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.

Bearing skidding is the primary factor restricting the development of aeroengines toward ultrahigh speed, low friction, and lightweight. Compared to typical bearing faults, analysis of bearing skidding presents greater challenges due to the weak signal properties, significant time-varying characteristics and coupling influence of multiple factors. It is crucial to fully utilize multisource signals to enhance skidding features and capture time-varying characteristics. This article proposes a prior knowledge-embedded dual feedback spatial-temporal graph convolutional network (DFSTGCN) for skidding assessment. Unlike existing adjacency matrix construction strategies, the correlation between multisource signals is described based on multiple prior knowledge, which includes dynamic model, structural dynamics, and expert experience. Furthermore, a DFSTGCN is designed to simultaneously focus on the spatial and temporal dependencies of time-varying skidding data. Specifically, a dual feedback mechanism that includes prediction error ratio and uncertainty loss function is employed to improve the generalization performance of skidding prediction model. The effectiveness of the proposed strategy is validated under different working conditions.

Currently, the extraction of the size characteristics of a tropical cyclone (TC) from satellite data is mainly based on subjective human experience and archived satellite data. Few objective methods have been developed to estimate the size of a TC. To overcome this problem, this study introduces a new deep learning (DL) model, called a TC multitask estimation (TCMSE) model. The goal of using the model is to accurately extract the spatiotemporal characteristics of the size of a TC from infrared satellite images acquired during the current and previous three moments, combined with a multitask learning (MTL) approach and physically assisted task learning, to produce more reliable estimates of the wind radii of a TC. We used the TCMSE model to estimate the TC wind radius on the infrared satellite image of the Northwest Pacific Ocean to verify the performance of the model. The experimental results show that the proposed deep spatiotemporal feature extraction network, which combines MTL and physically assisted tasks, is superior to existing methods for estimating the wind radii of a TC. More specifically, the TCMSE model reduces the MAE error in estimating the TC wind radii by 6%, compared to the MTL approach, and 14%, compared to separately learning a single wind radii task.

Current deep learning methods for predicting tropical cyclone intensity are restricted by inadequate utilization of climate factors, insufficient feature fusion, and the inability to automatically adapt input data to changes in the scale of cyclone structures. To address these challenges, this paper proposes a multimodal data fusion model guided by sequential infrared satellite images (IRGMF). The IRGMF model integrates a broader range of climate factors for a more comprehensive, enriched feature representation of tropical cyclone intensity. Additionally, we designed a temporal attention module that allows the model to focus on critical moments in the sequential data for a more precise capture of the dynamic features of climate factors as they evolve. To enhance prediction accuracy, we also developed an attention-cropping module that adaptively crops irrelevant information outside the scale of a tropical cyclone from reanalysis data. To validate the effectiveness of the proposed model, we conducted experiments on multiple datasets, including CMA-BST, ERA5, and GridSat-B1. Experimental results show that, compared to existing methods, the proposed IRGMF model demonstrates significant advantages in predicting tropical cyclone intensity and provides superior accurate information for meteorological forecasting and decision support.

The multifactor coupling influence on the skidding behavior of aeroengine rotors presents significant challenges in locating the skidding causes and developing effective skidding suppression measures. However, ongoing research into fault mechanism and knowledge graph (KG) facilitates the accurate tracing of complex faults. We propose a heterogeneous KG inference-assisted skidding tracing and regulation strategy for aeroengine rotor. First, a skidding heterogeneous KG is constructed based on the text and data knowledge, in which the skidding level classification rules are determined for the first time. Second, we design an adaptive distributed metalearning algorithm to extract data features by combining the structural characteristics of the skidding KG. Third, few-shot knowledge inference is performed using the relation-metalearning graph convolutional network. Finally, we develop skidding suppression measures by tracing the input knowledge under unknown working states, enabling effective regulation of skidding behavior.

The demand for bearing skidding diagnosis is widely present in aeroengines operating at high-speed and light-load conditions. However, the weak and time-varying characteristics of skidding signal raise challenges for accurate diagnosis. To address these issues, we propose a synergistic TransGCN strategy to extract rich feature information from time-varying weak bearing skidding signals. Unlike existing methods, the prior knowledge obtained from bearing skidding analysis and the alternate integration and synergistic optimization of various advantages are used to enhance algorithm performance. First, an adaptive chirplet transform is designed to measure the time-varying cage slip rate. Second, the skidding sensitive characteristics are determined, and the variation ranges of slip rate sensitivity are employed as prior knowledge to calculate the fusion weights of multisource information. Then, an unsupervised deep feature representation network is constructed to analyze the complex correlation of bearing skidding signals. Finally, a synergistic TransGCN is developed by alternately integrating and synergistic optimizing Bayesformer and graph convolutional network. The superiority of the proposed strategy has been verified.

Precipitation nowcasting is gaining increasing attention in the signal processing community. Existing deep learning-based studies focus on designing an effective model architecture, neglecting the influence of the severe imbalanced distribution of rainfall data that can compromise the predictive accuracy on heavy rainfall intensities. To address the uneven distribution of precipitation nowcasting data, we propose a novel data reweighting strategy, termed Hybrid Weighting, which hybrids reweighting and non-weighting strategies together, boosting the precipitation nowcasting performance. Experimental results on two natural radar echo benchmark datasets demonstrate the superior performance of our proposed approach for precipitation nowcasting over existing loss functions on high rainfall intensities, without degenerating on low rainfall intensities compared with state-of-art reweighting methods.

In this paper, an intelligent fractional-order integral sliding mode control (FOISMC) strategy based on an improved cascade observer is proposed. First, an FOISMC strategy is designed to control a permanent magnet synchronous motor. It has good tracking performance, is strongly robust, and can effectively reduce chattering. The proposed FOISMC strategy associates strong points of the integral action (which can eliminate steady-state tracking errors) and the fractional calculus (which is flexible). Second, an improved cascade observer is proposed to detect the rotor information with a smaller observation error. The proposed observer combines an adaptive sliding mode observer and an extended high-gain observer. In addition, an improved variable-speed grey wolf optimization algorithm is designed to enhance controller parameters. The effectiveness of the strategy is tested using simulations and an experiment involving model uncertainty and external disturbance.

By utilizing physical models of the atmosphere collected from the current weather conditions, the numerical weather prediction model developed by the European Centre for Medium-range Weather Forecasts (ECMWF) can provide the indicators of severe weather such as heavy precipitation for an early-warning system. However, the performance of precipitation forecasts from ECMWF often suffers from considerable prediction biases due to the high complexity and uncertainty for the formation of precipitation. The bias correcting on precipitation (BCoP) was thus utilized for correcting these biases via forecasting variables, including the historical observations and variables of precipitation, and these variables, as predictors, from ECMWF are highly relevant to precipitation. The existing BCoP methods, such as model output statistics and ordinal boosting autoencoder, do not take advantage of both spatiotemporal (ST) dependencies of precipitation and scales of related predictors that can change with different precipitation. We propose an end-to-end deep-learning BCoP model, called the ST scale adaptive selection (SSAS) model, to automatically select the ST scales of the predictors via ST Scale-Selection Modules (S3M/TS2M) for acquiring the optimal high-level ST representations. Qualitative and quantitative experiments carried out on two benchmark datasets indicate that SSAS can achieve state-of-the-art performance, compared with 11 published BCoP methods, especially on heavy precipitation.

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.