Meteorological forecasting is a typical and fundamental problem in the remote sensing field. Although many brilliant forecasting methods have been developed, long-term (a few days ahead) meteorological prediction still relies on traditional Numerical Weather Prediction (NWP) that is not competent for the oncoming flood of meteorological data. To improve the forecasting ability faced with meteorological big data, this article adopts the Automated Machine Learning (AutoML) technique and proposes a deep learning framework to model the dynamics of multi-modal meteorological data along spatial and temporal dimensions. Spatially, a convolution based network is developed to extract the spatial context of multi-modal meteorological data. Considering the complex relationship between different modalities, the Neural Architecture Search (NAS) technique is introduced to automate the designing procedure of the fusion network in a purely data-driven manner. As for the temporal dimension, an encoder-decoder structure is built to exhaustively model the temporal dynamics of the embedding sequence. Specializing for the numerical sequence representation transformation, the multi-head attention module endows the proposed model with the ability to forecast future data. Generally speaking, the whole framework could be optimized with the standard back-propagation, yielding an end-to-end learning mechanism. To investigate its feasibility, the proposed model is evaluated with four typical meteorological modalities including temperature, relative humidity, and two components of wind, which are all restricted under the region whose latitude and longitude range from $0°$ to $55°$ N and $70°$ E to $140°$ E, respectively. Experiments on two datasets with different resolutions verify that deep learning is effective as an operational technique for the meteorological forecasting task.

气象预报是遥感领域一个典型的基础性问题。尽管已经开发了许多出色的预测方法，但长期（提前几天）气象预测仍然依赖于传统的数值天气预报（NWP），无法应对即将到来的气象数据洪流。为了提高面对气象大数据的预测能力，本文采用自动机器学习（AutoML）技术，提出了一种深度学习框架，对多模态气象数据在时空维度上的动态进行建模。在空间上，开发了一种基于卷积的网络来提取多模态气象数据的空间上下文。考虑到不同模式之间的复杂关系，引入了神经架构搜索（NAS）技术，以纯数据驱动的方式自动化融合网络的设计过程。至于时间维度，构建了一个编码器-解码器结构来详尽地建模嵌入序列的时间动态。多头注意力模块专门用于数字序列表示转换，赋予所提出的模型预测未来数据的能力。一般来说，整个框架可以通过标准的反向传播进行优化，从而产生端到端的学习机制。为了研究其可行性，所提出的模型用四种典型的气象模式进行了评估，包括温度、相对湿度和风的两个分量，$0°$到$55°$N 和$70°$E到$140°$E，分别。在具有不同分辨率的两个数据集上进行的实验验证了深度学习作为气象预报任务的操作技术是有效的。