Super-Resolution Simulation for Real-Time Prediction of Urban Micrometeorology

Author:

Onishi Ryo1,Sugiyama Daisuke1,Matsuda Keigo1

Affiliation:

1. Center for Earth Information Science and Technology, Japan Agency for Marine-Earth Science and Technology

Publisher

Meteorological Society of Japan

Subject

Atmospheric Science

Reference21 articles.

1. Abadi, M., P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. A. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zhang, 2016: TensorFlow: A system for large-scale machine learning. Proc. of 12th USENIX Symp. on Operating Systems Design and Implementation (OSDI), 16, 265-283.

2. Cannon, A. J., 2011: Quantile regression neural networks: Implementation in R and application to precipitation downscaling. Comput. Geosci., 37, 1277-1284.

3. Dong, C., C. C. Loy, K. He, and X. Tang, 2014: Learning a deep convolutional network for image super-resolution, European Conf. on Computer Vision (ECCV), pp.184-199.

4. Duchon, C. E., 1979: Lanczos filtering in one and two dimensions. J. Appl. Meteor., 18, 1016-1022.

5. Ghosh, S., 2010: SVM-PGSL coupled approach for statistical downscaling to predict rainfall from GCM output. J. Geophys. Res. Atmos., 115, D22102.

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