Radar echo extrapolation based on real-world data is a fundamental problem in meteorological forecasting. Existing extrapolation models typically assume complete radar echo sequences, but in practice, data loss frequently occurs due to equipment failures and communication disruptions. Although traditional solutions can handle missing values through a interpolation-then-prediction pipeline, they suffer from a major limitation: interpolating the missing data and then extrapolating will introduce a cumulative error. To address these issues, we propose MissPred, a radar echo extrapolation model specifically designed for missing data patterns. MissPred employs a dual encoder–decoder architecture. Specifically, the training process involves the sequential execution of interpolation and extrapolation as two distinct serial tasks. In order to circumvent the occurrence of cumulative errors, interpolation and extrapolation are required to share encoder parameters. Furthermore, a missing spatiotemporal feature fusion module (MSTF) that is absent has been designed for the purpose of extracting fine-grained complete spatiotemporal features. Finally, the incorporation of adversarial training is introduced to enhance the authenticity of the prediction results. In order to evaluate the proposed model, case studies are conducted on real radar datasets. Our dataset covers missing rates ranging from 10% to 50%. The experimental results show that the model outperforms the baseline model with the prior interpolation of missing data in the missing mode with stable robustness.

To address the challenges of scarce labeled samples, strong instantaneity, and variable morphology in lightning optical classification—issues that traditional methods struggle to handle efficiently and often require extensive manual intervention—we propose a frame difference triplet network (FD-TripletNet) tailored for few-shot lightning recognition. The lightning optical dataset used in this study was collected from two observation stations over six months, comprising 459 video samples that include lightning events with diverse morphologies (e.g., branched, spherical) and non-lightning events prone to misclassification (e.g., strong light interference, moving objects). Considering the critical feature of lightning—abrupt single-frame changes—we introduce adjacent frame difference matrices as model input to explicitly capture transient brightness variations, reducing noise from static backgrounds. To enhance discriminative ability in few-shot scenarios, the model leverages Triplet Loss to compact intra-class features and separate inter-class features, combined with a dynamic sample matching strategy to focus on challenging cases. The experimental results show that FD-TripletNet achieves a classification accuracy of 94.8% on the dataset, outperforming traditional methods and baseline deep learning models. It effectively reduces the False Negative Rate (FNR) to 3.2% and False Positive Rate (FPR) to 7.4%, successfully distinguishing between lightning and non-lightning events, thus providing an efficient solution for real-time lightning monitoring in meteorological applications.