Polarimetric weather radar measurements vary nonlinearly with changes in radar frequency and scanning elevation. In comparative observation experiments between Ku-band weather radar and CINRAD/SA radar, there were systematic errors in the polarimetric data of the two radars, the reason was the difference of frequency and elevation angles of them. Because the observation data of the ground-based Ku-band weather radar is small, it cannot cover most of the precipitation. So the T-matrix method was used to simulate the differential reflectivity factors of the Ku-band and S-band radars at different elevation angles, and the BP neural network was trained based on the simulation data to realize the conversion of the differential reflectivity factor from S-band to Ku-band to correct the systematic errors. Using the measured data, it was verified that the BP neural network can correct the systematic errors between the differential reflectivity factors of the two radars, improve the data consistency of them, and provide possibility for data fusion of S-band and Ku-band weather radars.

Recent advances in deep learning have significantly improved the quality of precipitation nowcasting. Current approaches are either based on deterministic or generative models. Deterministic models perceive nowcasting as a spatiotemporal prediction task, relying on distance functions like L2-norm loss for training. While improving meteorological evaluation metrics, they inevitably produce blurry predictions with no reference value. In contrast, generative models aim to capture realistic precipitation distributions and generate nowcasting products by sampling within these distributions. However, designing a generative model that produces realistic samples satisfying meteorological evaluation indexes in real-time remains challenging, given the triple dilemma of generative learning: achieving high sample quality, mode coverage, and fast sampling simultaneously. Recently, diffusion models exhibit impressive sample quality but suffer from time-consuming sampling, severely hindering their application in nowcasting. Moreover, samples generated by the U-Net denoiser of current denoising diffusion model are prone to yield poor meteorological evaluation metrics such as CSI. To this end, we propose a spatiotemporal Transformer-based conditional diffusion model with rapid diffusion strategy. Concretely, we incorporate an adversarial mapping-based rapid diffusion strategy to overcome the time-consuming sampling process for standard diffusion models, enabling timely nowcasting. Additionally, a meticulously designed spatiotemporal Transformer-based denoiser is incorporated into diffusion models, remedying the defects in U-Net denoisers by estimating diffusion scores and improving nowcasting skill scores. Case studies of typical weather events such as thunderstorms, as well as quantitative indicators, demonstrate the effectiveness of the proposed method in generating sharper and more precise precipitation forecasts while maintaining satisfied meteorological evaluation metrics.

Deep learning (DL)-based precipitation nowcasting algorithms have garnered significant attention in recent years. However, the presence of variable spatial scales in precipitation patterns poses challenges for methods that solely focus on capturing spatiotemporal correlations at a single scale. Moreover, current DL-based algorithms tend to model short-term (e.g., 10-min time span) rainfall locally neglecting long-term, global (e.g., 2-h time span) life-cycle evolution. Furthermore, widely used pixel-wise losses are prone to produce low effective-spatial-resolution predictions. To this end, we introduce a multiscale deformable transformer network to leverage echo contexts from image patches of varying spatial scales. Meanwhile, a multihead deformable self-attention mechanism is introduced for capturing precipitation spatiotemporal dynamics in a global manner. Moreover, to improve the spatial resolution of predictions, the Fourier space regularization and adversarial losses are proposed by narrowing the discrepancy of the Fourier spectra of predictions and references. Thanks to the introduced loss function, our model generates highly effective spatial-resolution predictions with abundant details. Extensive experiments on two real datasets show the substantial superiority of our method in terms of critical success index (CSI) compared to recent competitive approaches. At the same time, our predictions have more realistic precipitation details and significantly better fidelity. For example, on a vertically integrated liquid (VIL) product dataset, compared to baseline methods, our approach reduces the Fréchet inception distance (FID) value by a factor of$2\sim 4$while improves the CSI score by 3%~5% approximately.

Nowadays deep learning based weather radar echo extrapolation methods have competently improved nowcasting quality. Current pure convolutional or convolutional recurrent neural network based extrapolation pipelines inherently struggle in capturing both global and local spatiotemporal interactions simultaneously, thereby limiting nowcasting performances, e.g., they not only tend to underestimate heavy rainfalls’ spatial coverage and intensity but also fail to precisely predict non-linear motion patterns. Furthermore, the usually adopted pixel-wise objective functions lead to blurry predictions. To this end, we propose a novel motion-guided global-local aggregation Transformer network for effectively combining spatiotemporal cues at different time scales, thereby strengthening global-local spatiotemporal aggregation urgently required by the extrapolation task. First, we divide existing observations into both short and long term sequences to represent echo dynamics at different time scales. Then, to introduce reasonable motion guidance to Transformer, we customize an end-to-end module for jointly extracting Motion Representation of Short and Long term echo sequences (MRS, MRL), while estimating optical flow. Subsequently, based on Transformer architecture, MRS is used as queries to retrospect the most useful information from MRL for an effective aggregation of global long-term and local short-term cues. Finally, the fused feature is employed for future echo prediction. Additionally, for the blurry prediction problem, predictions from our model trained with an adversarial regularization achieve superior performances not only in nowcasting skill scores but also in precipitation details and image clarity over existing methods. Extensive experiments on two challenging radar echo datasets demonstrate the effectiveness of our proposed method.

Recently, frequency-modulated continuous-wave (FMCW) radar-based hand gesture recognition (HGR) using deep learning has achieved favorable performance. However, many existing methods use extracted features separately, i.e., using one of the range, Doppler, azimuth, or elevation angle information, or a combination of any two, to train convolutional neural networks (CNNs), which ignore the interrelation among the 5-D time-varying-range-Doppler-azimuth-elevation feature space. Although there have been methods using the 5-D information, their mining of the interrelation among the 5-D feature space is not sufficient, and there is still room for improvements. This article proposes a new processing scheme of HGR based on 5-D feature cubes that are jointly encoded by a 3-D fast Fourier transform (3-D-FFT)-based method. Then, a CNN is proposed by building two novel blocks, i.e., the spatiotemporal deformable convolution (STDC) block and the adaptive spatiotemporal context-aware convolution (ASTCAC) block. Concretely, STDC is designed to cope with hand gestures’ large spatiotemporal geometric transformations in the 5-D feature space. Moreover, ASTCAC is designed for modeling long-distance global relationships, e.g., relationships between pixels of the feature at the upper left corner and lower right corner, and exploring the global spatiotemporal context, in order to enhance the target feature representation and suppress interference. Finally, our presented method is verified on a large radar dataset, including 19 760 sets of 16 common hand gestures, collected by 19 subjects. Our method obtains a recognition rate of 99.53% on the validation dataset and that of 97.22% on the test dataset, which is significantly better than state-of-the-art methods.

Grammatical error correction (GEC) is a promising natural language processing (NLP) application, whose goal is to change the sentences with grammatical errors into the correct ones. Neural machine translation (NMT) approaches have been widely applied to this translation-like task. However, such methods need a fairly large parallel corpus of error-annotated sentence pairs, which is not easy to get especially in the field of Chinese grammatical error correction. In this paper, we propose a simple yet effective method to improve the NMT-based GEC models by dynamic masking. By adding random masks to the original source sentences dynamically in the training procedure, more diverse instances of error-corrected sentence pairs are generated to enhance the generalization ability of the grammatical error correction model without additional data. The experiments on NLPCC 2018 Task 2 show that our MaskGEC model improves the performance of the neural GEC models. Besides, our single model for Chinese GEC outperforms the current state-of-the-art ensemble system in NLPCC 2018 Task 2 without any extra knowledge.

In this paper, we develop a simulator that could simulate polarimetric data of airship-borne typhoon observation radar. We use the Weather Research and Forecast (WRF) model to generate a `realistic' typhoon phenomenon, combined with the T-matrix method to calculate the scattering properties. Mixtures of rain, snow, graupel and ice relative to typhoon phenomenon are considered. Polarimetric observables also depend on the angle of the incident radar beam, so we analyzed the influence of incident angles on radar observables. The simulated polarimetric observables showed a good agreement with those in previous literature.