______ _____________________ 

42 

43 76 14 proximity but different settings. Low roughness 

44 76 16 airport. Then higher roughness at the SEARCH 

45 80 22 roughness sensitivity and this is more recent. 

46 81 9 assessment was that low surface roughness used to 

47 81 11 roughness typical of source locations, and 

48 81 13 roughness to address that. 

49 83 12 AERSURFACE pretty high roughness about 0.8 meters 

50 83 15 supplementation with its roughness which is 
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 3 Page Ref No. Keyword = "roughness" 

 4 ____ _______ _____________________ 

 5 

 6 83 22 the SEARCH site with the higher roughness. 

 7 84 7 to see due to surface roughness itself. 

 8 165 24 surface roughness to a 1 km radius of ASOS 

 9 166 4 by surface roughness of airport property. Better 

10 166 10 the surface modeling of the airport roughness. 

11 206 25 is too short of a fetch distance. Low roughness 

12 

13 Page Ref No. Keyword = "RUC" 

14 ____ _______ _______________ 

15 

16 197 5 parameters. Use of NCEP products (e.g., RUC 

17 

18 Page Ref No. Keyword = "rule" 

19 ____ _______ ________________ 

20 

21 161 6 developers recommend 4 km as a safe general rule, 

22 200 23 NSR implementation rule, PM2.5 PSD SILs, SMCs, and 

23 200 24 increments (proposed 9/21/07; final rule 

24 201 2 procedures (proposed 6/6/07; final rule pending). 

25 213 3 rulemakings associated with promulgation and 

26 214 18 notice-and-comment of rulemaking to change any 

27 218 23 Until the new recent rule, this was not much of 

28 

29 Page Ref No. Keyword = "run" 

30 ____ _______ _______________ 

31 

32 15 5 have the MM5 data that was run up there and have 

33 31 3 MM5 is run again and was initialized with 

34 31 5 was run with Great Plains with the exception we 

35 43 23 running with the NOOBS only and with P-G, and 

36 74 18 has run AERMOD and getting results they don’t 

37 75 9 SIP. Basically AERMOD was run initially with 

38 75 18 series plot running the model with the airport 

39 100 23 run. The concentration change is linearly 

40 102 17 you’re running the lagrangian model for all the 

41 103 25 recognize the geography. Why would I run Spain? 

42 106 11 this is running with 3-D Puffs and we are not 

43 108 24 running the 3D particle model the change in 

44 110 9 China in 2001. This was running the 3-D particle 

45 112 6 wildfire smoke forecast that is running. You can 

46 114 10 those tracer experiments we have run....the first 

47 144 4 meteorology output from MM5. CALPUFF was run in 

48 150 5 Rather than running each one individually we 

49 150 6 decided to do group analysis and run them in 

50 166 21 information is not provided until AERMOD is run. 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 303 

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 3 Page Ref No. Keyword = "run" 

 4 ____ _______ _______________ 

 5 

 6 178 23 issue is as new production was run in that 

 7 183 11 pull some of this data in and run it through 

 8 184 15 versions of AERMOD that couldn’t quite run in 

 9 185 5 concentration for SO2). NARR run within 5% of 

10 185 6 control for 1st high. NARR run within .07% for 

11 185 10 newbie when it comes to running AERMET and 

12 196 21 for running AERMOD or CALPUFF. I think it seems 

13 209 5 usage experience. We know how to run it and have 

14 209 6 been running it for years. It has better 

15 218 2 fixes that users have encountered in running 

16 

17 Page Ref No. Keyword = "rural" 

18 ____ _______ _________________ 

19 

20 124 4 •187m power plant stack, rural 

21 168 21 evaluate regional models in rural regions in the 

22 

23 Page Ref No. Keyword = "scale" 

24 ____ _______ _________________ 

25 

26 48 4 community multi scale air quality model from the 

27 82 8 were on the same scale. This is sort of model to 

28 101 16 scale type of situation. I’m not going to argue 

29 101 20 large scale experiments the resolution of the 

30 108 17 scale (inaudible) for global background, it is 

31 110 8 scale. This was the massive dust storm from 

32 111 2 the large scale weather patterns at the frontal 

33 112 18 down on the local scale. This is down to the 80 

34 112 19 km scale we’re looking at a tracer experiment we 

35 125 17 capture fine scale variability next to emissions 

36 126 21 captures the local scale variability and the grid 

37 127 16 grid scale)-developed by L-3 Communications/Titan 

38 202 21 Local Scale Analysis (2005). Any of these 

39 225 25 local scale analysis using AERMOD to (inaudible) 

40 

41 Page Ref No. Keyword = "SCRAM" 

42 ____ _______ _________________ 

43 

44 16 2 SCRAM for the evaluation data sets for the 

45 17 23 them up on the SCRAM web site. That was 2000 and 

46 184 9 How do I test it? He said to go on the SCRAM 

47 214 8 as SCRAM nor is it sufficient to publish the 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 304 

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 3 Page Ref No. Keyword = "screening" 

 4 ____ _______ _____________________ 

 5 

 6 142 8 screening metric states they obviously agree with 

 7 165 10 We understand the screening model, 

 8 

 9 Page Ref No. Keyword = "sensitivity" 

10 ____ _______ _______________________ 

11 

12 45 4 So we did that one additional sensitivity test 

13 80 22 roughness sensitivity and this is more recent. 

14 85 7 The other thing is the sensitivity of model 

15 86 7 you can have a whole lot of sensitivity or not 

16 134 13 emission scenarios and other emission sensitivity 

17 195 9 Evaluation variables, Sensitivity to prognostic 

18 196 25 There will likely be a large sensitivity of 

19 197 4 parameter. Sensitivity of prognostic model 

20 197 6 fields) and they are free. Sensitivity of 

21 206 20 Sensitivity of modeling to surface 

22 207 6 2008 Annual Meeting on sensitivity modeling. We 

23 226 23 also an intent to do an sensitivity studies as to 

24 

25 Page Ref No. Keyword = "service" 

26 ____ _______ ___________________ 

27 

28 42 21 Service and the Fish & Wildlife and the Park 

29 42 22 Service because they wanted some demonstrations, 

30 44 9 the Park Service we said okay and what is it that 

31 49 22 not doing a service to the community. 

32 112 7 go to our web page and also the weather service 

33 112 9 service page partly because we offer ways for 

34 181 13 service areas. I’ve only been there for one 

35 181 15 weather service for about 15 years. That’s 

36 225 18 Mark Garrison from ERM. We service the Air 

37 226 15 National Weather Service Stations. Then 

38 

39 Page Ref No. Keyword = "site" 

40 ____ _______ ________________ 

41 

42 8 24 that you can find on the EPA web site are done by 

43 12 14 on the NOAA webs site introduced a final metric 

44 15 7 can go on the web site and get that data and do 

45 15 22 assembled and get them up on the web site so that 

46 15 24 themselves similar to the datum web site and 

47 15 25 similar to what Roger has on the web site for 

48 17 23 them up on the SCRAM web site. That was 2000 and 

49 76 13 SEARCH site pretty closes by showing the 

50 76 17 site. It was sited direct within a neighborhood 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 305 

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 3 Page Ref No. Keyword = "site" 

 4 ____ _______ ________________ 

 5 

 6 76 24 site. It’s not real dramatic terrain features 

 7 78 16 compared with met SEARCH site and airport site to 

 8 79 6 light wind. For the SEARCH site you can clearly 

 9 79 18 the SEARCH site that’s matched with the model 

10 79 21 site. One of the things that is going on there 

11 80 4 Whereas the SEARCH site which is right next 

12 83 22 the SEARCH site with the higher roughness. 

13 84 2 airport and the SEARCH site didn’t seem to be 

14 85 3 And also another non standard airport site, 

15 85 4 the Texas (inaudible) site, I think we looked at. 

16 101 19 you have on site meteorology. But for these 

17 114 21 site download and convert that data so that you 

18 115 12 web site. Let’s look at one briefly. Of course 

19 167 17 etc.) as opposed to statistical fits to site 

20 176 5 distribution site and the red line is what 

21 182 9 Potentially a source for site specific data - 

22 183 4 might get some more site specific data but if 

23 184 10 site and use some of the cases that are there. 

24 184 18 only could get one site so I used this case 

25 184 22 data (and on site data). Re-run with NARR (ed. 

26 185 2 air site. 

27 190 10 tower, a site drawing as you might call it. Now 

28 207 8 for met and application site surface 

29 207 11 that differences in site surface characteristics 

30 207 23 Roger Brode: Technically, if a site 

31 212 4 site. And for those of you are interested, AWMA 

32 214 7 draft meeting agenda on the agency web site such 

33 215 2 on its web site several guidance memoranda that 

34 

35 Page Ref No. Keyword = "source" 

36 ____ _______ __________________ 

37 

38 39 18 terrain and also the source location relative to 

39 39 19 the Class I analysis and exactly where the source 

40 40 15 differenr source – Class I area pairs -- looking 

41 51 4 It’s a combination of several Open Source 

42 51 9 these are available open source and we designed 

43 52 23 Then often because R is open source users 

44 54 10 open source pretty much free of charge. 

45 80 2 monitor from the source that is the closest 

46 80 3 source. 

47 80 5 to the source the drainage flow is more from the 

48 80 12 pulling a different source. 

49 81 11 roughness typical of source locations, and 

50 81 24 context. Also we looked at the source 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 306 

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 3 Page Ref No. Keyword = "source" 

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 5 

 6 84 8 The next one is more on the source 

 7 85 8 results to source characterization options for 

 8 85 20 source with an initial Sigma Z or volume source 

 9 86 11 different met data and different source 

10 88 9 picked a certain source type, -- you may not be 

11 89 11 In Alabama, and these other source types, are 

12 89 13 these other source types which drive all these 

13 94 11 photochemical models in a more of a single source 

14 95 3 CAMx), Plume-in-Grid, Single Source Apportionment 

15 95 9 Grid and single Source Apportionment technique 

16 98 21 calculations in the meteorology for each source. 

17 101 4 PC and Mac executables, and UNIX (LINUX) source. 

18 102 5 ensemble, matrix, and source attribution options. 

19 111 5 off in the source location but you might be 

20 125 8 next part of this which is the Single Source 

21 130 6 especially near the source regions and even 

22 130 7 further away from the source regions. The 

23 130 14 the source region. Even further away it’s about 

24 130 22 source transport and chemistry of point source 

25 135 19 Morris on single source models and photochemical 

26 136 6 source modeling and tracking that type of thing. 

27 138 9 particular source plume kind of in its own 

28 138 18 single source is full state of the science gas29 139 7 Source Apportionment tools allow for tracking of 

30 139 10 More recently, Source Apportionment tools 

31 139 16 single source applications. I’ll show some 

32 139 17 examples in a minute. Source Apportionment 

33 140 3 pretty self explanatory. Source Apportionment 

34 140 6 contributions from emissions source groups, 

35 140 7 emissions source regions, and initial and 

36 140 9 model species for each contributing source. 

37 140 21 particular source you would just include 

38 141 7 This is an example of ozone source 

39 141 12 contribution from a source shown at right and 

40 141 14 source apportionment. 

41 141 15 This is an example of using Source 

42 141 19 estimation from that particular source in each 

43 141 21 the source would be located. The photochemical 

44 141 23 contribution from that source to ammonium 

45 141 25 species. So clearly this particular source has 

46 142 5 from a particular source over an entire year. 

47 142 12 Issues for using PCM for Single Source 

48 143 21 single source modeling with CAMx PSAT to compare 

49 143 23 States did single source visibility modeling for 

50 145 17 for credible single source modeling with Source 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 307 

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 3 Page Ref No. Keyword = "source" 

 4 ____ _______ __________________ 

 5 

 6 146 3 will get more details from Ralph on single source 

 7 146 17 photo grid models for the single source 

 8 146 21 seeing now more and more what is my source or are 

 9 146 23 the contributions of source to the Ozone and 

10 147 12 source plume chemistry and dispersion? 

11 147 15 the source to resolve near-source plume chemistry 

12 147 25 capability for PGM for single source but we do 

13 148 5 can specify fine grid to resolve point source 

14 148 9 chemistry of point source plumes. Both CMAx and 

15 148 10 CMAQ have PM and Ozone Source Apportionment and 

16 148 11 allows individual source(s) assessments. Of 

17 148 23 point source plumes. Available within the CAMx 

18 149 13 PM Source Apportionment so I don’t have to talk 

19 149 19 in the eastern U.S. Individual point source 

20 150 13 areas. Use IRON P-in-G for Texas BART Source. 

21 151 5 inconsistent source contributions with 2009 PM2.5 

22 151 7 inappropriate for individual point source 

23 151 9 treat chemistry and dispersion of point source 

24 151 11 point source plume would be computationally 

25 151 13 31 zero-out runs to get individual source 

26 151 17 chemistry and dispersion. PM Source 

27 151 19 individual source contributions. 

28 151 25 2009 base case with PSAT PM2.5 source 

29 152 14 Here’s the source apportionment. The 

30 153 6 contributions. The largest single source 

31 153 7 contribution is this source right near the 

32 153 9 contribution source on a monitor. In this case 

33 154 2 “single source” contributions to ozone, PM2.5, 

34 154 6 PM source apportionment. Full gas-phase and 

35 154 9 source” air quality, visibility and deposition 

36 154 11 source PM2.5 assessment. Oil and gas AQ and AQRV 

37 154 16 “single source” contributions to ozone, PM2.5, 

38 154 20 PM source apportionment. Full gas-phase and 

39 154 23 “single source” air quality, visibility and 

40 154 25 point source PM2.5 assessment. Oil and gas AQ and 

41 155 6 any questions on single source? 

42 156 9 modeling single source for Ozone PM2.5 seems to 

43 166 7 the pollutant source domain. For most pollutant 

44 166 13 characteristics of the pollutant source domain. 

45 176 9 questions. The issue is the source region 

46 178 11 this is a single source area in Central 

47 180 24 can use reanalysis as a source for 

48 182 9 Potentially a source for site specific data - 

49 183 8 attractive at least in upper air data source 

50 186 19 develop and release the Industrial Source [ed. 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 308 

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 3 Page Ref No. Keyword = "source" 

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 5 

 6 189 21 source characterization study which generally has 

 7 199 4 some condensing from vapor – source/fuel- 

 8 199 5 specific. It is emitted directly from a source 

 9 202 14 source types from EPA’s AP-42, SPECIATE, and FIRE 

10 203 2 PM2.5 NAAQS Impact: Background + Source Impact. 

11 203 4 source impact to peak percentile background, 

12 203 8 source impact concentrations. If daily 

13 206 24 source. For tall stack, buoyant releases, 1 km 

14 216 12 techniques in new source permitting situations to 

15 218 10 the evaluation of new source permitting. PM2.5 

16 218 20 component. Also, for single source new 

17 221 14 the problem at the source. Multi-core processors 

18 

19 Page Ref No. Keyword = "speed" 

20 ____ _______ _________________ 

21 

22 55 9 see the distribution and wind speed in your data 

23 55 12 mixing ratio, wind speed, wind direction, but 

24 56 4 and then (inaudible) and you see the wind speed, 

25 63 7 evaluation of low wind speed databases with API 

26 64 23 conc vs. downwind distance or wind speed, etc. 

27 66 6 dependent on the wind speed as well. When you 

28 74 25 into the wind speed issue as Bob Paine mentioned 

29 75 11 include sonic anemometer with lower wind speed 

30 75 12 stretched so they had lots of light wind speed 

31 77 12 lot of light wind speed, upward spike in the 

32 89 19 specifically under light wind speed conditions is 

33 104 7 like a plume because wind speed and direction 

34 104 14 speed shear with height. And that is really 

35 120 5 causality effects, low wind speed dispersion, 

36 193 8 ran AERMOD for these 3 cases for 1 wind speed. 

37 197 12 speed, wind direction, Frequency of light wind 

38 204 13 The Low wind speed issues. Modeling of 

39 207 3 wind speed issues. Moisture assigned only on an 

40 207 25 wind speed threshold is treated the same way as a 

41 209 7 handling, low wind speed stagnation, coastal and 

42 

43 Page Ref No. Keyword = "stack" 

44 ____ _______ _________________ 

45 

46 40 4 stack is in the valley -- with coarse resolution, 

47 40 9 and the peaks are higher so maybe the stack now 

48 56 19 specific; stack box plots (inaudible) box plots. 

49 57 21 entire year. Stack bar plots, this is more 

50 74 10 tall stack or evaluation data base that was used 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 309 

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 3 Page Ref No. Keyword = "stack" 

 4 ____ _______ _________________ 

 5 

 6 124 4 •187m power plant stack, rural 

 7 127 15 the science treatment of stack plumes at the sub- 

 8 189 9 and position that places stack in the correct 

 9 193 22 stack height regulation defines nearby terrain 

10 193 23 for the purpose of limiting stack heights. Past 

11 202 16 reviewed stack test data to develop emission 

12 202 22 factors are based on stack test methods known to 

13 206 24 source. For tall stack, buoyant releases, 1 km 

14 

15 Page Ref No. Keyword = "stacks" 

16 ____ _______ __________________ 

17 

18 40 6 stacks are no longer below the terrain height. 

19 71 20 of the applications are for tall stacks. For 

20 89 7 scheme of things, this has primarily been stacks, 

21 89 8 elevated stacks. To what degree do we have good 

22 208 22 quality impact from (inaudible) stacks from long 

23 

24 Page Ref No. Keyword = "statistical" 

25 ____ _______ _______________________ 

26 

27 7 20 such, we believe statistical measures should 

28 9 11 decide to augmented statistical measures focusing 

29 10 21 Draxler et al. (2001). These statistical 

30 10 22 measures are a broad set of statistical measures. 

31 11 14 These are the statistical measures and these 

32 13 23 for this statistical component of it. So that’s 

33 34 18 terms of the statistical data. It did marginally 

34 44 5 the arc statistical program and he was plotting 

35 51 7 MYSQL, another one is R a statistical package 

36 52 19 statistical metrics. Diurnal Statistics, Time 

37 67 2 determine the statistical significance of 

38 67 11 then that means the models are not statistically 

39 68 17 they cross zero, they are statistically unbiased 

40 167 9 monitoring data combined with statistical 

41 167 17 etc.) as opposed to statistical fits to site 

42 167 19 statistical models in place of more rigorous 

43 168 2 approving statistical fits. 

44 

45 Page Ref No. Keyword = "steady state" 

46 ____ _______ ________________________ 

47 

48 7 10 modeling so we use non steady state (inaudible) 

49 160 10 models such as MM5) for both steady state and 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 310 

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 3 Page Ref No. Keyword = "surface" 

 4 ____ _______ ___________________ 

 5 

 6 71 19 tower data, not just surface data because a lot 

 7 80 21 Another issue that comes up is surface 

 8 81 9 assessment was that low surface roughness used to 

 9 81 18 source surface characteristics of the sources. 

10 83 11 data process with surface characteristics using 

11 83 25 surface characteristics differences between the 

12 84 7 to see due to surface roughness itself. 

13 165 24 surface roughness to a 1 km radius of ASOS 

14 166 4 by surface roughness of airport property. Better 

15 166 10 the surface modeling of the airport roughness. 

16 174 5 surface relative humidity (RH). In reality, 

17 182 11 standard upper air and surface observations 

18 183 25 surface up to 700 mb and above 200 mb. Between 

19 184 21 It uses Pittsburgh PA surface and upper air 

20 185 17 requirements of surface data for AERMOD. 

21 206 20 Sensitivity of modeling to surface 

22 206 23 mismatch in surface type between met tower and 

23 207 8 for met and application site surface 

24 207 11 that differences in site surface characteristics 

25 

26 Page Ref No. Keyword = "surrogate" 

27 ____ _______ _____________________ 

28 

29 200 20 EPA had a PM10 surrogate policies for compliance 

30 218 25 a surrogate. But now with the EPA delegated 

31 

32 Page Ref No. Keyword = "temperature" 

33 ____ _______ _______________________ 

34 

35 54 23 temperature and the one on the right for wind 

36 55 3 different temperature ranges and then a box plot 

37 174 10 minimum temperature will overstate SO4 and 

38 197 13 speeds, etc., vertical wind and temperature 

39 197 14 structure, temperature & relative humidity, 

40 

41 Page Ref No. Keyword = "terrain" 

42 ____ _______ ___________________ 

43 

44 19 23 good complex terrain to it which would be useful. 

45 28 22 that explains why we’re not seeing the terrain of 

46 35 25 study are updated terrain and land use from old 

47 39 9 in CALPUFF such as you resolve the terrain 

48 39 18 terrain and also the source location relative to 

49 39 23 impacts where the terrain may channel the flow 

50 40 5 the terrain may get smoothed so much so that the 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 311 

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 3 Page Ref No. Keyword = "terrain" 

 4 ____ _______ ___________________ 

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 6 40 6 stacks are no longer below the terrain height. 

 7 40 14 the effect of terrain resolution from 90 

 8 40 24 a split of higher and lower terrain resolution. 

 9 44 10 the terrain causing this or the land use. So I 

10 44 13 flattened the terrain so that is was 1 meter 

11 44 14 terrain for the single land use. I had all the 

12 44 20 terrain and land use were making a difference 

13 70 24 complex terrain). Conditions of concern for 

14 76 21 This is a terrain plot and it’s not very clear 

15 76 23 more significant terrain features around the 

16 76 24 site. It’s not real dramatic terrain features 

17 100 6 high resolution terrain it would use that data 

18 105 24 (5000). If you don’t recognize the terrain this 

19 155 9 analysis do you treat terrain elevations of the 

20 155 17 as far as the terrain the receptors are at the 

21 155 19 receptor if you like. These models are terrain 

22 155 22 Joe Scire: There’s no terrain 

23 155 25 Ralph Morris: Yes, the terrain 

24 156 2 (inaudible) so any terrain effects are in the 

25 162 22 terrain, short term puff dispersion, chemical 

26 163 16 mountainous terrain, such as Wyoming where there 

27 165 20 distance limits and whether “complex terrain” is 

28 188 23 and Terrain Wake Effects. 

29 193 21 Terrain wake effects; currently the GEP 

30 193 22 stack height regulation defines nearby terrain 

31 193 25 terrain can be significant. Currently this effect 

32 194 4 two when terrain wake effect is accounted for 

33 194 7 upwind terrain wake effects should be considered. 

34 194 9 developed to determine wind up wind terrain and 

35 194 13 EPA wind tunnel where they showed these terrain 

36 194 25 accounting for upwind terrain wake effects. That 

37 198 11 flat, rolling terrain, mountainous, tracer or 

38 209 8 air issues. Complex terrain and slow reversal 

39 

40 Page Ref No. Keyword = "toxics" 

41 ____ _______ __________________ 

42 

43 132 3 large spatial variability in air toxics 

44 137 9 VOC, SOx, PM and toxics and use data science 

45 137 12 toxics, and even deposition. 

46 140 12 that. There are also some toxics components but 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 312 

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 3 Page Ref No. Keyword = "tracer" 

 4 ____ _______ __________________ 

 5 

 6 7 25 meteorological and tracer databases for 

 7 8 4 mesoscale tracer studies but there is no one 

 8 8 23 done on these mesocale tracer studies. The two 

 9 8 25 the Great Plains Tracer Mesocale Tracer Study and 

10 13 12 model; this is a European tracer experiment and I 

11 13 21 the model performed in that particular tracer 

12 14 24 mentioned earlier is to assemble a tracer and 

13 16 24 assembled tracer database. Then like I said 

14 17 2 LRT models for the assembled tracer database to 

15 17 20 awaiting any tracer evaluations that had been 

16 18 10 these tracer data bases looking at both 

17 19 2 The tracer experiments that we have 

18 19 3 currently we have the Great Plains Tracer 

19 19 5 lot today. Savannah River Laboratory Tracer 

20 19 8 the Cross-Appalachian Tracer Experiment but that 

21 19 12 Then the European Tracer Experiment which is a 

22 20 13 Then that’s not a good tracer evaluation to 

23 21 14 the Great Plains Mesoscale Tracer Experiment. 

24 21 18 perflourocarbaon tracer releases from Norman, OK 

25 26 4 duration and the time that the tracer cloud 

26 26 25 placed in the tracer cloud. What we did see here 

27 29 19 go back and look at with this tracer evaluation. 

28 30 2 tracer experiment and basically this is probably 

29 30 3 I call it the granddaddy of all the tracer 

30 30 5 tracer experiment we have. This was Europeans 

31 30 8 So the European’s tracer experiments or ETEX was 

32 30 15 two releases of perflourocarbon (PFC) tracer were 

33 34 25 are an insufficient number of tracer experiments 

34 35 9 Basically for the Great Plains Tracer 

35 36 13 The European Tracer Experiment and as you can 

36 43 20 was working on the Great Plains Tracer Experiment 

37 44 23 Plains Tracer Study did and that’s probably where 

38 63 12 tracer studies and short-term intensive studies, 

39 63 17 concentrations on tracer arcs that are used for 

40 64 13 mixture of tracer experiments and long-term 

41 64 24 They are generally used only for tracer 

42 65 6 only used for tracer databases. 

43 67 18 best suited to tracer databases and is widely 

44 69 9 distance. For a particular tracer arc if you 

45 73 20 the 1950’s. It is an intense tracer study as Bob 

46 112 19 km scale we’re looking at a tracer experiment we 

47 114 8 web all the tracer experiments we have been 

48 114 10 those tracer experiments we have run....the first 

49 117 23 version 4.9? We’ve got all these tracer 

50 123 14 measurements from three tracer 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 313 

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 3 Page Ref No. Keyword = "tracer" 

 4 ____ _______ __________________ 

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 6 143 11 PCMs, evaluate tracer studies. The picture on 

 7 143 12 the right was a tracer experiment we just did a 

 8 163 21 observations, tracer releases, and PM and 

 9 198 11 flat, rolling terrain, mountainous, tracer or 

10 205 6 traffic itself. Review of data from tracer 

11 

12 Page Ref No. Keyword = "turbulence" 

13 ____ _______ ______________________ 

14 

15 14 10 outdated. We had the new turbulence options; we 

16 18 4 recommend turbulence based dispersion (CALPUFF 

17 18 11 P-G and turbulence options there. Then the final 

18 24 25 turbulence here. I think that’s one I mean 

19 25 4 CALPUFF turbulence and the AERMOD turbulence in 

20 25 21 upon which P-G or turbulence we have a little bit 

21 35 5 encouraged with the turbulence in terms of the 

22 82 3 vehicle induced turbulence. Especially for the 

23 102 7 staggered WRF grids, turbulence ensemble, urban 

24 104 16 kind of turbulence on this it would have a minor 

25 107 14 from the turbulence from the previous time step, 

26 107 19 in proportion to the turbulence that comes out of 

27 108 6 had stationary homogeneous turbulence. You’re 

28 118 8 turbulence already in existing version and 

29 120 10 inhomogeneous (convective) turbulence. They are 

30 122 6 of turbulence. The tendency of neighboring puffs 

31 127 10 treatment of effect of atmospheric turbulence on 

32 205 5 turbulence and low wind speeds generated by 

33 

34 Page Ref No. Keyword = "urban" 

35 ____ _______ _________________ 

36 

37 19 21 on. The other one is the VTMX where the urban 

38 63 3 colleague here has done an urban evaluation and 

39 84 4 is right in the urban options so the urban 

40 102 7 staggered WRF grids, turbulence ensemble, urban 

41 136 17 photochemical models like urban REMSAD models. 

42 165 17 algorithms for use in urban areas, especially for 

43 204 15 small urban areas. Need for post-processor to 

44 205 12 Nocturnal urban mixing height (Ziu) is a 

45 218 17 nonattainment areas are urban areas where, 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 314 

 2 

 3 Page Ref No. Keyword = "variability" 

 4 ____ _______ _______________________ 

 5 

 6 120 3 variably of accounting for spatial variability of 

 7 120 7 variability in dispersion rates, etc. Lagrangian 

 8 125 17 capture fine scale variability next to emissions 

 9 125 21 cannot capture the subgrid-scale variability that 

10 126 21 captures the local scale variability and the grid 

11 132 3 large spatial variability in air toxics 

12 132 7 subgrid-scale variability in exposure levels. 

13 155 11 is do you treat any wind variability within the 

14 155 23 variability in the cell? That’s my question 

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16 Page Ref No. Keyword = "weather" 

17 ____ _______ ___________________ 

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19 111 2 the large scale weather patterns at the frontal 

20 112 7 go to our web page and also the weather service 

21 112 8 page. Our page is better than the weather 

22 112 10 verification whereas the weather page only shows 

23 181 15 weather service for about 15 years. That’s 

24 183 10 weather and technical geek, let me see if I can 

25 226 15 National Weather Service Stations. Then 

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27 Page Ref No. Keyword = "wind" 

28 ____ _______ ________________ 

29 

30 18 15 is very sensitive to wind field (inaudible) you 

31 26 24 seeing that in terms of where the wind shield was 

32 28 20 appears that the wind field was steering it l 

33 29 21 CALMET wind fields from the previous one. I 

34 32 10 gets into this area up here we start with wind 

35 33 8 getting caught up in the deforming wind field the 

36 54 18 to the met side includes Rawindsonde, Wind 

37 54 23 temperature and the one on the right for wind 

38 55 7 generated. And similarly on the wind direction 

39 55 8 side in the wind direction plots where you can 

40 55 9 see the distribution and wind speed in your data 

41 55 12 mixing ratio, wind speed, wind direction, but 

42 55 21 would also be to window this down to other 

43 56 3 This is a wind profiler comparison over time 

44 56 4 and then (inaudible) and you see the wind speed, 

45 56 6 you have wind profile information,. a nice plot. 

46 63 7 evaluation of low wind speed databases with API 

47 64 23 conc vs. downwind distance or wind speed, etc. 

48 66 6 dependent on the wind speed as well. When you 

49 69 10 have a cross wind concentration like this you 

50 71 14 understanding of the Low Level Jet and the wind 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 315 

 2 

 3 Page Ref No. Keyword = "wind" 

 4 ____ _______ ________________ 

 5 

 6 71 24 wind energy assessment towers that are available 

 7 74 25 into the wind speed issue as Bob Paine mentioned 

 8 75 11 include sonic anemometer with lower wind speed 

 9 75 12 stretched so they had lots of light wind speed 

10 77 12 lot of light wind speed, upward spike in the 

11 77 25 light wind conditions. There may be some 

12 78 4 pretty clear pattern as the light wind speeds go 

13 78 15 data. Just looking at the wind direction 

14 78 20 that the SEARCH wind directions were offset by 

15 79 6 light wind. For the SEARCH site you can clearly 

16 79 7 see low wind, drainage flow, showing up under 

17 79 10 West direction would be the typical light wind, 

18 80 15 wind conditions? It’s not a clear answer one way 

19 84 20 ASOS wind data to reduce the number of calms, 

20 89 19 specifically under light wind speed conditions is 

21 89 23 wind stable wind conditions. One reason for that 

22 103 21 cloud when the wind field varies in space and 

23 104 7 like a plume because wind speed and direction 

24 104 11 wind coming out of the East (inaudible). And so 

25 104 13 is a result of the wind direction shear and wind 

26 112 3 that had a high wind velocity. 

27 120 5 causality effects, low wind speed dispersion, 

28 124 15 wind integrated concentration (CIC). Very 

29 127 9 no treatment of wind shear or plume overlaps, no 

30 146 10 more about the regional or further down wind a 

31 155 11 is do you treat any wind variability within the 

32 155 13 Ralph Morris: No just using the wind 

33 155 15 whether it’s a gridded wind field or (inaudible). 

34 156 3 wind fields that come out of MM5. 

35 160 5 topography, wind persistence data and land use 

36 166 6 wind observations to the land characteristics of 

37 166 12 the airport wind observation to the land 

38 183 14 creating wind roses and finding out how many 

39 189 16 aspect ratios. Use of wind tunnel testing to 

40 190 12 wind residential tower to go in to the model. 

41 190 18 wind tunnel to determine the shape that would 

42 193 7 diagonals of wind. Now I have some input and I 

43 193 8 ran AERMOD for these 3 cases for 1 wind speed. 

44 193 13 wind tunnel so I will show you what 

45 193 14 concentrations looked like in the wind tunnel. 

46 194 9 developed to determine wind up wind terrain and 

47 194 13 EPA wind tunnel where they showed these terrain 

48 196 6 change the wind data. It’s exactly as it came 

49 196 14 output of MM5 converted to wind rose software. 

50 197 11 Evaluate all meteorological variables. Wind 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 316 

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 3 Page Ref No. Keyword = "wind" 

 4 ____ _______ ________________ 

 5 

 6 197 12 speed, wind direction, Frequency of light wind 

 7 197 13 speeds, etc., vertical wind and temperature 

 8 204 13 The Low wind speed issues. Modeling of 

 9 204 20 AERMOD impacts occur for very low wind speeds, 

10 204 25 hours with very low wind speeds. AERMOD needs 

11 205 5 turbulence and low wind speeds generated by 

12 207 3 wind speed issues. Moisture assigned only on an 

13 207 25 wind speed threshold is treated the same way as a 

14 209 7 handling, low wind speed stagnation, coastal and 

15 210 25 is a long hour and a lot of wind speeds in most 

16 226 11 approach which is to extract wind profiles from 

17 226 21 comparing the prognostic model derived wind 

18 

19 Page Ref No. Keyword = "wind speed" 

20 ____ _______ ______________________ 

21 

22 55 9 see the distribution and wind speed in your data 

23 55 12 mixing ratio, wind speed, wind direction, but 

24 56 4 and then (inaudible) and you see the wind speed, 

25 63 7 evaluation of low wind speed databases with API 

26 64 23 conc vs. downwind distance or wind speed, etc. 

27 66 6 dependent on the wind speed as well. When you 

28 74 25 into the wind speed issue as Bob Paine mentioned 

29 75 11 include sonic anemometer with lower wind speed 

30 75 12 stretched so they had lots of light wind speed 

31 77 12 lot of light wind speed, upward spike in the 

32 89 19 specifically under light wind speed conditions is 

33 104 7 like a plume because wind speed and direction 

34 120 5 causality effects, low wind speed dispersion, 

35 193 8 ran AERMOD for these 3 cases for 1 wind speed. 

36 204 13 The Low wind speed issues. Modeling of 

37 207 3 wind speed issues. Moisture assigned only on an 

38 207 25 wind speed threshold is treated the same way as a 

39 209 7 handling, low wind speed stagnation, coastal and 

40 

41 Page Ref No. Keyword = "wind speeds" 

42 ____ _______ _______________________ 

43 

44 78 4 pretty clear pattern as the light wind speeds go 

45 204 20 AERMOD impacts occur for very low wind speeds, 

46 204 25 hours with very low wind speeds. AERMOD needs 

47 205 5 turbulence and low wind speeds generated by 

48 210 25 is a long hour and a lot of wind speeds in most 
 1 Ninth Modeling Conference Keyword Index Vol. 2, p. 317 

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 3 Page Ref No. Keyword = "winds" 

 4 ____ _______ _________________ 

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 6 25 14 You can see here the CALMET winds did very 

 7 25 16 better than the MM5 winds in terms of the arrival 

 8 27 2 is that when we were feeding the MM5 only winds 

 9 27 12 where the MM5 winds did markedly better than 

10 27 20 the MM5 winds were doing slightly better, but you 

11 27 21 can see the MM5 winds have it displaced more 

12 70 25 dispersion modeling are how often are the winds 

13 77 20 variable winds we are looking at 25 or 30% of the 

14 77 21 data period missing either to calms or winds. 

15 79 23 light winds conditions that show up at the 

16 165 15 incorporate algorithms for near calm winds and 

17 211 7 method to define precisely when complex winds 

18 211 11 winds. But I think the answer is in the 

19 211 12 definition of complex winds. 

20 

21 Page Ref No. Keyword = "work group" 

22 ____ _______ ______________________ 

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24 210 5 AWMA supports an independent work group for 

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