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The development of Hurricane Catarina over the western South Atlantic Ocean in March 2004 marks the first time that the existence of a hurricane has been confirmed by analysis and satellite imagery in the South Atlantic basin. The storm undergoes a complex life cycle, beginning as an extratropical precursor that moves east-southeastward off the Brazilian coast and toward the midlatitudes. Its eastward progress is halted and the system is steered back westward toward the Brazilian coast as it encounters a strengthening dipole-blocking structure east of the South American continent. Entering the large region of weak vertical shear that characterizes this blocking pattern, Catarina begins a tropical transition process over anomalously cool 25°C ocean waters above which an elevated potential intensity is supported by the cold upper-level air associated with the trough component of the block. As the convective outflow from the developing tropical system reinforces the ridge component of the dipole block, the storm is accelerated westward toward the Santa Catarina province of Brazil and makes landfall there as a nominal category-1 hurricane, causing extensive damage with its heavy rains and strong winds. The complex evolution of the system is analyzed using a suite of diagnostic tools, and a conceptual model of the tropical transition and steering processes in the presence of a dipole block is developed. Once the essential properties of the upper-level flow are established, an analog study is undertaken to investigate lower-atmospheric responses to similar blocking regimes. Persistent dipole-blocking structures are found to be rare east of South America; however, the evolution of systems occurring during these periods is shown to be complex and to exhibit various subtropical development modes. [ABSTRACT FROM AUTHOR]

In recent years, many agriculture-related problems have been evaluated with the integration of artificial intelligence techniques and remote sensing systems. Specifically, in fruit detection problems, several recent works were developed using Deep Learning (DL) methods applied in images acquired in different acquisition levels. However, the increasing use of anti-hail plastic net cover in commercial orchards highlights the importance of terrestrial remote sensing systems. Apples are one of the most highly-challenging fruits to be detected in images, mainly because of the target occlusion problem occurrence. Additionally, the introduction of high-density apple tree orchards makes the identification of single fruits a real challenge. To support farmers to detect apple fruits efficiently, this paper presents an approach based on the Adaptive Training Sample Selection (ATSS) deep learning method applied to close-range and low-cost terrestrial RGB images. The correct identification supports apple production forecasting and gives local producers a better idea of forthcoming management practices. The main advantage of the ATSS method is that only the center point of the objects is labeled, which is much more practicable and realistic than bounding-box annotations in heavily dense fruit orchards. Additionally, we evaluated other object detection methods such as RetinaNet, Libra Regions with Convolutional Neural Network (R-CNN), Cascade R-CNN, Faster R-CNN, Feature Selective Anchor-Free (FSAF), and High-Resolution Network (HRNet). The study area is a highly-dense apple orchard consisting of Fuji Suprema apple fruits (Malus domestica Borkh) located in a smallholder farm in the state of Santa Catarina (southern Brazil). A total of 398 terrestrial images were taken nearly perpendicularly in front of the trees by a professional camera, assuring both a good vertical coverage of the apple trees in terms of heights and overlapping between picture frames. After, the high-resolution RGB images were divided into several patches for helping the detection of small and/or occluded apples. A total of 3119, 840, and 2010 patches were used for training, validation, and testing, respectively. Moreover, the proposed method's generalization capability was assessed by applying simulated image corruptions to the test set images with different severity levels, including noise, blurs, weather, and digital processing. Experiments were also conducted by varying the bounding box size (80, 100, 120, 140, 160, and 180 pixels) in the image original for the proposed approach. Our results showed that the ATSS-based method slightly outperformed all other deep learning methods, between 2.4% and 0.3%. Also, we verified that the best result was obtained with a bounding box size of 160 × 160 pixels. The proposed method was robust regarding most of the corruption, except for snow, frost, and fog weather conditions. Finally, a benchmark of the reported dataset is also generated and publicly available. [ABSTRACT FROM AUTHOR]