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Data about storm impacts are essential for the disaster risk reduction process, but unlike data about storm characteristics, they are not routinely collected. In this paper, we demonstrate the high potential of convolutional neural networks to automatically constitute storm impact database using timestacks images provided by coastal video monitoring stations. Several convolutional neural network architectures and methods to deal with class imbalance were tested on two sites (Biarritz and Zarautz) to find the best practices for this classification task. This study shows that convolutional neural networks are well adapted for the classification of timestacks images into storm impact regimes. Overall, the most complex and deepest architectures yield better results. Indeed, the best performances are obtained with the VGG16 architecture for both sites with F-scores of 0.866 for Biarritz and 0.858 for Zarautz. For the class imbalance problem, the method of oversampling shows best classification accuracy with F-scores on average 30% higher than the ones obtained with cost sensitive learning. The transferability of the learning method between sites is also investigated and shows conclusive results. This study highlights the high potential of convolutional neural networks to enhance the value of coastal video monitoring data that are routinely recorded on many coastal sites. Furthermore, it shows that this type of deep neural network can significantly contribute to the setting up of risk databases necessary for the determination of storm risk indicators and, more broadly, for the optimization of risk-mitigation measures. [ABSTRACT FROM AUTHOR]

Mesoscale eddies are ubiquitous in the world ocean and well researched both globally and regionally, while their properties and distributions across the whole Indonesian Seas are not yet fully understood. This study investigates for the first time the spatiotemporal variations and generation mechanisms of mesoscale eddies across the whole Indonesian Seas. Eddies are detected from altimetry sea level anomalies by an automatic identification algorithm. The Sulu Sea, Sulawesi Sea, Maluku Sea and Banda Sea are the main eddy generation regions. More than 80% of eddies are short-lived with a lifetime below 30 days. The properties of eddies exhibit high spatial inhomogeneity, with the typical amplitudes and radiuses of 2–6 cm and 50–160 km, respectively. The most energetic eddies are observed in the Sulawesi Sea and Seram Sea. Eddies feature different seasonal cycles between anticyclonic and cyclonic eddies in each basin, especially given that the average latitude of the eddy centroid has inverse seasonal variations. About 48% of eddies in the Sulawesi Sea are highly nonlinear, which is the case for less than 30% in the Sulu Sea and Banda Sea. Instability analysis is performed using high-resolution model outputs from Bluelink Reanalysis to assess mechanisms of eddy generation. Barotropic instability of the mean flow dominates eddy generation in the Sulu Sea and Sulawesi Sea, while baroclinic instability is slightly more in the Maluku Sea and Banda Sea. [ABSTRACT FROM AUTHOR]

For decades, the presence of a seasonal intrusion of the East Australian Current (EAC) has been disputed. In this study, with a Topographic Position Index (TPI)-based image processing technique, we use a 26-year satellite Sea Surface Temperature (SST) dataset to quantitatively map the EAC off northern New South Wales (NSW, Australia, 28–32°S and ~154°E). Our mapping products have enabled direct measurement ("distance" and "area") of the EAC's shoreward intrusion, and the results show that the EAC intrusion exhibits seasonal cycles, moving closer to the coast in austral summer than in winter. The maximum EAC-to-coast distance usually occurs during winter, ranging from 30 to 40 km. In contrast, the minimum distance usually occurs during summer, ranging from 15 to 25 km. Further spatial analyses indicate that the EAC undergoes a seasonal shift upstream of 29°40′S and seasonal widening downstream. This is the first time that the seasonality of the EAC intrusion has been confirmed by long-term remote-sensing observation. The findings provide new insights into seasonal upwelling and shelf circulation previously observed off the NSW coast. [ABSTRACT FROM AUTHOR]

The launch of the SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) satellites has led to the development of a series of L-band soil moisture retrieval algorithms. In these algorithms, many input parameters (such as leaf area index and soil texture) and empirical coefficients (such as roughness coefficient (hP, NRP) and crop structure parameter (bP, ttP)) are needed to calculate surface soil moisture (SSM) from microwave brightness temperature. Many previous studies have focused on how to determine the value of these coefficients and input parameters. Nevertheless, it can be difficult to obtain their 'real' values with low uncertainty across large spatial scales. To avoid this problem, a passive microwave remote sensing SSM inversion algorithm based on the principle of change detection was proposed and tested using theoretical simulation and a field SSM dataset for an agricultural area in northeastern China. This algorithm was initially used to estimate SSM for radar remote sensing. First, theoretical simulation results were used to confirm the linear relationship between the change rates for SSM and surface emissivity, for both H and V polarization. This demonstrated the reliability of the change detection algorithm. Second, minimum emissivity (or the difference between maximum emissivity and minimum emissivity) was modeled with a linear relationship between vegetation water content, derived from a three-year (2016–2018) SMAP L3 SSM dataset. Third, SSM values estimated by the change detection algorithm were in good agreement with SMAP L3 SSM and field SSM, with RMSE values ranging from 0.015~0.031 cm3/cm3 and 0.038~0.051 cm3/cm3, respectively. The V polarization SSM accuracy was higher than H polarization and combined H and V polarization accuracy. The retrieved SSM error from the change detection algorithm was similar to SMAP SSM due to errors inherited from the training dataset. The SSM algorithm proposed here is simple in form, has fewer input parameters, and avoids the uncertainty of input parameters. It is very suitable for global applications and will provide a new algorithm option for SSM estimation from microwave brightness temperature. [ABSTRACT FROM AUTHOR]