Previous post-processing studies on rainfall forecasts using numerical
weather prediction (NWP) mainly focus on statistics-based aspects, while
learning-based aspects are rarely investigated. Although some manually-designed
models are proposed to raise accuracy, they are customized networks, which need
to be repeatedly tried and verified, at a huge cost in time and labor.
Therefore, a self-supervised neural architecture search (NAS) method without
significant manual efforts called AdaNAS is proposed in this study to perform
rainfall forecast post-processing and predict rainfall with high accuracy. In
addition, we design a rainfall-aware search space to significantly improve
forecasts for high-rainfall areas. Furthermore, we propose a rainfall-level
regularization function to eliminate the effect of noise data during the
training. Validation experiments have been performed under the cases of
\emph{None}, \emph{Light}, \emph{Moderate}, \emph{Heavy} and \emph{Violent} on
a large-scale precipitation benchmark named TIGGE. Finally, the average
mean-absolute error (MAE) and average root-mean-square error (RMSE) of the
proposed AdaNAS model are 0.98 and 2.04 mm/day, respectively. Additionally, the
proposed AdaNAS model is compared with other neural architecture search methods
and previous studies. Compared results reveal the satisfactory performance and
superiority of the proposed AdaNAS model in terms of precipitation amount
prediction and intensity classification. Concretely, the proposed AdaNAS model
outperformed previous best-performing manual methods with MAE and RMSE
improving by 80.5\% and 80.3\%, respectively.

提出了一种名为 AdaNAS 的自我监督神经架构搜索方法，用于进行降雨预报后处理并高精度预测降雨，通过设计降雨感知搜索空间和降雨级别正则化函数，显著提高了对高降雨区域的预报准确性，与其他神经架构搜索方法和以前的研究相比，提出的 AdaNAS 模型在降水量预测和强度分类方面表现出令人满意的性能和优越性。