Markerless motion capture would permit the study of human biomechanics in environments where marker-based systems are impractical, e.g. outdoors or underwater. The visual hull tool may enable such data to be recorded, but it requires the accurate detection of the silhouette of the object in multiple camera views. This paper reviews the top-performing algorithms available to date for silhouette extraction, with the visual hull in mind as the downstream application; the rationale is that higher-quality silhouettes would lead to higher-quality visual hulls, and consequently better measurement of movement. This paper is the first attempt in the literature to compare silhouette extraction algorithms that belong to different fields of Computer Vision, namely background subtraction, semantic segmentation, and multiview segmentation. It was found that several algorithms exist that would be substantial improvements over the silhouette extraction algorithms traditionally used in visual hull pipelines. In particular, FgSegNet v2 (a background subtraction algorithm), DeepLabv3+ JFT (a semantic segmentation algorithm), and Djelouah 2013 (a multi-view segmentation algorithm) are the most accurate and promising methods for the extraction of silhouettes from 2D images to date, and could seamlessly be integrated within a visual hull pipeline for studies of human movement or biomechanics.

We present a novel silhouette extraction algorithm designed for the binary segmentation of swimmers underwater. The intended use of this algorithm is within a 2D-to-3D pipeline for the markerless motion capture of swimmers, a task which has not been achieved satisfactorily, partly due to the absence of silhouette extraction methods that work well on images of swimmers. Our algorithm, FISHnet, was trained on the novel Scylla dataset, which contains 3,100 images (and corresponding hand-traced silhouettes) of swimmers underwater, and achieved a dice score of 0.9712 on its test data. Our algorithm uses a U-Net-like architecture and VGG16 as a backbone. It introduces two novel modules: a modified version of the Semantic Embedding Branch module from ExFuse, which increases the complexity of the features learned by the layers of the encoder; and the Spatial Resolution Enhancer module, which increases the spatial resolution of the features of the decoder before they are skip connected with the features of the encoder. The contribution of these two modules to the performance of our network was marginal, and we attribute this result to the lack of data on which our network was trained. Nevertheless, our model outperformed state-of-the-art silhouette extraction algorithms (namely DeepLabv3+) on Scylla, and it is the first algorithm developed specifically for the task of accurately segmenting the silhouettes of swimmers.

<p>Tropical cyclones (TCs) are one of the costliest and deadliest natural disasters due to the combination of their strong winds and induced storm surges and heavy precipitation, which can cause devastating floods. Unfortunately, due to its high spatio-temporal variability, complex underlying physical process, and lack of high-quality observations, precipitation is still one of the most challenging aspects of a TC to model. However, as precipitation is a key forcing variable for hydrological processes acting across multiple space-time scales, accurate precipitation input is crucial for reliable hydrological simulations and forecasts.</p> <p>A popular source of precipitation data is the ERA5 reanalysis dataset, frequently used as input to hydrological models when studying floods. However, ERA5 systematically underestimates TC-induced precipitation compared to MSWEP, a multi-source observational dataset fusing gauge, satellite, and reanalysis-based data, currently one of the most accurate precipitation datasets. Moreover, the spatial distribution of TC-rainfall in ERA5 has large room for improvement.</p> <p>Here, we present a precipitation correction scheme based on U-Net, a popular deep-learning architecture. Rather than only adjusting the per-pixel precipitation values at each timestep of a given TC, we explicitly design our model to also adjust the spatial distribution of the precipitation; to the best of our knowledge, we are the first to do so. The key novelty of our model is a custom-made loss function, based on the combination of the fractions skill score (FSS) and mean absolute error (MAE) metrics. We train and validate the model on 100k time steps (with an 80:20 train:test split) from global TC precipitation events. We show how a U-Net trained with our loss function can reduce the per-pixel MAE of ERA5 precipitation by nearly as much as other state-of-the-art methods, while surpassing them significantly in terms of improved spatial patterns of precipitation. Finally, we discuss how the outputs of our model can be used for future research.</p>

Tropical cyclone genesis indices are valuable tools for studying the relationship between large-scale environmental fields and the genesis of tropical cyclones, supporting the identification of future trends of cyclone genesis. However, their formulation is generally derived from simple statistical models (e.g., multiple linear regression) and are not optimised globally. In this paper, we present a simple framework for optimising genesis indexes given a user-specified trade-off between two performance metrics, which measure how well an index captures the spatial and interannual variability of tropical cyclone genesis. We apply the proposed framework to the popular Emanuel and Nolan Genesis Potential Index, yielding new, optimised formulas that correspond to different trade-offs between spatial and interannual variability. Results show that our refined indexes can improve the performance of the Emanuel and Nolan index up to 8% for spatial variability and 16-22% for interannual variability; this improvement was found to be statistically significant (p<0.01). Lastly, by analysing the formulas found, we give some insights into the role of the different inputs of the index in maximising one metric or the other.

Genesis Potential Indices (GPIs) link the occurrence of Tropical Cyclones (TCs) to large‐scale environmental conditions favorable for TC development. In the last few decades, they have been routinely used as a way to overcome the limitations of climate models (GCM), whose resolution is too coarse to produce realistic TCs. Recently, the first GCM ensemble with high enough horizontal resolution to realistically reproduce TCs was made available. Here, we address the questions of whether GPIs are still relevant in the era of TC‐permitting climate model ensembles, and whether they have sufficient predictive skills. The predictive skills of GPIs are assessed against the TCs directly simulated in a climate model ensemble. We found that GPIs have poor skill in two key metrics: inter‐annual variability and multi‐decadal trends. We discuss possible ways to improve the understanding of the predictive skill of GPIs and therefore enhance their applicability in the era of TC‐permitting GCMs.