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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2020-135
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-135
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 03 Jun 2020

Submitted as: development and technical paper | 03 Jun 2020

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A revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

Image Processing Based Atmospheric River Tracking Method Version 1 (IPART-1)

Guangzhi Xu1, Xiaohui Ma1,3, Ping Chang2,3, and Lin Wang4 Guangzhi Xu et al.
  • 1Key Laboratory of Physical Oceanography, Institute for Advanced Ocean Studies, Ocean University of China and Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
  • 2Department of Oceanography and Department of Atmospheric Sciences, Texas A&M University, College Station, Texas, USA
  • 3The International Laboratory for High-Resolution Earth System Prediction, Texas A&M University, College Station, Texas, USA
  • 4Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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.

Guangzhi Xu et al.

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Interactive discussion

Status: closed
Status: closed
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Guangzhi Xu et al.

Model code and software

Image-Processing based Atmospheric River Tracking (IPART) algorithms version 1 G. Xu, X. Ma, P. Chang, and L. Wang https://doi.org/10.5281/zenodo.3864592

Guangzhi Xu et al.

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Latest update: 22 Sep 2020
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Short summary
We observed considerable limitations in existing atmospheric river (AR) detection methods, and looked into other disciplines for inspirations of tackling the AR detection problem. A new method is derived from an image-processing technique, and encodes the spatio-temporal scale information of AR systems, which is a key physical ingredient of ARs that is more stable than the vapor flux intensities, making it more suitable for climate scale studies when models often have different biases.
We observed considerable limitations in existing atmospheric river (AR) detection methods, and...
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