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DeepSD:通过单幅图像超分辨率生成高分辨率气候变化预测DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution |
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| 课程网址: | http://videolectures.net/kdd2017_vandal_single_image/ |
| 主讲教师: | Thomas Vandal |
| 开课单位: | 视频讲座网 |
| 开课时间: | 2017-10-09 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants. However, contemporary Earth System Models (ESM) are run at spatial resolutions too coarse for assessing effects this localized. Local scale projections can be obtained using statistical downscaling, a technique which uses historical climate observations to learn a low-resolution to high-resolution mapping. Depending on statistical modeling choices, downscaled projections have been shown to vary significantly terms of accuracy and reliability. The spatio-temporal nature of the climate system motivates the adaptation of super-resolution image processing techniques to statistical downscaling. In our work, we present DeepSD, a generalized stacked super resolution convolutional neural network (SRCNN) framework for statistical downscaling of climate variables. DeepSD augments SRCNN with multi-scale input channels to maximize predictability in statistical downscaling. We provide a comparison with Bias Correction Spatial Disaggregation as well as three Automated-Statistical Downscaling approaches in downscaling daily precipitation from 1 degree (100km) to 1/8 degrees (12.5km) over the Continental United States. Furthermore, a framework using the NASA Earth Exchange (NEX) platform is discussed for downscaling more than 20 ESM models with multiple emission scenarios. |
| 关 键 词: | 气候变化; 统计建模; 系统模型; 空间分辨 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-03-13：chenxin01 |
| 最后编审: | 2023-05-15：chenxin01 |
| 阅读次数: | 117 |