file134.txt

	
	
		
I. IntroductionC ontrails are clouds that are visible trails of water vapor made by the exhaust of aircraft engines.They appear and persist if the aircraft is flying in certain atmospheric conditions.The environmental impact of aircraft-induced persistent contrails has drawn attention in recent years. 1 Persistent contrails reduce incoming solar radiation and outgoing thermal radiation in a way that accumulates heat. 2 The global mean contrail cover observed in 1992 is estimated to double by 2015 and to quadruple by 2050 due to the increase in air traffic. 3Studies suggest that the environmental impact from persistent contrails may be three to four times, 4 or even ten times, 5 larger than that from aviation emissions.Therefore, methods to reduce aircraft-induced persistent contrails need to be explored to minimize the impact on the global environment.Efforts have been made in the past years to identify and reduce persistent contrail production.Gierens 6 and Noppel 7 reviewed various strategies for contrail avoidance including changing engine architecture, enhancing airframe and engine integration, using alternate fuels, and modifying traffic flow management procedures.Among the traffic flow management solutions, Mannstein 8 proposed a strategy to reduce the climate impact of contrails significantly by small changes to each aircraft's flight altitude.Campbell 9 presented a mixed integer programming methodology to optimally reroute aircraft trajectories to avoid the formation of persistent contrails.Both methodologies require a flexible flight plan and onboard contrail detection system.Fichter 10 showed that the global annual mean contrail coverage could be reduced by reducing the cruise altitude.Williams 11,12 proposed strategies for contrail reduction by identifying fixed and varying maximum altitude restrictions.These restrictions generally require more fuel burn and add congestion to the already crowded airspace at lower altitudes.The objective of this paper is to derive a class of indices that can identify and predict, up to six hours in advance, regions of airspace with high potential for contrail formation.Traffic and weather forecasts were used to generate the predicted contrail frequency index.The indices are used to identify air traffic control centers and altitudes with high contrail formation activities over the next one to six hours.The method uses actual air traffic data and provides a one-hour temporal resolution of predicted contrail frequency.The results show that the predicted indices are highly correlated with the actual contrail frequencies and have a high success rate in identifying the centers and flight levels with high contrail frequencies over the next one to three hours.The remainder of the paper is organized as follows.Section II provides the descriptions of atmospheric and aircraft data and the contrail model used in this paper.Section III describes contrail frequency index, predicted contrail frequency index, and their use for contrail reduction strategies.The results are demonstrated in Section IV.Finally, a summary and conclusions are presented in Section V.
II. Atmospheric and Aircraft Data and Contrail ModelThe atmospheric data, contrail model, and aircraft data used to generate the contrail formation frequency are described in this section.
A. Atmospheric DataContrails can be observed from surface observation data 13 and detected by satellite data. 14Duda 15 has related the observations to numerical weather analysis output and demonstrated that persistent contrail formation can be computed using atmospheric temperature and humidity data retrieved from the Rapid Updated Cycle (RUC) data, provided by the National Oceanic and Atmospheric Administration (NOAA).Contrails can persist when ambient air is supersaturated with respect to ice, which means the environmental relative humidity with respect to ice (RHi) is greater than one hundred percent. 16The RHi can be computed from relative humidity with respect to water (RHw) and temperature, which are available in the RUC data.The one-hour, two-hour, three-hour, and six-hour forecasts and the forty-kilometer (40km) resolution RUC data were used in this paper.The data have a temporal resolution of one hour, a horizontal resolution of 40 kilometers, and isobaric pressure levels from 100 to 1000 hectopascals (hPa) in 25 hPa increments.The vertical range is from 150 hPa to 400 hPa, which are equivalent to 23, 600 feet to 44, 400 feet in the standard atmosphere.As an example, snap shots of temperature and RHw contours at 8AM eastern daylight time (EDT) on August 1, 2007 at a pressure altitude of 250 hPa, or about 34, 100 feet, are shown in Fig. 1.
B. Contrail ModelContrails are clouds produced by aircraft operating at high altitudes.Persistent contrail formation areas are defined as areas with RHi greater than or equal to 100%.RHi can be computed from RHw and temperature using the saturation vapor pressure coefficients of Alduchov, 17 formulated as RHi = RHw × 6.0612e 18.102T /(249.52+T ) 6.1162e 22.577T /(237.78+T ) ,where T is the temperature in Celsius.The temperature and relative humidity shown in Fig. 1 can be translated to RHi.The 40-km RUC data consist of a grid of (113 × 151) data points at each isobaric pressure level.The altitude level index, l, is defined as l = 1 . . .11 corresponding to isobaric pressure levels at 400, 375, . . ., 150 hPa.The level index, isobaric pressure level, and approximate corresponding flight levels are listed in Table 1.The potential persistent contrail formation matrix (contrail matrix) at hour h at level l is defined asR l h =     r 1,1 r 1,2 . . .    ,(2)where r i,j is 1 if RHi ≥ 100% at grid (i, j), and 0 if RHi < 100%.To indicate the location of the twenty U.S. air traffic control centers in the grid, the center grid matrix is defined asC k =     c 1,    ,(3)where k is the center index corresponding to the twenty continental U.S. air space control centers (see Table 2), and c i,j is one if the grid point is within the center and zero if not.The potential persistent contrail formation coverage ratio (contrail coverage ratio) of one center can be defined by the total area of the contrail regions in the center divided by the area of the center.Assuming all of the grid points have the same area, the contrail coverage ratio of center k at time t at altitude level l can be defined as113 i=1 151 j=1 r i,j c i,j 113 i=1 151 j=1 c i,j ,(4)where r i,j is an element of R l t , and c i,j is an element of C k .As an example, the contrail area at flight level 341 at 8AM EDT on August 1, 2007 is shown in Fig. 2a.The corresponding center contrail coverage ratio, computed from Eq. ( 4), is shown in Fig. 2b.The center contrail coverage ratio indicates the portion of center airspace that would form contrails when aircraft fly through it.When the ratio is zero, there will be no contrail formed in the center.
C. Aircraft DataContrails form when aircraft fly through a potential contrail formation area.Aircraft locations are needed to determine the contrail formation frequency.The aircraft data used in this paper were extracted from the Federal Aviation Administration's Aircraft Situation Display to Industry (ASDI) data.The ASDI has a sampling rate of one minute.The same grid used in the RUC data was used to generate the aircraft position matrix.The aircraft position matrix is defined asA l t =     a 1,    ,(5)where a i,j is the number of aircraft within grid (i, j) flying closest to altitude level l at time t.The aircraft position matrix indicates the air traffic density in the grid scale at different altitudes.
III. MethodologyThe concept of contrail frequency index, predicted contrail frequency index, and its use for contrail reduction strategies are described in this section.
A. Contrail Frequency IndexAs discussed in the previous section, the size and coverage ratio of the persistent contrail formation areas are not sufficient indications of severity of contrail activities.The center contrail frequency index consists of both potential contrail formation area and air traffic information.It is defined as the number of aircraft flying through an area that would form persistent contrails at time t at level l.It is formulated as113 i=1 151 j=1 r i,j c i,j a i,j ,(6)where r i,j , c i,j , and a i,j are defined in Eq. ( 2), (3), and (5).As an example, the center contrail frequency indices at flight level 341 were computed at 8AM EDT on August 1, 2007 and are shown in Fig. 3.Even though in Fig. 2b the contrail coverage ratio of Houston Center is higher than Atlanta Center, in Fig. 3 the contrail frequency of Houston Center is zero.Figure 4a
B. Predicted Contrail Frequency IndexThe contrail frequency index derived in the previous section indicates the actual contrail activities.For strategic planning, prediction of the contrail frequency for the next few hours is needed.The predicted contrail frequency index is defined as a convolution of traffic data and atmospheric conditions.They are similar to the concept of Weather Impacted Traffic Index (WITI) introduced by Callaham et al. 18 and Sridhar, 19 and the three dimensional index derived by Chen. 20The index consists of the RUC forecast data and the predicted aircraft locations.The center predicted contrail frequency index is defined as the predicted number of aircraft flying through the forecasted potential contrail area at time t at level l in center k.It is formulated as113 i=1 151 j=1 r i,j c i,j a i,j ,(7)where r i,j is defined in Eq. ( 2) with RUC forecast data, c i,j is defined in (3), and a i,j is defined in (5). a i,j is based on the historic air traffic data during the planning period.As in the case of WITI, the index is affected more by the changing atmospheric conditions than by small daily variations to the nominal traffic plan. 19In Eq. ( 7) the coefficient a i,j can be thought of as an air traffic weighting coefficient.
C. Contrail Reduction StrategyThe feasibility of using predicted contrail frequency index for contrail reduction is investigated.The center predicted contrail frequency index can be used to identify the flight level that would have formed the most contrails and find an alternate altitude with less contrail activities.The contrail frequency index after the contrail reduction strategy has been applied is formulated as113 i=1 151 j=1 r i,j c i,j âi,j ,(8)where r i,j and c i,j are defined in Eqs. ( 2) and ( 3), and âi,j is defined in Eq, ( 5) with the aircraft location after the contrail reduction strategy is applied.The contrail reduction strategies need to consider extra fuel burn to minimize overall environmental impact and not to add congestions in the center.The strategy in Ref. 21 uses the predicted contrail frequency index to identify the area that would have formed the most contrail activities, and change the cruise altitudes of a group of aircraft to reduce contrails.The changes need to have minimal extra fuel utilization and maintain the air traffic density below airspace capacity.In general, changing the cruise altitude of a group of aircraft will not increase the air traffic density within the center and sectors.
IV. ResultsThe temperature and relative humidity from RUC data and aircraft position from ASDI data in 2007 were processed and analyzed.Figure 5 shows four average hourly indices at each of the twenty continental U.S. centers at different altitudes in 2007.They are the contrail coverage ratio derived in Section II.B, the aircraft position matrix derived in Section II.C, the contrail frequency index derived in Section III.A, and the contrail frequency density derived later in this section.As shown in the figure, most of the contrails were formed between flight level 301 and 387.These flight levels account for 78% of contrail frequency over all centers and altitudes.Seattle Center (ZSE), Oakland Center (ZOA), Los Angeles Center (ZLA), and New York Center (ZNY) have lower contrail activities.The reasons are there are less flight activities at ZSE, ZOA, and ZLA, and the size of ZNY is small bringing the index low.To observe the density of the center contrail frequency index, the center contrail frequency density is defined by the center contrail frequency index divided by the number of grid points in the center, formulated as113 i=1 151 j=1 r i,j c i,j a i,j 113 i=1 151 j=1 c i,j ,(9)where r i,j , c i,j , and a i,j are defined in Eq. ( 2), (3), and (5). Figure 5d shows the average hourly center contrail frequency density at each of the eleven flight levels and twenty centers.It is shown that there are high contrail activities between flight level 320 and 363, having some centers with density higher than 0.5.The highest contrail density is 0.69 at flight level 363 at Indianapolis Center (ZID).Figure 6 shows the contrail frequency density at flight level 363 on a U.S. map.As shown in the figures, Indianapolis Center (ZID) has the highest contrail density, and its surrounding five centers, Kansas City Center (ZKC), Chicago Center (ZAU), Memphis Center (ZME), Cleveland Center (ZOB), and Atlanta Center (ZTL), also have high contrail density ranged from 0.45 to 0.57.The seasonal variation can be observed by the monthly average center contrail frequency at ZID in 2007, as shown in Fig. 7.In general, there are less contrail activities in summer.The contrail frequency index provides a way to quantify the contrail activities.Next, for the predicted contrail frequency index, one-hour, two-hour, three-hour, and six-hour predicted indices in August 2007 were generated using Eq. ( 7) and analyzed.a i,j in Eq. ( 7) was based on the air traffic data on the same day of week of July 15-21.For example, to generate the predicted contrail frequency indices on August 1, 8, 15, 22, and 29, the air traffic data on July 18 was used since they are all Wednesdays.As an example, actual and one-hour predicted contrail frequency indices at flight level 363 at Indianapolis center are shown in Fig. 8a.As shown in the figure, the one-hour predicted contrail index is highly correlated with actual index, with a correlation coefficient of 0.94.It is mentioned in Ref. 19 that the index is affected more by the changing atmospheric conditions than by small daily variations to the nominal traffic plan.To show the effect on different choices of the historical air traffic data used, the predicted contrail frequency index was regenerated using a i,j computed by the average air traffic in July 2007.The indices at flight level 363 at Indianapolis Center using the average traffic in July 2007 are shown in Fig. 8b with a correlation coefficient of 0.94.The result is very similar to using the air traffic data on July 15-21.Table 3 shows the average correlation coefficients between actual and predicted indices in twenty centers at different flight levels using historical data of July 15-21 and the average of July 2007.Note that there is no significant difference between the two types of historical data.The accuracy of the prediction decays with longer prediction time.The correlations at flight level 414 and 444 are small mainly because of the lower frequency of contrail formation and the resulting higher sensitivity to the noise.The mean correlation coefficients between actual index and one-hour, two-hour, three-hour, and six-hour predicted index are 0.85, 0.72, 0.64, and 0.52 using July 15-21 data, and 0.84, 0.72, 0.63, and 0.51 respectively.To use the prediction contrail frequency index for contrail reduction strategies, center prediction indices were generated and analyzed.Figure 9  In general, when the actual contrail frequency is high, the predicted contrail frequency is high for prediction up to three hours.The six-hour predicted index is under-predicted, most likely due to the prediction inaccuracy.For implementing a contrail reduction strategy, the centers with high contrail frequency indices need to be identified.As an example, the contrail reduction strategy may be enabled when the centers have indices higher than 100.This would affect seven centers including Los Angeles Center (ZLA), Salt Lake City Center (ZLC), Albuquerque Center (ZAB), Dallas/Fort Worth Center (ZFW), Houston Center (ZHU), Jacksonville Center (ZJX), and Miami Center (ZMA).All of the one-hour, two-hour, and three-hour prediction indices are able to correctly identify the centers that need a reduction strategy with the exception that the threehour predicted index fails to identify ZHU.There is one case that the one-hour prediction falsely identifying Denver Center (ZDV) with low contrail activity as having an index greater than 100.The success rate of the identification is defined as the rate of the predicted contrail index correctly identifying the center with high or low contrail activities.In this example, the one-hour, two-hour, three-hour, and six-hour predicted indices have success rates of 95%, 100% , 95% and 65% for identifying the correct centers respectively.The performance of predicted indices for identifying centers with high contrail frequency index is shown in Table 4.As expected, the success rate decays with longer prediction time due to the prediction inaccuracy.Also noticeable is that the success rate decays with higher threshold.There is a 83.47% success rate to identify centers with contrail frequency index greater than 100 using one-hour predicted index, 69.24% using two-hour index, 58.31% using three-hour index, and down to 38.92% using six-hour index.It is harder to successfully identify centers with index greater than 400.There is a 76.19% success rate using one-hour index, and down to 21.99% using six-hour index.
V. ConclusionsThis paper described a methodology to predict contrail frequency index for contrail reduction.A class of predicted indices that reflects the severity of airspace contrail formation frequency was derived.The indices consist of weather forecast and actual and historical air traffic data.The results show that the predicted indices are affected more by changing atmospheric conditions than by small daily variations of traffic.For the data tested, the one-hour predicted contrail index is highly correlated with the actual index, resulting in an average correlation coefficient of 0.85 and is lower with longer prediction time.The average correlation coefficient between the actual index and the two-hour, three-hour, and six-hour predicted index are 0.72, 0.64, and 0.52, respectively.In terms of developing strategies for contrail reduction, there is a 83.47% success rate to identify centers with contrail frequency index greater than a threshold, 69.24% using two-hour index, 58.31% using three-hour index, and 38.92% using six-hour index.The method of using predicted contrail frequency index for contrail reduction shows promise but requires detailed future evaluation in a fast-time traffic flow management simulation environment.Relative humidity with respect to water
Figure 1 .1Figure 1.Contours of temperature and RHw at 34,100 feet at 8AM EDT on August 1, 2007.
Figure 2 .2Figure 2. Potential persistent contrail formation area and coverage ratio at flight level 341 at 8AM EDT on August 1, 2007.
Figure 3 .3Figure 3. Center contrail frequencies at flight level 341 at 8AM EDT on August 1, 2007.
Figure 4 .4Figure 4. Aircraft location and persistent contrail formation areas at 8AM EDT on August 1, 2007.
Figure 5 .Figure 6 .Figure 7 .567Figure 5. Average hourly center contrail coverage ratio, air traffic density, contrail frequency and contrail frequency density in 2007.
Use of average air traffic data in July 2007
Figure 8 .8Figure 8. Actual and predicted center contrail frequency index at flight level 363 at Indianapolis Center in August 2007.
shows the actual and predicted center contrail frequency indices at flight level 363 at 8PM EDT on August 1, 2007.The blue bars are the actual, and the light blue, green, orange, and red color bar are the one-hour, two-hour, three-hour, and six-hour predicted contrail frequency indices computed by Eq. (7) using traffic data on July 18, 2007.As shown in the figure, the actual contrail frequency index and the one-hour, two-hour, and three-hour predicted contrail frequency indices are correlated.
Table 1 .1Altitude level index, isobaric pressure level, and pressure altitude.level index1234567891011pressure level (hPa ) 400 375 350 325 300 275 250 225 200 175 150flight level (100 feet) 236 251 267 283 301 320 341 363 387 414 444
Table 2 .2Center index of twenty continental U.S. air traffic control centers.IndexNameIndexName1Seattle Center (ZSE)11Chicago Center (ZAU)2Oakland Center (ZOA)12Indianapolis Center (ZID)3Los Angeles Center (ZLA)13Memphis Center (ZME)4Salt Lake City Center (ZLC)14Cleveland Center (ZOB)5Denver Center (ZDV)15Washington D. C. Center (ZDC)6Albuquerque Center (ZAB)16Atlanta Center (ZTL)7Minneapolis Center (ZMP)17Jacksonville Center (ZJX)8Kansas City Center (ZKC)18Miami Center (ZMA)9Dallas/Fort Worth Center (ZFW)19Boston Center (ZBW)10Houston Center (ZHU)20New York Center (ZNY)
Table 3 .3Average correlation coefficient between actual and predicted contrail frequency index over twenty U.S. centers in August 2007.Two types of historical data are used, air traffic on July 15-21, 2007 (ref. 1) and the average air traffic in July 2007 (ref.2).prediction timeflight levelone-hourtwo-hourthree-hoursix-hourref. 1 ref. 2 ref. 1 ref. 2 ref. 1 ref. 2 ref. 1 ref. 24440.640.630.580.570.530.530.460.464140.730.690.660.630.600.570.540.503870.890.900.810.820.730.740.570.583630.920.920.790.790.700.690.550.543410.910.910.770.770.680.680.530.523200.910.900.770.750.680.670.530.533010.880.880.730.740.650.650.530.532830.860.860.710.710.620.610.500.482670.860.840.700.700.620.610.490.482510.860.850.700.700.630.620.500.502360.870.860.710.710.630.610.500.48350Actual300one-hourContrail frequency index100 150 200 250two-hour three-hour six-hour500ZSEZOAZLAZLCZDVZABZMPZKCZFWZHUZAUZIDZMEZOBZDCZTLZJXZMAZBWZNYFigure 9. Actual and predicted center contrail frequency index at flight level 363 at 8PM EDT on August 1, 2007.
Table 4 .4Success percentage of identifying the center with index greater than the threshold index for twenty U.S. centers in August 2007.thresholdprediction time one-hour two-hour three-hour six-hour10083.47%69.24%58.31%38.92%20081.43%64.37%51.27%30.69%30079.14%60.75%46.36%26.18%40076.19%57.28%41.69%21.99%
		
		
			

				


	
		
			IWaitz
		
		
			JTownsend
		
		
			JCutcher-Gershenfeld
		
		
			EGreitzer
		
		
			JKerrebrock
		
		Report to the United States Congress: Aviation and the Environment, A National Vision, Framework for Goals and Recommended Actions
		London, UK
		
			December 2004
		
	
	Tech. rep
	Partnership for AiR Transportation Noise and Emissions Reduction
	Waitz, I., Townsend, J., Cutcher-Gershenfeld, J., Greitzer, E., and Kerrebrock, J., "Report to the United States Congress: Aviation and the Environment, A National Vision, Framework for Goals and Recommended Actions," Tech. rep., Partnership for AiR Transportation Noise and Emissions Reduction, London, UK, December 2004.



	
		Radiative forcing by contrails
		
			RMeerkötter
		
		
			USchumann
		
		
			DRDoelling
		
		
			PMinnis
		
		
			TNakajima
		
		
			YTsushima
		
		10.1007/s00585-999-1080-7
	
	
		Annales Geophysicae
		Ann. Geophys.
		1432-0576
		
			17
			8
			
			1999
			Copernicus GmbH
		
	
	Meerkotter, R., Schumann, U., Doelling, D. R., Minnis, P., Nakajima, T., and Tsushima, Y., "Radiative forcing by contrails," Annales Geophysicae, Vol. 17, 1999, pp. 1080-1094.



	
		Future Development of Contrail Cover, Optical Depth, and Radiative Forcing: Impacts of Increasing Air Traffic and Climate Change
		
			SMarquart
		
		
			MPonater
		
		
			FMager
		
		
			RSausen
		
		10.1175/1520-0442(2003)016<2890:fdocco>2.0.co;2
	
	
		Journal of Climate
		J. Climate
		0894-8755
		1520-0442
		
			16
			17
			
			September 2003
			American Meteorological Society
		
	
	Marquart, S., Ponater, M., Mager, F., and Sausen, R., "Future Development of Contrail Cover, Optical Depth, and Radiative Forcing: Impacts of Increasing Air Traffic and Climate Change," Journal of Climate, Vol. 16, September 2003, pp. 2890-2904.



	
		A Standing Royal Commission
		
			SusanOwens
		
		10.1093/acprof:oso/9780198294658.003.0003
	
	
		Knowledge, Policy, and Expertise
		London, UK
		
			Oxford University Press
			2002
			
		
	
	"The Environmental Effects of Civil Aircraft in Flight," Tech. rep., Royal Commission on Environmental Pollution, London, UK, 2002.



	
		Aircraft induced contrail cirrus over Europe
		
			HermannMannstein
		
		
			UlrichSchumann
		
		10.1127/0941-2948/2005/0058
	
	
		Meteorologische Zeitschrift
		metz
		0941-2948
		
			14
			4
			
			2005
			Schweizerbart
		
	
	Mannstein, H. and Schumann, U., "Aircraft induced contrail cirrus over Europe," Meteorologische Zeitschrift, Vol. 14, No. 4, 2005, pp. 549-554.



	
		A Review of Various Strategies for Contrail Avoidance
		
			KlausGierens
		
		
			LingLim
		
		
			KostasEleftheratos
		
		10.2174/1874282300802010001
	
	
		The Open Atmospheric Science Journal
		TOASCJ
		1874-2823
		
			2
			1
			
			2008
			Bentham Science Publishers Ltd.
		
	
	The Open Atmospheric
	Gierens, K., Limb, L., and Eleftheratos, K., "A Review of Various Strategies for Contrail Avoidance," The Open Atmo- spheric Science Journal, Vol. 2, 2008, pp. 1-7.



	
		Overview on Contrail and Cirrus Cloud Avoidance Technology
		
			FNoppel
		
		
			RSingh
		
		10.2514/1.28655
	
	
		Journal of Aircraft
		Journal of Aircraft
		0021-8669
		1533-3868
		
			44
			5
			
			2007
			American Institute of Aeronautics and Astronautics (AIAA)
		
	
	Noppel., F. and Singh, R., "Overview on Contrail and Cirrus Cloud Avoidance Technology," Journal of Aircraft, Vol. 44, No. 5, 2007, pp. 1721-1726.



	
		A note on how to avoid contrail cirrus
		
			HermannMannstein
		
		
			PeterSpichtinger
		
		
			KlausGierens
		
		10.1016/j.trd.2005.04.012
	
	
		Transportation Research Part D: Transport and Environment
		Transportation Research Part D: Transport and Environment
		1361-9209
		
			10
			5
			
			September 2005
			Elsevier BV
		
	
	Mannstein, H., Spichtinger, P., and Gierens, K., "A note on how to avoid contrail cirrus," Transportation Research. Part D, Transport and environment, Vol. 10, No. 5, September 2005, pp. 421-426.



	
		An Optimal Strategy for Persistent Contrail Avoidance
		
			ScotCampbell
		
		
			NatashaNeogi
		
		
			MichaelBragg
		
		10.2514/6.2008-6515
		AIAA-2008-6515
	
	
		AIAA Guidance, Navigation and Control Conference and Exhibit
		Honolulu, HI
		
			American Institute of Aeronautics and Astronautics
			August 2008
		
	
	Campbell1, S. E., Neogi, N. A., and Bragg, M. B., "An Optimal Strategy for Persistent Contrail Avoidance," AIAA Guidance, Navigation and Control Conference, AIAA-2008-6515, AIAA, Honolulu, HI, August 2008.



	
		The impact of cruise altitude on contrails and related radiative forcing
		
			ChristineFichter
		
		
			SusanneMarquart
		
		
			RobertSausen
		
		
			DavidSLee
		
		10.1127/0941-2948/2005/0048
	
	
		Meteorologische Zeitschrift
		metz
		0941-2948
		
			14
			4
			
			August 2005
			Schweizerbart
		
	
	Fichter, C., Marquart, S., Sausen, R., and Lee, D. S., "The impact of cruise altitude on contrails and related radiative forcing," Meteorologische Zeitschrift, Vol. 14, No. 4, August 2005, pp. 563-572.



	
		Reducing the climate change impacts of aviation by restricting cruise altitudes
		
			VictoriaWilliams
		
		
			RobertBNoland
		
		
			RalfToumi
		
		10.1016/s1361-9209(02)00013-5
	
	
		Transportation Research Part D: Transport and Environment
		Transportation Research Part D: Transport and Environment
		1361-9209
		
			7
			6
			
			November 2002
			Elsevier BV
		
	
	Williams, V., Noland, R. B., and Toumi, R., "Reducing the climate change impacts of aviation by restricting cruise altitudes," Transportation Research. Part D, Transport and environment, Vol. 7, No. 5, November 2002, pp. 451-464.



	
		Variability of contrail formation conditions and the implications for policies to reduce the climate impacts of aviation
		
			VictoriaWilliams
		
		
			RobertBNoland
		
		10.1016/j.trd.2005.04.003
	
	
		Transportation Research Part D: Transport and Environment
		Transportation Research Part D: Transport and Environment
		1361-9209
		
			10
			4
			
			July 2005
			Elsevier BV
		
	
	Williams, V. and Noland, R. B., "Variability of contrail formation conditions and the implications for policies to reduce the climate impacts of aviation," Transportation Research. Part D, Transport and environment, Vol. 10, No. 4, July 2005, pp. 269-280.



	
		Contrail Frequency over the United States from Surface Observations
		
			PatrickMinnis
		
		
			JKirkAyers
		
		
			MicheleLNordeen
		
		
			StevenPWeaver
		
		10.1175/1520-0442(2003)016<3447:cfotus>2.0.co;2
	
	
		Journal of Climate
		J. Climate
		0894-8755
		1520-0442
		
			16
			21
			
			November 2003
			American Meteorological Society
		
	
	Minnis, P., Ayers, J. K., Nordeen, M. L., and Weaver, S. P., "Contrail Frequency over the United States from Surface Observations," Journal of Climate, Vol. 16, No. 21, November 2003, pp. 34473462.



	
		Contrail coverage derived from 2001 AVHRR data over the continental United States of America and surrounding areas
		
			RabindraPalikonda
		
		
			PatrickMinnis
		
		
			DavidPDuda
		
		
			HermannMannstein
		
		10.1127/0941-2948/2005/0051
	
	
		Meteorologische Zeitschrift
		metz
		0941-2948
		
			14
			4
			
			August 2005
			Schweizerbart
		
	
	Palikonda, R., Minnis, P., Duda, D. P., and Mannstein, H., "Contrail coverage derived from 2001 AVHRR data over the continental United States of America and surrounding areas," Meteorologische Zeitschrift, Vol. 14, No. 4, August 2005, pp. 525-536.



	
		Relating observations of contrail persistence to numerical weather analysis output
		
			RPalikonda
		
		
			PMinnis
		
		
			DPDuda
		
		
			HMannstein
		
	
	
		Atmospheric Chemistry and Physics
		
			9
			4
			
			February 2009
		
	
	Palikonda, R., Minnis, P., Duda, D. P., and Mannstein, H., "Relating observations of contrail persistence to numerical weather analysis output," Atmospheric Chemistry and Physics, Vol. 9, No. 4, February 2009, pp. 1357-1364.



	
		Estimated contrail frequency and coverage over the contiguous United States from numerical weather prediction analyses and flight track data
		
			DavidPDuda
		
		
			PatrickMinnis
		
		
			RabindraPalikonda
		
		10.1127/0941-2948/2005/0050
	
	
		Meteorologische Zeitschrift
		metz
		0941-2948
		
			14
			4
			
			June-July 2003
			Schweizerbart
			Friedrichshafen at Lake Constance, Germany
		
	
	Duda, D. P., Minnis, P., Costulis, P. K., and Palikonda, R., "CONUS Contrail Frequency Estimated from RUC and Flight Track Data," European Conference on Aviation, Atmosphere, and Climate, Friedrichshafen at Lake Constance, Germany, June- July 2003.



	
		Improved Magnus Form Approximation of Saturation Vapor Pressure
		
			OlegAAlduchov
		
		
			RobertEEskridge
		
		10.1175/1520-0450(1996)035<0601:imfaos>2.0.co;2
	
	
		Journal of Applied Meteorology
		J. Appl. Meteor.
		0894-8763
		1520-0450
		
			35
			4
			
			April 1996
			American Meteorological Society
		
	
	Alduchov, O. A. and Eskridge, R. E., "Improved Magnus Form Approximation of Saturation Vapor Pressure," Journal of Applied Meteorology, Vol. 35, No. 4, April 1996, pp. 601-609.



	
		Assessing NAS Performance: Normalizing for the Effects of Weather
		
			MBCallaham
		
		
			JSDearmon
		
		
			ACooper
		
		
			JHGoodfriend
		
		
			DMoch-Mooney
		
		
			GSolomos
		
	
	
		4th USA/Europe Air Traffic Management R&D Symposium
		Santa Fe, NM
		
			December 2001
		
	
	Callaham, M. B., DeArmon, J. S., Cooper, A., Goodfriend, J. H., Moch-Mooney, D., and Solomos, G., "Assessing NAS Performance: Normalizing for the Effects of Weather," 4th USA/Europe Air Traffic Management R&D Symposium, Santa Fe, NM, December 2001.



	
		Relationship Between Weather, Traffic and Delay Based on Empirical Methods
		
			BanavarSridhar
		
		
			SeanSwei
		
		10.2514/6.2006-7760
	
	
		6th AIAA Aviation Technology, Integration and Operations Conference (ATIO)
		Wichita, KS
		
			American Institute of Aeronautics and Astronautics
			September 2006
		
	
	Sridhar, B. and Swei, S., "Relationship between Weather, Traffic and Delay Based on Empirical Methods," 6th AIAA Aviation Technology, Integration and Operations Conference (ATIO), Wichita, KS, September 2006.



	
		Estimation of Air Traffic Delay Using Three Dimensional Weather Information
		
			NeilChen
		
		
			BanavarSridhar
		
		10.2514/6.2008-8916
	
	
		The 26th Congress of ICAS and 8th AIAA ATIO
		Anchrorage, AK
		
			American Institute of Aeronautics and Astronautics
			September 2008
		
	
	Chen, N. Y. and Sridhar, B., "Estimation of Air Traffic Delay Using Three Dimensional Weather Information," 8th AIAA Aviation Technology, Integration and Operations Conference (ATIO), Anchrorage, AK, September 2008.



	
		Fuel efficient strategies for reducing contrail formations in United States airspace
		
			BanavarSridhar
		
		
			NeilYChen
		
		10.1109/dasc.2010.5655533
	
	
		29th Digital Avionics Systems Conference
		Salt Lake City, UT
		
			IEEE
			October 2010
		
	
	to appear
	Sridhar, B., Chen, N. Y., and Ng, H. K., "Fuel Efficient Strategies for Reducing Contrail Formations in United State National Air Space," 29th Digital Avionics Systems Conference, Salt Lake City, UT, October 2010, to appear.