International Corporation Air Sciences

DRAFT FINAL REPORT 

DETERMINATION OF REPRESENTATIVENESS 

OF 2002 OZONE SEASON FOR OZONE 

TRANSPORT REGION SIP MODELING

Prepared for 

Tom Frankiewicz 

Ozone Transport Commission 

444 North Capitol St., NW, Suite 638 

Washington, DC 20001 

Prepared by 

Till Stoeckenius 

Sue Kemball-Cook 

ENVIRON International Corporation 

101 Rowland Way, Suite 220 

Novato, CA 94945 

31 March 2005

 101 Rowland Way, Suite 220, Novato, CA 94945 415.899.0700 
March 2005 

i 

TABLE OF CONTENTS 

Page 

1. INTRODUCTION................................................................................................................ 1-1 

2. DATA GATHERING AND INITIAL ANALYSIS........................................................... 2-1 

Data .............................................................................................................................. 2-1 

Identification Of Ozone Monitoring Sub-Regions ................................................................ 2-1 

Spatial Ozone Pattern Analysis.............................................................................................. 2-6

Meteorological Variable Selection ........................................................................................ 2-7 

3. EPISODE CLASSIFICATION ANALYSIS ..................................................................... 3-1 

Clustering Analysis................................................................................................................ 3-1

Development Of An Objective Classification Procedure .................................................... 3-12 

Classification Of 2002 Ozone Episodes .............................................................................. 3-15 

4. CONCLUSIONS AND RECOMMENDATIONS............................................................. 4-1 

5. REFERENCES.................................................................................................................... R-1 

APPENDICES 

Appendix A: Composites Representing Mean Meteorological Conditions 

During Each Ozone Episode Pattern 

Appendix B: Boxplots of Key Meteorological Variables 

Appendix C: Wind Direction Frequency Tables 

TABLES 

Table 3-1. Mean z-scores for the AvgEx00 ozone summary statistic 

 within each monitoring sub-region under four different 

 candidate sets of cluster designations. The episode pattern 

 ID in the far right-hand column is keyed to the episode patterns 

 described in the text. ............................................................................................ 3-4 

Table 3-2. Key characteristics of each OTR episode type. ................................................. 3-10 

Table 3-3. Frequencies of occurrence of OTR episode types. ............................................ 3-12 
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ii 

Table 3-4. Distribution of episode types during the 1997-2001/2003 

 historical period (as determined via the clustering analysis) 

 for days in each terminal node of the classification tree 

 shown in Figure 3-8. .......................................................................................... 3-14 

Table 3-5. Number of days during May-September with 8-hour daily 

 maximum ozone greater than 0.08 ppm in each monitoring 

 sub-region averaged over the 1997-2001/2003 historical 

 period and in 2002. ............................................................................................ 3-18

FIGURES 

Figure 1-1. Ozone monitoring sites in the Ozone Transport Region 

 which is comprised of DC, CT, DE, ME, MD, MA, NH, 

 NJ, NY, PA, RI, VT, and that portion of Virginia contained 

 within the DC Consolidated Metropolitan Statistical Area. ................................ 1-1 

Figure 1-2. 8-Hour ozone nonattainment classifications in the OTR 

 and adjacent areas. ............................................................................................... 1-2 

Figure 2-1. Ozone monitoring station cluster assignments for k = 5 clusters. ....................... 2-3 

Figure 2-2. Ozone monitoring station cluster assignments for k = 10 clusters. ..................... 2-4 

Figure 2-3. Number of 8-hour ozone exceedance days at each 

 monitoring site during the study period (1997 – 2001 and 2003). ...................... 2-5 

Figure 2-4. Ozone monitoring sub-regions in the OTR.......................................................... 2-6 

Figure 3-1. Dendrogram from application of Ward’s hierarchical 

 agglomerative clustering to the combined daily ozone and 

 meteorological data. Each leaf at the bottom of the figure 

 epresents one day; the vertical height at which pairs of leaves 

 (or pairs of clusters of leaves) are joined represents a measure 

 of the distance between the leaves (or cluster centroids) in the 

 multivariate data space......................................................................................... 3-3 

Figure 3-2. Composite 850 mb height and wind fields for each episode 

 (pattern) type (pattern numbers refer to the episode types 

 listed in Table 3-2)............................................................................................... 3-6 

Figure 3-3. Composite 850 mb temperature fields for each episode 

 (pattern) type (pattern numbers refer to the episode types 

 listed in Table 3-2)............................................................................................... 3-7 

Figure 3-4. Composite surface sea level pressure for each episode 

 (pattern) type (pattern numbers refer to the episode types 

 listed in Table 3-2)............................................................................................... 3-8 

Figure 3-5. Composite surface temperature and 10 m wind fields for 

 each episode (pattern) type (pattern numbers refer to the 

 episode types listed in Table 3-2). ....................................................................... 3-9 

Figure 3-6. Deviance as a function of tree size (number of terminal nodes) 

 for sequence of trees generated by the pruning algorithm. .............................. 3-13 

Figure 3-7. Deviance from 10-fold cross-validation as a function of 

 tree size (number of terminal nodes) for sequence of trees 

 generated by pruning algorithm......................................................................... 3-14 
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Figure 3-8. Classification tree used to group days by episode type. 

 Variable names and values used to divide data at each splitting 

 node are shown: days meeting the specified criterion are moved 

 down the left branch in each case. Terminal nodes are 

 numbered 1 – 5 and are keyed to the summary in Table 3-4............................. 3-15 

Figure 3-9. Percent of episode days by type in the 1997 – 2001/2003 

 historical period (as determined by the cluster analysis and 

 by the classification tree) and in 2002 ............................................................... 3-17 

Figure 3-10. Percent of episode days by type in the 1997 – 2001/2003 historical 

 period (as determined by the cluster analysis and by the 

 classification tree) and in 2002 with Type E events removed 

 and frequencies re-normalized........................................................................... 3-17 

Figure 3-11a. Boxplots of average daily maximum ozone (ppb) in each 

 monitoring sub-region during 2002 and during the 1997-2001 

 and 2003 “historical” period: Type E (Pattern No. 1) events.. ......................... 3-19 

Figure 3-11b. Boxplots of average daily maximum ozone (ppb) in each 

 monitoring sub-region during 2002 and during the 1997-2001 

 and 2003 “historical” period: Type B (Pattern No. 2) events........................... 3-20 

Figure 3-11c. Boxplots of average daily maximum ozone (ppb) in each 

 monitoring sub-region during 2002 and during the 1997-2001 

 and 2003 “historical” period: Type A (Pattern No. 3) events........................... 3-21 

Figure 3-11d Boxplots of average daily maximum ozone (ppb) in each 

 monitoring sub-region during 2002 and during the 1997-2001 

 and 2003 “historical” period: Type D (Pattern No. 4) events........................... 3-22 

Figure 3-11e. Boxplots of average daily maximum ozone (ppb) in each 

 monitoring sub-region during 2002 and during the 1997-2001 

 and 2003 “historical” period: Type C (Pattern No. 5) events........................... 3-23 

Figure 3-12. Key to boxplot symbols. .................................................................................... 3-24 

Figure 3-13. Composite meteorological fields by episode type (pattern) 

 in 2002: 850 mb heights and winds. .................................................................. 3-25 

Figure 3-14. Composite meteorological fields by episode type (pattern) 

 in 2002: 850 mb temperature. ............................................................................ 3-26 

Figure 3-15. Composite meteorological fields by episode type (pattern) 

 in 2002: sea level pressure. ................................................................................ 3-27 

Figure 3-16. Composite meteorological fields by episode type (pattern) 

 in 2002: surface temperature and 10 m winds. .................................................. 3-28 
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1. INTRODUCTION 

The Ozone Transport Commission is coordinating a photochemical modeling study of the Ozone 

Transport Region (OTR) in support of State Implementation Plan development for certain areas 

recently designated by the U.S. EPA as being in nonattainment of the 8-hour ozone National 

Ambient Air Quality Standard. The OTR is comprised of 12 states (DC, CT, DE, ME, MD, MA, 

NH, NJ, NY, PA, RI, VT) and that portion of Virginia contained within the Washington DC 

Consolidated Metropolitan Statistical Area (see Figure 1-1). Areas within the OTR designated 

nonattainment for 8-hour ozone are shown in Figure 1-2; detailed attainment demonstrations are 

required for the nonattainment areas within the OTR classified as “moderate”. 

Figure 1-1. Ozone monitoring sites in the Ozone Transport Region which is comprised of DC, 

CT, DE, ME, MD, MA, NH, NJ, NY, PA, RI, VT, and that portion of Virginia contained within the 

DC Consolidated Metropolitan Statistical Area.
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Figure 1-2. 8-Hour ozone nonattainment classifications in the OTR and adjacent areas.

Development of effective 8-hour ozone attainment strategies requires application of 

photochemical models to a set of episodes that adequately represent the range of meteorological 

conditions associated with violations of the ambient standard. EPA’s 8-hour ozone modeling 

guidance (EPA, 1999) lists four criteria for episode selection: 

1. Select episodes representing a variety of meteorological conditions, which frequently 

correspond to exceedances of the 8-hour standard. 

2. Select episodes during which the daily maximum 8-hour averages are close to the 8-hour 

design value, i.e., the average annual 4th highest daily maximum 8-hour average. 

3. Select episodes for which extensive meteorological and air quality data sets are available. 

4. Select a sufficient number of episode days for modeling so that the modeled attainment 

test specified in EPA’s guidance is based on several days. 

In practice, it is difficult, if not impossible, to simultaneously meet all of these criteria. In 

general, it is important to include episodes that represent as completely as possible the full range 

of meteorological conditions associated with exceedances of the ozone standard. Differences 

among episode types are important in so far as they influence the predicted effectiveness of 

alternative emission control strategies. 

CATEGORY/CLASSIFICATION

Severe 17 Los Angeles 

Moderate

Marginal

Subpart 1 (Basic)

Subpart 1 EAC (Basic)

Moderate EAC Greensboro, NC

Serious - Riverside Co (Coachella Valley), 

San Joaquin, Sacramento, CA

8-Hour Ozone Nonattainment 

Classifications in the OTR

Source: 

http://www.epa.gov/oar/oaqps/greenbk/state_8hr_maps.zip
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The OTC recognizes that the OTR is a large region that experiences a wide variety of weather 

patterns associated with 8-hour exceedances and has decided to perform ozone SIP modeling of 

the full 2002 ozone season extending from May 15-September 15. This has the advantage of 

incorporating a fairly large number of episode days in different portions of the OTR. Thus, there 

should be a good chance that all of the important episode types are covered within this period. 

However, the 2002 season is somewhat unique in that some of the most prolonged and severe 

ozone episodes in recent years occurred during this season. This raises the possibility that one or 

more episode types of interest are not adequately represented within the 2002 season. The goal 

of this study, therefore, is to assess the representativeness of conditions during the 2002 season 

with respect to exceedance events that have occurred in other years and determine if there are 

any types of episodes which are not adequately represented within the 2002 season. 

EPA’s 1999 draft guidance recommends joint use of subjective and statistical methods for 

characterizing and classifying 8-hr ozone episodes. Subjective methods include “typing” of 

episode meteorological conditions in which episodes are classified via inspection on the basis of 

similarities in meso- and synoptic-scale weather patterns. In contrast, statistical methods can 

produce objective classifications either by use of tree models1

 or and various forms of cluster 

analysis (often in conjunction with a principal components analysis). A predictive classification 

procedure such as a classification tree model (which can be viewed as a non-parametric form of 

least-squares regression) does not actually classify episodes, although it can be used to identify 

potential episodes with common meteorological features. This information can then be used to 

inform the episode selection process. A cluster analysis, on the other hand, is designed to 

identify natural groupings of conditions within the set of candidate episodes. In either case, 

considerable expert judgment is required in variable selection, selection of different modeling 

methods, and interpretation of results so even the statistical methods are not wholly objective. 

Nevertheless, the these approaches are well suited to the development of valid, defensible 

episode classification schemes that are sufficiently robust to explain the major characteristics of 

ozone episode types. 

In this study, we apply a combination of exploratory statistical techniques, cluster analyses, and 

classification tree building algorithms to ozone and meteorological data from the OTR to assess 

the representativeness of 8-hour ozone episodes occurring during the 2002 season. Data sources 

and preliminary analyses are described in Section 2. Procedures and results used to identify the 

major Northeastern U.S. ozone episode types and their key characteristics are presented in 

Section 3 along with a comparison of the frequency of occurrence and features of each episode 

type in 2002 versus those in other recent years. Our conclusions regarding the representativeness 

of the 2002 season are detailed in Section 4. 

 

1

 A commonly used tree modeling approach is based on the CART methodology (Breiman et al., 1984). 
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2. DATA GATHERING AND INITIAL ANALYSIS 

DATA 

Daily ozone and meteorological data required for the episode representativeness analysis were 

obtained from a variety of sources. To capture the full range of OTR episode characteristics and 

insure statistical significance, a seven year period (1997 – 2003) was chosen for analysis. Data 

prior to 1997 were not used to avoid any confounding influences of long-term air quality trends. 

For purposes of this study, data from the warm season months (May – September) were used to 

capture most if not all high ozone events during the year. 

Ozone and meteorological data were separated into two groups: data from 1997 – 2001 and 2003 

were treated as the “historical” period and were used to define the types of ozone episode 

conditions occurring in the OTR. Data from 2002 were treated as an independent data set with 

data in this year to be compared against the types of conditions found in the historical period. 

Hourly ozone concentrations at monitoring sites throughout the OTR for the period 1997-2003 

were provided by the New York State Department of Environmental Conservation. Stations 

missing more than one year of data were excluded from the study, leaving a total of 158 stations 

with nearly complete data. Daily maximum 8-hour averages were calculated from the hourly 

data using the data handling conventions specified in 40 CFR 50, Appendix I. Because the 

spatial pattern analysis procedure requires a complete data set, missing daily maxima were set to 

the station mean daily maximum (this conforms to the procedure used by Cox, 1997). 

Hourly surface meteorological data (winds, temperature, etc.) from airports and other locations 

in the OTR were obtained from the National Center for Atmospheric Research (NCAR) as 

dataset ds472. Upper air data were extracted from the ETA Data Assimilation System (EDAS) 

files available from the National Climatic Data Center. EDAS contains 3-hourly objective 

analysis initialization and forecast fields from the National Center for Environmental Prediction's 

(NCEP) ETA model at 40 km resolution. By using the EDAS data, we were able to obtain a 

consistent set of surface and upper air variables covering the entire eastern half of the U.S. at 

high temporal resolution. 

IDENTIFICATION OF OZONE MONITORING SUB-REGIONS 

Monitoring sub-regions were defined within the OTR to reduce the number of variables required 

to describe the spatial ozone distribution under different episode patterns. Sub-regions were 

defined by combining results of a station clustering analysis with information on typical ozone 

concentration patterns provided by air quality analysts from several OTR states. A variable 

clustering procedure (VARCLUS) based on principal components analysis was used to group the 

OTR ozone monitoring sites into disjoint geographic clusters (Sarle, 1990, Harrell 1999). This 

procedure essentially divides the monitoring stations into groups of highly correlated sites. 

Station clusters are selected so as to explain most of the day-to-day variation in ozone levels over 

the OTR using just a handful of station groups. VARCLUS works by performing a principal 

components analysis on the ozone values in each candidate cluster and seeks to find the set of 
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clusters that maximize the total (across clusters) of the variance explained by the first principal 

components. 

The VARCLUS procedure requires one to specify as input the number of clusters to be formed. 

As with any clustering procedure, this introduces an element of subjectivity which can be 

minimized by repeating the analysis several times, each time varying the number of clusters to be 

formed and examining the robustness of the cluster memberships as the number of requested 

clusters (k) changes. 

Application of the clustering algorithm for various values of k showed that, for a given value of 

k, the VARCLUS procedure produced several spatially coherent clusters as well as other clusters 

which were not spatially coherent. Clusters which were not spatially coherent were always made 

up of just 5 or fewer member stations. For example, setting k=5 produced 2 coherent clusters 

(clusters 1 and 2) and 3 smaller clusters (clusters 3-5) whose members tended to be widely 

separated in space (see Figure 2-1). The version of VARCLUS used for our analysis assigns the 

lowest cluster identification numbers to the “tightest” (i.e., most easily identifiable and robust) 

clusters. As the results in Figure 2-1 show, these lowest numbered clusters (in this case Clusters 

1 and 2) turned out to also be the most spatially coherent (note that the clustering is based on 

ozone correlations only – the locations of each monitoring site are not an input to the clustering 

algorithm). This is consistent with our expectation that sites located close to one another will be 

highly correlated. Clusters 1 and 2 are similar to the two northeast clusters found by Cox (1997), 

who used a similar analysis technique applied over the entire eastern U.S.. Successive increases 

in k over the range 6-10 produced additional coherent clusters which subdivided the two large 

clusters seen in Figure 2-1. The smaller, non-contiguous clusters remained largely unchanged 

for all values of k. 
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Ozone Spatial Clusters in the Ozone Transport Region

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36

38

40

42

44

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k = 5

Cluster 1 Cluster 4

Cluster 2 Cluster 5

Cluster 3 

Figure 2-1. Ozone monitoring station cluster assignments for k = 5 clusters.

With k = 10, VARCLUS produced 6 spatially coherent clusters, and 4 smaller, non-coherent 

clusters (Figure 2-2).1 As in the k=5 case, the spatially coherent clusters are the lowest 

numbered clusters, 1-5, and the non-contiguous clusters are 6-8, and 10. The k=10 case is 

unusual because cluster 9 (located on the Rhode Island/Massachusetts coast) turned out to be 

spatially coherent, even though the lower numbered clusters 6-8 were not. We investigated the 

possibility that cluster 9 should be treated as a separate sub-region. After examining the way 

exceedances in cluster 9 vary with those in surrounding clusters, however, we concluded that this 

area could be adequately treated by including it in with cluster 4 (along the Washington – New 

York City corridor). In order to use only the clusters which seemed robust under variations in k, 

we therefore based the final ozone monitoring sub-regions largely on the first five clusters 

obtained under the k=9 scenario (which were slightly more coherent than those under the k=10 

 

1

 As k was increased beyond 10, the coherent clusters produced were judged to be too small in spatial dimension to be useful in 

classifying ozone exceedance regimes. 
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case). However, the RI – MA coastal sites were associated with the New York City metropolitan 

area sites rather than the other MA sites based both on the k=10 result described above and input 

from several state air quality analysts. In addition, all stations on the ME coast were assigned to 

the southern New England group (Cluster 1) based on input from state air quality analysts. 

Stations from the other higher numbered, non-contiguous clusters were integrated into the 

surrounding clusters; there were no such stations for which the appropriate cluster assignment 

was ambiguous. 

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36

38

40

42

44

46

Ozone Spatial Clusters in the Ozone Transport Region 

k = 10

Cluster 1 Cluster 6

Cluster 2 Cluster 7

Cluster 3 Cluster 8

Cluster 4 Cluster 9

Cluster 5 Cluster 10

Figure 2-2. Ozone monitoring station cluster assignments for k = 10 clusters. 

Another adjustment to the VARCLUS results was made along the Philadelphia – New York 

corridor. Figure 2-3 shows the number of 8-hour exceedances at each monitoring site during the 
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period analyzed. Exceedance events in the Washington – Philadelphia corridor are more 

frequent than within and downwind (northeast) of the New York City metropolitan area. 

Furthermore, based on our discussions with state ozone forecasters in the OTR, we expect 

transport of ozone and ozone precursors along the I-95 corridor to play an important role in 

exceedance events. This suggests that leaving the entire Washington to New York City cluster 

intact might cause our final episode classification scheme to overlook events in which transport 

northeast from Washington-Baltimore-Philadelphia to New York is an important feature. We 

therefore decided to split this cluster into two parts as shown in the final ozone monitoring subregion assignments presented in Figure 2-4. Cluster 5 extends from the Washington area through 

Trenton and a new cluster 6 covers the New York City-Long Island-Southern Connecticut 

region. 

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36

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40

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44

46

 0 to 25

 25 to 50

 50 to 75

 75 to 100

 100 to 125

Number of 8-Hour Station Ozone Exceedances for 1997-2003 

 Excluding 2002

Figure 2-3. Number of 8-hour ozone exceedance days at each monitoring site during the study 

period (1997 – 2001 and 2003).
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36

38

40

42

44

46

Ozone Spatial Clusters in the Ozone Transport Region 

Cluster 1 Cluster 4

Cluster 2 Cluster 5

Cluster 3 Cluster 6 

 

Figure 2-4. Ozone monitoring sub-regions in the OTR.

SPATIAL OZONE PATTERN ANALYSIS 

An initial analysis of episode patterns was performed based on just 8-hour ozone concentrations 

within the sub-regions (spatial clusters) described above. For each day, a cluster was determined 

to be in exceedance if any one monitoring site in the cluster recorded an exceedance. We then 

counted the number of joint exceedance events between each pair of clusters and examined 

exceedance patterns across all six clusters. Detailed results from this analysis were provided in a 

technical memorandum to the OTC (Stoeckenius and Kemball-Cook, 2004) but are not repeated 

here because this approach was eventually discarded in favor of an integrated analysis approach 

in which the daily ozone levels in each sub-region were combined with daily meteorological data 

to determine the key characteristics of the major types of ozone episodes occurring in the OTR. 
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METEOROLOGICAL VARIABLE SELECTION 

The extensive amount of meteorological data collected for this study had to be reduced to a 

manageable size for to allow processing of days into groups with similar conditions as described 

in Section 3. Selection of key meteorological variables that best represent conditions across the 

OTR on exceedance days was based on a review of previous studies (Deuel and Douglas, 1996; 

McHenry et al., 2004) and on discussions with state and local agency air quality personnel 

involved in ozone forecast programs within the OTR. Key variables focused on both surface 

conditions (maximum temperature, morning and afternoon average wind direction and speed, 

pressure) and conditions aloft (500 and 850 mb heights, temperatures, and winds). The final 

selected set of key daily meteorological parameters are: 

Surface resultant wind speed and direction computed for both morning (05:00 – 10:00 

EST) and afternoon (12:00 – 17:00 EST) hours at New York City (LaGuardia), 

Philadelphia, Boston, Buffalo, Albany, Washington DC, Portland ME, Atlantic City, Islip 

(Long Island), Hyannis (Cape Cod), Worcester MA, and Hartford CT.2

Surface daily maximum temperatures at New York City NY (LaGuardia), Philadelphia 

PA, Boston MA, Buffalo NY, Albany NY, Washington DC, Portland ME, Atlantic City 

NJ, Islip NY (Long Island), Hyannis MA (Cape Cod), Worcester MA, and Hartford CT.2

Temperatures, heights, and winds at 850 mb pressure surface at Washington DC, New 

York NY, Boston MA, Pittsburgh PA, Buffalo NY, and Portland ME. 

Surface pressure gradients across the OTR computed as pressure differences between: 

 Washington DC and New York City 

 Washington DC and Boston 

 Washington DC and Pittsburgh 

 Pittsburgh and Buffalo 

 Buffalo and Boston 

 

2

 Surface wind and temperature data from Concord, NH and New Haven, CT were also examined but these sites had 

a high frequency of missing data which prevented their use in this analysis. 
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3. EPISODE CLASSIFICATION ANALYSIS 

In this section we describe a series of clustering and exploratory analyses performed on the 

ozone and meteorological data discussed in Section 2. Clustering was performed with data from 

the historical (1997 – 2001 and 2003) period to identify the major types of ozone episodes in the 

OTR and their key characteristics. Once the key episode types were identified, we developed a 

decision rule for classifying any given day into one of the identified episode types based on 

ozone levels and meteorological conditions. This decision rule was then used to classify days 

during the 2002 ozone season by episode type. The resulting distribution of episode types and 

the ozone and meteorological conditions occurring within each type in 2002 were subsequently 

compared with results from the historical period to determine the representativeness of the 2002 

with respect to conditions during the historical period. 

CLUSTERING ANALYSIS 

Clustering was performed with data for the 329 days in the 1997-2001/2003 historical period on 

which an 8-hour ozone exceedance was recorded at one or more of the monitoring sites shown in 

Figure 2-4. As the clustering algorithms require numerical variables, wind directions were 

decomposed into u (east-west) and v (north-south) components. Meteorological data were 

prepared for clustering by first filling in missing values with exceedance day means. This step 

was necessary as the clustering procedures cannot process any days that have missing values for 

one or more variables. While the fraction of data that are missing for any individual variable is 

fairly small, roughly two-thirds of the 329 8-hour ozone exceedance days in our historical dataset 

had at least one missing value, so it was important to impute the missing values in some fashion 

even though the clustering results are not likely to be too sensitive to the exact method of 

imputation. All of the data were then standardized by computing z-scores (i.e., subtracting the 

mean and dividing by the standard deviation) prior to clustering so that variables with different 

scales of measure are given equal weight. 

Ozone data were also prepared at this time for use in the clustering analysis. Two daily ozone 

summary statistics, AvgEx08 and AvgEx00 were computed for each monitoring sub-region 

shown in Figure 2-4. AvgEx08 was defined as the average, over all sites in a given sub-region, 

of the amount by which the daily maximum 8-hour average exceeded 0.08 ppm (with values for 

sites below 0.08 ppm set equal to zero). AvgEx00 is identical to AvgEx08 but with the 

exceedance threshold set to 0 ppm. As with the meteorological data, z-scores were computed for 

the daily AvgEx08 in each sub-region for use in the clustering analysis. Preliminary clustering 

analyses were performed using the methods described below with first the AvgEx08 measure 

and then the AvgEx00 measure. Cluster results based on the AvgEx00 measure appeared to be 

more robust and physically meaningful then results based on the AvgEx08 measure so the 

AvgEx08 measure was dropped in favor of AvgEx00. 

Initially, clustering was applied to the meteorological variables only. Both agglomerative and 

divisive hierarchical clustering techniques were used. Classifications of days under the resulting 

meteorological clusters were compared with the classification of days by ozone exceedance 

pattern which had previously been reported (Stoeckenius and Kemball-Cook, 2004). These 

comparisons showed that, while some pairs of exceedance and meteorological patterns showed a 
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dominant one-to-one relationship, others did not. In other words, some of the exceedance 

patterns were typically associated with more than one meteorological pattern and some 

meteorological patterns were typically associated with more than one exceedance pattern. This 

result was found to be robust in the sense that it occurred under a variety of clustering 

approaches. We interpreted this to mean that at least some of the ozone exceedance patterns 

described by Stoeckenius and Kemball-Cook were not sufficiently unique from a meteorological 

perspective to serve as adequate archetypes of different types of ozone episodes. Given this 

result, we decided to examine clustering approaches based on using both the meteorological and 

ozone (AvgEx00) data simultaneously. 

Before proceeding further, we performed a principal components analysis (PCA) on the 

combined ozone and meteorological data set prior to clustering to determine if it would be 

possible to reduce the number of variables required for the analysis. Preliminary results showed, 

however, that the first four components only explained 14% of the total variance. As a result, we 

did not pursue the PCA any further but simply retained all of the key variables in the clustering 

analysis. 

Several different clustering procedures were applied to the data. Application of single and 

complete linkage hierarchical agglomerative clustering methods (Venables and Ripley, 1994) to 

the combined ozone and meteorological data resulted in the formation of one large cluster 

containing most of the days in the dataset and a large number of additional clusters containing at 

most a few days each. Use of Ward's method (Ward, 1963) produced a more even distribution of 

cluster membership at each stage of the agglomeration but with fairly evenly spaced reductions 

in deviance (see resulting dendrogram in Figure 3-1). In other words, these results did not 

provide much guidance as to what would constitute a reasonable number of clusters to use in 

describing the data. 
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G:\OTC-EpisodeClassification\Report\Draft_Final\Sec3(Analysis).doc3/31/2005 3-3 19 24 414243 445051 52 53545556 61 6259576064 68 6769 7072 737475 76 77 78 82 798087 8893 89 95 94 100101 103 102104 108120 129 130131 154 167168 169 170 171172 173 174 181 180182 183 184186 204 205 207 221 217 218208 209 210 216223 228 229 227 233 224 231 230 234235 238 237 236239 242 241 243 247 248 244 249 250 251 253 252257 258 260 259 261 263 266268 267 269 270 271273 274 275 276277 278279280 281 282 283288289 291 290302 292 293 303 310 327 317 318 328 334 335336 337 338 339 343 342344 345 346 347 348 349351358 359 361 360 362364 363 370 371372 373374 375 376 381382 383 384 385386 387 389390 391396 392 393 394 395398 397 402 399403 404 405 408409 410 411 412 415 414416 417 423 426431432 441 433 442455460 465 467 466 464 468471 476 490 472 491492 498 499 500 501 502 506 503 504 507515 514 516 517 523 524 522 525526 529533 541 538558 559 560561 578592602 614 615 616 613 617 622 623 624 636 654 652655 656 657661 658662 663 664 669 670671 672 673682 683 689 690691 694 692 693 695696 697698 704 705 706707 709 710711 712 713 714719 720728 729 742743 748754 807819 820 821 822823 824 825826 827 828829 830 831833 846 834 841 852 853 859865 870 871872 873 877 878879 8828850 20 40 60 80 Height

Figure 3-1. Dendrogram from application of Ward’s hierarchical agglomerative clustering to the 

combined daily ozone and meteorological data. Each leaf at the bottom of the figure represents 

one day; the vertical height at which pairs of leaves (or pairs of clusters of leaves) are joined 

represents a measure of the distance between the leaves (or cluster centroids) in the 

multivariate data space. 

Based on the agglomerative clustering results, we decided to apply Hartigan’s k-means 

clustering algorithm (Hartigan, 1979) several times, specifying a different value for the number 

of clusters to form in each application. Under the k-means algorithm, data are arranged into a 

pre-specified number of clusters so as to minimize the total within-cluster sum of squares. Initial 

cluster centroids are determined via agglomerative hierarchical clustering. After this initial step, 

each day is assigned to the nearest cluster centroid where “nearest” is in this case defined as the 

minimum least squares distance computed over all of the standardized variables. After this 

initial assignment phase, the algorithm iteratively reassigns days to different clusters until the 

sum of the within-cluster sums of squares is minimized.1

 

Due to the large number of variables used in the clustering procedure, it is difficult to obtain a 

complete picture of the meteorological and air quality conditions associated with days falling in 

each cluster, especially when looking at several alternative cluster configurations. As one of the 

most import features of each cluster is the spatial ozone distribution, we tabulated the mean 

 

1

 As finding the global minimum of this objective function is not computationally feasible, Hartigan’s algorithm 

actually finds a local minimum such that switching any single observation from one cluster to another does not 

reduce the objective. As a result, the final cluster assignments may be sensitive to the selection of initial cluster 

centroids. 
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values of the ozone measure described above (AvgExc00) for each sub-region within each 

cluster identified by the k-means algorithm when the data are divided into between 4 and 7 

clusters (see Table 3-1). We also examined similar sets of results for each key meteorological 

variable. Inspection of these results revealed the presence of five distinct sets of ozone and 

meteorological conditions that are robust in the sense that they show up consistently whether the 

data are divided into 4, 5, 6 or 7 clusters. 

Table 3-1. Mean z-scores for the AvgEx00 ozone summary statistic within each monitoring 

sub-region under four different candidate sets of cluster designations. The episode pattern ID in 

the far right-hand column is keyed to the episode patterns described in the text. 

a) 4 

clusters 

Episode 

Pattern 

Cluster # 

Sub-Region 

1 

Sub-Region 

2 

Sub-Region 

3 

Sub-Region 

4 

Sub-Region 

5 

Sub-Region 

5a ID 

1 0.51 0.08 0.40 -0.08 0.26 0.56 C 

2 -0.86 0.49 -0.86 -0.40 -0.15 -0.37 B 

3 0.27 0.05 0.22 0.30 0.30 0.20 A 

4 -0.72 -0.57 -0.45 -0.31 -0.93 -0.96 E 

b) 5 

clusters 

Cluster # 

Sub-Region 

1 

Sub-Region 

2 

Sub-Region 

3 

Sub-Region 

4 

Sub-Region 

5 

Sub-Region 

5a 

1 -0.74 0.07 -0.40 -0.58 -0.34 -0.78 E 

2 -0.70 0.45 -0.73 -0.37 -0.06 -0.17 B 

3 0.45 0.16 0.32 0.35 0.45 0.46 A 

4 -0.49 -0.97 -0.35 0.22 -1.07 -0.91 D 

5 0.54 -0.04 0.47 -0.10 0.17 0.42 C 

c) 6 

clusters 

Cluster # 

Sub-Region 

1 

Sub-Region 

2 

Sub-Region 

3 

Sub-Region 

4 

Sub-Region 

5 

Sub-Region 

5a 

1 -0.83 0.47 -0.88 -0.47 -0.17 -0.27 B 

2 0.38 0.21 0.23 0.44 0.49 0.46 A 

3 0.05 -1.62 0.17 0.58 -1.32 -0.86 D 

4 -1.13 -0.17 -0.91 -0.52 -0.89 -1.10 E1 

5 0.49 0.03 0.43 -0.10 0.21 0.48 C 

6 -0.19 -0.14 0.11 -0.39 -0.17 -0.48 E2 

d) 7 

clusters 

Cluster # 

Sub-Region 

1 

Sub-Region 

2 

Sub-Region 

3 

Sub-Region 

4 

Sub-Region 

5 

Sub-Region 

5a 

1 0.59 -0.30 0.56 -0.39 -0.02 -0.01 C 

2 0.08 0.02 0.02 0.32 0.32 0.10 D1 

3 0.08 -1.77 0.21 0.65 -1.52 -1.00 D2 

4 -1.17 -0.15 -0.97 -0.54 -0.88 -1.11 E1 

5 0.60 0.44 0.39 0.45 0.60 1.03 A 

6 -0.38 -0.12 -0.03 -0.45 -0.27 -0.58 E2 

7 -0.89 0.47 -0.89 -0.47 -0.19 -0.39 B 
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We prepared summaries of the meteorological characteristics of each of these five episode types 

as follows: 

1) composite maps of surface and upper air (850 mb) meteorological variables for each 

cluster, 

2) Side-by-side box plots comparing the distributions of selected key meteorological 

variables within each cluster, and 

3) Tables of morning and afternoon resultant wind direction frequencies within each cluster. 

Full results of items 1 – 3 above are presented in Appendix A, B, and C, respectively. By way of 

example, we show the 850 mb height and wind fields, 850 mb temperature, surface pressure, and 

surface daily maximum temperature and 10 m wind fields composited for each episode type in 

Figures 3-2 to 3-5, respectively. Comparing these composite fields for different episode types 

reveals that each episode type is characterized by a distinct meteorological pattern and these 

patterns are consistent with the ozone patterns noted in Table 3-1. Key characteristics of the five 

episode types are presented in Table 3-2. In the description of each episode type, “average” 

refers to averages over all OTR exceedance days used in the cluster analysis.
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Figure 3-2. Composite 850 mb height and wind fields for each episode (pattern) type. Pattern 

numbers refer to the episode types listed in Table 3-2: Pattern 1 = Episode Type E, 2 = B, 3 = 

A, 4 = D, 5 = C). 
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Figure 3-3. Composite 850 mb temperature fields for each episode (pattern) type. Pattern 

numbers refer to the episode types listed in Table 3-2: Pattern 1 = Episode Type E, 2 = B, 3 = 

A, 4 = D, 5 = C). 
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Figure 3-4. Composite surface sea level pressure for each episode (pattern) type. Pattern 

numbers refer to the episode types listed in Table 3-2: Pattern 1 = Episode Type E, 2 = B, 3 = 

A, 4 = D, 5 = C). 
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Figure 3-5. Composite surface temperature and 10 m wind fields for each episode. Pattern 

numbers refer to the episode types listed in Table 3-2: Pattern 1 = Episode Type E, 2 = B, 3 = 

A, 4 = D, 5 = C). 
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Table 3-2. Key characteristics of each OTR episode type. 

Episode 

Type 

Pattern 

No. 

Description 

A 3 High ozone throughout the OTR. This pattern is characterized by strong 

high pressure over the southeastern states extending from the surface to 

500 mb with high temperatures extending into New England and southwest 

surface winds throughout the OTR. 850 mb temperatures and heights, and 

surface temperatures are above average at all locations except Washington 

DC; winds are SW to W throughout the OTR except more variable at 

LaGuardia and magnitudes of resultant wind vectors are higher than 

average (indicative of a fairly steady, well defined flow regime), E-W surface 

pressure gradients are near neutral but SW-NE gradients both along the I95 corridor and in the west (Pittsburgh to Buffalo) are positive which is 

consistent with the SW flow. Ozone formation under these conditions is 

promoted throughout the OTR by the stable air mass and high 

temperatures. 

B 2 High ozone confined to the extreme southeastern OTR. This pattern is 

characterized by an upper-level trough offshore of the OTR and a surface 

high centered over Kentucky. This results in cooler air advection over 

nearly all of the OTR with northwest flow aloft and a more westerly flow at 

the surface. 850 mb heights are lower than average (especially in New 

England) and surface winds are more frequently from NW along the I-95 

corridor than under Type A. Temperatures at 850 mb along the I-95 

corridor are only slightly cooler than under Type A but inland temperatures, 

especially in the north, are much cooler (e.g., at Buffalo); similarly, surface 

temperatures along the I-95 corridor are about the same as under Type A 

but temperatures are cooler in Buffalo and Albany. Type B events have the 

strongest positive W – E surface pressure gradients of any category, 

consistent with the NW winds but gradients from Washington to New York 

and Boston are positive. The cooler air over the western OTR and westerly 

to northwesterly flow result in the higher ozone levels being confined to just 

the extreme southern portion of the OTR under this pattern. 

C 5 High ozone along I-95 corridor and northern New England. This pattern is 

characterized by an extension of the semi-permanent Bermuda high into the 

southeastern U.S. and an area of high surface and 850 mb temperatures 

extending from Maryland to Maine; the 500 mb pattern is nearly zonal (east 

– west flow) while flow at the surface is generally from the SW. 850 mb 

heights intermediate between Type A and Type B but 850 mb temperatures 

are very high along the I-95 corridor and slightly cooler further inland. 

Winds are more consistently S - SW at all sites than under other episode 

types and almost no NW-N-NE winds are seen at LaGuardia in contrast to 

other types. Resultant wind vector magnitudes are much higher than 

average, consistent with the steady SW flow. SW – NE pressure gradients 

along I-95 corridor and from Pittsburgh to Buffalo are positive, consistent 

with the SW flow. Average E-W pressure gradients are near zero. These 

conditions result in above average ozone levels all along the I-95 corridor 

with advection north into coastal and interior New England. Ozone levels 

are slightly below average in the extreme southeastern and western OTR 

(subregions 2 and 4). 

D 4 High ozone in the western OTR. This pattern is characterized by an area of 

mean upper level divergence with associated cut-off low at 850 mb off the 

Outer Banks of North Carolina. A relatively vigorous mean low pressure 

center can be seen at the surface. An east-west temperature gradient 

across the OTR is evident at 850 mb. Surface temperatures along the I-95 

corridor and in Albany are below average but surface temperature is above 

average at Buffalo. 850 mb heights are the highest of any episode type due 

to a strong ridge over New England. Surface winds are mostly E - NE along
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Episode 

Type 

Pattern 

No. 

Description 

I-95 corridor from DC to NY but more variable further north. In contrast to 

episode types A, B, or C, SW – NE pressure gradients along the I-95 

corridor are negative, consistent with the NE surface winds. W – E 

pressure gradients are flat. These conditions result in below average ozone 

in the eastern OTR (sub-regions 1, 2, 3, 5, and 6) due to the on-shore flow 

in the north and cyclonic conditions in the south but above average ozone 

levels in the western OTR (sub-region 4) due to stable, warm conditions 

with light winds. 

E 1 Generally low ozone throughout OTR. This category includes days with 

moderately low to lowest average ozone readings of all OTR exceedance 

days included in the cluster analysis. The Bermuda high is shifted east 

relative to the other types and flow over the southeastern U.S. is only 

weakly anti-cyclonic with a nearly zonal flow pattern at the 850 and 500 mb 

levels over the OTR. Temperatures at the surface and aloft are the coolest 

of any episode type. While winds aloft are nearly westerly, surface winds 

are generally S – SE over most of the OTR. SW – NE pressure gradients 

are negative along the I-95 corridor and E-W gradients are positive, 

consistent with the SE flow. These conditions result in below average 

ozone throughout the OTR due to the relatively low temperatures and 

southeasterly onshore flow at coastal locations. 

The five episode types described in Table 3-2 exhibit characteristics which are largely consistent 

across the different cluster allocations noted in Table 1 (4, 5, 6 or 7 clusters). When four clusters 

are specified, the Type D events are subsumed into the remaining four episode types. Finer 

division of days into six clusters results in a split of the Type E events into two groups (denoted 

as E1 and E2 in Table 1) with generally very similar meteorological conditions but distinguished 

in part by E-W pressure gradient anomalies that are slightly greater under type E2. Further 

division into seven clusters appears to preserve the Type A, B, and, to a lesser extent, Type C 

events along with the Type E1 and E2 events found in the seven cluster result while the Type D 

events are split into two new categories (denoted D1 and D2 in Table 1). Both D1 and D2 events 

are associated with high ozone in the west (sub-region 4) under S – SW flow as is typical of 

Type D but differ in the surface wind pattern, and hence ozone anomalies, along the I-95 

corridor. 

It is important to keep in mind that there is no a priori expectation that all ozone exceedance 

events in the OTR fall into one of a finite number of distinct patterns: daily conditions differ 

from one another to varying degrees and some days will always have characteristics that cross 

over any predetermined classification boundaries. This means that an episode classification 

system will always have a certain degree of arbitrariness to it and division of days into bins will 

always result in some days that do not fit particularly well into any single bin. Nevertheless, for 

purposes of this study, we seek a reasonable classification system based on a handful of pattern 

types each of which is uniquely identifiable by a set of characteristics related to ozone formation 

across the entire OTR.2

 Based on the clustering results described above, it appears that the 

 

2

 It is worth reiterating here that we are seeking a general classification system applicable to the whole of the OTR. 

More precise classification systems could be developed for individual sub-regions within the OTR but the resulting 

two dimensional system (consisting of a unique set of episode types for each of several sub-regions) would not only 

be very time-consuming to develop but would lead to results from which it would most likely be very difficult to 

draw any conclusions regarding the representativeness of a single season with respect to conditions over the whole 

of the OTR. 
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episode Types A – E meet these requirements reasonably well. Frequencies of occurrence for 

these five types are shown in Table 3-3. 

Table 3-3. Frequencies of occurrence of OTR episode types. 

Type A Type B Type C Type D Type E 

No. Days 46 50 123 44 66 

Pct. 14% 15% 37% 13% 20% 

DEVELOPMENT OF AN OBJECTIVE CLASSIFICATION PROCEDURE 

In order to complete our analysis, we needed to develop a final episode classification rule based 

on results of the above analysis of the 1997 – 2001 and 2003 data which can then be applied to 

the 2002 data to determine the classification of episodes in 2002 to the five ozone event types 

described above. A classification tree model was created for this purpose using the ozone and 

meteorological data from 1997-2001 and 2003 as predictors and the episode pattern type as the 

response variable. In the classification tree model, data from all exceedance days start out 

together in the root node of the tree and are then split into two daughter nodes based on the value 

of one of the predictor variables. For example, a split might consist of sending all days with 

resultant afternoon wind speed at Hartford, CT less than 4.8 m/s to one node and all remaining 

days to the other. The variable and value of that variable used to perform a split is determined by 

examining all possible splits and finding the one which results in the greatest reduction in 

deviance in the response variable (deviance is a measure of the degree of heterogeneity of the 

response variable in a node). The splitting process is then repeated for each resulting daughter 

node and so on until a stopping criterion is reached. The daughter nodes resulting from the last 

split along each branch of the tree are referred to as terminal nodes. The resulting classification 

tree, grown using the 1997-2001/2003 data as the learning dataset, can then be applied to the 

2002 data for which the episode classifications are unknown by running the 2002 daily data 

down the tree, separating days at each node according to the previously determined splitting 

criteria. Each day from the 2002 data will fall into one of the terminal nodes of the tree, and the 

probability of that day belonging to the ith episode type is estimated from the fraction of days 

from the learning dataset in the terminal node belonging to the ith episode type. The predicted

episode type for days in 2002 falling in the terminal node is taken to be the episode type with the 

highest probability of occurrence. 

Initially, the classification tree was grown by making successive splits until only a small number 

of days (in this case five) ends up in each terminal node. This results in a relatively large tree 

with many terminal nodes, each of which will typically be very homogeneous: most of the days 

in any one terminal node will belong to the same episode type. This large tree represents an over 

fit to the data in the learning dataset. In other words, if the tree were to be validated against an 

independent set of days for which the episode types are known (i.e., a test dataset) the frequency 

of misclassification will generally be higher than the low misclassification frequency determined 

by applying the full tree against the learning dataset. Thus, a smaller tree (one with fewer splits 

and therefore fewer terminal nodes), is likely to perform at least as well against a test dataset as 

the initial, large tree. We therefore applied a recursive tree-pruning algorithm known as costcomplexity pruning to the large tree (Venables and Ripley, 1994). This results in a sequence of 

trees, each of which can be characterized by the number of terminal nodes and the costcomplexity parameter, which is a measure of the trade off between growth in tree size and 

reduction in deviance. The resulting tree sequence is shown in Figure 3-6. As this figure shows, 
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there is a diminishing return in deviance reduction as the size of the tree increases beyond about 

5 terminal nodes. 

To further evaluate the relative value of different size trees, we performed a ten-fold crossvalidation using the learning dataset. The ten-fold cross-validation consists of setting aside 

1/10th of the days in the learning sample as a test sample, building a tree using the remaining 

90% of days, and evaluating the deviance reduction using the reserved days. This process is 

repeated 10 times with a different set days set aside in each case. Results from the crossvalidation (Figure 3-7) suggests that the residual deviance is minimized at a tree size of about 

five or six terminal nodes. These results, together with an examination of the misclassification 

rates from the learning dataset for the pruned tree sequence shows that the 6 terminal node tree is 

about the optimal size. 

size

deviance

200 400 600 800 1000

5 10 15 20

190.0 65.0 32.0 26.0 19.0 14.0 13.0 8.9

Figure 3-6. Deviance as a function of tree size (number of terminal nodes) for sequence of 

trees generated by the pruning algorithm. 
March 2005 

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size

deviance

800 900 1000 1100 1200

5 10 15 20

190.0 65.0 32.0 26.0 19.0 14.0 13.0 8.9

Figure 3-7. Deviance from 10-fold cross-validation as a function of tree size (number of 

terminal nodes) for sequence of trees generated by pruning algorithm. 

The selected classification tree is shown in Figure 3-8; Table 3-4 summarizes the distribution of 

days by episode type in each terminal node. Two nodes are made up of predominantly Type E 

days, each of the rest are most representative of one of the four other episode types. Each 

terminal node has a dominant episode type accounting for between 64 and 81% of days assigned 

to the node. To use the classification tree for assigning an episode type to a previously 

unclassified day, we define the predicted episode type for all days reaching a given terminal 

node as the dominant episode type for the node as shown by the shaded boxes in Table 3-4. 

When this rule is applied to the 329 episode days during the historical period, a comparison of 

the predicted episode types with the episode types assigned by the cluster analysis shows an 

overall misclassification rate of 23%.

Table 3-4. Distribution of episode types during the 1997-2001/2003 historical period (as 

determined via the clustering analysis) for days in each terminal node of the classification tree 

shown in Figure 3-8. 

Episode Type 

Node No. 1 2 3 4 5 Total 

4 16 3 6 0 0 25

5 0 16 100 1 9 126

7 5 3 5 0 53 66

8 2 25 1 0 3 31

10 19 0 5 4 0 28

11 4 3 6 39 1 53

Total 46 50 123 44 66 329
March 2005 

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| resPMwd.KPHL.u<0.156411

resPMws.KBOS<4.84

sfcTmax.KHRT<300.5 resPMwd.KISP.v<0.0958421

H850.BUFFALO<1520.75

1 3 5 2

1 4

Classification Tree

Figure 3-8. Classification tree used to group days by episode type. Variable names and values 

used to divide data at each splitting node are shown: days meeting the specified criterion are 

moved down the left branch in each case. Terminal nodes are numbered 1 – 5 and are keyed 

to the summary in Table 3-4. 

CLASSIFICATION OF 2002 OZONE EPISODES 

Data from the 2002 ozone season were analyzed using the classification tree described above to 

yield a division of the ozone exceedance days into the five episode types. The resulting 

frequency distribution of episode types in 2002 was then compared with the historical episode 

type frequency distribution shown in Table 3-4, thereby providing an indication of the degree to 

which conditions during 2002 are representative of conditions observed in other years. We also 

compared ozone concentration distributions and composite meteorological fields by episode type 

in 2002 with those during the historical period as a way of further evaluating the 

representativeness of conditions during the 2002 ozone season. 

Episode Type Classification

We applied the 6-node tree shown in Figure 3-8 to all 8-hour ozone exceedance days in 2002. 

Of the 71 exceedance days, 69 could be assigned to terminal nodes on the tree; missing data 

prevented classification of two of the days. Examination of the classification results showed that 
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if surrogate splits3 were used to assign these two days to one of the terminal nodes, the number 

of days falling into the node would change by no more than 3 percentage points, so the two days 

with missing data were simply ignored. The predicted episode type for each exceedance day in 

2002 was taken to be the predominant episode type in the terminal node to which it was assigned 

(as indicated by the shaded boxes in Table 3-4. The resulting distributions of days by episode 

type for the 2002 season and the 1997-2001/2003 historical period are shown in Figure 3-9.4 For 

the historical days, both the episode type assignments based on the classification tree and the 

episode types as originally assigned in the clustering analysis are shown. The overall pattern of 

episode type occurrence frequencies for the historical period is similar between the classification 

tree and the clustering analysis, as we would expect. Frequencies of occurrence of the episode 

types are within two percentage points of each other except for Type D events (slightly more 

Type D days assigned by the classification tree) and Type B events (about a third fewer Type B 

days determined by the classification tree). 

Comparison of the occurrence frequencies over the historical period with the 2002 data also 

suggest a generally similar pattern of episode types. Note that the error bars in Figure 3-9 show 

the 10th and 90th percentile range in the frequencies of occurrence of each episode type observed 

within individual years during the historical period: an individual year would be expected to fall 

within this range with 80% probability. The 2002 type frequencies generally fall within these 

error bars except for a somewhat higher frequency of Type C events and a lower frequency of 

Type E events. As Type E events are characterized by below average ozone (relative to all 

exceedance days) throughout all but the southernmost OTR, this difference reflects the higher 

frequency of exceedance days in 2002 relative to the historical period as noted above. If we 

ignore the Type E events and renormalize (see Figure 3-10), the occurrence frequencies in 2002 

of the remaining episode types are found to be similar to those in the historical period and fall 

within the 10th to 90th percentile range in each case. Thus, each of the event types A – D appear 

to be well represented within the 2002 season. 

 

3 Surrogate splitting uses the best alternative splits (based on the non-missing variable that produces nearly the same 

split as the primary splitting variable). 

4

 The bars in this figure are scaled to the fraction of OTR exceedance days assigned to each episode type. Thus, 

these comparisons are not effected by the above average number of exceedance days in 2002 noted earlier. 
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Episode Type Frequencies

37%

15%

20%

13% 14%

38%

9%

20%

16% 16%

33%

12%

29%

17%

9%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Type A Type B Type C Type D Type E

Clust ering (Hist orical Dat a)

Classif icat ion Tree (Hist orical Dat a)

Classif icat ion Tree (2002 Dat a)

Figure 3-9. Percent of episode days by type in the 1997 – 2001/2003 historical period (as 

determined by the cluster analysis and by the classification tree) and in 2002; error bars show 

10th and 90th percentiles of annual frequencies of occurrence during 1997-2001&2003.

Episode Type Frequencies

( Re nor ma li z e d wi t h Type E Ev ent s Remov ed)

43%

18%

23%

16%

46%

11%

24%

19%

37%

13%

32%

19%

0%

10%

20%

30%

40%

50%

60%

Type A Type B Type C Type D

Clust ering (Hist orical Dat a)

Classif icat ion Tree (Hist orical Dat a)

Classif icat ion Tree (2002 Dat a)

Figure 3-10. Percent of episode days by type in the 1997 – 2001/2003 historical period (as 

determined by the cluster analysis and by the classification tree) and in 2002 with Type E 

events removed and frequencies re-normalized; error bars show 10th and 90th percentiles of 

annual frequencies of occurrence during 1997-2001&2003.
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Ozone Concentration levels 

An exceedance of the 8-hour ozone standard occurred at one or more sites in the study region on 

71 days during 2002, representing 46% of the 153 days during the May – September season 

analyzed in this study. For the 1997 – 2001/2003 historical period, the corresponding percentage 

was 36% so exceedances were more frequent during 2002. The greater frequency of ozone 

exceedance events was distributed throughout the OTR as shown by the comparison by 

monitoring sub-region in Table 3-5. Exceedances occurred with 20 – 50% greater frequency in 

2002 in all sub-regions (100% greater in sub-region 3). This difference in the frequency of 

exceedances in 2002 as compared to the historical period does not necessarily mean, however, 

that the exceedance events themselves have characteristics that significantly differ from those 

seen during the historical period. 

Table 3-5. Number of days during May-September with 8-hour daily maximum ozone greater 

than 0.08 ppm in each monitoring sub-region averaged over the 1997-2001/2003 historical 

period and in 2002. 

Sub-Region 

1 2 3 4 5 6 

>0.08 ppm 

1997-2001/2003 22.3 31.0 8.5 27.3 42.0 30.8 

2002 34 38 17 39 58 44 

Pct. Difference 52% 23% 100% 43% 38% 43% 

Distributions of daily maximum 8-hour average ozone concentrations averaged over monitors in 

each sub-region (the AvgExc00 statistic) in 2002 and the historical period are compared for each 

event type in Figures 3-11(a-e). A key to the boxplot symbols used to summarize the ozone 

distribution is shown in Figure 3-12. Overall, the range of ozone under each event type in 2002 

is similar to that under the corresponding event type in the historical period. The most notable 

exceptions are higher ozone levels during Type D events in 2002 along the Washington – New 

York City corridor (sub-regions 2, 5, and 6). This is consistent with a less pronounced low 

pressure center off the NC coast in the 2002 Type D events as compared to the historical period 

(see further discussion below). Aside from this difference, the overall ozone levels during the 

2002 exceedance events were generally very consistent with those observed during the historical 

period, not withstanding the fact that exceedance days were more frequent during 2002.
March 2005 

G:\OTC-EpisodeClassification\Report\Draft_Final\Sec3(Analysis).doc3/31/2005 3-19 0.03 0.04 0.05 0.06

2002 Historical

ppb

Sub-Region 1

0.04 0.06 0.08

2002 Historical

ppb

Sub-Region 2

0.02 0.04 0.06

2002 Historical

ppb

Sub-Region 3

0.03 0.05 0.07

2002 Historical

ppb

Sub-Region 4

0.04 0.06 0.08

2002 Historical

ppb

Sub-Region 5

0.02 0.04 0.06

2002 Historical

ppb

Sub-Region 6

Figure 3-11a. Boxplots of average daily maximum ozone (ppb) in each monitoring sub-region 

during 2002 and during the 1997-2001 and 2003 “historical” period: Type E (Pattern No. 1) 

events. 
March 2005 

G:\OTC-EpisodeClassification\Report\Draft_Final\Sec3(Analysis).doc3/31/2005 3-20 0.03 0.04 0.05 0.06 0.07

2002 Historical

ppb

Sub-Region 1

0.05 0.06 0.07 0.08 0.09

2002 Historical

ppb

Sub-Region 2

0.03 0.04 0.05 0.06

2002 Historical

ppb

Sub-Region 3

0.04 0.06 0.08

2002 Historical

ppb

Sub-Region 4

0.05 0.07 0.09

2002 Historical

ppb

Sub-Region 5

0.02 0.04 0.06 0.08 0.10

2002 Historical

ppb

Sub-Region 6

Figure 3-11b. Boxplots of average daily maximum ozone (ppb) in each monitoring sub-region 

during 2002 and during the 1997-2001 and 2003 “historical” period: Type B (Pattern No. 2) 

events. 
March 2005 

G:\OTC-EpisodeClassification\Report\Draft_Final\Sec3(Analysis).doc3/31/2005 3-21 0.04 0.06 0.08 0.10

2002 Historical

ppb

Sub-Region 1

0.04 0.06 0.08 0.10

2002 Historical

ppb

Sub-Region 2

0.03 0.05 0.07 0.09

2002 Historical

ppb

Sub-Region 3

0.04 0.06 0.08 0.10

2002 Historical

ppb

Sub-Region 4

0.04 0.06 0.08 0.10

2002 Historical

ppb

Sub-Region 5

0.04 0.06 0.08 0.10

2002 Historical

ppb

Sub-Region 6

Figure 3-11c. Boxplots of average daily maximum ozone (ppb) in each monitoring sub-region 

during 2002 and during the 1997-2001 and 2003 “historical” period: Type A (Pattern No. 3) 

events. 
March 2005 

G:\OTC-EpisodeClassification\Report\Draft_Final\Sec3(Analysis).doc3/31/2005 3-22 0.03 0.05 0.07

2002 Historical

ppb

Sub-Region 1

0.03 0.05 0.07 0.09

2002 Historical

ppb

Sub-Region 2

0.03 0.05 0.07

2002 Historical

ppb

Sub-Region 3

0.04 0.06 0.08

2002 Historical

ppb

Sub-Region 4

0.02 0.04 0.06 0.08

2002 Historical

ppb

Sub-Region 5

0.03 0.05 0.07

2002 Historical

ppb

Sub-Region 6

Figure 3-11d. Boxplots of average daily maximum ozone (ppb) in each monitoring sub-region 

during 2002 and during the 1997-2001 and 2003 “historical” period: Type D (Pattern No. 4) 

events. 
March 2005 

G:\OTC-EpisodeClassification\Report\Draft_Final\Sec3(Analysis).doc3/31/2005 3-23 0.03 0.05 0.07 0.09

2002 Historical

ppb

Sub-Region 1

0.03 0.05 0.07 0.09

2002 Historical

ppb

Sub-Region 2

0.03 0.05 0.07

2002 Historical

ppb

Sub-Region 3

0.03 0.05 0.07 0.09

2002 Historical

ppb

Sub-Region 4

0.05 0.07 0.09

2002 Historical

ppb

Sub-Region 5

0.04 0.08

2002 Historical

ppb

Sub-Region 6

Figure 3-11e. Boxplots of average daily maximum ozone (ppb) in each monitoring sub-region 

during 2002 and during the 1997-2001 and 2003 “historical” period: Type C (Pattern No. 5) 

events. 
March 2005 

G:\OTC-EpisodeClassification\Report\Draft_Final\Sec3(Analysis).doc3/31/2005 3-24 

Median

75th 

Percentile

25th

Percentile

IInter-Quartile 

Distance 

(IQD) 

Largest value < 

75%'tile + 1.5 * IQD

Smallest value > 

25%'tile - 1.5 * IQD

Extreme Value

Figure 3-12. Key to boxplot symbols. 

Meteorological Conditions 

Selected composite meteorological fields for each episode type in 2002 as predicted by 

application of the classification tree were computed and displayed for comparison with the 

historical period composite fields. Results are shown in Figure 3-13 through 3-16. Comparing 

of these results with those for the historical period (Figures 3-2 to 3-5), we see a remarkable 

degree of similarity:5

 the surface and upper air meteorological patterns for a given episode type 

in 2002 are very similar to those for the same episode type observed in the historical period. In 

other words, the key characteristics of each type observed in the historical dataset are reproduced 

within the 2002 data. Perhaps the most significant difference is the less pronounced low pressure 

center off the NC coast under Type D events in 2002 which allowed for the formation of higher 

ozone concentrations along the Washington – New York City corridor for these event types in 

2002 as compared to the historical period. Overall, however, the close match in weather patterns 

associated with each event type in 2002 and the historical period strongly supports the 

conclusion that the 2002 ozone episodes, although more numerous than in other years, are of 

substantially similar character. 

 

5

 In making these comparisons, note that different color and wind vector scales had to be used in some plots of the 

2002 data. 
March 2005 

G:\OTC-EpisodeClassification\Report\Draft_Final\Sec3(Analysis).doc3/31/2005 3-25 

Figure 3-13. Composite meteorological fields by episode type (pattern) in 2002: 850 mb 

heights and winds. Pattern numbers refer to the episode types listed in Table 3-2: Pattern 1 = 

Episode Type E, 2 = B, 3 = A, 4 = D, 5 = C). 
March 2005 

G:\OTC-EpisodeClassification\Report\Draft_Final\Sec3(Analysis).doc3/31/2005 3-26 

Figure 3-14. Composite meteorological fields by episode type (pattern) in 2002: 850 mb 

temperature. Pattern numbers refer to the episode types listed in Table 3-2: Pattern 1 = 

Episode Type E, 2 = B, 3 = A, 4 = D, 5 = C). 
March 2005 

G:\OTC-EpisodeClassification\Report\Draft_Final\Sec3(Analysis).doc3/31/2005 3-27 

Figure 3-15. Composite meteorological fields by episode type (pattern) in 2002: sea level 

pressure. Pattern numbers refer to the episode types listed in Table 3-2: Pattern 1 = Episode 

Type E, 2 = B, 3 = A, 4 = D, 5 = C). 
March 2005 

G:\OTC-EpisodeClassification\Report\Draft_Final\Sec3(Analysis).doc3/31/2005 3-28 

Figure 16. Composite meteorological fields by episode type (pattern) in 2002: surface 

temperature and 10 m winds. Pattern numbers refer to the episode types listed in Table 3-2: 

Pattern 1 = Episode Type E, 2 = B, 3 = A, 4 = D, 5 = C). 
March 2005 

G:\OTC-EpisodeClassification\Report\Draft_Final\Sec4(Conclusions).doc3/31/2005 4-1 

4. CONCLUSIONS AND RECOMMENDATIONS 

Results from the application of statistical clustering analyses presented in Section 3 show that 

regional ozone episode conditions over the OTR can be reasonably well described by a set of 

five different episode types. Our examination of mean ozone and meteorological conditions 

shows that each of these episode types is associated with a unique set of distinguishing 

characteristics. While we would not expect every exceedance day to fully exhibit all of the 

characteristics of one type or another, our results provide no clear evidence for the existence of 

any other additional sufficiently unique types which occurred frequently enough to be 

distinguishable within the six year historical period analyzed. 

Data from the 2002 ozone season were analyzed within the framework of the five identified 

episode types with respect to: a) frequencies of occurrence of each type and b) characteristics of 

the ozone and meteorological conditions within each type in 2002 as compared to the 1997 – 

2001/2003 historical period. 

A key feature of the 2002 season is that ozone episodes (defined as an exceedance of the 8-hour 

ozone standard at one or more monitoring sites within the OTR) occurred more frequently than 

during the historical period (71 exceedance days during the May – September season in 2002 as 

compared to an average of 55 days per season during the historical period). Taken by itself, 

however, this difference does not necessarily mean that region-wide meteorological and ozone 

concentration patterns during exceedance days were significantly different in 2002 as 

compared to other years: the greater number of exceedance days in 2002 may just reflect a 

lower than average frequency of days with meteorological conditions not conducive to ozone 

formation in 2002. As these days are of little interest to us in terms of modeling in support of 

ozone SIP development, however, the higher than average exceedance rate in 2002 is by itself 

not an indication of any lack of representativeness of the 2002 exceedance events.

Our examination of conditions during exceedance days in 2002 showed that: 

• Except for the Type E events during which ozone exceedances are typically confined to 

the extreme southeastern corner of the OTR, each of the five episode types identified in 

the historical period was found to occur on about as many days in 2002 as one would 

expect based on their rate of occurrence during the historical record. Thus the 

meteorological conditions on episode days in 2002 exhibit a normal range of variation and 

each of the five types of episodes are well represented. 

• Type E events are under represented in the 2002 season. This is consistent with the higher 

than average frequency of exceedance days in 2002. The relative lack of Type E events in 

2002 should not be of concern from a SIP modeling standpoint, however, as these events 

are characterized by relatively low ozone levels throughout nearly all of the OTR (except 

the Washington and Virginia area). 

• The distribution of daily maximum 8-hour average ozone levels during each event type in 

2002 is generally very similar to that within the same event type during the historical 

period. The only significant exception is higher ozone along the Washington – New York 

City corridor under Type D events in 2002 as compared to the historical average.
March 2005 

G:\OTC-EpisodeClassification\Report\Draft_Final\Sec4(Conclusions).doc3/31/2005 4-2 

• Regional-scale meteorological conditions during each event type in 2002 exhibit the same 

key characteristics as observed for the event types during the historical period. A less 

pronounced low pressure center off of the NC coast under the 2002 Type D events appears 

to be responsible for the higher Washington – New York City ozone levels under this 

event type noted in the previous bullet. 

In summary, while ozone exceedances were more frequent during 2002, conditions during the 

2002 exceedance events were for the most part very similar to those found to occur in other 

years. This leads us to conclude that the 2002 season can be considered to be representative for 

purposes of photochemical modeling in support of SIP development. 
March 2005 

G:\OTC-EpisodeClassification\Report\Draft_Final\Sec5(refs).doc R-1

REFERENCES 

Breiman, L., J.H. Friedman, R.A. Olshen and C.J. Stone, 1984. Classification and Regression 

Trees. Wadsworth, Belmont, CA 

Cox, W.M., 1997. Spatial Clustering of Ambient Ozone in Eastern U.S., Draft, U.S. 

Environmental Protection Agency, January (available at 

http://www.epa.gov/ttn/naaqs/ozone/areas/misc/ozclus2a.htm) 

Deuel, H.P. and S.G. Douglas, 1996. “Regional Ozone Patterns in the Eastern U.S. 1985-1995: 

Statistical Pattern Analysis and Classification and Regression Tree (CART) Analysis in 

Support of the Ozone Transport and Assessment Group (OTAG) Modeling Effort.” 

Systems Applications Inc. San Rafael, CA October. 

EPA, 1999. “Draft Guidance on the Use of Models and Other Analyses in Attainment 

Demonstrations for the 8-Hour Ozone NAAQS.” EPA-54/R-99-004, U.S. Environmental 

Protection Agency, May. 

Harrell, F.E., 1999. Hmisc S-PLUS function library. Programs available from lib.stat.cmu.edu. 

Hartigan, J. A. and M.A. Wong, 1979. A k-means clustering algorithm. Applied Statistics 28, 

100-108. 

McHenry, J.N., W.F. Ryan, N.L. Seaman, C.J. Coats, J. Pudykiewicz, S. Arunachalam and J.M. 

Vukovich, 2004. A real-time Eulerian photochemical model forecast system: Overview 

and initial ozone forecast performance in the northeastern U.S. corridor. Bulletin of the 

American Meteorological Society, 85(4), April. 

Sarle, WS: The VARCLUS Procedure. SAS/STAT User's Guide, 4th Edition, 1990. Cary NC: 

SAS Institute, Inc. 

Stoeckenius, T.E. and S. Kemball-Cook, 2004. Ozone episode classification project: spatial 

ozone pattern analysis (Task 2a). Memo to T. Frankiewicz, Ozone Transport Comission, 

21 September. 

Venables, W.N. and B.D. Ripley, 1994. “Modern Applied Statistics with S-Plus.” SpringerVerlag, New York. 

Ward, J.H., 1963. Hierarchical groupings to optimize an objective function. J. Amer. Statistical 

Assoc., 58:236-244. 
Appendix A 

Composites Representing Mean Meteorological Conditions 

During Each Ozone Episode Pattern 
Composites Representing Mean Meteorological Conditions During Each Ozone 

Episode Pattern 

Mean meteorological fields were computed over days falling into each of the five ozone 

episode patterns in the Ozone Transport Region defined in the text. The five episode 

patterns and their composite pattern identifiers are: 

Composite Pattern Episode Type 

3 

Type A: High ozone 

throughout the OTR 

2 

Type B: High ozone confined 

to extreme southeastern OTR 

5 

Type C: High ozone along I95 corridor and northern New 

England 

4 

Type D: High ozone in the 

western OTR 

1 

Type E: Generally low ozone 

throughout the OTR 

Mean fields were computed for the following parameters extracted from the EDAS data: 

Parameter ID Description 

H850 850 mb height 

850 mb Wind Resultant wind vector at 850 mb 

T(850 mb) 850 mb temperature (deg K) 

MSLP Mean sea level pressure (mb) 

TSFC Surface temperature (deg K) 

10m Wind Resultant wind vector at 10 m height 

w_500 w (vertical) component of 500 mb wind 

vector 

H500 500 mb heights 






Appendix B 

Boxplots of Key Meteorological Variables 
Boxplots Of Key Meteorological Variables 

Boxplots in this Appendix summarize distributions of the sub-regional ozone summary 

statistic, AvgEx00, described in the text along with selected key daily meteorological 

variables by episode pattern membership for the five cluster case. Pattern membership 

identifiers (“Met Cluster”) used in these plots correspond to the episode types described 

in the text as follows: 

Met Cluster Episode Type 

1 Type E 

2 Type B 

3 Type A 

4 Type D 

5 Type C 

Ozone and meteorological variables are: 

Variable Description 

clnx AvgEx00 ozone summary statistic for ozone 

monitoring cluster x (x = 1,2...6; see Figure 1 in 

text) 

DelPsfc.edas.DCtoNYC Surface pressure gradient: Washington DC – New 

York City 

DelPsfc.edas.DCtoBOSTON Surface pressure gradient: Washington DC to 

Boston 

DelPsfc.edas.DCtoPITTSBURGH Surface pressure gradient: Washington DC to 

Pittsburgh 

DelPsfc.edas.BUFFALOtoBOSTON Surface pressure gradient: Buffalo to Boston 

DelPsfc.edas.PITTSBURGHtoBUFFALO Surface pressure gradient: Pittsburgh to Buffalo 

H850.DC 850 mb height: Washington DC 

H850.BOSTON 850 mb height: Boston 

H850.PITTSBURGH 850 mb height: Pittsburgh 

H850.BUFFALO 850 mb height: Buffalo 

H850.PORTLAND 850 mb height: Portland, ME 

H850.NYC 850 mb height: New York City 

T850.DC 850 mb temperature: Washington DC 

T850.BOSTON 850 mb temperature: Boston 

T850.PITTSBURGH 850 mb temperature: Pittsburgh 

T850.BUFFALO 850 mb temperature: Buffalo 

T850.PORTLAND 850 mb temperature: Portland, ME 

T850.NYC 850 mb temperature: New York City 

sfcTmax.KLGA Daily max surface temperature: La Guardia 

sfcTmax.KPHL Daily max surface temperature: Philadelphia 

sfcTmax.KBOS Daily max surface temperature: Boston 

sfcTmax.KBUF Daily max surface temperature: Buffalo 

sfcTmax.KALB Daily max surface temperature: Albany 

sfcTmax.KDCA Daily max surface temperature: Washington DC 
0.04 0.06 0.08

12345

Met Cluster

cln1

0.04 0.06 0.08 0.10

12345

Met Cluster

cln2

0.02 0.04 0.06 0.08

12345

Met Cluster

cln3

0.04 0.06 0.08 0.10

12345

Met Cluster

cln4
0.02 0.04 0.06 0.08 0.10

12345

Met Cluster

cln5

0.02 0.06 0.10

12345

Met Cluster

cln6

-6 -4 -2 0 2 4

12345

Met Cluster

delPsfc.edas.DCtoNYC

-5 0 5

12345

Met Cluster

delPsfc.edas.DCtoBOSTON
-6 -4 -2 0 2 4 6

12345

Met Cluster

delPsfc.edas.DCtoPITTSBURGH

-15 -10 -5 0 5 10

12345

Met Cluster

delPsfc.edas.BUFFALOtoBOSTON

-6 -4 -2 0 2 4 6 8

12345

Met Cluster

delPsfc.edas.PITTSBURGHtoBUFFALO

1450 1500 1550 1600 1650

12345

Met Cluster

H850.DC
1400 1450 1500 1550 1600

12345

Met Cluster

H850.BOSTON

1400 1500 1600

12345

Met Cluster

H850.PITTSBURGH

1350 1450 1550 1650

12345

Met Cluster

H850.BUFFALO

1400 1450 1500 1550 1600

12345

Met Cluster

H850.PORTLAND
1450 1500 1550 1600

12345

Met Cluster

H850.NYC

282 286 290 294

12345

Met Cluster

T850.DC

275 280 285 290 295

12345

Met Cluster

T850.BOSTON

280 285 290 295

12345

Met Cluster

T850.PITTSBURGH
275 280 285 290 295

12345

Met Cluster

T850.BUFFALO

270 275 280 285 290

12345

Met Cluster

T850.PORTLAND

275 280 285 290 295

12345

Met Cluster

T850.NYC
290 295 300 305 310

12345

Met Cluster

sfcTmax.KLGA

290 295 300 305 310

12345

Met Cluster

sfcTmax.KPHL

285 290 295 300 305 310

12345

Met Cluster

sfcTmax.KBOS

290 295 300 305

12345

Met Cluster

sfcTmax.KBUF

290 295 300 305

12345

Met Cluster

sfcTmax.KALB

295 300 305 310

12345

Met Cluster

sfcTmax.KDCA
290 295 300 305

12345

Met Cluster

sfcTmax.KPWM

290 295 300 305 310

12345

Met Cluster

sfcTmax.KHVN

290 295 300 305 310

12345

Met Cluster

sfcTmax.KACY

290 295 300 305 310

12345

Met Cluster

sfcTmax.KISP
Appendix C 

Wind Direction Frequency Tables 
Wind Direction Frequency Tables 

Contingency tables showing resultant surface wind direction frequencies were prepared 

for the five cluster membership cases. These results show relative frequency of days with 

the indicated wind direction in each cluster, i.e., the values for each cluster (column) sum 

to 100%. Tabulations are shown for both morning (AM) and afternoon (PM) resultant 

wind direction. Site location codes referenced in these tables are shown below. 

Site Code Location 

KLGA LaGuardia airport, New York, NY 

KPHL Philadelphia, PA 

KBOS Boston, MA 

KBUF Buffalo, NY 

KALB Albany, NY 

KDCA Washington, DC 

KPWM Portland, ME 

KHVN New Haven, CT 

KACY Atlantic City, NJ 

KISP Islip, Long Island, NY 

KHYA Hayannis, Cape Cod, MA 

KWOR Worcester, MA (KORH) 

KHRT Hartford, CT (KHFD) 
Table B-1. Morning and afternoon daily resultant wind direction frequencies (%) by 

cluster membership for the five cluster case (columns sum to 100%). Header row for 

each table indicates AM or PM and four letter site ID as described in text (e.g., KLGA = 

LaGuardia, NY). Cluster identifier (A, B, C, D, E) is shown in first row of each table. 

a) Morning wind directions 

$resAMwd.KLGA: 

 E B A D C 

 E 2 2 5 47 3 

 N 4 4 1 0 0 

NE 27 4 7 35 0 

NW 7 42 10 0 0 

 S 33 6 12 5 6 

SE 11 0 7 7 2 

SW 16 10 34 2 76 

 W 0 32 23 5 14

$resAMwd.KPHL: 

 E B A D C 

 E 9 0 2 32 2 

 N 4 4 1 2 0 

NE 7 0 0 34 0 

NW 4 28 4 5 0 

 S 24 4 17 9 29 

SE 20 0 4 7 2 

SW 26 34 55 9 64 

 W 7 30 17 2 5

$resAMwd.KBOS: 

 E B A D C 

 E 2 0 4 9 2 

 N 2 0 1 0 0 

NE 9 4 1 14 0 

NW 9 22 13 20 0 

 S 17 8 11 16 11 

SE 15 2 2 7 3 

SW 22 26 43 20 77 

 W 24 38 25 14 8

$resAMwd.KBUF: 

 E B A D C 

 E 16 2 3 16 0 

 N 0 0 1 0 0 

NE 13 0 0 5 3 

NW 0 10 0 0 5 

 S 31 29 44 16 17 

SE 24 0 24 57 6 

SW 16 40 25 7 59 

 W 0 19 2 0 11

$resAMwd.KALB: 

 E B A D C 

 E 2 0 8 6 0 

 N 10 7 5 20 2 

NE 2 0 1 3 0 

NW 10 20 4 3 0 

 S 34 20 55 49 87 

SE 15 0 9 6 3 

SW 20 26 12 9 8 

 W 7 28 5 6 0

$resAMwd.KDCA: 

 E B A D C 

 E 4 0 0 26 0 

 N 2 14 2 2 0 

NE 7 0 1 28 0 

NW 9 24 5 5 0 

 S 46 24 38 16 68 

SE 4 0 5 16 2 

SW 22 28 45 2 29 

 W 7 10 5 5 2

$resAMwd.KPWM: 

 E B A D C 

 E 10 8 3 2 3 

 N 2 0 1 7 0 

NE 7 2 0 0 2 

NW 12 17 14 17 2 

 S 12 8 11 5 27 

SE 2 4 7 2 5 

SW 15 8 16 29 50 

 W 39 52 49 38 12

$resAMwd.KACY: 

 E B A D C 

 E 7 0 1 26 0 

 N 2 2 2 10 0 

NE 5 0 1 23 0 

NW 5 22 7 10 0 

 S 29 0 17 10 41 

SE 12 2 6 8 3 

SW 22 29 45 8 52 

 W 17 45 22 5 5

$resAMwd.KISP: 

 E B A D C 

 E 9 0 5 14 0 

 N 9 9 6 19 0 

NE 9 0 7 36 0 

NW 0 34 10 5 0 

 S 32 2 8 2 11 

SE 14 0 3 14 2 

SW 11 21 44 7 80 

 W 16 34 18 2 8

$resAMwd.KHYA: 

 E B A D C 

 E 6 0 0 8 0 

 N 3 13 3 14 0 

NE 11 2 1 43 0 

NW 6 20 6 0 0 

 S 14 7 6 11 3 

SE 8 4 6 3 0 

SW 31 22 41 19 60 

 W 22 33 38 3 37

$resAMwd.KWOR: 

 E B A D C 

 E 5 2 0 7 0 

 N 0 4 3 10 0 

NE 10 0 1 15 2 

NW 5 23 10 17 0 

 S 5 2 0 5 2 

SE 5 0 2 7 0 

SW 33 4 19 12 62 

 W 38 65 65 27 35 

$resAMwd.KHRT: 

 E B A D C 

 E 5 5 5 19 0 

 N 5 2 2 6 0 

NE 8 2 2 9 0 

NW 3 7 0 0 0 

 S 51 36 71 41 94 

SE 5 12 13 16 3 

SW 19 19 2 6 3 

 W 3 17 5 3 0 
Table B-1 (concl). 

b) Afternoon wind directions 

$resPMwd.KLGA: 

 E B A D C 

 E 9 0 7 20 0 

 N 0 6 1 0 0 

NE 30 0 15 68 0 

NW 4 68 26 0 8 

 S 30 6 12 2 8 

SE 13 2 8 7 0 

SW 11 6 17 0 48 

 W 2 12 14 2 36

$resPMwd.KPHL: 

 E B A D C 

 E 13 2 4 57 0 

 N 0 8 2 0 0 

NE 4 0 0 32 0 

NW 2 40 5 0 2 

 S 22 0 20 2 8 

SE 26 4 3 9 2 

SW 30 22 50 0 73 

 W 2 24 16 0 17

$resPMwd.KBOS: 

 E B A D C 

 E 20 2 13 34 5 

NE 9 2 2 9 0 

NW 4 30 2 0 0 

 S 17 4 23 18 8 

SE 15 2 20 25 0 

SW 26 10 24 7 65 

 W 9 50 16 7 23

$resPMwd.KBUF: 

 E B A D C 

 E 7 2 3 19 0 

 N 0 2 0 0 0 

NE 11 4 1 5 5 

NW 2 6 6 0 8 

 S 28 12 20 33 3 

SE 11 6 2 16 0 

SW 37 51 61 28 68 

 W 4 16 7 0 17

$resPMwd.KALB: 

 E B A D C 

 E 2 0 3 8 0 

 N 2 2 2 11 0 

NE 4 2 2 3 0 

NW 7 41 6 0 2 

 S 59 2 45 50 58 

SE 15 2 5 18 2 

SW 7 20 17 8 24 

 W 4 31 20 3 15

$resPMwd.KDCA: 

 E B A D C 

 E 7 4 5 23 0 

 N 0 4 2 2 0 

NE 9 6 2 48 0 

NW 2 34 4 0 5 

 S 57 14 53 9 59 

SE 11 6 9 11 3 

SW 11 6 17 2 21 

 W 4 26 8 5 12

$resPMwd.KPWM: 

 E B A D C 

 E 11 8 17 20 5 

 N 0 4 2 0 0 

NE 13 2 2 7 2 

NW 11 42 11 0 2 

 S 20 6 24 27 29 

SE 20 4 16 25 6 

SW 16 14 15 14 33 

 W 9 20 13 7 24

$resPMwd.KACY: 

 E B A D C 

 E 15 2 3 42 0 

 N 0 6 1 2 0 

NE 7 4 1 47 0 

NW 2 48 11 0 0 

 S 33 4 21 0 23 

SE 24 0 10 9 0 

SW 13 8 26 0 55 

 W 7 28 27 0 22

$resPMwd.KISP: 

 E B A D C 

 E 11 0 6 36 0 

 N 2 10 5 2 0 

NE 11 0 3 36 0 

NW 2 52 11 2 3 

 S 38 0 17 2 14 

SE 20 0 8 18 0 

SW 11 8 39 2 68 

 W 4 29 13 0 15

$resPMwd.KHYA: 

 E B A D C 

 E 16 11 7 9 0 

 N 0 9 2 7 0 

NE 9 4 5 27 0 

NW 0 11 6 0 0 

 S 13 9 12 20 5 

SE 18 6 8 20 0 

SW 38 21 48 16 75 

 W 7 30 11 0 20

$resPMwd.KWOR: 

 E B A D C 

 E 14 4 3 17 2 

 N 5 4 1 2 0 

NE 14 4 4 22 0 

NW 9 35 9 2 0 

 S 9 0 7 22 5 

SE 5 0 3 10 0 

SW 37 4 28 7 54 

 W 7 49 44 17 40 

$resPMwd.KHRT: 

 E B A D C 

 E 11 2 16 16 0 

 N 4 9 3 3 0 

NE 0 4 7 22 0 

NW 2 22 1 0 0 

 S 56 13 43 22 52 

SE 13 11 18 24 2 

SW 11 11 9 11 35 

 W 2 28 4 3 11