MACHINE LEARNING ON GCM ATMOSPHERIC VARIABLES FOR SPATIAL DOWNSCALING OF PRECIPITATION
Author:
Affiliation:
1. Dept. of Civil and Earth Resources Eng., Kyoto University
2. P&S ITS Division, Global Technology Solution BG, Fujitsu Ltd.
Publisher
Japan Society of Civil Engineers
Link
https://www.jstage.jst.go.jp/article/journalofjsce/12/2/12_23-16152/_pdf
Reference10 articles.
1. 1) Wilby R. L. and Wigley. T. M. L. (1997). Downscaling general circulation model output: a review of methods and limitations. Progress in Physical Geography, 21-4, 530-548.
2. 2) Wilby R. L., Dawson, C. W., Barrow, E. M. (2002). SDSM - A decision support tool for the assessment of regional climate change impacts. Environ. Modell. Software, 17-2, 145-157.
3. 3) Walton D., Berg N., Pierce D. et al. (2010). Understanding differences in California climate projections produced by dynamical and statistical downscaling. JGR Atmospheres, 125-19, https://doi.org/10.1029/2020JD032812.
4. 4) Hashmi, M. Z., Shamseldin, A. Y., and Melville, B. W. (2009). Statistical downscaling of precipitation: state-of-the-art and application of bayesian multi-model approach for uncertainty assessment. Hydrol. Earth Syst. Sci. Discuss., 6, 6535–6579, https://doi.org/10.5194/hessd-6-6535-2009.
5. 5) Misra, S., Sarkar, S., & Mitra, P. (2018). Statistical downscaling of precipitation using long short-term memory recurrent neural networks. Theoretical and Applied Climatology, 134(3), 1179–1196.
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