[1] 刘永和, 郭维栋, 冯锦明, 等.气象资料的统计降尺度方法综述[J].地球科学进展, 2011, 26(8):837-847. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dqkxjz201108007
[2] OSHIMA N, KATO H, KADOKURA S. An application of statistical downscaling to estimate surface air temperature in Japan[J]. J Geophys Res Atmos, 2002, 107(D10):ACL-1-ACL 14-10. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=10.1029/2001JD000762
[3] MPELASOKA F S, MULLAN A B, HEERDEGEN R G. New Zealand climate change information derived by multivariate statistical and artificial neural networks approaches[J]. Int J Climatol, 2001, 21(11):1 415-1 433. doi: 10.1002/joc.617
[4] ANANDHI A, SRINIVAS V V, KUMAR D N, et al. Role of predictors in downscaling surface temperature to river basin in India for IPCC SRES scenarios using support vector machine[J]. Int J Climatol, 2009, 29(4):583-603. doi: 10.1002/joc.1719
[5] LIAN S M, WANG K, CUI M C, et al. Exercise in downscaling on sea surface temperature along Chinese coast[J]. Chinese Journal of Oceanology & Limnology, 2000, 18(2):110-116.
[6] CUI B, TOTH Z, ZHU Y, et al. Bias correction for global ensemble forecast[J]. Wea Forecasting, 2012, 27(2): 396-410. doi: 10.1175/WAF-D-11-00011.1
[7] 李新, 程国栋, 卢玲.空间内插方法的比较[J].地球科学进展, 2000, 25(13) : 261-265
[8] 彭彬, 周艳莲, 高苹, 等.气温插值中不同空间插值方法的适用性分析——以江苏为例[J].地球信息科学学报, 2011, 13(4) : 539-547. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dqxxkx201104016
[9] 徐振亚, 任福民, 杨修群, 等.日最高温度统计降尺度方法的比较研究[J].气象科学, 2012, 32(4):395-402. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=qxkx201204005
[10] KEYS R G. Cubic convolution interpolation for digital image processing[J]. IEEE Trans.on Acoust.speech.Signal Processing, 1981, 29(6): 1 153-1 160.
[11] DONG C, LOY C C, HE K, et al. Image super-resolution using deep convolutional networks[J]. Pattern Analysis & Machine Intelligence IEEE Transactions on, 2016, 38(2):295-307.
[12] LEGID C, THEIS L, HUSZARF, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017: 105-114.
[13] KIM J, LEE J K, Lee K M. Accurate image super-resolution using very deep convolutional networks[C].IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016: 1 646-1 654.
[14] MAO X J, SHEN C, YANG Y B. Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections[C]. Advances in Neural Information Processing Systems, 2016: 2 802-2 810.
[15] SHI W, CABALLERO J, HUSZAR F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 1 874-1 883.
[16] LIM B, SON S, KIM H, et al. Enhanced deep residual networks for single image super-resolution[C]. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017: 1 132-1 140.
[17] 姜丽黎, 余晖.基于动力相似方法的台风极端降水概率预报研究[J].热带气象学报, 2019, 35(3):353-364. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=rdqxxb201903008
[18] 李欣韵, 余锦华, 梁信忠.基于CWRF模式夏季日降水量的订正技术对比[J].热带气象学报, 2019, 35(6):842-851. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=rdqxxb201906012
[19] LECUN Y, BOTTOU L. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2 278-2 324. doi: 10.1109/5.726791
[20] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]. IEEE Conference on Computer Vision and Pattern Recognition(CVPR), 2016: 770-778.
[21] NAIR V, HINTON G E. Rectified linear units improve restricted boltzmann machines vinod nair[C]. Proceedings of the 27th International Conference on Machine Learning(ICML), Haifa, Israel, 2010: 807-814.
[22] KINGMA D P, BA J. Adam: A method for stochastic optimization[C]. Proceedings of the 3rd International Conference for Learning Representations(ICLR), 2015: 1-15.
[23] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]. IEEE Conference on Computer Vision and Pattern Recognition(CVPR), 2018: 7 132-7 141.
[24] 张阿珍, 刘政林.基于双三次插值算法的图像缩放引擎设计[J].微电子学与计算机, 2007, 24(1): 49-51. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=wdzxyjsj200701013