Sheng Wang

This paper proposed a graphical and statistical features-based broad learning system (GSF-BLS) to detect tropical cyclogenesis with the Cross-Calibrated Multi-Platform version 2.0 (CCMP V2.0) wind products. The framework of the proposed model is composed of three modules: the data preprocessing module, the feature extraction module, and the basis broad learning system (BLS). At the stage of data preprocessing, we use the CCMP V2.0 data to match the best tracks and the Global Tropical Cloud Cluster tracks to obtain the developed and undeveloped samples. At the feature extraction stage, a convolution module with pre-trained weights is used to extract the graphical features (GFs). Meanwhile, the statistical features (SFs) are calculated based on the divided sub-regions of each sample. Thus, the combination of these GFs and SFs forms the input vectors. Then, the training time of GSF-BLS on CPU is only one-twentieth of that of deep learning models, showing its simplicity and efficiency in model training. The overall accuracy, probability of detection (POD), and false alarm rate (FAR) on the testing set are 89.46%, 86.78%, and 8.31%, respectively. More importantly, the incremental learning ability of GSF-BLS makes it superior to most deep learning models in model updating, which can avoid the computational burden caused by retraining. Finally, the case study results show that GSF-BLS can predict tropical cyclogenesis in 52 of 70 cases in advance, and the average lead times are 13.54 hours. Therefore, the experimental results demonstrate that GSF-BLS is a promising tropical cyclogenesis detection model.

The tangential wind speed increases from the center to the eyewall of tropical cyclones (TC) along the radial direction and begins to decay when it extends outward. The tangential wind profile model is one of the most effective and widely used methods to reconstruct the TC radial wind speed. This article proposes a parametric tangential wind profile (TWP) model based on high-spatial-resolution synthetic aperture radar (SAR) imagery. The new model functions are piecewise with maximum tangential wind speed as a threshold, and all of them are designed as nonlinear. Notably, the derivative at the segmentation threshold is zero to ensure a smooth transition of the estimated wind speed profile. With the SAR-derived azimuth-averaged wind speed, we can determine the model parameters and get the tangential wind speed. The TWP model outperforms the commonly used single-modified Rankine vortex (SMRV) model, as it better resolves the tangential wind profile shape as depicted by both SAR-derived winds and hurricane hunter stepped-frequency microwave radiometer derived winds. A comprehensive analysis of the TWP model parameters is carried out by fitting tangential winds for 620 hurricane hunter flights. Interestingly, the tangential wind profiles for major hurricanes show a similar shape. The proposed TWP model can be used for improved TC characterization and forecasting purposes.

The C-band synthetic aperture radar (SAR) observation is one of the most popular sources for high-resolution tropical cyclone (TC) wind speed estimation. The scarcity of high wind speed data with good quality restricts the inversion accuracy of high wind speed. It is a challenge to obtain a high-precision wind speed inversion model with sparse data points. In this article, the TC wind speed is successfully estimated from the dual-polarization SAR signal. First, the training dataset with a total of 327 data points is formed using the Sentinel-1A (S-1A) extra-wide / interferometric-wide mode images and their temporal–spatial matched stepped frequency microwave radiometer (SFMR) measurements. Then, a novel Nonparametric TC Wind Speed Estimation model (hereafter NWSE model) is proposed with this dataset by using the Bayesian nonparametric general regression method. The wind speed is interpreted as a function of the cross-polarized normalized radar cross sections and incident angle for the NWSE model. Moreover, the wind speed obtained from the co-polarized signal is used to improve the accuracy of NWSE model under low wind speed. Finally, the validation results show the excellent overall consistency between the model retrieved wind speed and the collocated SFMR and Soil Moisture Active and Passive (SMAP) measurements. Specifically, considering all the wind speeds, the overall root-mean-square error (RMSE) and the absolute bias of NWSE model are 2.85 and 2.26 m/s compared with the SMAP wind speed, respectively. When considering the wind speeds larger than 30 m/s, the RMSE and bias of NWSE model are 3.75 and 2.78 m/s, respectively.

Synthetic Aperture Radar (SAR) has been proven to be a useful tool in monitoring hurricane structure and intensity. By far, SAR is the most promising spaceborne sensor to obtain high resolution hurricane wind field on the ocean surface. In this paper, an improved asymmetric hurricane parametric (IMAHP) model has been proposed to reconstruct the asymmetric wind speed. Where, the high-resolution cross-polarization SAR imagery is used to determine the value of model parameters. Compared to other models, the new model can better reconstruct hurricane wind speed with a more concise model function. For verification, taking SAR-retrieved wind speed as a reference, the root means square error (RMSE) and bias of the wind speed estimated by the IMAHP model is 1.86 m/s, 1.89 m/s for Hurricane Arthur (2014), 2.01 m/s, 1.77 m/s for Iselle (2014), and 1.99 m/s, 1.74 m/s for Norbert (2014), respectively. Finally, comparisons with airborne stepped-frequency microwave radiometer (SFMR) and dropwindsondes measurements show that the wind speed simulated by the IMAHP model is close to these measurements.

In this paper we present a novel efficient energy-aware approach for clustering nodes in wireless sensor networks. We use different cluster head (CH) declaration delays for each node to characterize the qualification to be a CH. The approach guarantees the fairly uniform cluster distribution while incurring low overheads. Additionally, we do not make any assumptions about the distribution or node capabilities, e.g., location-awareness. The simulation results show that our clustering approach outperforms LEACH both in cluster characteristics and in the efficiency of prolonging the network lifetime.