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STAMLSGRU: a spatiotemporal radar echo extrapolation algorithm with edge computing for shortterm forecasting
Journal of Cloud Computing volume 13, Article number: 100 (2024)
Abstract
With the advent of Mobile Edge Computing (MEC), shifting data processing from cloud centers to the network edge presents an advanced computational paradigm for addressing latencysensitive applications. Specifically, in radar systems, the realtime processing and prediction of radar echo data pose significant challenges in dynamic and resourceconstrained environments. MEC, by processing data near its source, not only significantly reduces communication latency and enhances bandwidth utilization but also diminishes the necessity of transmitting large volumes of data to the cloud, which is crucial for improving the timeliness and efficiency of radar data processing. To meet this demand, this paper proposes a model that integrates a spatiotemporal Attention Module (STAM) with a Long ShortTerm Memory Gated Recurrent Unit (STConvLSGRU) to enhance the accuracy of radar echo prediction while leveraging the advantages of MEC. STAM, by extending the spatiotemporal receptive field of the prediction units, effectively captures key interframe motion information, while optimizations to the convolutional structure and loss function further boost the model’s predictive performance. Experimental results demonstrate that our approach significantly improves the accuracy of shortterm weather forecasting in a mobile edge computing environment, showcasing an efficient and practical solution for processing radar echo data under dynamic, resourcelimited conditions.
Introduction
The precision and timeliness of weather forecasting are crucial for addressing extreme weather events, agricultural production, aviation safety, and many other domains. Radar echo extrapolation, as a vital weather prediction technique, provides essential information on shortterm weather changes. However, the effectiveness of this method greatly depends on the accurate capture and analysis of spatiotemporal features in radar data [1, 2].
Traditional radar extrapolation methods primarily rely on linear or simple mathematical models to predict weather patterns, which often perform poorly in handling complex weather systems [3]. With the advancement of MEC and Artificial Intelligence (AI) technologies, new solutions have emerged for radar echo extrapolation. The lowlatency characteristic of MEC allows for rapid processing of data near its point of origin, while AI, particularly deep learning technologies, demonstrate immense potential in analyzing largescale, complex datasets [4, 5].
Firstly, MEC plays a pivotal role in processing radar data. Traditionally, radar data required transmission to remote servers for processing and analysis, which was not only timeconsuming but could also lead to data delays [6]. MEC significantly reduces data transmission time by providing computational resources near the data source, thus accelerating data processing [7]. This type of nearsource processing is particularly wellsuited for weather forecasting, as it necessitates rapid response and realtime analysis [8]. Secondly, AI technologies, especially machine learning and deep learning, have proven highly effective in interpreting radar data and enhancing forecast accuracy. Deep learning models can learn from historical weather data and predict future changes in weather patterns [9]. These models are particularly adept at handling large volumes of radar data and extracting meaningful insights, aiding meteorologists in making more accurate predictions [10].
In recent years, there has been growing interest in developing algorithms to infer radar echoes beyond the instrument range and to forecast the evolution of echoes over time [11,12,13,14]. Currently, weather forecasting methods can be broadly categorized into two main approaches: numerical weather prediction (NWP) methods and radar echo extrapolation. NWP methods utilize fluid dynamics and thermodynamic laws to simulate the physical processes of the lower atmosphere, providing predictions based on complex physical state equations and supercomputers [15]. While NWP methods offer valuable insights, they face challenges such as prediction delays, low resolution, and limitations in forecasting sudden severe weather events [16, 17]. On the other hand, radar echo extrapolation methods, such as artificial neural networks [18], support vector machines [19], and decision trees [20], leverage radar data to understand the relationship between radar echoes and other variables, enabling the prediction of future weather conditions. These datadriven methods have gained attention in recent years due to the availability of large amounts of historical data and have shown superior performance in various fields [21, 22].
The purpose of radar echo extrapolation is to predict future radar echo maps for a specific area based on previously observed radar echoes. This prediction task poses significant challenges as it requires spatiotemporal modeling of radar data to accommodate high resolution, thereby rendering it a spatiotemporal forecasting problem. Convolutional Neural Networks and Recurrent Neural Networks have been extensively utilized in such spatiotemporal forecasting tasks [23, 24]. However, existing models still confront challenges in handling high spatiotemporal resolution and complex nonstationary information, particularly in the context of convection formation and dissipation. Moreover, theoretical models for radar echo extrapolation are capable of generating prediction sequences of any length. Yet, as the prediction length increases, error accumulation can lead to image blurriness and loss of details.
In this context, the integration of MEC and AI offers a new perspective for radar echo extrapolation. The core advantage of MEC lies in its lowlatency characteristics, enabling rapid processing near the data generation point, which is particularly crucial for realtime radar data analysis. This capability for rapid response, coupled with advanced abilities in handling and analyzing largescale complex datasets, provides robust support for enhancing the efficiency and accuracy of radar data processing. Therefore, this paper proposes a STAMLSGRU network to address key challenges in radar echo extrapolation, including error accumulation and effective extraction of highorder nonstationary information. The main contributions of this paper can be summarized as follows:

By designing STAM, this model achieves longterm prediction in MEC environments and effectively captures global spatiotemporal dependencies, significantly reducing error accumulation during the prediction process.

A predictive RNN unit is devised, integrating the Inception network structure, which effectively captures highorder nonstationary information by employing multiscale layers and receptive fields, thereby enhancing the model’s prediction accuracy.

By incorporating Critical Success Index (CSI) and Heidke Skill Score (HSS) evaluation metrics, improvements are made to the loss function, reducing the ambiguity and distortion of the prediction results, and enhancing predictive performance in heavy rainfall regions.
The remainder of this article is organized as follows: Related Work, Methodology, Experiments, and Conclusion. The Related Work section provides an overview of previous studies, highlighting existing methods and findings in the field. The Methodology section details the theoretical frameworks and techniques used in the research, outlining the design of the proposed model. The Experiments section introduces the experimental setup, dataset description, and obtained results, offering empirical validation and comparison. Finally, the Conclusion section summarizes the contributions and proposes directions for future research.
Related work
The MEC with AI technologies demonstrates significant potential in fields such as radar echo extrapolation and weather forecasting. As an emerging computational paradigm, MEC shifts computational tasks from the cloud to the network edge, achieving lowlatency and highefficiency data processing. For instance, zhou [25] noted that ‘Edge Intelligence’ is a product of the convergence of MEC and AI. This concept aims to provide superior solutions for key issues in edge computing. It also explores how to establish AI models on edge devices, including model training and inference processes. This approach exemplifies the innovative strides being made in combining AI with edge computing, to optimize computational efficiency and enhance the capabilities of edge devices in processing complex tasks.
MEC technology enhances data processing efficiency by providing computational resources at the network edge, thereby significantly reducing latency and bringing processing closer to the data source. The integration of AI technologies further elevates MEC’s data processing capabilities. AI algorithms, particularly deep learning models, have demonstrated exceptional performance in areas like image recognition, pattern detection, and predictive analytics. AlHabob and Dobre [26] explored the symbiotic relationship between MEC and AI, highlighting AI’s critical role in the MEC offloading process, such as resource management and scheduling. Huang [27] proposed an infrastructure for executing machine learning tasks on MEC servers, assisted by Reconfigurable Intelligent Surfaces. Deng [28] discussed the role of AI with software orchestration and hardware acceleration in reducing edge computing latency. Yazid [29] provided a comprehensive review of Unmanned Aerial Vehicles (UAVs) in the application of MEC and AI, exploring their role in enhancing the efficiency of IoT applications. Dahmane [30] introduced a blockchainbased AI paradigm for secure implementation of UAVs in MEC. Wang [31] surveyed the convergence of MEC, Metaverse, 6G wireless communications, AI, and blockchain, and their impact on modern applications. Chakraborty and Sukapuram [32] examined the application of MEC in urban informatics, emphasizing its contribution to the development of smart cities.
Recently, deep learningbased radar echo extrapolation models have been proposed that are more accurate than traditional methods. In 2015, Shi [21] introduced the Convolutional LongShort Term Memory (ConvLSTM) model for precipitation nowcasting. This model is designed to handle time series data with spatial structures and uses convolutional operations instead of the Hadamard product in the FCLSTM [33]. In 2016, Shi [34] improved the ConvGRU model and proposed the TrajGRU model, which can dynamically learn the network recursive structure. Wang [35, 36] proposed PredRNN and PredRNN++ models based on ConvLSTM. The LSTM unit was rebuilt by the team, and the Spatiotemporal LSTM (STLSTM) unit was created, allowing memory state to propagate in both vertical and horizontal directions and no longer being restricted to each individual LSTM unit. They developed the Gradient Highway Units (GHU), which were added between the first and second layers of the model at each time step, and the Causal LSTM unit in subsequent research. This greatly shortened the gradient propagation path and solved the issue of information loss in longterm predictions. Due to the relatively simple state transition function and the ineffective differential signal processing in the majority of RNNs used for spatiotemporal prediction, it is challenging for the model to learn complicated spatiotemporal changes. They proposed the MIM structure [37], which is used to extract stationary features and nonstationary features (MIMS layer and MIMN layer, respectively). The model achieved better performance on radar datasets. Lin [38] proposed the SaConvLSTM, which added a selfattention mechanism at the output end of ConvLSTM. By using an additional memory unit M and a selfattentive feature aggregation mechanism, it computed pairwise similarity scores to fuse the previous features that contain the global spatial receptive field. Wu [39] has made further advances in the utilization of spatiotemporal information by proposing the MotionRNN architecture and designing the MotionGRU unit. This unit can model transient changes and motion trends in a unified manner, and a new motion highway has been introduced, which significantly enhances the ability to predict variable motion and avoids the issue of vanishing motion when stacking multiple prediction models. Chang [40] proposed a spatialtemporal residual prediction model applicable for highresolution video prediction. The model employs a spatialtemporal encodingdecoding scheme to capture complex motion information in highresolution videos. Jin [41] proposed a novel spatiotemporal graph neural network model called BGGRU, which integrates spatial and temporal information to explore the temporal patterns and spatial propagation effects of time series, aiming to enhance prediction accuracy. However, in the above methods, the global spatiotemporal dependencies of radar echoes have not been fully explored. This paper analyzes existing spatiotemporal prediction models and proposes an STAM module that addresses the issue of error accumulation. Additionally, the convolutional structure and loss function of the basic unit are improved, resulting in more accurate predictions of highecho regions at different scales.
Methodology
The task of radar echo extrapolation aims to learn the mapping from input sequences to a latent space. To achieve this objective, we constructed a convolutional recurrent neural network, STAMLSGRU. As shown in Fig. 1, its recurrent connections bestow it with memory functionality, enabling the capture and storage of previously inputted information. This memory capability allows the network to consider the information of the entire sequence comprehensively, rather than being limited to the inputs at the current timestep. Within STAMLSGRU, this memory mechanism is crucial, allowing the network to effectively learn patterns and regularities within sequence data, thereby achieving the mapping from input sequences to latent space. By stacking three STConvLSGRUs and one STAMLSGRU, an encoderdecoder network is formed, which is currently the mainstream method for spatiotemporal sequence prediction. The STConvLSGRU integrates the temporal information flow processing capabilities of the conventional GRU with a newly added spatial memory flow propagation mechanism, achieving simultaneous capture of temporal and spatial dependencies. The STAMLSGRU predicts the next time step’s radar echo image, not solely relying on the output of the previous time step, allowing for better handling of long sequence inputs and outputs. At a single time step, the vertical arrows represent the direction of memory and state updates along the spatial dimension, while the horizontal arrows represent the direction of updates along the time dimension. The spatiotemporal memory M is transferred from the lowest layer of the recurrent unit to the highest layer within a single time step, and is then transferred to the lowest layer of the following time step in a “Z”shaped direction, first along the spatial dimension and then along the time dimension. The input radar data is downsampled by a factor of four and goes through three layers of STConvLSGRU for information extraction and transformation before being input into the STAMLSGRU. This enables the model to focus on past input states, avoiding error accumulation. The output is then upsampled to obtain the final prediction results. In order to improve the prediction of high echo areas, this paper proposes an enhanced loss function. In addition, the gating mechanisms has been optimized with an inception module for all the basic units of RNNs. Experimental results demonstrate that employing the STAMLSGRU network leads to a significant improvement in prediction accuracy, thereby enabling more precise forecasting of future echo image sequences at different time points.
STConvLSGRU
Inspired by STLSTM [35], this paper introduces the concept of LSTM memory units into the ConvGRU model, resulting in the STConvLSGRU model depicted in Fig. 2 This model serves as the foundation for subsequent improvements. The original STLSTM model utilized a dual LSTM structure to process images with high spatiotemporal resolution effectively, storing and transmitting spatiotemporal information within and outside the memory cells. However, this structure tends to increase the model’s complexity and the number of parameters, leading to overfitting issues. To address this challenge, this work integrates LSTM memory units into ConvGRU, aiming to reduce the model’s parameter count while maintaining effective spatiotemporal information processing. This enhanced STConvLSGRU model, compared to STLSTM, not only reduces the model’s complexity but also enhances its capability to handle spatiotemporal information, avoiding overfitting and thus enabling more accurate predictions. As shown in Fig. 2, \(Z_{t}\), \(R_{t}\) and \(X_{t}\) are update gates, reset gates and input states, respectively. \(\tilde{h}_{t}\), i and f are new information, input gates, and forget gates, respectively. g is used as a temporary variable to update M. t denotes the \(t_{t h}\) time step and l denotes that the loop cell is located at the \(l_{t h}\) level of the stacked structure.
For a single STConvLSGRU unit at time t, if the unit is located in the first layer (i.e., when l=1), the input state \(X_{t}^{l}\) is a tensor converted from the radar echo map input at the current time. If the unit is not in the first layer (i.e., \(l>\)1), the hidden state \(H_{t}^{l1}\) output at time t is used as the input state \(X_{t}^{l}\) for the unit. The STConvLSGRU unit first passes the input state \(X_{t}^{l}\) and the hidden state \(H_{t1}^{l}\) output by the unit in the same layer at time t1 through a gating structure. Two different convolution filters are applied to obtain the reset gate \(R_{t}\), the update gate \(Z_{t}\), and the new information vector \(\tilde{h}_{t}\). The calculation method for \(R_{t}\) and \(Z_{t}\) is consistent with that of ConvGRU, and is shown as follows, ‘*’ denotes the convolution operation:
Similar to LSTM, the input state \(X_{t}^{l}\) and spatiotemporal memory \(M_{t1}^{l}\) are fed into a gated structure. Three different convolution filters are applied to obtain the forget gate \(f_{t}\), input gate \(i_{t}\), and input modulation gate \(g_{t}\). The forget gate \(f_{t}\) and elementwise Hadamard product are used to forget unimportant features of past time steps in the temporal memory M. The input gate and input modulation gate are used to update the features in memory through element multiplication, resulting in the updated spatiotemporal memory \(M_{t}^{l}\). The ‘\(\circ\)’ represents the Hadamard product. This process can be represented as follows:
Next, the input state \(X_{t}^{l}\), hidden state \(H_{t}^{l1}\), and updated spatiotemporal memory \(M_{t}^{l}\) are convolved using convolution filters to obtain new information \(\tilde{h}_{t}\). The hidden state \(H_{t}^{l}\) is updated using the reset gate \(R_{t}\) and update gate \(Z_{t}\), which extract abundant and rich spatiotemporal features through two gating mechanisms. As a result, the extrapolation network is able to accurately model the motion of radar echoes and make precise predictions on whether they will continue to expand or dissipate in the future. This process can be represented as follows:
Spatiotemporal attention memory module
The extrapolation of radar echoes can also be classified as a regression problem. The extrapolation model theoretically has the ability to generate prediction sequences of arbitrary lengths. However, as the prediction length increases, there is a strong interdependence between adjacent frames, resulting in cumulative errors, which leads to blurred and distorted extrapolated images with missing details. To address this issue and enable the model to review the historical input sequence at each predicted time step, a STAM is constructed to utilize the input \(H_{t}^{l}\) of the \(l_{t h}\) layer, to recall the historical input \(X_{h}^{l}\). The model can adaptively learn the mapping from \(X_{\textrm{0}:n}\) to \(X_{\mathrm {n+1}:T}\) based on a rich history of data.
The specific implementation is illustrated in Fig. 3, and its design is inspired by the dualattention mechanism, which is named as STAM. STAM receives three inputs, including the predicted results of past time series \(H_{t\tau :t1}^{l}\), the multilayer results \(H_{t}^{l\tau :l1}\) of the current time series, and the lowlevel results \(H_{t}^{l}\) of the current time series, where \(\tau\) represents the step size. STAM consists of two modules, the attention module and the fusion module. The convolutional layer passes the current hidden state \(H_{t}^{l} \in R^{C \times H \times W}\), and generates the query \(Q \in R^{C \times H \times W}\) through a 1x1 convolution operation, where C, H, and W represent the number of channels, the length and width of the input data, respectively. Similarly, the key value \(K_{t} \in R^{\tau \times C \times H \times W}\) and value \(V_{t} \in R^{\tau \times C \times H \times W}\) can be obtained from the predicted results of the past time series \(H_{t\tau :t1}^{l} \in R^{\tau \times C \times H \times W}\) through two independent convolutions. The weight matrix \(A_{t}\) is obtained by multiplying Q and \(K_{t}\) , and then applying sum and softmax operations:
Subsequently, the new temporal state \(T_{t}^{l}\) can be calculated according to the formula of temporal attention:
Finally, the reshaped \(T_{t}^{l}\) is resized to the same size as the original hidden state, and is used as the input of the fusion module.
The above approach enables adaptive learning of the historical input \(X_{t}^{l}\) in the temporal dimension. In order to address the problem of information loss during the propagation process from the lowlevel to highlevel layers, the output of each convolutional neural network layer is kept in the multilayer state. Then, the toplevel hidden state \(H_{t}^{l}\) is used to recall \(H_{t}^{l\tau :l1}\) and generate a new spatial hidden state \(S_{t}^{l}\) as the input of the fusion module.
The computation process is akin to temporal attention, whereby the current hidden state \(H_{t}^{l} \in R^{C \times H \times W}\) can be transformed into a query \(Q \in R^{C \times H \times W}\) by reshaping the convolutional layer. Subsequently, the keys \(K_{s} \in R^{\tau \times C \times H \times W}\) and values \(V_{s} \in R^{\tau \times C \times H \times W}\) can be generated via two independent 1x1 convolutions using \(H_{t}^{0: l1} \in R^{\tau \times C \times H \times W}\). The weight matrix \(A_{s}\) is obtained by multiplying Q and \(K_{s}\), followed by summation and softmax operations.
Subsequently, the new spatial state \(S_{t}^{l}\) can be calculated according to the formula for spatial attention:
Finally, the reshaped \(S_{t}^{l}\) is resized to the same size as the original hidden state and serves as the input to the fusion module.
The fusion module aggregates the temporal state \(T_{t}^{l}\) and the spatial state \(S_{t}^{l}\), and uses gating mechanisms to control the output of the current time sequence. First, \(T_{t}^{l}\) and \(S_{t}^{l}\) are concatenated along the channel dimension, and the number of channels is adjusted through convolutional operations to obtain the fusion features:
Subsequently, to effectively control the fusion of historical attention information and the current hidden state \(H_{t}^{l}\), two gating mechanisms are used:
The fusion features G are used to generate the input gate \(e_{i}\) and the forget gate \(e_{f}\) through convolution, and the output of the STAM, represented as \(\tilde{H}_{t}^{l}\), is given by:
In the STAMLSTM unit, the STAM will be embedded into the RNN unit shown in Fig. 4, forming the STAMLSGRU.
Convolutional Inception optimization
The radar echo image exhibits varying strengths and sizes of echoes in different regions, and the commonly used 5x5 convolution is insufficient for capturing the multiscale radar echoes and highorder nonstationary information. In this paper, we propose an improved RNN unit that integrates an Inception network structure with different gating mechanisms and replaces the original 5x5 convolution. The Inception network architecture exhibits strong capabilities in extracting image features across various dimensions and orientations, enhancing the model’s generalization ability and feature extraction performance [42]. Initially, it adopts a multiscale feature extraction strategy, where multiple convolutions of varying sizes operating in parallel within the Inception modules can capture features at different scales simultaneously. This design allows the network to process both local and global features within a single layer, leading to a more comprehensive understanding of image content. Furthermore, the branches within the Inception module utilize convolutional kernels of different sizes and types, working in parallel to perform convolutions in various directions. This parallel operation facilitates the network’s effective learning of multiple feature representations, encompassing both local details and global structures. Additionally, by employing multiple convolutions of different sizes in parallel, the Inception module significantly reduces the number of parameters in the network, thereby decreasing the risk of overfitting and enhancing the model’s generalization capability. Lastly, the Inception module aggregates information by concatenating features of different scales along the channel dimension. This capacity for information aggregation enables the network to better integrate abstract features at different levels, further improving its understanding of image content.
As shown in Fig. 5, the enhanced convolutional network structure comprises three branches, each undergoing 1x1 convolution, 3x3 convolution, and two consecutive 3x3 convolution operations, respectively. By concatenating two consecutive 3x3 convolutions, our structure not only achieves the same receptive field as a single 5x5 convolution but also significantly reduces the parameter count compared to the latter, offering a more efficient computational approach. Furthermore, the improved Inception convolutional structure effectively captures features at different scales by synthesizing convolution kernels of different sizes, thereby demonstrating superior performance in capturing image features compared to a singlesize 5x5 convolution. This design optimizes parameter usage, reduces computational burden, enhances the network’s ability to capture multiscale information in images, and improves the model’s expressive power in handling complex image tasks.
Loss function optimization
Currently, most of the existing deep learning radar echo algorithms use mean squared error (MSE) loss as a loss function, which is a common loss function used to evaluate the difference between the predicted value and the real value of the model, and is suitable for regression problems. The smaller the value, the smaller the difference between the predicted and true results. The calculation of the MSE is as follows:
In this equation, H and W are the length and width of the radar image, and MSE loss is the sum of the squared errors for each pixel in every extrapolated radar image \(\hat{y}\) and its corresponding true image y. However, since MSE is sensitive to outliers and penalizes large prediction errors, using MSE as the loss function on datasets with outliers can be influenced by those outliers. In practical images, noise or other interference may exist, which could greatly affect MSE and thereby impact the model’s predictive ability. MSE only considers the difference between the predicted and actual values of each pixel, without considering the correlation between pixels. In image prediction, there is usually some correlation between pixels, and ignoring this correlation may lead to a decrease in prediction performance. Therefore, to improve upon MSE, commonly used meteorological indicators such as the CSI and the HSS can be incorporated into the evaluation metrics. The improved loss function is as follows:
Considering that the CSI and the HSS may not be differentiable, they can be made differentiable by applying the sigmoid function, and incorporated into the final differentiable loss function.
Experiments
Dataset
This paper utilizes the data from the China Central Meteorological Observatory’s radar network in the eastern region of China from 2020 to 2022, with a spatial resolution of 0.01\(^{\circ }\) and a temporal resolution of 6 minutes. The radar data is sliced at the central point to achieve a spatial resolution of 400x400. dBZ represents the radar echo value, with larger dBZ values indicating a higher likelihood and intensity of severe convective weather. Atmospheric motion exhibits periodicity, particularly in the same region, with many similar samples in both realtime observations and model forecasts, leading to overfitting. Additionally, severe convective weather occurs only a few days out of the year, thus filtering out some relatively mediocre data is necessary. Ultimately, 10,000 sequential samples are selected, with 6,000 sequences serving as the training set, 2,000 sequences as the validation set, and 2,000 sequences as the test set. The radar echo values range from 0 to 70, and normalization is employed to distribute the values between 0 and 1, facilitating better model convergence.
Implementation
For all experiments, a Nvidia GeForce RTX 3090 GPU was used for training. The default hyperparameters and experimental setup were as follows: the model was trained using a batch size of four image sequences, the Adam optimizer with an initial learning rate of 0.001, and momentum decay set to 0.90. The fourlayer model was configured with 64 channels, and a total of 70,000 training steps were performed. At every 5000 training steps, evaluation metrics were recorded for both the training and validation sets. During training, the model was designed to predict the next 10 time sequences, with the attention mechanism of the STAM module focusing on a time step of 5. All experiments were conducted using the same set of hyperparameter values to ensure consistency and comparability. To enhance training performance and explore different strategies, various techniques were employed. These included the use of teacher forcing, which involved providing the correct sequence as input during training, and bidirectional training, where the model was trained in both forward and backward directions. To prevent overfitting, early stopping techniques were utilized. Specifically, training was terminated if the validation loss did not decrease for 10,000 consecutive steps, indicating that the model’s performance had plateaued. This ensured that the model was not trained excessively and retained its generalization ability.
Evaluation indicators
This paper evaluates the effectiveness of various models based on MSE, CSI, HSS, and Structural Similarity (SSIM). MSE measures the average difference between model predictions and true values, reflecting the model’s precision in predicting future radar echoes. CSI assesses the model’s detection capability for precipitation events, taking into account false alarms and missed detections. HSS compares the correctness of the model’s predictions to random forecasting, used to measure the predictive capability of the model. The SSIM is used to evaluate the degree of similarity between the modelpredicted radar echo images and the actual radar echo images, considering aspects such as luminance, contrast, and structure. Considering the correlation between radar echo values and actual weather, three thresholds of 20dBZ, 35dBZ, and 45dBZ were chosen to evaluate the efficacy of the algorithm for radar extrapolation. Under these thresholds, radar echo images were binarized by designating a value of 1 if the echo value exceeded the threshold and a value of 0 otherwise. TP represents a predicted event that actually occurred, FP represents a predicted event that did not occur, FN represents an unpredicted event that did occur, and TN represents a predicted event that did not occur. Following are the formulas used to calculate the evaluation metrics in this paper:
where \(\mu _{x}\) and \(\mu _{y}\) are the means of x and y, respectively. \(\sigma _{x}^{2}\) and \(\sigma _{y}^{2}\) denote the variances. \(\sigma _{xy}\) is the covariance of x and y. c1 and c2 are constants.
Results and analysis
Currently, radar echo extrapolation models are primarily based on stacking multiple layers of basic Convolutional Recurrent Units. There is no fixed standard for the number of layers in the stacked radar echo extrapolation model, as it is contingent on variables such as data volume, data complexity, network structure, and hardware. Increasing the number of layers can improve the network’s expressive power and the model’s capacity to represent and abstract data, but it may also increase the network’s computational and storage burden, resulting in overfitting and other issues. The number of layers is typically determined by the extent and complexity of the dataset as well as the training effect of the network. For smaller datasets and relatively straightforward problems, a shallower network structure may be optimal, whereas a deeper network structure may be preferable for larger datasets and more complex problems. Using experimental methods, this paper determines the optimal number of stacked layers to avoid overfitting and underfitting issues. Table 1 presents the MSE scores of various models at different numbers of layers. It is observed that as the number of layers increases, the MSE value of each model typically decreases to reach a minimum value before starting to rise again. For most models, the MSE value reaches its minimum when the number of layers is four, indicating that the models perform best at this depth. As the depth of the model increases, it is able to learn more complex features and deeper data representations. There comes a point where this learning capability is optimized, and further increases in the number of layers make the model more complex, increasing the number of parameters. This complexity can lead to gradients gradually vanishing or exploding during the backpropagation process, making it difficult to train the model. The STAMLSGRU model achieves its best performance at four layers; when the number of layers is less than or greater than four, the model’s performance metrics decrease. Therefore, this paper sets the number of layers of the STAMLSGRU model to four.
Through ablation experiments and comparative experiments, we validated the best performance of radar echo extrapolation at different thresholds. The results are shown in Tables 2 and 3, where CSI and HSS scores were calculated at the thresholds of 20 dBZ, 35 dBZ, and 45 dBZ, while MSE and SSIM were averaged across all thresholds. The symbol \(\uparrow\) indicates that higher values indicate better performance in radar echo extrapolation, while \(\downarrow\) indicates the opposite.
The ablation experiment results are presented in Table 2, where evaluation metrics of the original STConvLSGRU model and various improvement methods are compared. Across all metrics and thresholds, STConvLSGRU1, STAMLSGRU0, and STAMLSGRU1 outperform STConvLSGRU. Specifically, STAMLSGRU* demonstrates improvements of 6.87%, 6.45%, 5.8%, and 7.7% in CSI, HSS, SSIM, and MSE, respectively, compared to the STConvLSGRU network. Figure 6 illustrates the radar echo extrapolation results in order to visually compare the experimental outcomes of various enhancement techniques. Indicating that these modules can enhance the performance of intensity prediction, the prediction results of STAMLSGRU* are clearer, have more distinct edges, and pay closer attention to high echo regions.
As shown in Table 3, different evaluation metrics have been improved. Compared to the stateoftheart MotionRNN model, the CSI score has increased by an average of 1.6%, the HSS score has increased by 1.1%, the SSIM score has increased by 2.7%, and the mean squared error has increased by 3.2%, under different thresholds. Figure 7 displays the hourly scores of each model in the prediction of the next hour. The models exhibit similar performances, but as time progresses, the scores of other models sharply decline, whereas STAMLSGRU shows a more gradual decrease in scores.
For a more intuitive observation, a visual example is also shown in Fig. 8. From Fig. 8, it can be observed that the STAMLSGRU model designed in this research outperformed the other five models. The ConvLSTM model produced smoother results than other methods and suffered from severe detail loss and prediction errors in the high reflectivity regions of radar images. TrajGRU and PredRNN had poor performance in predicting the central echo region and also suffered from distortion to some extent. Since radar image changes are a highorder nonstationary process, these methods were able to effectively predict the radar motion trend. MIM and STAMLSGRU better captured the overall changing trend characteristics of the echo region, but STAMLSGRU captured the high and low echo characteristics better than MIM. The prediction results of MotionRNN and STAMLSGRU were similar, but the predicted image blocks of STAMLSGRU were closer to the actual observation results. From the edge of the echo main body, its predicted image edges were more in line with the actual ones, and its blurring degree was lower, showing greater consistency with the real image.
Conclusion
This research introduces a neural networkbased radar echo extrapolation algorithm named STAMLSGRU. By deploying the STAMLSGRU model in an edge computing environment, we not only achieve enhanced realtime data processing capabilities but also significantly reduce data transmission delays. Compared with traditional radar echo extrapolation algorithms and other deep learningbased algorithms, STAMLSGRU exhibits markedly improved predictive performance in complex environments, particularly in heavy rain areas. This paper designs STAM to capture reliable interframe motion information by expanding the temporal and spatial receptive fields of the prediction units. The convolutional structure and loss function of the basic unit have been improved to enhance the robustness of model predictions. Compared to the MotionRNN model, the CSI score has increased by an average of 1.6%, the HSS score by 1.1%, and the SSIM score by 2.7%. In the future, we plan to further advance meteorological forecasting by integrating more observational data and model outputs, aiming to improve the accuracy and timeliness of weather predictions. With the continuous advancements in MEC and AI technologies, along with the increasing abundance of meteorological observation data, we anticipate that the STAMLSGRU model will demonstrate higher predictive capabilities in a wider range of meteorological scenarios, bringing new breakthroughs to the field of weather forecasting.
Availability of data and materials
No datasets were generated or analysed during the current study.
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Hailang Cheng conducted thematic research, collected and organized data, and wrote the paper. Mengmeng Cui provided innovative points, and developed a detailed outline and structure. Yuzhe Shi undertook comprehensive editing and proofreading of the draft paper, including linguistic refinement, logical verification, and formatting. Mengmeng Cui is the corresponding author of this paper.
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Cheng, H., Cui, M. & Shi, Y. STAMLSGRU: a spatiotemporal radar echo extrapolation algorithm with edge computing for shortterm forecasting. J Cloud Comp 13, 100 (2024). https://doi.org/10.1186/s13677024006606
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DOI: https://doi.org/10.1186/s13677024006606