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 spatio-temporal scale of ARs to achieve the detection, thus making it magnitude independent and applicable to both IWV- and IVT-based AR detections. Compared with conventional thresholding methods, it displays lower sensitivity to parameters and a greater tolerance to 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.
Removal of Adherent Noises from Image Sequences by Spatio-Temporal Image Processing
