High-quality meteorological grid data are essential for meteorological research and applications, especially in regional scales. Statistical downscaling (SD) is an efficient method to provide more detailed information at spatial scale, and has already been implemented in many regions. In recent years, deep convolutional neural networks have exhibited promising performance in SD, effectively learning non-linear mappings from the low-resolution (LR) meteorological data to its corresponding high-resolution (HR) one. Nevertheless, existing deep-learning-based SD approaches may encounter two potential limitations. First, most of the previous deep-learning-based downscaling algorithms utilize a supervised learning framework, which necessitates the formation of data pairs consisting of HR labels and LR data for model training. However, the acquisition of meteorological data at regional scale is more challenging, making it difficult to meet the training requirements of traditional supervised-learning-based downscaling models in some cases. Second, learning the non-linear mapping between LR and HR meteorology data is typically an ill-posed issue, which means that there are infinite HR solutions for the same LR sample, making it harder to find the optimal solution within the large solution space, especially in the case of insufficient HR training labels. In this study, we propose a closed-loop circular regression network for simultaneous restoration of medium-resolution (MR) and HR 2m air temperature over Sichuan and surrounding areas, China. The model leverages the circular structure consistency to train both the downscaling and upscaling networks simultaneously. Specifically, in terms of insufficient HR labels, we introduce an additional constraint of MR supervision information to reduce the space of possible functions, forming a gradual downscaling process from LR to MR to HR data. Besides, we also establish an extra upscaling mapping from HR to MR to LR, which forms a circular consistency constraint on LR and MR data to provide additional supervision. Extensive experiments demonstrate that the proposed algorithm attains a Root Mean Square Error (RMSE) of 0.84 when utilizing 50% of the training data and 0.77 when using 75%. This performance surpasses that of many classic supervised-learning-based SD methods, even the complete supervised information is not utilized.

Currently, integrating mathematical-physical (MP) knowledge into the deep learning (DL) model in an interpretable manner for tropical cyclone (TC) intensity estimation and prediction remains a challenge. In this article, we propose the statistical weather prediction for tropical cyclone intensity (SWP-TCI) model, focusing on real-time estimation and prediction of the TC intensity over the Numerical weather prediction (NWP). SWP-TCI incorporates multiple physical factors and utilizes the causal statistical fusion ensemble module (CSFEM) to integrate this physical knowledge into the model. By leveraging constraints from various physical factors, SWP-TCI can identify more distinct TC features from the satellite cloud images. CSFEM is developed using causal inference (CI), establishing the causal relationship between the TC and physical factors, thus supporting the learning of SWP-TCI and enhancing the model’s interpretability. In experiments, our results are obtained without using the historical sequence of TC intensities. The average MAE of 24-h predictions across from 2015 to 2021 is 5.9 m/s, standing in a close comparison with the official object forecast methods employed worldwide.

Analyzing the temporal evolution of historical tropical cyclone (TC) structures is essential for accurate TC intensity estimation. In this article, a novel spatiotemporal interaction attention (STIA) model is proposed to estimate TC intensity using Himawari-8 data in the western North Pacific (WNP) basin. The model incorporates a spatial feature extraction module and a spatiotemporal interaction module, which leverage historical satellite images. Based on a sequence of observed satellite images, the spatial feature extraction module is expected to extract spatial features of each TC frame. After that, the spatiotemporal interaction module comprising the temporal–spatial (TS) module and the spatial–temporal (ST) module is responsible for fusing the temporal and spatial features of each frame. The experimental data are composed of Himawari-8 infrared (IR) and water vapor (WV) images from 2015 to 2020 with a time interval of 1 h. The model is trained on images from 2015 to 2018 and evaluated on images from 2019 to 2020. Ablation experiments are conducted to analyze the impact of the number of frames, and the ST and TS modules. The results demonstrate that using 18-frame inputs yields the best performance, achieving an overall root-mean-square error (RMSE) of 3.61 m/s and a mean absolute error (MAE) of 2.83 m/s. In addition, the ST and TS modules significantly contribute to enhancing the accuracy of TC intensity estimation. The performance of the STIA model already surpasses the state-of-the-art benchmarks, demonstrating its excellence in TC intensity estimation.

This article presents a novel temporal-differential attention rapid intensification (TDA-RI) model for predicting tropical cyclone (TC) rapid intensification (RI) in the western North Pacific (WNP) Basin. The model leverages satellite image data from the Digital Typhoon dataset, effectively capturing the characteristics of TC RI through temporal-differential images. It incorporates two key components: an intensity embedding spatial-temporal (IEST) module, and a temporal-differential normed attention (TDNA) module. The IEST module embeds intensity information into the spatial-temporal (ST) contexts of image sequences, capturing dynamic changes in intensity and image features over time and space. The TDNA module focuses on capturing changes and evolution patterns of TCs in the temporal dimension. The dataset is divided into a training dataset spanning from 1989 to 2017 and a test dataset covering 2018 to 2022. Experimental results demonstrate that the TDA-RI model exhibits excellent RI predictive capabilities, achieving ROC AUC and PR AUC scores of 0.901 and 0.465, respectively. Compared to other advanced models, the TDA-RI model shows significant advantages in terms of recall and false positive rate. Furthermore, the model can achieve satisfactory performance even without historical intensity information, highlighting its potential for real-time RI forecasting.

Recent deep learning methods for change detection focus on excavating more discriminative context within individual images. However, due to seasonal change, noise, and so on, the appearance of objects tends to be more heterogeneous among various scenes. Consequently, the above intra-image context is inadequate to represent specific-category objects and pseudo changes would be inevitable in detection results. To deal with this issue, we propose a context aggregation network (CANet) to mine inter-image context over all training images for further enhancing intra-image context. Specifically, a Siamese network attached with temporal attention modules is served as a feature encoder to extract multi-scale temporal features from bitemporal images. Then, a context extraction module is devised to capture long-range spatial-channel context within individual images. Meanwhile, context representations of underlying categories in the scene are inferred using all training images in an unsupervised manner. Finally, these two kinds of contextual information are aggregated to one which is subsequently fed into a multi-scale fusion module to produce the detection map. CANet is compared with several state-of-the-art methods on three benchmark datasets, including the season-varying change detection (SVCD) dataset, the Sun Yat-sen University change detection (SYSU-CD) dataset, and the Learning Vision and Remote Sensing Laboratory building change detection (LEVIR-CD) dataset. It is demonstrated that our method outperforms all comparison methods in terms of F1, overall accuracy (OA), and Intersection-of-Union (IoU). The results of CANet on three datasets are available at https://github.com/NuistZF/CANet-for-change-detection and codes will be public soon.

This article proposes a deep learning model to predict tropical cyclogenesis (TCG) from gridded satellite and ERA5 reanalysis data in the western North Pacific basin. The proposed model contains two modules. First, convolutional neural network (CNN)-based deep features are extracted for each predictor, and then, the extracted features are fused with two fully connected layers to differentiate and investigate the relationship between predictors and TCG. The experimental data of this study are composed of 3232 developing tropical cluster clouds and 6657 nondeveloping ones; 90% of the collected data are utilized to train the model, and the rest are used to evaluate the trained model. Totally, nine predictors have been considered for the study, and the results show that the brightness temperature (IR), relative vorticity (Vo), and geopotential height (Z) perform better than the other predictors. A combined model with six predictors [IR, Z, RH (relative humidity), Vo, WS10 m(wind speed at the height of ten meters above the surface of the Earth), and mslp (mean sea-level pressure)] achieves the best TCG predicting performance, i.e., 97.1% of developing tropical cyclones are detected at a probability threshold of 0.13 with a false alarm rate of 20.3%. The experimental results demonstrate that the proposed method is superior to the existing methods and also indicate that the fusion of satellite and reanalysis data is a promising method to predict TCG.

This article proposes a two-branch convolutional neural network model (TCIENet) to estimate the intensity of tropical cyclone (TC) from infrared and water vapor images in the northwest Pacific basin. Three different sizes of input images are explored to train the TCIENet model, and the size of 60 × 60 pixels (radius 450 km) achieves the best performance with an overall root mean square error (RMSE) of 5.13 m/s and mean absolute error (MAE) of 4.03 m/s. TCs are divided into six categories whose RMSEs range from 4.07 to 6.05 m/s. In addition, the TCs in the year 2017 are used to analyze the correlation between the rainfall intensity from the global precipitation measurement (GPM) mission and the estimation errors of the TCIENet model. Preliminary results suggest that the model performs the best at the categories of tropical storm and super typhoon, but it degrades in performance for moderate intense categories and the weakest category of the tropical depression. The correlation coefficient between the estimation error and the rainfall intensity is 0.19. It is far from certain that the rainfall intensity accounts for the error achieved by the TCIENet model.