Refined Spatialization of 10-Day Precipitation in China Based on GPM IMERG Data and Terrain Decomposition Using the BEMD Algorithm
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Materials Science
Link
https://link.springer.com/content/pdf/10.1007/s13351-023-2171-1.pdf
Reference75 articles.
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2. Bhuiyan, M. A. E., E. I. Nikolopoulos, E. N. Anagnostou, et al., 2018: A nonparametric statistical technique for combining global precipitation datasets: Development and hydrological evaluation over the Iberian Peninsula. Hydrol. Earth Syst. Sci., 22, 1371–1389, doi: https://doi.org/10.5194/hess-22-1371-2018.
3. Bhuiyan, M. A. E., F. F. Yang, N. K. Biswas, et al., 2020: Machine learning-based error modeling to improve GPM IMERG precipitation product over the Brahmaputra River basin. Forecasting, 2, 248–266, doi: https://doi.org/10.3390/forecast2030014.
4. Chang, X. Y., B. B. Wang, Y. Yan, et al., 2019: Characterizing effects of monsoons and climate teleconnections on precipitation in China using wavelet coherence and global coherence. Climate Dyn., 52, 5213–5228, doi: https://doi.org/10.1007/s00382-018-4439-1.
5. Chen, D. L., T. H. Ou, L. B. Gong, et al., 2010: Spatial interpolation of daily precipitation in China: 1951–2005. Adv. Atmos. Sci., 27, 1221–1232, doi: https://doi.org/10.1007/s00376-010-9151-y.
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