Extreme precipitating events in satellite and rain-gauge products over the Sahel

Over the recent decades, Extreme Precipitation Events (EPE) have become more frequent over the Sahel. Their properties, however, have so far received little attention. In this study the spatial distribution, intensity, seasonality and interannual variability of EPEs are examined, using both a reference dataset, based on a high-density rain-gauge network over Burkina Faso and 24 precipitation gridded datasets. The gridded datasets are evaluated in depth over Burkina Faso while their commonalities are used to document the EPE properties over the Sahel. EPEs are defined as the occurrence of daily-accumulated precipitation exceeding the all-day 99th percentile over a 1°x1° pixel. Over Burkina Faso, this percentile ranges between 21 and 33 mm day-1. The reference dataset show that EPEs occur in phase with the West African monsoon annual cycle, more frequently during the monsoon core season and during wet years. These results are consistent among the gridded datasets over Burkina Faso but also over the wider Sahel. The gridded datasets exhibit a wide diversity of skills when compared to the Burkinabe reference. The Global Precipitation Climatology Centre Full Data Daily version 1 (GPCC-FDDv1) and the Global Satellite Mapping of Precipitation gauge Reanalysis version 6.0 (GSMaP-gauge-RNL v6.0) are the only products that properly reproduce all of the EPE features examined in this work. The datasets using a combination of microwave and infrared measurements are prone to overestimate the EPE intensity, while infrared-only products generally underestimate it. Their calibrated versions perform than their uncalibrated (near-real-time) versions. This study finally emphasizes that the lack of rain-gauge data availability over the whole Sahel strongly impedes our ability to gain insights in EPE properties.

The Southern Ocean Radiolarian (SO-RAD) dataset: a new compilation of modern radiolarian census data

Radiolarians (holoplanktonic protozoa) preserved in marine sediments are commonly used as palaeoclimate proxies for reconstructing past Southern Ocean environments. Generating reconstructions of past climate based on microfossil abundances, such as radiolarians, requires a spatially and environmentally comprehensive reference dataset of modern census counts. The Southern Ocean Radiolarian (SO-RAD) dataset includes census counts for 238 radiolarian taxa from 228 surface sediment samples located in the Atlantic, Indian, and southwest Pacific sectors of the Southern Ocean. This compilation is the largest radiolarian census dataset derived from surface sediment samples in the Southern Ocean. The SO-RAD dataset may be used as a reference dataset for palaeoceanographic reconstructions, or for studying modern radiolarian biogeography and species diversity. As well as describing the data collection and collation, we include recommendations and guidelines for cleaning and subsetting the data for users unfamiliar with the procedures typically used by the radiolarian community. The SO-RAD dataset is available to download from https://doi.org/10.1594/PANGAEA.929903 (Lawler et al., 2021).

BSE49, a diverse, high-quality benchmark dataset of separation energies of chemical bonds

We present an extensive and diverse dataset of bond separation energies associated with the homolytic cleavage of covalently bonded molecules (A-B) into their corresponding radical fragments (A. and B.). Our dataset contains two different classifications of model structures referred to as “Existing” (molecules with associated experimental data) and “Hypothetical” (molecules with no associated experimental data). In total, the dataset consists of 4502 datapoints (1969 datapoints from the Existing and 2533 datapoints from the Hypothetical classes). The dataset covers 49 unique X-Y type single bonds (except H-H, H-F, and H-Cl), where X and Y are H, B, C, N, O, F, Si, P, S, and Cl atoms. All the reference data was calculated at the (RO)CBS-QB3 level of theory. The reference bond separation energies are non-relativistic ground-state energy differences and contain no zero-point energy corrections. This new dataset of bond separation energies (BSE49) is presented as a high-quality reference dataset for assessing and developing computational chemistry methods.

Numerical Investigations through ANNs for Solving COVID-19 Model

The current investigations of the COVID-19 spreading model are presented through the artificial neuron networks (ANNs) with training of the Levenberg-Marquardt backpropagation (LMB), i.e., ANNs-LMB. The ANNs-LMB scheme is used in different variations of the sample data for training, validation, and testing with 80%, 10%, and 10%, respectively. The approximate numerical solutions of the COVID-19 spreading model have been calculated using the ANNs-LMB and compared viably using the reference dataset based on the Runge-Kutta scheme. The obtained performance of the solution dynamics of the COVID-19 spreading model are presented based on the ANNs-LMB to minimize the values of fitness on mean square error (M.S.E), along with error histograms, regression, and correlation analysis.

Are elevated moist layers a blind spot for hyperspectral infrared sounders? A model study

The ability of the hyperspectral satellite-based passive infrared (IR) instrument IASI to resolve elevated moist layers (EMLs) within the free troposphere is investigated. EMLs are strong moisture anomalies with significant impact on the radiative heating rate profile and typically coupled to freezing level detrainment from convective cells in the tropics. A previous case study by Stevens et al. (2017) indicated inherent deficiencies of passive satellite-based remote sensing instruments in resolving an EML. In this work, we first put the findings of Stevens et al. (2017) into the context of other retrieval case studies of EML-like structures, showing that such structures can in principle be retrieved, but retrievability depends on the retrieval method and the exact retrieval setup. To approach a first more systematic analysis of EML retrievability, we introduce our own basic optimal estimation (OEM) retrieval, which for the purpose of this study is based on forward-modelled (synthetic) clear-sky observations. By applying the OEM retrieval to the same EML case as Stevens et al. (2017), we find that a lack of independent temperature information can significantly deteriorate the humidity retrieval due to a strong temperature inversion at the EML top. However, we show that by employing a wider spectral range of the hyperspectral IR observation, this issue can be avoided and EMLs can generally be resolved. We introduce a new framework for the identification and characterization of moisture anomalies, a subset of which are EMLs, to specifically quantify the retrieval's ability to capture moisture anomalies. The new framework is applied to 1288 synthetic retrievals of tropical ocean short-range forecast model atmospheres, allowing for a direct statistical comparison of moisture anomalies between the retrieval and the reference dataset. With our basic OEM retrieval, we find that retrieved moisture anomalies are on average 17 % weaker and 15 % thicker than their true counterparts. We attribute this to the retrieval smoothing error and the fact that rather weak and narrow moisture anomalies are most frequently missed by the retrieval. Smoothing is found to also constrain the magnitude of local heating rate extremes associated with moisture anomalies, particularly for the strongest anomalies that are found in the lower to mid troposphere. In total, about 80 % of moisture anomalies in the reference dataset are found by the retrieval. Below 5 km altitude, this fraction is only of the order of 52 %. We conclude that the retrieval of lower- to mid-tropospheric moisture anomalies, in particular of EMLs, is possible when the anomaly is sufficiently strong and its thickness is at least of the order of about 1.5 km. This study sets the methodological basis for more comprehensively investigating EMLs based on real hyperspectral IR observations and their operational products in the future.

Graph Analysis Using Fast Fourier Transform Applied on Grayscale Bitmap Images

There is spiking interest in graph analysis, mainly sparked by social network analysis done for various purposes. With social network graphs often achieving very large size, there is a need for capable tools to perform such an analysis. In this article, we contribute to this area by presenting an original approach to calculating various graph morphisms, designed with overall performance and scalability as the primary concern. The proposed method generates a list of candidates for further analysis by first decomposing a complex network into a set of sub-graphs, transforming sub-graphs into intermediary structures, which are then used to generate grey-scaled bitmap images, and, eventually, performing image comparison using Fast Fourier Transform. The paper discusses the proof-of-concept implementation of the method and provides experimental results achieved on sub-graphs in different sizes randomly chosen from a reference dataset. Planned future developments and key considered areas of application are also described.

TephraNZ: a major- and trace-element reference dataset for glass-shard analyses from prominent Quaternary rhyolitic tephras in New Zealand and implications for correlation

Although analyses of tephra-derived glass shards have been undertaken in New Zealand for nearly four decades (pioneered by Paul Froggatt), our study is the first to systematically develop a formal, comprehensive, open-access reference dataset of glass-shard compositions for New Zealand tephras. These data will provide an important reference tool for future studies to identify and correlate tephra deposits and for associated petrological and magma-related studies within New Zealand and beyond. Here we present the foundation dataset for TephraNZ, an open-access reference dataset for selected tephra deposits in New Zealand. Prominent, rhyolitic, tephra deposits from the Quaternary were identified, with sample collection targeting original type sites or reference locations where the tephra's identification is unequivocally known based on independent dating and/or mineralogical techniques. Glass shards were extracted from the tephra deposits, and major- and trace-element geochemical compositions were determined. We discuss in detail the data reduction process used to obtain the results and propose that future studies follow a similar protocol in order to gain comparable data. The dataset contains analyses of glass shards from 23 proximal and 27 distal tephra samples characterising 45 eruptive episodes ranging from Kaharoa (636 ± 12 cal yr BP) to the Hikuroa Pumice member (2.0 ± 0.6 Ma) from six or more caldera sources, most from the central Taupō Volcanic Zone. We report 1385 major-element analyses obtained by electron microprobe (EMPA), and 590 trace-element analyses obtained by laser ablation (LA)-ICP-MS, on individual glass shards. Using principal component analysis (PCA), Euclidean similarity coefficients, and geochemical investigation, we show that chemical compositions of glass shards from individual eruptions are commonly distinguished by major elements, especially CaO, TiO2, K2O, and FeOtt (Na2O+K2O and SiO2/K2O), but not always. For those tephras with similar glass major-element signatures, some can be distinguished using trace elements (e.g. HFSEs: Zr, Hf, Nb; LILE: Ba, Rb; REE: Eu, Tm, Dy, Y, Tb, Gd, Er, Ho, Yb, Sm) and trace-element ratios (e.g. LILE/HFSE: Ba/Th, Ba/Zr, Rb/Zr; HFSE/HREE: Zr/Y, Zr/Yb, Hf/Y; LREE/HREE: La/Yb, Ce/Yb). Geochemistry alone cannot be used to distinguish between glass shards from the following tephra groups: Taupō (Unit Y in the post-Ōruanui eruption sequence of Taupō volcano) and Waimihia (Unit S); Poronui (Unit C) and Karapiti (Unit B); Rotorua and Rerewhakaaitu; and Kawakawa/Ōruanui, and Okaia. Other characteristics, including stratigraphic relationships and age, can be used to separate and distinguish all of these otherwise-similar tephra deposits except Poronui and Karapiti. Bimodality caused by K2O variability is newly identified in Poihipi and Tahuna tephras. Using glass-shard compositions, tephra sourced from Taupō Volcanic Centre (TVC) and Mangakino Volcanic Centre (MgVC) can be separated using bivariate plots of SiO2/K2O vs. Na2O+K2O. Glass shards from tephras derived from Kapenga Volcanic Centre, Rotorua Volcanic Centre, and Whakamaru Volcanic Centre have similar major- and trace-element chemical compositions to those from the MgVC, but they can overlap with glass analyses from tephras from Taupō and Okataina volcanic centres. Specific trace elements and trace-element ratios have lower variability than the heterogeneous major-element and bimodal signatures, making them easier to fingerprint geochemically.

Effects of Age and Sex on Properties of Lumbar Erector Spinae in Healthy People: Preliminary Results From a Pilot Study

Background: The influences of age and sex on properties of lumbar erector spinae have not been previously studied. Changes in the performance of lumbar erector spinae properties associated with age represent a valuable indicator of risk for lower-back-related disease.Objective: To investigate the lumbar erector spinae properties with regard to age and sex to provide a reference dataset.Methods: We measured muscle tone and stiffness of the lumbar erector spinae (at the L3–4 level) in healthy men and women (50 young people, aged 20–30 years; 50 middle-aged people, aged 40–50 years; and 50 elderly people, aged 65–75 years) using a MyotonPRO device.Results: In general, there are significant differences in muscle tone and stiffness among young, middle-aged, and elderly participants, and there were significant differences in muscle tone and stiffness between men and women, and there was no interaction between age and sex. The muscle tone and stiffness of the elderly participants were significantly higher than those of the middle-aged and young participants (P < 0.01), and the muscle tone and stiffness of the middle-aged participants were significantly higher than those of the young participants (P < 0.01). In addition, the muscle tone and stiffness of men participants were significantly higher than that of women participants (P < 0.01).Conclusion: Our results indicate that muscle tone and stiffness of the lumbar erector spinae increase with age. The muscle tone and stiffness of the lumbar erector spinae in men are significantly higher than in women. The present study highlights the importance of considering age and sex differences when assessing muscle characteristics of healthy people or patients.

Deep Learning for Caries Detection and Classification

Objectives: Deep learning methods have achieved impressive diagnostic performance in the field of radiology. The current study aimed to use deep learning methods to detect caries lesions, classify different radiographic extensions on panoramic films, and compare the classification results with those of expert dentists. Methods: A total of 1160 dental panoramic films were evaluated by three expert dentists. All caries lesions in the films were marked with circles, whose combination was defined as the reference dataset. A training and validation dataset (1071) and a test dataset (89) were then established from the reference dataset. A convolutional neural network, called nnU-Net, was applied to detect caries lesions, and DenseNet121 was applied to classify the lesions according to their depths (dentin lesions in the outer, middle, or inner third D1/2/3 of dentin). The performance of the test dataset in the trained nnU-Net and DenseNet121 models was compared with the results of six expert dentists in terms of the intersection over union (IoU), Dice coefficient, accuracy, precision, recall, negative predictive value (NPV), and F1-score metrics. Results: nnU-Net yielded caries lesion segmentation IoU and Dice coefficient values of 0.785 and 0.663, respectively, and the accuracy and recall rate of nnU-Net were 0.986 and 0.821, respectively. The results of the expert dentists and the neural network were shown to be no different in terms of accuracy, precision, recall, NPV, and F1-score. For caries depth classification, DenseNet121 showed an overall accuracy of 0.957 for D1 lesions, 0.832 for D2 lesions, and 0.863 for D3 lesions. The recall results of the D1/D2/D3 lesions were 0.765, 0.652, and 0.918, respectively. All metric values, including accuracy, precision, recall, NPV, and F1-score values, were proven to be no different from those of the experienced dentists. Conclusion: In detecting and classifying caries lesions on dental panoramic radiographs, the performance of deep learning methods was similar to that of expert dentists. The impact of applying these well-trained neural networks for disease diagnosis and treatment decision making should be explored.