Climate Downscaling: A Deep-Learning Based Super-resolution Model of
  Precipitation Data with Attention Block and Skip Connections
Chia-Hao Chiang Zheng-Han Huang Liwen Liu Hsin-Chien Liang Yi-Chi Wang Wan-Ling Tseng Chao Wang Che-Ta Chen Ko-Chih Wang
Human activities accelerate consumption of fossil fuels and produce
greenhouse gases, resulting in urgent issues today: global warming and the
climate change. These indirectly cause severe natural disasters, plenty of
lives suffering and huge losses of agricultural properties. To mitigate impacts
on our lands, scientists are developing renewable, reusable, and clean energies
and climatologists are trying to predict the extremes. Meanwhile, governments
are publicizing resource-saving policies for a more eco-friendly society and
arousing environment awareness. One of the most influencing factors is the
precipitation, bringing condensed water vapor onto lands. Water resources are
the most significant but basic needs in society, not only supporting our
livings, but also economics. In Taiwan, although the average annual
precipitation is up to 2,500 millimeter (mm), the water allocation for each
person is lower than the global average due to drastically geographical
elevation changes and uneven distribution through the year. Thus, it is crucial
to track and predict the rainfall to make the most use of it and to prevent the
floods. However, climate models have limited resolution and require intensive
computational power for local-scale use. Therefore, we proposed a deep
convolutional neural network with skip connections, attention blocks, and
auxiliary data concatenation, in order to downscale the low-resolution
precipitation data into high-resolution one. Eventually, we compare with other
climate downscaling methods and show better performance in metrics of Mean
Absolute Error (MAE), Root Mean Square Error (RMSE), Pearson Correlation,
structural similarity index (SSIM), and forecast indicators.