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. Automated detection of atmospheric rivers (ARs) has been heavily relying onmagnitude thresholding on either the integrated water vapor (IWV) orintegrated vapor transport (IVT). Magnitude-thresholding approaches canbecome problematic when detecting ARs in a warming climate, because ofthe increasing atmospheric moisture. A new AR detection method derivedfrom an image-processing algorithm is proposed in this work. Differentfrom conventional thresholding methods, the new algorithm appliesthreshold to the spatiotemporal scale of ARs to achieve the detection,thus making it magnitude independent and applicable to both IWV- andIVT-based AR detection. Compared with conventional thresholdingmethods, it displays lower sensitivity to parameters and a greatertolerance towards a wider range of water vapor flux intensities. A newmethod of tracking ARs is also proposed, based on a new AR axisidentification method and a modified Hausdorff distance that gives ameasure of the geographical distances of AR axes pairs.

Image-processing-based atmospheric river tracking method version 1 (IPART-1)