동아시아 대기의 강 탐지 알고리즘 비교
Comparison of Atmospheric River Detection Algorithms in East Asia
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초록
This study compares the three detection algorithms of East Asian summer atmospheric rivers (ARs). The algorithms developed by Guan and Waliser (GW15), Park et al. (P21), and Tian et al. (T23) are particularly compared in terms of the AR frequency, the number of AR events, and the AR duration for the period of 2016-2020. All three algorithms show similar spatio- temporal distributions of AR frequency, centered along the edge of the North Pacific high. The maximum AR frequency gradually shifts northward in early summer as the edge of the North Pacific High expands, and retreats in late summer. However, the detailed pattern and the maximum value differ among the algorithms. When the AR frequency is decomposed into the number of AR events and the AR duration, the AR frequencies detected by GW15 and P21 are equally explained by both factors. However, the number of AR events primarily determine the AR frequency in T23. This difference occurs as T23 utilizes the machine learning algorithm applied to moisture field while GW15 and P21 apply the threshold value to moisture transport field. When evaluating AR-related precipitation, the ARs detected by P21 show the closest relationship with total precipitation in East Asia by up to 60%. These results indicate that AR detection in the East Asian summer is sensitive to the choice of the detection algorithm and can be optimized for the target region.
Abstract
This study compares the three detection algorithms of East Asian summer atmospheric rivers (ARs). The algorithms developed by Guan and Waliser (GW15), Park et al. (P21), and Tian et al. (T23) are particularly compared in terms of the AR frequency, the number of AR events, and the AR duration for the period of 2016-2020. All three algorithms show similar spatio- temporal distributions of AR frequency, centered along the edge of the North Pacific high. The maximum AR frequency gradually shifts northward in early summer as the edge of the North Pacific High expands, and retreats in late summer. However, the detailed pattern and the maximum value differ among the algorithms. When the AR frequency is decomposed into the number of AR events and the AR duration, the AR frequencies detected by GW15 and P21 are equally explained by both factors. However, the number of AR events primarily determine the AR frequency in T23. This difference occurs as T23 utilizes the machine learning algorithm applied to moisture field while GW15 and P21 apply the threshold value to moisture transport field. When evaluating AR-related precipitation, the ARs detected by P21 show the closest relationship with total precipitation in East Asia by up to 60%. These results indicate that AR detection in the East Asian summer is sensitive to the choice of the detection algorithm and can be optimized for the target region.
참고문헌 (44)
- Adler, R. F., "Global Precipitation Climatology Project (GPCP) monthly analysis (new version 2.3) and a review of 2017 global precipitation", Atmosphere, 9, pp.138~ (2018)[DOI]
- Bao, J. -W., "Interpretation of enhanced integrated water vapor bands associated with extratropical cyclones : Their formation and connection to tropical moisture", Mon. Wea. Rev, 134, pp.1063~1080 (2006)[DOI]
- Chen, L.-C, "Encoder-decoder with atrous separable convolution for semantic image segmentation", Proceedings of the European conference on computer vision, 15, pp.801~818 (2018)
- Dettinger, M. D, "A tmospheric rivers, floods and the water resources of California", Water, 3, pp.445~478 (2011)[DOI]
- Dettinger, M. D., "Atmospheric rivers as drought busters on the US West Coast", J. Hydrometeor, 14, pp.1721~1732 (2013)[DOI]
- Espinoza, V., "Global analysis of climate change projection effects on atmospheric rivers", Geophys. Res. Lett, 45, pp.4299~4308 (2018)[DOI]
- Gimeno, L., "Major mechanisms of atmospheric moisture transport and their role in extreme precipitation events", Annu. Rev. Environ. Res, 41, pp.117~141 (2016)[DOI]
- Goldenson, N., "Influence of atmospheric rivers on mountain snowpack in the western United States", J. Climate, 31, pp.9921~9940 (2018)[DOI]
- Guan, B, "Extreme snowfall events linked to atmospheric rivers and surface air temperature via satellite measurements", Geophys. Res. Lett, 37, pp.L20401~ (2010)[DOI]
- Guan, B., "Detection of atmospheric rivers : Evaluation and application of an algorithm for global studies", J. Geophys. Res. Atmospheres, 120, pp.12514~12535 (2015)[DOI]
- Hersbach, H., "The ERA5 global reanalysis", Quart. J. Roy. Meteor. Soc, 146, pp.1999~2049 (2020)[DOI]
- Higgins, T, "Using deep learning for an analysis of atmospheric rivers in a high‐resolution large ensemble climate data set", J. Adv. Modeling Earth Syst, 15, pp.e2022MS003~ (2023)[DOI]
- Kamae, Y., "Climatological relationship between warm season atmospheric rivers and heavy rainfall over East Asia", J. Meteor. Soc. Japan. Ser. II, 95, pp.411~431 (2017)[DOI]
- Kamae, Y., "Atmospheric rivers over the northwestern Pacific : Climatology and interannual variability", J. Climate, 30, pp.5605~5619 (2017)[DOI]
- Kashinath, K., "ClimateNet : An expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather", Geoscientific Model Development, 14, pp.107~124 (2021)[DOI]
- Kim, J, "Precipitation characteristics related to atmospheric rivers in East Asia", Int. J. Climatol, 41, pp.E2244~E2257 (2021)[DOI]
- Konrad, C. P., "Flood runoff in relation to water vapor transport by atmospheric rivers over the western United States, 1949~2015", Geophys. Res. Lett, 44, pp.11456~11462 (2017)[DOI]
- 권예은, "대기의 강이 한반도 지역별 강수에 미치는 영향", 대기, 32(2), pp.135~148 (2022), 한국기상학회[DOI]
- Lavers, D. A., "The detection of atmospheric rivers in atmospheric reanalyses and their links to British winter floods and the large-scale climatic circulation", J. Geophys. Res. Atmospheres, 117, pp.13~ (2012)[DOI]
- Liang, J, "Climatology of atmospheric rivers in the Asian monsoon region", Int. J. Climatol, 41, pp.E801~E818 (2021)[DOI]
- 문혜진, "Atmospheric River 상륙이 한반도 강수와 기온에 미치는 영향 연구", 대기, 29(4), pp.343~353 (2019), 한국기상학회[DOI]
- Mundhenk, B. D., "All-season climatology and variability of atmospheric river frequencies over the North Pacific", J. Climate, 29, pp.4885~4903 (2016)[DOI]
- Muszynski, G., "Topological data analysis and machine learning for recognizing atmospheric river patterns in large climate datasets", Geoscientific Model Development, 12, pp.613~628 (2019)[DOI]
- Neiman, P. J., "Flooding in western Washington : The connection to atmospheric rivers", J. Hydrometeor, 12, pp.1337~1358 (2011)[DOI]
- Newman, M., "Relative contributions of synoptic and low-frequency eddies to timemean atmospheric moisture transport, including the role of atmospheric rivers", J. climate, 25, pp.7341~7361 (2012)[DOI]
- O’Brien, T. A, "Increases in future AR count and size: Overview of the ARTMIP Tier 2CMIP5", 6 experiment, 2022, pp.127, e2021JD036~
- Pan, M., "A novel atmospheric river identification algorithm", Water Resour. Res, 55, pp.6069~6087 (2019)[DOI]
- Pan, M, "East Asia atmospheric river catalog: Annual cycle, transition mechanism, and precipitation", Geophys. Res. Lett, 47, pp.e2020GL089~ (2020)[DOI]
- Park, C, "Distinct features of atmospheric rivers in the early versus late East Asian summer monsoon and their impacts on monsoon rainfall", J. Geophys. Res. Atmospheres, 126, pp.JD033537~ (2021)[DOI]
- Radić, V., "Future changes in autumn atmospheric river events in British Columbia, Canada, as projected by CMIP5global climate models", J. Geophys. Res. Atmospheres, 120, pp.9279~9302 (2015)[DOI]
- Ralph, F. M., "Satellite and CALJET aircraft observations of atmospheric rivers over the eastern North Pacific Ocean during the El Niño winter of 1997", 98, 2004, pp.132, 1721~1745
- Ralph, F, "Flooding on California’s Russian River: Role of atmospheric rivers", Geophys. Res. Lett, 33 (2006)[DOI]
- Ralph, F. M., "Historical and national perspectives on extreme West Coast precipitation associated with atmospheric rivers during December 2010", Bulletin of the American Meteorological Society, 93, pp.783~790 (2012)[DOI]
- Ronneberger, O, "U-net:Convolutional networks for biomedical image segmentation", Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, 9351, pp.234~241 (2015)
- Rutz, J. J., "Climatological characteristics of atmospheric rivers and their inland penetration over the western United States", Mon. Wea. Rev, 142, pp.905~921 (2014)[DOI]
- Rutz, J. J., "The atmospheric river tracking method intercomparison project(ARTMIP) : quantifying uncertainties in atmospheric river climatology", J. Geophys. Res. Atmospheres, 124, pp.13777~13802 (2019)[DOI]
- Shields, C. A., "Atmospheric river tracking method intercomparison project(ARTMIP) : project goals and experimental design", Geoscientific Model Development, 11, pp.2455~2474 (2018)[DOI]
- Tian, Y, "A deep-learning ensemble method to detect atmospheric rivers and its application to projected changes in precipitation regime", J. Geophys. Res. Atmospheres, 128, pp.e2022JD037~ (2023)[DOI]
- Wang, P., "Understanding convolution for semantic segmentation", 2018 IEEE winter conference on applications of computer vision, pp.1451~1460 (2018)
- Wick, G. A., "Evaluation of forecasts of the water vapor signature of atmospheric rivers in operational numerical weather prediction models", Wea. Forecasting, 28, pp.1337~1352 (2013)[DOI]
- Wu, T., "Cgnet : A light-weight context guided network for semantic segmentation", IEEE Trans. Image Processing, 30, pp.1169~1179 (2020)[DOI]
- Yang, Y., "Atmospheric rivers over the Bay of Bengal lead to northern Indian extreme rainfall", Int. J. Climatol, 38, pp.1010~1021 (2018)[DOI]
- Zhao, H., "Pyramid scene parsing network", Computer Vision and Pattern Recognition, pp.2881~2890 (2017)[DOI]
- Zhu, Y., "A proposed algorithm for moisture fluxes from atmospheric rivers", Mon. Wea. Rev, 126, pp.725~735 (1998)[DOI]