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

Abstract

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

How well can persistent contrails be predicted? – An update

“…While this is certainly a possibility to improve the predicted humidity field per se, it is not clear whether the methods are fast and accurate enough for flight planning. Duda and Minnis (2009b) conclude that "reductions in the uncertainties of meteorological variables to a point where acceptable contrail forecasts are produced would be a good goal for NWA (numerical weather analysis) modelers".…”

Mid-season climate diagnostics of jet contrail ëoutbreaksí and implications for eastern US sky‑cover trends

“…Thus, in common with other studies (e.g. Stuefer et al 2005, Rädel & Shine 2008, Duda & Minnis 2009a, we emphasize the RH measure of UT moisture content and assume that higher values of RH(300) increase the likelihood of ice supersaturation (Atlas & Wang 2010). The RH also is useful for inferring the effect of vertical motion (i.e.…”

“…The original studies relating contrail formation and persistence to UT meteorological conditions (Appleman 1953, Pilié & Jiusto 1958, and confirmed more recently (e.g. Schumann 1996, Moss 1999, Stuefer et al 2005, Duda & Minnis 2009a, showed a strong dependence on low temperature and high relative humidity (RH) at jet flight altitudes. These conditions typically occur at or near the tropo pause in middle latitudes.…”

“…Although a considerable number of UT variables are available, many authors (e.g. Hanson & Hanson 1995, 1998, Chlond 1998, Jensen et al 1998a,b, Kästner et al 1999, Stuefer et al 2005, Car-leton et al 2008, Duda & Minnis 2009a show that those most likely to re present contrail outbreaks include the air tem per ature (T); the humidity; the vertical motion of air (or omega (ω), = dp/dt, where p = pressure and t = time), and the u-wind, zonal (i.e. west-east) component, of the total (vector) wind.…”

Aviation-Contrail Impacts on Climate and Climate Change: A Ready-to-Wear Research Mantle for Geographers

“…Although detailed analysis of an individual contrail or contrail outbreak yields insights into the physical processes in the immediate area and for that time interval, it does not speak to the larger meso-scale to synoptic-scale environments typically promoting the formation and persistence of outbreaks or their variations between subregions. Accordingly, it is less readily applied to the problem of improving the prediction of contrail-susceptible areas in near real time, which is increasingly a goal of contrail investigations by atmospheric scientists using either mesoscale dynamical models or empirical techniques (e.g., Stuefer, Meng, and Wendler 2005;Duda and Minnis 2009). Although atmospheric scientists researching contrail impacts on weather and climate have not tended to explicitly consider the inherently geographic concepts of scale, they have readily utilized geographic techniques to portray the spatial variations in their observation data and also numerical model output (e.g., Minnis et al 2003;Minnis et al 2004;Duda, Minnis, and Palikonda 2005).…”