Potential Improvement of GK2A Clear-Sky Atmospheric Motion Vectors Using the Convolutional Neural Network Model
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Published:2024-02-08
Issue:3
Volume:60
Page:245-253
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ISSN:1976-7633
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Container-title:Asia-Pacific Journal of Atmospheric Sciences
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language:en
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Short-container-title:Asia-Pac J Atmos Sci
Author:
Choi Hwayon, Choi Yong-SangORCID, Song Hyo-Jong, Kang Hyoji, Kim Gyuyeon
Abstract
AbstractIn this study, we propose a new approach to improve the accuracy of the horizontal atmospheric motion vector (AMV) in cloud-free skies and its forecasting. We adapted the optical flow of the convolutional neural network (CNN) framework model using two 10-min interval infrared images at water vapor channels (centered at 6.3, 7.0, and 7.3 $$\mu m$$
μ
m
) from the Korean geostationary satellite GEO-KOMPSAT-2A (GK2A). Since all pixels had seamless AMVs calculated by CNN (CNN AMVs), we could also predict AMVs using the linear regression method. The tracking performance of the CNN-based algorithm was validated using AMVs retrieved from GK2A (GK2A AMVs) by estimating the difference between those values and the ECMWF (European Centre for Medium-Range Weather Forecasts) Reanalysis v5 (ERA5) wind data over Korea in 2022. CNN AMVs showed similar or better root-mean-square vector differences (RMSVDs) than GK2A AMVs (12.33–12.86 vs. 15.89–19.96 m/s). The RMSVDs of the forecasted AMVs were 2.74, 2.95, 3.41, and 4.79 m/s at lead times of 10, 20, 30, and 60 min, respectively. Consequently, our method showed higher accuracy for tracking motion in the production of AMVs and succeeded in forecasting AMVs. We expect that such potential improvements in computational accuracy for operational GK2A AMVs will contribute to increased accuracy when forecasting meteorological phenomena related to wind.
Funder
National Research Foundation of Korea Korea Meteorological Institute
Publisher
Springer Science and Business Media LLC
Reference32 articles.
1. Bell, B., Hersbach, H., Simmons, A., Berrisford, P., Dahlgren, P., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Radu, R., Schepers, D.: The ERA5 global reanalysis: Preliminary extension to 1950. Q. J. R. Meteorol. Soc. 147(741), 4186–4227 (2021) 2. Bresky, W., Daniels, J.: The feasibility of an optical flow algorithm for estimating atmospheric motion. In Proceedings of the Eighth Int. Winds Workshop, Beijing, China pp. 24–28 (2006) 3. Carr, J.L., Wu, D.L., Kelly, M.A., Gong, J:. MISR-GOES 3D Winds: Implications for Future LEO-GEO and LEO-LEO Winds. Remote Sensing. 10(12), 1885 (2018) 4. Chen, Y., Shen, J., Fan, S., Meng, D., Wang, C.: Characteristics of fengyun-4a satellite atmospheric motion vectors and their impacts on data assimilation. Adv. Atmos. Sci. 37, 1222–1238 (2020) 5. Choi, Y.-S., Ho, C.-H.: Earth and environmental remote sensing community in South Korea: A review. Remote Sens. Appl.: Soc. Environ. 2, 66–76 (2015)
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