Improving Summer Precipitation Prediction in China Using Deep Learning
<p>Summer precipitation in China exhibits considerable spatial-temporal variation with direct social and economic impact. Yet seasonal prediction remains a long-standing challenge. The dynamical models even with a 1-month lead still shows limited forecast skill over China in summer. The present study focuses on applying deep learning to summer precipitation prediction in China. We train a convolutional neural network (CNN) on seasonal retrospective forecast from forecast centres in several European countries, and subsequently use transfer learning on reanalysis and observational data of 160 stations over China. <span>The Pearson&#8217;s correlation coefficient (PCC) and the root mean square error (RMSE) </span><span>are used to evaluate the performance of precipitation forecasts.</span> <span>The results demonstrate</span> that deep learning approach produces skillful forecast better than those of current state-of-the-art dynamical forecast systems and traditional statistical methods in downscaling, with <span>PCC increasing by 0.1&#8211;0.3, at 1&#8211;3 months leads</span>. <span>Moreover, experiments show that </span>the data-driven model is capable to learn the complex relationship of input atmospheric state variables from reanalysis data and precipitation from station observations, with PCC of about 0.69. Image-Occlusion technique are also performed to determine variables and&#160; spatial features of the general circulation in the Northern Hemisphere which contribute maximally to the spatial distribution of summer precipitation in China <span>through the automatic feature representation learning</span>, and help evaluate the weakness of dynamic models, in order to gain a better understanding of the factors that limit the capability to seasonal prediction. It suggests that deep learning is a powerful tool suitable for both seasonal prediction and for dynamical model assessment.</p>