[American Institute of Aeronautics and Astronautics 9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO) - Hilton Head, South Carolina ()] 9th AIAA Aviation

Analyzing the Share of Individual Weather Factors Affecting NAS Performance Using the Weather Impacted Traffic

Index

Lara Shisler Cook1 and Bryan Wood

2

Mosaic ATM, Inc., Leesburg, VA 20175

Alexander Klein3

Air Traffic Analysis, Inc., Fairfax, VA 22031

and

Robert Lee4 and Bahar Memarzadeh

5

AvMet Applications, Inc., Reston, VA 20191

We quantify the impacts of both convective and con-convective weather factors air traffic using metrics based on the 4-component NAS Weather Index (NWX). The adverse impacts of seven weather factors are calculated for over three years of data. The impacts are then broken out by airport, geographic region, and season. The quantitative results highlight extreme values that validate well with common knowledge of NAS behavior.

I. Introduction One of the main motivations to modernizing the National Airspace System (NAS) is to address the problem of

flight delays. In 2008, 77% of flights departed on time and only 75% arrived on time*, according to the Aviation

System Performance Metrics (ASPM) system†. The

average delay in gate arrival time over all flights for

the year was more than 15 minutes. There are various

causes for flight delays in the NAS, but the

predominant factor is weather. A study conducted by

the Cooperative Program for Operational

Meteorology, Education and Training (COMET)

Program found that 76% of the flight delays in 2004

were caused by weather‡, as depicted in Figure 1.

In this paper we present the results of research to

better understand the impact that weather has on the

NAS. Using the Weather Impacted Traffic Index

(WITI), a methodology to calculate the number of

flights impacted by weather, we look at these impacts

by season, geography, and type of weather. We also

analyze the frequency of occurrence of specific types

1 Principle Analyst, Mosaic ATM, 801 Sycolin Rd., SE, Ste. 212, Leesburg, VA 20175, AIAA Member

2 Senior Analyst, Mosaic ATM, 801 Sycolin Rd., SE, Ste. 212, Leesburg, VA 20175, AIAA Member

3 President, 3802 Ridgelea Drive, Fairfax, VA 22031, AIAA Member

4 Senior Systems Analyst, 1801 Robert Fulton Dr., Ste. 570, Reston, VA 20191

5 Systems Analyst, 1801 Robert Fulton Dr., Ste. 570, Reston, VA 20191

* A flight is considered to operate on-time if it departs/arrives within 15 minutes of schedule

† http://aspm.faa.gov/aspm/ASPMframe.asp

‡ http://meted.ucar.edu/

Figure 1. Causes of NAS Delays as Produced by the COMET Program

9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO) <br>and <br>Air21 - 23 September 2009, Hilton Head, South Carolina

AIAA 2009-7017

Copyright © 2009 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

2

American Institute of Aeronautics and Astronautics

Figure 2. NWS 2004 Study—Impact of Weather on the NAS by Type

of weather vs. the severity of the impact to better understand those weather factors that can have a significant impact

when they occur, even if the overall yearly frequency is low. This information can be valuable for highlighting

weather events that require more extensive Traffic Flow Management (TFM) planning when forecasted. This study

also highlights cases where there are weather factors that do not have a big impact on a given day, but due to the

high frequency throughout the year, end up significantly contributing to overall NAS-wide delays. These types of

factors may point to areas where slight improvements in TFM efficiency could result in large reductions in overall

NAS-wide delay. Importantly, while most previous analyses have focused on convective weather, this research

extends to numerous non-convective weather factors, year round.

II. Previous Research In 2004, the National Weather Service (NWS) completed a study of impacts to the entire NAS in terms of

weather element frequency relative to traffic volume. Eight weather elements were considered: wind (terminal cross

winds and tail winds), ceiling, visibility, snow, freezing rain/drizzle, turbulence, icing (in-flight), and thunderstorms.

Elements that occurred frequently in high volume areas scored the highest (high impact), while elements that

occurred infrequently or in low traffic areas scored the lowest (low impact). The results of the study are available on

a public Web site§ and are summarized in Figure

2.

To support this study, the FAA provided a list

of the 68 busiest airports (31 rated as large, 37 as

medium) in the U.S. and Puerto Rico, ranked

according to air traffic. Weather thresholds for

each type of weather were used to identify the

frequency of each type of weather at each airport.

The traffic at each airport was used to weigh the

frequency of occurrence to calculate the weighted

impact factor.

Since this methodology weighs the occurrence

of weather by the volume of traffic impacted by

that weather, it is similar to the concept of the

WITI. However, this study focused on weather at

just the 68 busiest airports and weighted the

weather by the airport’s ranking in traffic volume, not by the actual operations. Additionally, the study did not

attempt to quantify the impact of individual weather factors on specific airports’ capacity; rather, it measured the

occurrence of each weather phenomenon. In our study using the WITI, we analyze the impact of weather both in the

terminal area at the major airports and in the en-route airspace throughout the entire NAS, using the scheduled

traffic for that particular day, 365 days a year. We also consider the ratio of airport capacity (possibly degraded by

weather) to traffic demand, i.e., the non-linear impact of weather-induced queuing delays on the system, both in the

terminal and en-route environments.

III. Weather Impacted Traffic Index (WITI) Background The WITI measures the number of flights impacted by weather. Each weather constraint is weighted by the

number of flights encountering that weather constraint in order to measure the impact of weather on NAS traffic at a

given location. Early development of the WITI focused on en-route convective weather, but the approach is now

applied to other weather hazard types as well. In the WITI’s basic form, every grid cell of a weather grid W is

assigned a value of 1 if severe weather is indicated and a value of 0 otherwise. Other models, such as the Convective

Weather Avoidance Model (CWAM), can be used to identify whether a pilot will fly through a weather hazard or

will deviate around it. The number of aircraft T in each grid cell of the weather grid W is counted. The WITI can

then be computed for any time period (such as one-minute intervals) as the sum over all grid cells of the product of

W and T for each grid cell1. A WITI-B variation evaluates the extent to which a flight would have to reroute in order

to avoid severe weather2. If a flight route encounters severe weather, the algorithm will find the closest point in a

perpendicular direction to the flow where no severe weather is present. The WITI score for that route is then

weighted by the number of cells between the original impeded cell and the unimpeded cell found for the reroute.

§ http://www.srh.noaa.gov/abq/avclimate/index.php

3


Various methods for determining the traffic count have been explored. WITI can use scheduled flight tracks

from ―good weather days‖ as the traffic data source3, current day flight plan trajectories

4, or great circle tracks

between the origin and destination airports as the ideal, shortest-path unimpeded flight trajectories5. Scheduled flight

frequencies on these flows for the day in question are used. The En-route WITI (E-WITI) for a flow is the product of

its hourly flight frequency and the amount of convective reports in rectangular or hexagonal grid cells. This is then

aggregated to the NAS level and to a 24-hour day, as well as by Air Traffic Control (ATC) Center, sector, or general

airspace geometry. Another approach apportions all en-route WITI measures to the origin and destination airports.

Even though en-route delays may not be due to any local airport weather, the resulting delays will originate and/or

eventuate at the departure or arrival airports. A grid cell’s WITI score for a flow is apportioned to each airport

proportional to the square root of the distance from the cell to those airports. The closer a weather area is to an

airport, the larger the portion of the WITI will be assigned to that airport. This provides a national WITI score

broken out by airport, consistent with how NAS delays are recorded in ASPM today5.

Given that the WITI is an estimation of ―front-end‖ impact of weather and traffic demand on NAS performance,

WITI has also been used as a proxy for NAS delays6. Multiple years of weather, traffic, and delay data have been

analyzed, and a strong correlation exists between the WITI metric and NAS delays. Recent research considers other

NAS operational outcome factors in addition to delay, such as the number of cancellations, diversions, and excess

miles flown in reroutes7.

The correlation between the WITI and delays has improved as additional types of weather besides en-route

convection have been considered. Terminal WITI (T-WITI) considers terminal area weather, ranked by severity of

impact, and weighs it by the departures and arrivals at an airport. Types of weather include local convection

(thunderstorms directly at, or in vicinity of, an airport), surface wind velocity (impact on runway usability is

evaluated taking dry/wet surface condition into account), winter precipitation, rain, and low ceilings/visibility. The

impact of turbulence on en-route flows is also being studied as an inclusion to WITI8.

The NAS Weather Index (NWX) implements the WITI for the FAA. In addition to calculating E-WITI and T-

WITI, it estimates the additional (non-linear) delays due to queuing during periods where demand exceeds capacity,

both at airports and in the en-route airspace. In the latter case, the index is referred to as the 4-component NWX, or

NWX48. Current research is now exploring the use of the WITI for airline route evaluation, departure and arrival fix

availability evaluation at Terminal Radar Approach Control facilities (TRACONs), and principal fix evaluation in

ATC Centers9. Future research should also consider the altitude dimension (evaluating echo tops) in order to identify

whether or not an aircraft can fly over a weather hazard instead of diverting around it.

IV. Approach We use the NWX4 implementation of the WITI to study the impact of weather on the NAS. The four

components of the NWX4 are the E-WITI, T-WITI, Terminal Queuing Delay, and En-Route Queuing Delay. Each

of the components is calculated based on contributions from the following seven factors:

1) En-route convective weather. The convective weather impact on an airport’s inbound/outbound routes

within approximately a 500 NM range of the airport. This contributes to the E-WITI and the En-Route

Queuing Delay components.

2) Local convective weather. The impact of convective weather in the vicinity (<= 100 NM) or directly over

an airport. This contributes to the T-WITI and Terminal Queuing Delay components.

3) Wind. The wind speeds are used to determine whether the current conditions are within one of four wind

scenarios. Each scenario corresponds to certain percent capacity degradations. These percentages were

calculated through calibration of WITI vs. ASPM delay metrics. This contributes to the T-WITI and

Terminal Queuing Delay components.

4) Snow, freezing rain, ice, etc. There are no specific benchmarks for winter weather (as distinct from low

ceilings that often accompany it). Averages obtained from the ASPM database were used for calculating

airport capacity (both departures and arrivals) in these conditions. This factor contributes to the T-WITI

and Terminal Queuing Delay components.

5) IMC. This is an impact at an airport if the ceiling or visibility (or both) are below airport specific minima.

Specific runway configurations (and related airport capacities) are then used for VMC, IMC, and Low-

ceiling IMC (ceilings < 700 Ft). Airport capacity benchmarks from FAA publications, as well as the ASPM

database, are used for calculating airport capacity (both departures and arrivals) in these conditions.

Reduced capacity means higher T-WITI and Terminal Queuing Delay scores.

6) Terminal Queuing Delay (No Weather) due to Volume, plus Ripple effects. No particular weather factor is

recorded locally for the given airport and hour, but queuing delays were observed and recorded. This can be

4


simply due to high traffic demand. Also, this can happen in an aftermath of a major weather event when

queuing delays linger on even as the weather has moved out. Additionally, Ripple Effects are included in

this measure. For example, if ORD arrival queuing delays are computed by the WITI tool, the

corresponding origin airports will get some additional ―queuing delay‖ type WITI. This contributes to the

Terminal Queuing Delay component.

7) Other. Includes minor impacts due to light/moderate rain or drizzle but ceilings/visibility above VFR

minima; also unfavorable runway configuration usually due to light-to-moderate winds (15-20 Kt or even

10 Kt) that prevent optimum-capacity runway configurations from being used. Airports such as ORD,

LGA, and others are susceptible to this factor. This contributes to the T-WITI and Terminal Queuing Delay

components.

These factors are used to calculate the four NWX components. The four components are then combined as a

weighted sum to calculate the NWX4 score. The weights were selected by conducting a linear regression of the four

components vs. ASPM delays.

Each of the factors and the resulting NWX is calculated NAS-wide, but can also be broken out by airport. The

en-route convection factor is apportioned to airports using

the routes that serve that airport that encounter en-route

convection. We can also break out the NAS-wide NWX

metric by ATC Center as an alternative to breaking it out

by airport. A Center’s NWX metric includes the impact of

the en-route convection within the Center, along with the

impact of regional weather on each airport located within

that Center.

The use of the NWX4 to measure the impact on the

NAS due to weather is supported by the strong correlation

between the daily NWX4 values and ASPM delays, as

shown in Figure 3.

The correlation is .84 during the severe weather season

(April–September) and .73 during the rest of the year. So

by calculating the NWX4, we are determining the impact

on the NAS due to the actual weather and traffic. But

using this methodology also allows us to break up the

NWX scores by each of the seven factors listed earlier,

such as winds and IMC. By doing this, we can better

understand how each individual factor is contributing to

the NWX score, and thus, NAS impact.

V. Results We use the NWX4 toolset to study the relative

magnitudes of each weather phenomena’s effect on the

NAS. We analyze and group the metrics by (a) weather

factor’s contribution; (b) geography; and (c) season of the

year. We also analyze the relationship between the

frequencies of weather occurrences vs. the impact of those

occurrences. The results are based on three years of data

covering the fiscal years of 2006 through 2008.

A. NAS-Wide Analysis Figure 4 shows the breakdown of the NWX by factor for

fiscal year 2007. The factor with the highest impact is IMC,

or ceilings and visibility. Second is en-route convection, and

third is surface wind. The remaining factors all have a much

smaller contribution to the overall NWX.

The percent contribution of each factor to NWX was very consistent across the three years analyzed, as shown in

Table 1.

Figure 3. NWX4 vs. ASPM Delay—Severe Weather Season

0

50

100

150

200

250

300

0 50 100 150 200 250 300

NW

X4

Delay

4-Component NWX vs. ASPM Delay,Normalized OEP34 Daily Averages Apr-Sep, 2005-2007

Figure 4. Contribution of Each Factor to NWX

25%

2%

16%

2%

42%

7%

6%

NAS FY07

Enrt_conv

Local_Conv

Wind

Snow

IMC

UnfavRWY

VolRippl

5


Table 1. Percent Contribution by Factor to NWX by Fiscal Year Factor FY06 FY07 FY08

En-Route Convection 28% 25% 26%

Local Convection 2% 2% 2%

Wind 15% 16% 17%

Winter Precipitation 1% 2% 2%

Ceilings and Visibility 40% 42% 41%

Unfavorable Runway Configuration 7% 7% 6%

Volume and Ripple Effects 7% 6% 6%

B. Regional Analysis There are various ways we can ―slice and dice‖ the NWX in terms of geography. We can break the NWX down

by airport, by Center, and by region.

Figure 5 shows the NWX for each airport, proportioned by contributing factor. ORD is the airport that

contributes the most towards NAS-wide NWX,

followed closely by ATL. The next five airports,

LGA, IAH, DFW, PHL, and LAX, have much lower

NWX values than the top two airports. Of the top two,

they have very different contributing factors. In ORD,

the big contributors are IMC and wind, while in ATL

they are IMC and convection. In fact, ORD has a

much higher impact from wind than any other airport,

and ATL has a higher impact from convection than

any other airport. The volume ripple effect is a

contributor at ORD, but not at ATL, and winter

precipitation has a higher impact at ORD than ATL.

In the top 10 airports, LAX stands out as having a

much different breakdown by factor. Winds and

convection are not a factor at all, but IMC has a huge

impact.

The Figure 6 pie chart captures the percent

contribution of each Center towards NAS-wide NWX.

Chicago and Atlanta Centers are the top two

Figure 5. NWX by Airport, Proportioned by Factor

0

50

100

150

200

250

300

350

OR

D

ATL

LGA

IAH

DFW PH

L

LAX

DTW BO

S

JFK

CLT

MSP

MC

O

DEN

EWR

CV

G

IAD

CLE ST

L

DC

A

SFO

MEM SE

A

MIA

MD

W

BW

I

PIT

TPA

FLL

SAN

PH

X

SLC

LAS

PD

X

NW

X

Average NWX by Airport, Proportioned by FactorJan 2005 - Sep 2008

VolRippl

UnfavRWY

IMC

Snow

Wind

Local_Conv

Enrt_conv

Figure 6. NWX by Center

ZAU12%

ZTL11%

ZNY11%

ZDC10%

ZOB8%

ZLA6%

ZMA5%

ZHU5%

ZFW4%

ZID4%

ZBW3%

ZJX3%

ZMP3%

ZME3%

ZDV3%

ZSE3%

ZKC2%

ZOA2%

ZAB1%

ZLC1%

Average NWX by CenterJan. 2005 - Sep. 2008

6


contributing Centers, which is consistent with ORD and ATL being the top two contributing airports. They are then

followed by ZDC, ZOB, and ZLA.

The following figures show the breakout of NWX by factor for a few of the Centers in FY2007. These charts

help to identify the major weather contributors in each specific Center location. The Los Angeles area is mainly

impacted by IMC, while the Chicago and New York areas have a more diverse weather pattern resulting in more

evenly distributed weather factors for IMC, snow, and en-route convection. The Miami and Albuquerque ARTCC

main weather contributor is by far the convection that impacts airlines en-route to and from their airports.

To get a yet better idea of the impact of geography on NWX, we divided the country into five regions. The

airports included in each region are as follows:

1) North East: CLE, PIT, BOS, EWR, LGA, JFK, PHL, BWI, IAD, DCA

2) South East: CVG, CLT, ATL, MCO, TPA, FLL, MIA

3) Mid West: DEN, STL, MSP, ORD, MDW, DTW

4) South West: PHX, DFW, MEM, IAH

5) West: SEA, PDX, SFO, LAX, SAN, LAS, SLC

Figure 8 shows the NWX value by region, broken out by weather factor. The North East is by far the region with

the highest NWX, which is consistent with

anecdotal evidence. The Mid West experiences

the second highest impact, primarily due to

ORD. The West and Southwest have the

smallest impacts, as expected.

A summary of the results of the regional

analysis can best be captured in Figure 9. The

dashed lines represent the areas encompassed

by each region. Note that the airport names on

the map are not consistent with the airports

included in the NWX metric. The size of each

pie chart is in proportion to the contribution of

that region towards NAS-wide NWX, and each

pie chart is broken down by factor.

Figure 7. Breakout of Factors for Key ARTCC Facilities

15%

1%

21%

2%44%

13%4%

ZNY FY07

Enrt_conv

Local_Conv

Wind

Snow

IMC

UnfavRWY

VolRippl

14%

0%

11%

0%

65%

6%4%

ZLA FY07

Enrt_conv

Local_Conv

Wind

Snow

IMC

UnfavRWY

VolRippl

17%

1%

28%

2%

38%

7%7%

ZAU FY07

Enrt_conv

Local_Conv

Wind

Snow

IMC

UnfavRWY

VolRippl

57%

4%

11%

0%

13%

8%7%

ZMA FY07

Enrt_conv

Local_Conv

Wind

Snow

IMC

UnfavRWY

VolRippl

56%

3%

13%

0%

13%

13%2%

ZAB FY07

Enrt_conv

Local_Conv

Wind

Snow

IMC

UnfavRWY

VolRippl

Figure 8. NWX by Region, Proportioned by Season

0

50000

100000

150000

200000

250000

300000

350000

400000

NorthEast SouthEast MidWest SouthWest West

Seasonal NWX by RegionFY07

Summer

Spring

Winter

Fall

7


Figure 9. NWX by Region and Factor

West

Southwest

Midwest

Southeast

Northeast

C. Seasonal Analysis As one may expect, the impact of weather on the NWX varies significantly across times of the year. We break

up the year into the following four seasons:

1) Fall: Sep, Oct, Nov

2) Winter: Dec, Jan, Feb

3) Spring: Mar, Apr, May

4) Summer: Jun, Jul, Aug

Figure 10 shows the average NWX by season, stacked

for each of the three fiscal years analyzed. The summer

season has the highest NWX and is 16% greater than the

second dominant season, winter. Fall and spring are fairly

close, with spring having a slightly higher NWX.

Figure 11 shows the percent contribution of each

factor towards each season’s NWX. The results are very

consistent across the three years analyzed. IMC

dominates all of the graphs, but has its largest impact in the winter. En-route convection is also very significant,

showing a much higher impact during the summer than any other season.

Figure 10. Seasonal NWX—FY2006-2008

Seasonal NWX

74 94 77114

8897

84

12277

11092

112

0

50

100

150

200

250

300

350

400

Fall Winter Spring Summer

NW

X FY08

FY07

FY06

Figure 11. Breakout of Weather Factors by Season

0%

20%

40%

60%

80%

100%


Wx Factors Contribution by seasonFY07

VolRippl

UnfavRWY

IMC

Snow

Wind

Local_Conv

Enrt_conv

0%

20%

40%

60%

80%

100%


Wx Factors Contribution by seasonFY06

VolRippl

UnfavRWY

IMC

Snow

Wind

Local_Conv

Enrt_conv

Wx Factors Contribution by season

FY08

0%

20%

40%

60%

80%

100%


VolRippl

UnfavRWY

IMC

Snow

Wind

Local_Conv

Enrt_conv

8


Figure 12. Seasonal Trends of Factors

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

Jan

-05

Mar

-05

May

-05

Jul-

05

Sep

-05

No

v-0

5

Jan

-06

Mar

-06

May

-06

Jul-

06

Sep

-06

No

v-0

6

Jan

-07

Mar

-07

May

-07

Jul-

07

Sep

-07

No

v-0

7

Jan

-08

Mar

-08

May

-08

Jul-

08

Sep

-08

NW

X

Trend of Monthly Average NWX Values by Factor

Enrt_conv

Local_Conv

Wind

Snow

IMC

UnfavRWY

VolRippl

The next two figures in this section illustrate in another way the seasonal nature of the weather factors. Figure 12

plots the average monthly NWX over 3.75 years for each of the seven factors. It is clear to see the seasonal

fluctuations in each factor. IMC (green series) peaks in the winter months, while en-route convection (orange series)

peaks during the summer. The wind (blue series) is very interesting; it lags slightly behind IMC, peaking in the early

spring months.

Figure 13 is a similar plot, but for only the four smallest factors, since their fluctuations are hard to see in the

previous figure due to scale. Snow (purple series) peaks during each winter season, as expected, while local

convective weather (maroon series) peaks during the summer. The volume ripple effect (navy blue series) is also

cyclical, peaking each winter. This is due to the higher T-WITI during the winter months, which results from IMC

and snow. The last factor, unfavorable runway configurations (brown series), is the only factor without a clear

seasonal cycle.

D. Frequency of Occurrence vs. Severity of Impact The previous sections illustrate which factors have the largest impact on average over time on the NAS, and

analyze those impacts by region and time of year. But those results do not capture the frequency of occurrence. For

example, IMC has the largest impact on the NAS. Is this due to high frequency, or is the frequency smaller but with

large impact? And a factor such as snow, which only contributes to less than 2% of the NWX metric, could still be a

factor that has a large daily impact on the NAS, but the frequency is so small that the impact is lost when looking at

yearly averages, or even seasonal averages.

We now analyze the frequency of occurrence of each factor against the severity of the impact to better

understand those factors that can have a big impact when they occur, even if the yearly frequency is low. This can be

valuable information in order to highlight those factors that will require more extensive TFM planning when they

are forecasted to occur. This analysis can also highlight any cases where there are factors that do not have a big

impact on a given day, but due to the high frequency throughout the year, end up significantly contributing to overall

NAS-wide delays. These types of factors may point to areas that slight improvements in TFM efficiency could result

in large reductions in overall NAS-wide delay.

The following set of figures use 3.75 years of WITI/NWX data, covering from January 2005 through September

2008. The scale of the underlying data is the average daily values by airport, over 34 airports.

One way to measure frequency of occurrence is to count the number of days at each airport in which a particular

factor was the predominant factor at the airport on that day. Figure 14 uses this measure of frequency for the x-axis

and plots both the average NWX and ASPM delay values on the y-axis. This average value is the average of the

airport values of only those days where that weather factor was predominant. As discussed earlier, there is a strong

correlation between NWX and delay, but that correlation is stronger for some factors than others. We include both

measures in the following figures in order to better assess each individual weather factor.

The first interesting item to note in the figure is that the factor with the highest impact has the lowest frequency

(snow) and the factor with the lowest impact has almost the highest frequency (unfavorable runway configurations).

Of all of the factors, snow stands out as the outlier in terms of average daily impact on the NAS. Snow has less than

a 2% overall average impact NAS-wide, yet these results highlight the fact that although snow may happen

Figure 13. Seasonal Trend of Just Those Factors with Less Impact

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

Jan

-05

Mar

-05

May

-05

Jul-

05

Sep

-05

No

v-0

5

Jan

-06

Mar

-06

May

-06

Jul-

06

Sep

-06

No

v-0

6

Jan

-07

Mar

-07

May

-07

Jul-

07

Sep

-07

No

v-0

7

Jan

-08

Mar

-08

May

-08

Jul-

08

Sep

-08

NW

X

Trend of Monthly Average NWX Values by Factor

Local_Conv

Snow

UnfavRWY

VolRippl

9


infrequently, when it does, the impact is

very significant, much larger than those of

any other factor**

. The other interesting

element of snow captured in this figure is

that it has the widest spread between NWX

and delay, NWX having a much larger

value. This is predominantly due to the large

number of cancellations that tend to occur

during snow events. Cancellations are not

accounted for in the ASPM delay numbers,

thus delay alone is not a good indicator of

NAS impact during snow events.

Local convection also has a very low

frequency, but has a much smaller average

impact. Low values in both frequency and

impact suggest a factor that requires less

attention. These values track well with the

yearly breakdowns of NAS impact by

factor; local convection accounts for less

than 2% of the impact. Typically if there is local convection, there is also en-route convection, and so there are very

few instances where local convection was the predominant factor over en-route convection.

En-route convection has the highest frequency of all

factors, followed very closely by unfavorable runway

configurations; however, IMC is not far behind. All three

of these factors have very different values in average daily

NWX, though. This is more clearly illustrated in Figure 15.

After snow, IMC has the second largest impact, which is

well above the average value over all factors; while

unfavorable runway configurations has a very small

average impact, much smaller than all other factors. En-

route convection is closer to the average NWX across all

factors.

Figure 14 shows one data point for each factor, which is

the average over all airports. Figure 16 illustrates how the

values vary across airports by including a single data point

for each airport per weather factor. A few of the outlier

data points are labeled. This figure is helpful in

understanding whether or not there are consistent results in

frequency vs. impact over the airports, or whether they

have wide variances. In the case of snow, the frequency is

consistently low, but the impact varies, higher impacts occurring at larger airports. Local convection is similar,

though the variance in impact is smaller. In the case of unfavorable runway configurations, it is the impact that is

fairly consistent (small), but the frequency varies significantly, with the most frequent being PHX. Wind is fairly

clustered, but there is variance in both frequency and impact. There is one clear outlier in the case of wind, ORD.

En-route convection varies significantly in both frequency and impact, with a somewhat normal distribution profile.

The airport with the highest average overall weather impact is ATL. In the case of IMC, the airports with the largest

frequencies are those on the west coast, but the highest impact is ATL due to the large volume of traffic.

**

WITI tool records the impact of weather exactly as reported in meteorological observations (METARs). In case of

snowfall, when it stops, no ―Snow‖ type weather will be recorded (and no impact counted). However, we know that

the impact of ice precipitation or snowfall can last for hours and even days after the event. Exactly how long is

specific to airport, local resources, etc., and this data is not formally recorded as METARs themselves would be.

Therefore, WITI tool is at this stage unable to reflect and quantify these longer-lasting effects; the duration of winter

precipitation events shown by WITI is shorter than the duration of disruptions to traffic that may ensue.

Figure 14. Frequency (Days Predominant) vs. Severity of NAS Impact Factors

Dly

NWXDly

NWX

Dly

NWX

Dly

NWX

Dly

NWX

Dly

NWX

0

50

100

150

200

250

300

350

400

0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0%

Avg

NW

X/A

SPM

De

lay

Percent Days/Apts Predominant

Average NWX & ASPM Delay vs. Percent of Days/Apts Weather Factor is Predominant

Jan 2005 - Sep 2008

En Route Convection

Local Convection

Wind

Snow

IMC

Unfav Rwy Config

Volume Ripple

0

50

100

150

200

250

300

350

400

Average Daily NWX by Factor when Factor is Predominant

NWX

Avg NWX

Figure 15. Average Daily NWX by Factor when Predominant

10


VI. Conclusion This analysis effort to better understand weather impacts on the NAS has also served as an NWX4 model

validation exercise. The fact that many ―obvious‖ combinations of airport and impact appear as relative outliers in

the data contributes to the validity of this approach. For example, ORD is the airport most frequently and most

severely impacted by wind, and ATL is impacted by IMC on roughly one out of three days, which has a tremendous

impact on the NAS. We were able to quantify the impact of various weather factors on different NAS regions and

for different seasons, and all of our results are consistent with previous research and with general knowledge about

the weather phenomena.

However, we also show that NWX and delay are not always consistent. This is captured in Figure 14 where

frequency of occurrence is plotted against both average NWX and ASPM delay by factor. Some factors have

extremely close values, but snow shows a big difference between NWX and delay. We know that the impact on an

airport due to a weather event cannot always be captured in delays alone; an airport may experience a high number

of cancellations, thus decreasing demand and delay as well. This can result in NWX being higher than delay, which

explains the example of snow events. On the flip side, delays can occur at an airport for reasons other than weather,

such as mechanical delays or an airline delaying flights due to late inbounds (airline network effect). Also, delays

can sometimes linger at an airport long after weather has cleared. These factors may result in delay being higher

than NWX, such as with unfavorable runway configurations. We recognize that the NWX metric is a front-end

impact assessment tool and that many other factors on the back-end can contribute to the actual impacts on the NAS.

There are wide variances of magnitude and nature of weather impact by airport, region, and season. This

approach can be used to guide investment decisions regarding technologies for mitigating the impact of adverse

weather. The SFO Stratus Forecast System is a good example of an investment justified by analysis similar to this.

Due to its location on the edge of San Francisco Bay, SFO has a unique local climatology which, when combined

with the airport layout and traffic pattern, leads to a recurring 50% capacity reduction, which then results in

Figure 16. Frequency vs. Impact by Factor and Airport

ATL

MCO

ORD

ATL

DFW

ORD

ATL

LAX

ORD

SAN

SEA

PHX0

200

400

600

800

1000

1200

1400

1600

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0%

Ave

rage

NW

X

Percent Days Predominant

Average Daily NWX vs. Percent of Days Weather Factor is Predominant

Jan 2005 - Sep 2008

En Route Convection

Local Convection

Wind

Snow

IMC

Unfav Rwy Config

Volume Ripple Effect

11


tremendous ground delays during the summer months. The combined severity and frequency of the impact has lead

to the development of a highly specialized forecast product to predict the time SFO can return to full operations.

These impact measures include a measure of the efficacy of current and historical technology and practice for

mitigating the impact of weather. In this regard, we should also note that forecast accuracy and the ability to make

effective TFM decisions, not just weather, are also important factors in NAS operational impact when measured this

way. The analysis of weather forecast accuracy through a derivative of the WITI/NWX metric is currently

underway.

Whereas convective and winter precipitation weather events perhaps draw the most attention and publicity, the

results of this research effort indicate that the three weather phenomena with the largest impact at the NAS level are

(in decreasing order of importance): low ceilings/visibility (IMC), convection, and wind. However, the impact of

different weather phenomena also varies significantly by airport. In conclusion, the NWX4 implementation of the

WITI has proved to be very useful in understanding and quantifying the impact on the airspace system from various

types of weather—regionally, seasonally, and NAS-wide.

Acknowledgments The authors appreciate the support and guidance at NASA Ames Research Center from William Chan, Shon

Grabbe, and Cedric Walker.

References 1Callaham, M., J. DeArmon, A. Cooper, J. Goodfriend, D. Moch-Mooney, and G. Solomos, ―Assessing NAS Performance:

Normalizing for the Effects of Weather,‖ 4th USA/Europe ATM R&D Seminar, Santa Fe, NM, 2001. 2Klein, A., Cook, L., Wood, B., Simenauer, D., and Walker, C., ―Airspace Capacity Estimation using Flows and Weather

Impacted Traffic Index,‖ Integrated Communications, Navigation and Surveillance Conference, Bethesda, MD, 2008. 3Chatterji, G., and Sridhar, B., ―National Airspace System Delay Estimation Using Weather Weighted Traffic Counts,‖

AIAA Guidance, Navigation, and Control Conference, San Francisco, CA, 2004. 4Post, J., Bonn, J., Bennett, M., Howell, D., and Knorr, D., ―The Use of Flight Track and Convective Weather Densities for

National Airspace System Efficiency Analysis,‖ 21st Digital Avionics Systems Conference, Piscataway, NY, 2002. 5Klein, A., Jehlen, R., and Liang, D., Weather Index with Queuing Component for National Airspace System Performance

Assessment, 7th USA/Europe ATM R&D Seminar, Barcelona, Spain, 2007. 6Sridhar, B., ―Relationship between Weather, Traffic, and Delay based on Empirical Methods,‖ NEXTOR NAS Performance

Workshop, Asilomar, CA, 2006. 7Klein, A., ―Cost Index as a Metric for Assessing NAS Performance and Weather Impact,‖ Proceedings of the 2005 ATCA

Conference, Ft. Worth, TX, 2005. 8Cook, L. Klein, A. and Wood, B., Translating Weather Information into TFM Constraints: Report of the Weather

Translation Model, NASA Contract Deliverable, June 30, 2008. 9Klein, A., MacPhail, T., Kavoussi, D., Hickman, D., Phaneuf, M., Lee, R., and Simenauer, D., ―NAS Weather Index:

Quantifying Impact of Actual and Forecast En-Route and Surface Weather on Air Traffic,‖ 14th Conference on Aviation, Range

and Aerospace Meteorology, Phoenix, AZ, 2009.

Documents

[American Institute of Aeronautics and Astronautics 9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO) - Hilton Head, South Carolina ()] 9th AIAA Aviation