Image-processing-based atmospheric river tracking method version 1 (IPART-1)
Guangzhi Xu, Xiaohui Ma, Ping Chang, Lin Wang
Abstract
Abstract. Automated detection of atmospheric rivers (ARs) has been heavily relying on magnitude thresholding on either the integrated water vapor (IWV) or integrated vapor transport (IVT). Magnitude-thresholding approaches can become problematic when detecting ARs in a warming climate, because of the increasing atmospheric moisture. A new AR detection method derived from an image-processing algorithm is proposed in this work. Different from conventional thresholding methods, the new algorithm applies threshold to the spatiotemporal scale of ARs to achieve the detection, thus making it magnitude independent and applicable to both IWV- and IVT-based AR detection. Compared with conventional thresholding methods, it displays lower sensitivity to parameters and a greater tolerance towards a wider range of water vapor flux intensities. A new method of tracking ARs is also proposed, based on a new AR axis identification method and a modified Hausdorff distance that gives a measure of the geographical distances of AR axes pairs.