An Empirical Model to Predict Widespread Occurrences of Contrails

Travis, David J.; Carleton, Andrew M.; Changnon, Stanley A.

doi:10.1175/1520-0450(1997)036<1211:aemtpw>2.0.co;2

Cited by 16 publications

(17 citation statements)

References 19 publications

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“…Although contrails have been reported to often occur in clusters, or 'outbreaks' (Travis et al, 1997;Duda et al, 2001), it was only necessary for a minimum of one contrail to be located within a particular grid cell for its inclusion in the GIS database. This duplicates the approach taken to construct the contrail frequency database for the 1977-1979period (DeGrand et al, 2000.…”

Section: Satellite Imagery and Detection Of Contrailsmentioning

confidence: 99%

“…However, contrail frequency is not only dependent on the number of flights but also on the atmospheric conditions occurring at typical flight cruising altitudes (i.e. 10-12 km) (Schmidt, 1941;Appleman, 1953;Pilie and Jiusto, 1958;Schrader, 1997;Travis et al, 1997). Numerous studies have shown that contrail formation and persistence time are controlled by variations in temperature and moisture, especially near the tropopause, with the most ideal set of conditions for extended contrail persistence being an atmosphere that is supersaturated with respect to ice (Pilié and Jiusto, 1958;Hanson and Hanson, 1995;Kastner et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

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US jet contrail frequency changes: influences of jet aircraft flight activity and atmospheric conditions

Travis

Carleton

Johnson

et al. 2006

Intl Journal of Climatology

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In the United States, the dramatic increase in jet fuel usage and kilometers flown has led to speculation of a similar increase in jet contrails. However, contrail occurrence depends heavily upon the meteorological conditions near cruising altitudes (i.e. the tropopause, 10–12 km altitude). This study reports a contrail mid‐season contemporary climatology for the coterminous United States (2000–2002), and compares the frequencies with those previously reported for an earlier (1977–1979) period, to determine spatial and seasonal contrail frequency changes. For both climatologies, contrail occurrence is derived from the analysis of high‐resolution satellite imagery. Data on US jet aircraft flight activity were obtained to assess their relationship to contrail frequency, as were NCEP‐NCAR reanalysis data to determine the changes in tropopause‐level atmospheric conditions.For the 2000–2002 period, contrails comprise a distinct high (low) frequency pattern in the East (West) halves of the United States. Seasonally, there is a contrail association with the latitudinal migration of the jet stream and a US‐wide peak contrail frequency during winter (January). The inter‐monthly variations in contrail frequency are significantly different from each other but show no association with variations in jet flight activity, indicating a greater role for meteorological conditions. Between the 1977–1979 and 2000–2002 periods, there were strong spatial and seasonal asymmetries to the contrail frequency change. These involve a cooling (warming) of the tropopause for the largest (smallest) frequency increases, which shows some association with the switch in positive and negative phases of the Arctic Oscillation. The role of upper tropospheric conditions and links to hemispheric‐scale teleconnections should be considered when projecting contrail frequency changes and their future impacts on climate. Copyright © 2006 Royal Meteorological Society

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Section: Satellite Imagery and Detection Of Contrailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

US jet contrail frequency changes: influences of jet aircraft flight activity and atmospheric conditions

Travis

Carleton

Johnson

et al. 2006

Intl Journal of Climatology

Self Cite

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“…The 6.5-µm channel is sensitive to the top three millimeters of water vapor profile in the atmospheric column, and most of the detected emission is from the layer between 500 to 200 hPa, with a peak sensitivity near 400 hPa (Travis et al, 1997;Schmit et al, 2001). The water vapor channel on GOES-12 is spectrally widened compared to the corresponding channel on GOES-8, and thus measures radiation slightly deeper in the atmosphere than previous sensors.…”

Section: B Satellite Datamentioning

confidence: 99%

“…Travis et al (1997) used a combination of rawinsonde temperature and geostationary satellite water vapor absorption data to develop a logistic model of the occurrence of widespread persistent contrail coverage. Jackson et al (2001) created a contrail prediction model using surface observations of contrails and rawinsonde measurements of temperature, humidity and winds.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the success of the probabilistic forecast models described in Travis et al (1997) and Jackson et al (2001), several questions remain about the usefulness of these models. The former study used only a limited number of observations, while the latter only considered contrail observations within limited geographic (New England states) and temporal (two weeks in September) domains.…”

Section: Introductionmentioning

confidence: 99%

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Basic Diagnosis and Prediction of Persistent Contrail Occurrence Using High-Resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part II: Evaluation of Sample Models

Duda

Minnis

2009

Journal of Applied Meteorology and Climatology

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Previous studies have shown that probabilistic forecasting may be a useful method for predicting persistent contrail formation. A probabilistic forecast to accurately predict contrail formation over the contiguous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and from the Rapid Update Cycle (RUC) as well as GOES water vapor channel measurements, combined with surface and satellite observations of contrails. Two groups of logistic models were created. The first group of models (SURFACE models) is based on surface-based contrail observations supplemented with satellite observations of contrail occurrence. The second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The mean accuracies for both the SURFACE and OUTBREAK models typically exceeded 75 percent when based on the RUC or ARPS analysis data, but decreased when the logistic models were derived from ARPS forecast data.2

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Potential climatic effects of cirrus contrails for the subarctic setting of Fairbanks, Alaska

Wendler

Shulski

Hartmann

2005

Theor. Appl. Climatol.

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An Empirical Model to Predict Widespread Occurrences of Contrails

Cited by 16 publications

References 19 publications

US jet contrail frequency changes: influences of jet aircraft flight activity and atmospheric conditions

US jet contrail frequency changes: influences of jet aircraft flight activity and atmospheric conditions

Basic Diagnosis and Prediction of Persistent Contrail Occurrence Using High-Resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part II: Evaluation of Sample Models

Potential climatic effects of cirrus contrails for the subarctic setting of Fairbanks, Alaska

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