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http://dx.doi.org/10.7780/kjrs.2020.36.5.1.9

Improvements for Atmospheric Motion Vectors Algorithm Using First Guess by Optical Flow Method  

Oh, Yurim (Department of Atmospheric Sciences, Division of Earth Environmental System, Pusan National University)
Park, Hyungmin (Department of Atmospheric Sciences, Division of Earth Environmental System, Pusan National University)
Kim, Jae Hwan (Department of Atmospheric Sciences, Division of Earth Environmental System, Pusan National University)
Kim, Somyoung (Department of Atmospheric Sciences, Division of Earth Environmental System, Pusan National University)
Publication Information
Korean Journal of Remote Sensing / v.36, no.5_1, 2020 , pp. 763-774 More about this Journal
Abstract
Wind data forecasted from the numerical weather prediction (NWP) model is generally used as the first-guess of the target tracking process to obtain the atmospheric motion vectors(AMVs) because it increases tracking accuracy and reduce computational time. However, there is a contradiction that the NWP model used as the first-guess is used again as the reference in the AMVs verification process. To overcome this problem, model-independent first guesses are required. In this study, we propose the AMVs derivation from Lucas and Kanade optical flow method and then using it as the first guess. To retrieve AMVs, Himawari-8/AHI geostationary satellite level-1B data were used at 00, 06, 12, and 18 UTC from August 19 to September 5, 2015. To evaluate the impact of applying the optical flow method on the AMV derivation, cross-validation has been conducted in three ways as follows. (1) Without the first-guess, (2) NWP (KMA/UM) forecasted wind as the first-guess, and (3) Optical flow method based wind as the first-guess. As the results of verification using ECMWF ERA-Interim reanalysis data, the highest precision (RMSVD: 5.296-5.804 ms-1) was obtained using optical flow based winds as the first-guess. In addition, the computation speed for AMVs derivation was the slowest without the first-guess test, but the other two had similar performance. Thus, applying the optical flow method in the target tracking process of AMVs algorithm, this study showed that the optical flow method is very effective as a first guess for model-independent AMVs derivation.
Keywords
AMVs; Optical Flow; Target Tracking; Wind; Satellite;
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Times Cited By KSCI : 1  (Citation Analysis)
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