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Total electron content (TEC) is a vital parameter for describing the state of the ionosphere, and precise prediction of TEC is of great significance for improving the accuracy of the Global Navigation Satellite System (GNSS). At present, most deep learning prediction models just consider TEC temporal variation, while ignoring the impact of spatial location. In this paper, we propose a TEC prediction model, ED-ConvLSTM, which combines convolutional neural networks with recurrent neural networks to simultaneously consider spatiotemporal features. Our ED-ConvLSTM model is built based on the encoder-decoder architecture, which includes two modules: encoder module and decoder module. Each module is composed of ConvLSTM cells. The encoder module is used to extract the spatiotemporal features from TEC maps, while the decoder module converts spatiotemporal features into predicted TEC maps. We compared the predictive performance of our model with two traditional time series models: LSTM, GRU, a spatiotemporal mode1 ConvGRU, and the TEC daily forecast product C1PG provided by CODE on a total of 135 grid points in East Asia (10°–45°N, 90°–130°E). The experimental results show that the prediction error indicators MAE, RMSE, MAPE, and prediction similarity index SSIM of our model are superior to those of the comparison models in high, normal, and low solar activity years. The paper also analyzed the predictive performance of each model monthly. The experimental results indicate that the predictive performance of each model is influenced by the monthly mean of TEC. The ED-ConvLSTM model proposed in this paper is the least affected and the most stable by the monthly mean of TEC. Additionally, the paper compared the predictive performance of each model during two magnetic storm periods when TEC changes sharply. The results indicate that our ED-ConvLSTM model is least affected during magnetic storms and its predictive performance is superior to those of the comparative models. This paper provides a more stable and high-performance TEC spatiotemporal prediction model. [ABSTRACT FROM AUTHOR]

Central Asia is one of the most important sources of mineral saline dust worldwide. A comprehensive understanding of Central Asian dust transport is essential for evaluating its impacts on human health, ecological safety, weather and climate. This study first puts forward an observation-based climatology of Central Asian dust transport flux by using the 3-D dust detection of Cloud-Aerosol LiDAR with Orthogonal Polarization (CALIOP). The seasonal difference of transport flux and downstream contribution are evaluated and compared with those of the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). Central Asian dust can be transported not only southward in summer under the effect of the South Asian summer monsoon, but also eastward in other seasons under the control of the westerly jet. Additionally, the transport of Central Asian dust across the Pamir Plateau to the Tibetan Plateau is also non-negligible, especially during spring (with a transport flux rate of 150 kg m−1 day−1). The annual CALIOP-based downstream contribution of Central Asian dust to South Asian (164.01 Tg) is 2.1 times that to East Asia (78.36 Tg). This can be attributed to the blocking effect of the higher terrain between Central and East Asia. Additionally, the downstream contributions to South and East Asia from MERRA-2 are only 0.36 and 0.84 times that of CALIOP, respectively. This difference implies the overestimation of the wet and dry depositions of the model, especially in the low latitude zone. The quantification of the Central Asian dust transport allows a better understanding of the Central Asian dust cycle, and supports the calibration/validation of aerosol-related modules of regional and global climate models. [ABSTRACT FROM AUTHOR]