Weather radar echo extrapolation is one of the essential means for weather nowcasting. It has been considerably inspired over the last decade by deep learning. However, the internal similarity of the echo evolution process has little been exploited. To investigate this merit, a deep contrastive model with an encoder–projector structure is proposed in this letter, which projects the subsequences sampled from the same evolution process into the neighborhood of latent space by contrastive learning. Thus, the internal evolution similarity of the input echo sequence itself can be discovered and exploited for promoting prediction. To make the training smoother, we also adopt a cumulative sampling strategy that follows a simple-to-hard manner. Experimental results on two real-world radar datasets demonstrate the superiority of our model in comparison to state-of-the-art. The effectiveness of the sampling strategy and extrapolation ability on limited input is also analyzed and verified. Training code and pretrained models are available athttps://github.com/tolearnmuch/ESCL.

Radar echo extrapolation (REE) plays a crucial role in convective nowcasting. Existing deep learning (DL)-based methods for REE are predominantly based on the analysis of echo composite reflectivity (CR). However, CR product solely offers single-layered echo intensity information, thereby losing vertical details of convective systems such as echo top heights, resulting in lower accuracy in REE. To address these limitations, this article proposes a multiproduct fusion network (i.e., MPFNet) for REE. First, residual convolutional encoders (RCEs) are designed, which adopt the ResNet to reuse features and combine attention mechanisms to improve focus on convective features. In addition, to leverage the correlations and complementarities among multiproduct features, a multiproduct fusion module (MPFM) that adopts multihead attention for modeling the interrelations among multiproduct features and depthwise separable convolution (DSC) for feature fusion is proposed. Finally, a residual decoder (RD) is designed instead of a conventional deconvolution decoder to aggregate fused features for the restoration of predicted echo sequences. The proposed MPFNet is verified by convective nowcasting experiments, and the experimental results demonstrate that it can effectively utilize multiple radar products for guiding REE. It significantly outperforms the state-of-the-art (SOTA) methods, such as Earthformer and PreDiff. Compared to the previously best-performing Earthformer, MPFNet achieves an average improvement of 1.3% and 2.4% in critical success index (CSI) and Heidke skill score (HSS), respectively, in convective nowcasting experiments on the SWAN dataset, and an average improvement of 1.2% and 3.2% in CSI and HSS on the MeteoNet dataset.

Radar echo extrapolation is a common approach to weather nowcasting, which has become a significant support to detect potential disastrous weather a few hours ahead. The dynamics pattern inside an echo intensity sequence is beneficial for echo prediction. However, existing extrapolation methods have limited ability to consider entire time series echo context in an entire echo sequence from a given historical timestamp to a given future timestamp, leading to low long-term extrapolation accuracy. To solve this issue, we introduce spatiotemporal self-attention and propose a deep learning model named the nonlocal echo dynamics (NLED) network to capture the dependencies of the entire time domain. The NLED network has an encoder-decoder architecture for extrapolation. The encoder decomposes historical echoes into features of multiple spatial scales, which makes it better at learning echo dynamics from the global scale to the local scale. The decoder employs nonlocal blocks with sparse self-attention related to echo dynamics to learn correlations in the entire echo event, which is beneficial for predicting long-term echo distributions. Our model is evaluated on radar reflectivity datasets from Shanghai and Hong Kong. The experimental results indicate that the NLED model achieves a more accurate long-term forecast, and alleviates the forgetting of stronger echo dynamics, validating the effectiveness of entire time series modeling by the NLED network.

Weather radar echo extrapolation, which predicts future echoes based on historical observations, is one of the complicated spatial–temporal sequence prediction tasks and plays a prominent role in severe convection and precipitation nowcasting. However, existing extrapolation methods mainly focus on a defective echo-motion extrapolation paradigm based on finite observational dynamics, neglecting that the actual echo sequence has a more complicated evolution process that contains both nonlinear motions and the lifecycle from initiation to decay, resulting in poor prediction precision and limited application ability. To complement this paradigm, we propose to incorporate a novel long-term evolution regularity memory (LERM) module into the network, which can memorize long-term echo-evolution regularities during training and be recalled for guiding extrapolation. Moreover, to resolve the blurry prediction problem and improve forecast accuracy, we also adopt a coarse–fine hierarchical extrapolation strategy and compositive loss function. We separate the extrapolation task into coarse and fine two levels which can reduce the downsampling loss and retain echo fine details. Except for the average reconstruction loss, we additionally employ adversarial loss and perceptual similarity loss to further improve the visual quality. Experimental results from two real radar echo datasets demonstrate the effectiveness of our methodology and show that it can accurately extrapolate the echo evolution while ensuring the echo details are realistic enough, even for the long term. Our method can further be improved in the future by integrating multimodal radar variables or introducing certain domain prior knowledge of physical mechanisms. It can also be applied to other spatial–temporal sequence prediction tasks, such as the prediction of satellite cloud images and wind field figures.

Radar quantitative precipitation estimation (RQPE) is the most common measurement for area rainfall estimation with high spatial and temporal resolution. The radar reflectivity ($Z$) measured by the Doppler weather radar is strongly related to precipitation rate ($R$). However, conventional RQPE methods have limited capability of modeling the complex relationship between the radar echoes and the precipitation field. In this article, we propose a graph neural network (GNN)-based RQPE model named categorical node graph attention network (CNGAT) to model complex spatial–temporal features of precipitation field reflected by the radar echo field. CNGAT is derived from graph attention networks (GAT) utilizing attention mechanism to learn the importance of neighboring points to the central point, which is beneficial for learning varying local spatial patterns. Furthermore, CNGAT can handle multiple types of graph nodes by using different transform functions for different types of nodes, which makes it better at capturing diverse features of precipitation field indicated by strong and weak radar echo areas. The proposed model was trained and tested on radar network and rain gauge data distributed in East China during 2017 and 2018. The results of several experiments show that CNGAT greatly improves the estimation precision and detection rate than$Z$–$R$relation models and conventional data-driven RQPE methods, and alleviates under-estimation of higher precipitation rates, which validates that CNGAT can effectively represent complex spatial–temporal features of precipitation field.

Weather radar echo extrapolation has been one of the most important means for weather forecasting and precipitation nowcasting. However, the effective forecasting time of the most current extrapolation methods is usually short. In this paper, to meet the demand for long-term extrapolation in actual forecasting practice, we propose a hierarchical prediction recurrent neural network (HPRNN) for long-term radar echo extrapolation. HPRNN is composed of hierarchically stacked RNN modules and a refinement module, it employs both a hierarchical prediction strategy and a recurrent coarse-to-fine mechanism to alleviate the accumulation of prediction error with time and contribute to making long-term extrapolation. The extrapolation experiments conducted on the HKO-7 radar echo dataset demonstrate the effectiveness of our model.