## 2 Data Collection
### ConvGRU Algorithm Architecture  
The advancement in spatiotemporal forecasting methodologies, particularly through the utilization of ConvGRU-based models, necessitates the implementation of a sophisticated Encoder-Forecaster architecture with multi-input and multi-output layers. Drawing upon insights from prior studies [19; 20; 36; 37], and considering the specific input parameters of the network, this research has led to the development of an encoder-forecaster architecture. This approach is vital for the accurate prediction of ionospheric scintillation patterns. The proposed model in this research comprises two integral components: an encoder and a forecaster. The encoder segment of the model integrates a dual-layer framework, consisting of ConvGRU and convolution layers. This design enables the effective processing of image features, facilitating a transition from higher to lower dimensional representations. Simultaneously, the ConvGRU units in this part are responsible for sequential learning of the tensor features, culminating in the generation of an intermediate matrix. The forecaster segment of the model further advances this process. Here, the intermediate matrix is initially engaged with a deconvolution layer (unsampled layer), aimed at expanding the image dimensions. Subsequently, the ConvGRU layer undertakes the task of refining the image features. This dual-layer process ensures that the trained values revert precisely to their original dimensions in alignment with the input tensor. In the schematic presented as Figure 1, the architecture of our encoder-forecaster model is depicted. This structure incorporates both down-sampling and up-sampling layers, strategically positioned between ConvGRUs. These layers are realized through the application of convolution and deconvolution operations, characterized by their stride. A notable aspect of our design is the inversion of the conventional order within the forecasting network. This modification is premised on the hypothesis that high-level states, which encapsulate a global spatiotemporal representation, can effectively inform and refine the updates occurring in lower-level states. Consequently, these lower-level states are posited to exert a reciprocal influence on the overall predictive capacity of the model [37]. This configuration aims to leverage the hierarchical nature of state interactions to enhance predictive accuracy. The architectural design of the algorithm is composed of four distinct components: the input module, the encoder, the decoder, and the output module. The input and output maps, specifically focusing on the S4, are identically sized at 70 units in height and 50 units in width. The input comprises a sequence of 12 successive S4 maps, which are processed through a series of three encoder blocks. The encoders employ progressively increasing filter counts of 16, 32, and 64 for each block, respectively. Within each of these blocks, a convolutional layer is paired with a ConvGRU layer, with the former facilitating down-sampling and the latter dedicated to the extraction of spatiotemporal features. This arrangement is mirrored in the forecaster section, which also features three blocks, each comprising a deconvolutional layer and a ConvGRU layer. The purpose of the forecaster is to reconstruct and project a subsequent S4 map based on the encoded data. The outputs of the ConvGRU layers in the encoder, which include hidden and cell states, serve as inputs for the corresponding layers in the forecaster. This model, as illustrated in Figure 2, presents a comprehensive network structure for effectively modeling and predicting the ionospheric scintillation parameter. This model specifically targets the prediction of ionospheric scintillation values within the South American region.