Two new contrail detection methods for the compilation of a global climatology of contrail occurrence

One of the recommendations of the Federal Aviation Administration's (FAA) Aviation-Climate Change Research Initiative is the development of a global climatology of linear contrail occurrence detectable via satellite remote sensing methods. Such a contrail climatology is necessary for the validation of a new generation of atmospheric models that represent contrail formation explicitly. We present two new automated contrail detection algorithms (CDAs) that can be used to develop this climatology.

The first algorithm is a modified version of the technique described by Mannstein et al. (1999), which detects linear contrails in multi-spectral thermal IR satellite imagery using only two channels (11 & 12 µm) from the Advanced Very High Resolution Radiometer. This method requires only the brightness temperatures (BT) from the IR channels, with no other ancillary data, and can be applied to both day and night scenes. It uses a scene-invariant threshold to detect cloud edges produced by contrails, and 3 binary masks to determine if the detected linear features are truly contrails. However, these masks are not always sufficient to remove all non-contrail edge features. To reduce the number of false positive detections due to lower cloud streets and surface features, we add observations from other infrared radiance channels available on the MODerate-resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua. The new modified method uses additional masks derived from the added thermal infrared channels to screen out linear cloud features that appear as contrails in the original algorithm.

The second algorithm is based primarily on image analysis and pattern recognition techniques. It uses the BT difference of the MODIS thermal bands that produce the highest contrast for the contrails in a particular image. The core of the algorithm consists of several stages of correlation analysis that use predefined linear patterns to match contrails with different widths and orientations. The map of likely contrail vectors obtained from the initial analysis then undergoes a geometry analysis that merges and extends the detected linear fragments by including the pixels missed during the correlation stages. To discriminate contrails from common cirrus streamers, the geometry analysis also suppresses the lines that follow the most dominant local direction. The output contrail mask has different weights for each detected contrail indicating a combined confidence level of the detection. This allows some flexibility for the end user when deciding whether a more strict or more liberal detection is needed.

As a preliminary test of the new CDAs, the contrail masks produced by both algorithms have been compared to the global dataset of commercial aircraft waypoints provided by the FAA. The waypoint data have also been used as a reference to distinguish thick diffused contrails from natural cirrus clouds.