Observing System Research Articles

Analysis of Tactical-Technical Attack Performance Factors: A Case Study of a Professional Women's Handball Team

Game analysis based on tactical-technical sports performance provides important information for coaches to periodize and evaluate team processes more accurately. The aim of this study was to analyze the probabilities of success in offensive situations by a Brazilian professional women’s handball team. The method used was a quantitative, descriptive and case study approach, which was a professional women’s handball team with national and international achievements. Data collection was based on the Handball Observation System, which allowed a total of 726 game actions to be evaluated from the two target competitions (world and national), 143 for the Women’s Super Globe and 583 for the national handball league. The data was analyzed using the Lince program, which helps with video analysis (games from different sports), optimizing data verification, and the SPSS 23.0 software, using multinomial regression. The results showed that the team had 10.538 more probabilities of scoring when using basic tactical means, compared to 1-on-1 actions (CR = 1.061) and complex tactical means (CR = 1). The offensive organization showed greater probabilities (OR = 1.668) of scoring with the full team, in attacks from the central area of the court of less than 9 meters (OR = 1.917) and with shots between 21 and 40 minutes (OR = 1.393). There were also 7.968 more chances for the opposing goalkeeper to make a save when the team attacked with basic tactical means and 2.562 more chances for him to save the shot between 21 and 40 minutes.

Retrieval of boundary layer precipitable water from GOES ABI using machine learning techniques

Abstract Low-level moisture is an important ingredient for forecasting severe storms, especially over the Great Plains where severe storms often develop along the dryline. Although ground-based observation systems such as lidar or radiosondes on weather balloons provide accurate information on low-level moisture, data are provided at limited locations, and the low temporal resolution of radiosondes makes it difficult to track a rapidly developing dryline. Geostationary satellites provide high spatial and temporal observation, but the channels of current geostationary satellites are mostly sensitive to water vapor at mid to upper levels. However, the split window difference (SWD) between the “clean” window channel (10.3 μm) and the “dirty” window channel (12.3 μm) is commonly used for estimating low-level water vapor. However, this estimation is complicated by surface temperature contributions, dependence on the lapse rate, and nonlinear relationships between SWD and moisture. This study applies machine learning techniques to infer boundary layer precipitable water (BLPW) from Geostationary Operational Environmental Satellite (GOES) Advanced Baseline Imager (ABI) data. Since there are few observations that cover wide regions for training convolutional neural networks, especially for the atmosphere above the surface, High-Resolution Rapid Refresh (HRRR) model outputs are used as the truth for training. Statistical results show that using ABI channel 13, 14, and 15 brightness temperatures, skin temperature, solar zenith angle, and GOES total precipitable water product as additional inputs show the lowest MSE. Case study results show that the model developed in this study is good at capturing strong moisture gradients and depicting a dryline.

Machine Learning Modelling for Soil Moisture Retrieval from Simulated NASA-ISRO SAR (NISAR) L-Band Data

Soil moisture is a critical factor that supports plant growth, improves crop yields, and reduces erosion. Therefore, obtaining accurate and timely information about soil moisture across large regions is crucial. Remote sensing techniques, such as microwave remote sensing, have emerged as powerful tools for monitoring and mapping soil moisture. Synthetic aperture radar (SAR) is beneficial for estimating soil moisture at both global and local levels. This study aimed to assess soil moisture and dielectric constant retrieval over agricultural land using machine learning (ML) algorithms and decomposition techniques. Three polarimetric decomposition models were used to extract features from simulated NASA-ISRO SAR (NISAR) L-Band radar images. Machine learning techniques such as random forest regression, decision tree regression, stochastic gradient descent (SGD), XGBoost, K-nearest neighbors (KNN) regression, neural network regression, and multilinear regression were used to retrieve soil moisture from three different crop fields: wheat, soybean, and corn. The study found that the random forest regression technique produced the most precise soil moisture estimations for soybean fields, with an R2 of 0.89 and RMSE of 0.050 without considering vegetation effects and an R2 of 0.92 and RMSE of 0.042 considering vegetation effects. The results for real dielectric constant retrieval for the soybean field were an R2 of 0.89 and RMSE of 6.79 without considering vegetation effects and an R2 of 0.89 and RMSE of 6.78 with considering vegetation effects. These findings suggest that machine learning algorithms and decomposition techniques, along with a semi-empirical technique like Water Cloud Model (WCM), can be effective tools for estimating soil moisture and dielectric constant values precisely. The methodology applied in the current research contributes essential insights that could benefit upcoming missions, such as the Radar Observing System for Europe in L-band (ROSE-L) and the collaborative NASA-ISRO SAR (NISAR) mission, for future data analysis in soil moisture applications.

Three-dimensional observation system of direct current method based on overlapping electrodes and its application

Three-dimensional observation system of direct current method based on overlapping electrodes and its application

High‐dimensional ensemble Kalman filter with localization, inflation, and iterative updates

AbstractAccurate estimation of forecast‐error covariance matrices is an essential step in data assimilation, which becomes a challenging task for high‐dimensional data assimilation. The standard ensemble Kalman filter (EnKF) may diverge due to both the limited ensemble size and the model bias. In this article, we propose to replace the sample covariance in the EnKF with a statistically consistent high‐dimensional tapering covariance matrix estimator to counter the estimation problem under high dimensions. A high‐dimensional EnKF scheme combining covariance localization with the inflation method and an iterative update structure is developed. The proposed assimilation scheme is tested on the Lorenz‐96 model with spatially correlated observation systems. The results demonstrate that the proposed method could improve the assimilation performance under multiple settings.

Optimizing Numerical Weather Prediction Utility of the Maryland Mesonet with Observing System Simulation Experiments

Abstract The Maryland Mesonet Project will construct a network of 75 surface observing stations with aims that include mitigating the statewide impact of severe convective storms and improving analyses of record. The spatial configuration of mesonet stations is expected to affect the utility newly provided observations will have via data assimilation, making it desirable to study the effects of mesonet configuration. Furthermore, the impact associated with any observing system configuration is constrained by errors inherent to the prediction systems used to generate forecasts, which may change with future advances in data assimilation methodology, physical parameterization schemes, and resource availability. To address such possibilities, we perform sets of observing system simulation experiments using a high-resolution regional modeling system to assess the expected impact of four candidate mesonet configurations. Experiments cover seven 18-hour case-study events featuring moist convective regimes associated with severe weather over the state of Maryland and are performed using two versions of our experimental modeling system: a ’standard-uncertainty’ configuration tuned to be representative of existing convective-allowing prediction systems, and a ’constrained-uncertainty’ configuration with reduced boundary condition and model error that reflects a possible trajectory for future prediction systems. We find that the assimilation of mesonet data produces definitive improvements to analysis fields below 1000 m that are mediated by modeling system uncertainty. Conversely, mesonet impact on forecast verification is inconclusive and strongly variable across verification metrics. The impact of mesonet configuration appears limited by a saturation effect that caps local analysis improvements past a minimal density of observing stations.

Assimilation of ground-based GNSS data using a local ensemble Kalman filter

Tropical cyclones become increasingly nonlinear and dynamically unstable in high-resolution models. The initial conditions are typically sub-optimal, leaving scope to improve the accuracy of forecasts with improved data assimilation. Simultaneously, the lack of real ground-based GNSS observations over the ocean poses significant challenges when evaluating the assimilation results in oceanic regions. In this study, an Observation System Simulation Experiment is carried out based on a tropical cyclone case. Assimilation experiments using the WRF-PDAF framework are conducted. Conventional and GNSS observation operators are implemented. A diverse array of synthetic observations, encompassing temperature (T), wind components (U and V), precipitable water (PW), and zenith total delay (ZTD), are assimilated utilizing the Local Error-Subspace Transform Kalman filter (LESTKF). The findings highlight the improvement in forecast accuracy achieved through the assimilation process over the ocean. Multiple observation types further improve the forecast accuracy. The study underscores the crucial role of GNSS data assimilation techniques. The assimilation of GNSS data presents potential for advancing weather forecasting capabilities. Thus, the construction of ground-based GNSS observation stations over the ocean is promising.

The Built Environment Data platform for experimental test data in earthquake engineering

AbstractExperimental testing of full structural systems and their components is crucial for understanding their response to earthquakes. Since the 1960s, global interest in such testing has grown, supported by numerous national and international funding initiatives. This has resulted in valuable data that has improved understanding of structural behaviour, spurred the development of new mitigation solutions and helped validate numerical models critical for simulation studies. These advancements have enabled engineers to improve building codes and guidelines, and have allowed risk modellers to more accurately assess risk. With advanced computational resources, integrating experimental findings into broader initiatives becomes crucial. This article discusses a recent European initiative, Built Environment Data (BED) (https://builtenvdata.eu/), which currently offers a platform to store and manage data from experimental research, embodied carbon and simulated design services. BED aims to serve the European Plate Observing System (EPOS) distributed research infrastructure as one of its Thematic Core Services (TCSs). This paper focuses on the Experiments service for managing experimental data, compares it to similar global efforts and outlines the specific requirements and system architecture, including the web services and datasets currently offered. The Experiments service is expected to significantly support engineers worldwide by making experimental research and data more findable, accessible, inter‐operable and re‐usable.

Predominant technical actions used in the European judo championship

Judo is an Olympic combat sport that is divided into male and female weight categories. Each weight category implies significant differences in technical and strategic dimensions, as well as physiological, performance, and body composition among competitors. This study aimed to determine the technical actions predominance used by judokas in the European Judo Championship 2021. We observed 398 judo combats, in the various weight categories male and female, disputed in the European Judo Championship 2021. It was used an observation system in accordance with the classification of judo techniques proposed by Kodokan. An observation system was used according to the Kodokan classification of judo techniques. In Nage-Waza combat, there was a predominance leg technique (Ashi-Waza), arm technique (Te-Waza), hip technique (Koshi-Waza), side sacrifice techniques (Yoko-Sutemi-Waza) and frontal sacrifice techniques (Ma-Sutemi-Waza). When we focused our observation on the effectiveness of applied techniques, we observed more techniques scored with Ippon than Wazari. We can also observe that Shido was the significantly more recurrent punishment during male combats. Keywords: Judo, Kodokan, European Championship, Combat performance, Competitive performance analysis.

DeLVE into Earth's Past: A Visualization-Based Exhibit Deployed Across Multiple Museum Contexts.

While previous work has found success in deploying visualizations as museum exhibits, it has not investigated whether museum context impacts visitor behaviour with these exhibits. We present an interactive Deep-time Literacy Visualization Exhibit (DeLVE) to help museum visitors understand deep time (lengths of extremely long geological processes) by improving proportional reasoning skills through comparison of different time periods. DeLVE uses a new visualization idiom, Connected Multi-Tier Ranges, to visualize curated datasets of past events across multiple scales of time, relating extreme scales with concrete scales that have more familiar magnitudes and units. Museum staff at three separate museums approved the deployment of DeLVE as a digital kiosk, and devoted time to curating a unique dataset in each of them. We collect data from two sources, an observational study and system trace logs. We discuss the importance of context: similar museum exhibits in different contexts were received very differently by visitors. We additionally discuss differences in our process from Sedlmair et al.'s design study methodology which is focused on design studies triggered by connection with collaborators rather than the discovery of a concept to communicate. Supplemental materials are available at: https://osf.io/z53dq/.

What types of objective measures have been used to assess core ADHD symptoms in children and young people in naturalistic settings? A scoping review

ObjectivesWe described the range and types of objective measures of attention-deficit/hyperactivity disorder (ADHD) in children and young people (CYP) reported in research that can be applied in naturalistic settings.DesignScoping review...

The family alliance as a facilitator of therapeutic change in systemic relational psychotherapy for borderline personality disorder: A case study.

Managing the Therapeutic Alliance is often complex when it comes to the treatment of borderline personality disorder (BPD), but the alliance is crucial for the success of the therapy. Combined individual and family interventions have been shown to be very useful in treating of these cases. This study has two objectives. First, to describe how the family therapeutic alliance facilitates therapeutic change through family psychotherapy for families with a member diagnosed with BPD. Second, to analyze how the therapeutic change achieved through combined individual and family systemic relational psychotherapy affects the individual functioning of the patient with BPD. This single case study used the System of Observation of Family Therapy Alliances (SOFTA-o) to analyze the therapeutic alliance, along with two semi-structured clinical interviews, one at the beginning and one at the end of therapy. Results show a dynamic and positive evolution of the therapeutic alliance throughout the therapeutic process and how this alliance facilitated therapeutic change, both reducing the symptomatology of the patient with BPD and improving family communication and functioning. Results contribute to highlighting the importance of including family therapy as an intervention unit in protocols for patients with BPD.

Unravelling the impacts of stratospheric intrusions on near-surface ozone during the springtime ozone pollution episodes in Lhasa, China

While air pollution due to fine particulate matter (PM2.5) has been effectively controlled in China; the photochemical pollution characterized by elevated ozone (O3) has emerged as a major concern for air quality improvement. Except for ozone, Lhasa is one of the cleanest cities in China with the lowest annual PM2.5 concentration in 2017. The levels of major air pollutants in Lhasa are much lower than those of other cities in the eastern region of China, especially in May, when the O3 concentration peaks. This study was based on multi-source observations in combination with the Goddard Earth Observing System coupled with chemistry (GEOS-Chem) and the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) models to explore the causes of O3 pollution in Lhasa in May 2023. The results indicated that during the high O3 episodes, the concentrations of O3 precursors were low in Lhasa. Surrounding cities in other parts of the Tibetan Plateau also experienced high ozone concentrations despite being in different airsheds, suggesting that the O3 pollution in Lhasa was caused by regional transport rather than purely local emissions. Stratospheric intrusion events modulated by the westerly jet led to elevated ozone in the troposphere above Lhasa City. The results of the GEOS-Chem model indicated that horizontal advection, turbulence, diffusion, and other effects led to high concentrations of ozone in the near-surface above Lhasa. Vertical transport was the dominant factor leading to ozone concentration increases during high ozone days, with a contribution of 6.33 Gg/day. In addition, high-altitude air masses with a maximum altitude of over 8000 m, were observed arriving in Lhasa during the high ozone days. This study revealed that stratospheric intrusions have a greater contribution to the high O3 concentration in Lhasa in spring and provided a scientific basis for mitigating O3 pollution in the plateau cities.

A Space Non-Cooperative Target Recognition Method for Multi-Satellite Cooperative Observation Systems

A Space Non-Cooperative Target Recognition Method for Multi-Satellite Cooperative Observation Systems

Development of a Simple Observation System to Monitor Regional Wind Erosion

Dryland occupies about 46% of the global land surface area (except Antarctica) and is the most vulnerable area to climate change. From the conditions of vegetation and land surface wetness and blown sand phenomena, a simple observation system was developed to monitor regional wind erosion and applied to Khuld of Mongolia, which is sensitive to drought and desertification. The system was composed of instruments that observed blown sand, vegetation amount, land surface wetness, and landscape features related to regional wind erosion. Sixteen blown sand and eight sandstorm events were evaluated from 5 March to 5 June 2023 (i.e., during the Asian dust season in northeast Asia). The normalized difference vegetation index and visible images showed that the vegetation amount was considerably less, and the developed moisture index related to land surface wetness indicated dry conditions. Combining the results of blown sand, these indices, and visible images, land surface conditions during the analysis period were likely to occur with blown sand events.

Iterative UKF under generalized maximum correntropy criterion for intermittent observation systems with complex non-Gaussian noise

The traditional unscented Kalman filters (UKFs) under the maximum correntropy criterion provide a powerful tool for nonlinear state estimation with heavy-tailed non-Gaussian noise. Nevertheless, the above-mentioned filters may yield biased estimates because the Gaussian kernel function can only handle certain types of non-Gaussian noise. Additionally, the use of statistical linearization methods can result in approximation errors when solving linear observation equations, while the system may also experience observation data loss. Therefore, a new iterative UKF with intermittent observations under the generalized maximum correntropy criterion is proposed for systems with complex non-Gaussian noise, called GMCC-IO-IUKF. Firstly, the connection between the UKF with and without intermittent observations is established by designing a coefficient matrix including intermittent observation variables, so as to derive the UKF with intermittent observations under the maximum correntropy criterion. Secondly, for the measurement update of GMCC-IO-IUKF, a nonlinear regression augmented model that can deal with both prediction and observation errors is established using the coefficient matrix and the nonlinear function. To better adapt to different types of non-Gaussian noise, the generalized Gaussian kernel function is substituted for the traditional Gaussian kernel function. Theoretically, GMCC-IO-IUKF can achieve better estimation performance by directly employing the nonlinear function and the latest iteration value. Finally, a classical target tracking model is used to evaluate the estimation performance and feasibility of our proposed GMCC-IO-IUKF algorithm. It appears from the experiment results that our proposed GMCC-IO-IUKF can not only promote estimation precision but also handle complex non-Gaussian noise flexibly.

Design of an infrared/visible endoscope system for HL-3 divertor observation

This paper presents the design and test results of an infrared/visible endoscope system for diagnosing divertor in HL-3 tokamak. The system is designed to view the plasmas tangentially with multi-imaging systems including two infrared imaging systems and three visible imaging systems. Several filters based visible imaging measurements are thus available simultaneously for divertor physics investigation. The performance of the optical design is explored, and the manufacture of the system and the test results are presented. The characteristics of the advanced divertor configurations, such as multi-strike points and two X-points, are observed clearly with the endoscope system.

A theoretical framework for robust implementation of in situ measurements of ocean currents and waves in dynamics of mooring systems

We develop an approximated method to solve analytically the equations of motion that describe mooring line dynamics in a one-dimensional model. For the first time, we derive integral closed-form expressions to compute dynamic properties of mooring lines subject to ocean currents and waves of arbitrary time and spatial dependence, in terms of modified Bessel functions. This is done by decomposing the mooring line in three regions where different approximations and mathematical techniques of solution are carried out. Our analytical results provide a robust framework to simulate and analyze extreme realistic oceanic events when data from in situ ocean observation systems are available, regardless of the resolution or coarseness of subsurface measurements and even for long acquisition times. In order to prove the advantages of this approach, we have processed data from two stations in the National Data Buoy Center of the National Oceanic and Atmospheric Administration. From simulations with ocean currents data, we have gained insights into the coupling of the spatial modulation of ocean currents with the characteristic wavelengths of elastic lines. From simulations with ocean waves data, we have defined a scheme to analyze wave data and identify the contribution of each subset of frequency peaks to the net fluctuation of mooring line tension. This could be useful for classification of irregular waves based on their impact on mooring line tension. The development of better tools that integrate theoretical and experimental findings is necessary for the assessment of marine structures under the environmental conditions associated with climate change.

Differences among the total electron content derived by radio occultation, global ionospheric maps and satellite altimetry

In recent years, significant progress has been in ionospheric modeling research through data ingestion and data assimilation from a variety of sources, including ground-based global navigation satellite systems, space-based radio occultation and satellite altimetry (SA). Given the diverse observing geometries, vertical data coverages and intermission biases among different measurements, it is imperative to evaluate their absolute accuracies and estimate systematic biases to determine reasonable weights and error covariances when constructing ionospheric models. This study specifically investigates the disparities among the vertical total electron content (VTEC) derived from SA data of the Jason and Sentinel missions, the integrated VTEC from the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) and global ionospheric maps (GIMs). To mitigate the systematic bias resulting from differences in satellite altitudes, the vertical ranges of various VTECs are mapped to a standardized height. The results indicate that the intermission bias of SA-derived VTEC remains relatively stable, with Jason-1 serving as a benchmark for mapping other datasets. The mean bias between COSMIC and SA-derived VTEC is minimal, suggesting good agreement between these two space-based techniques. However, COSMIC and GIM VTEC exhibit remarkable seasonal discrepancies, influenced by the solar activity variations. Moreover, GIMs demonstrate noticeable hemispheric asymmetry and a degradation in accuracy ranging from 0.7 to 1.7 TECU in the ocean-dominant Southern Hemisphere. While space-based observations effectively illustrate phenomena such as the Weddell Sea anomaly and longitudinal ionospheric characteristics, GIMs tend to exhibit a more pronounced mid-latitude electron density enhancement structure.

Tropical Cyclones in the GEOS-S2S-2 Subseasonal Forecasts

Abstract This paper analyzes the climatology, prediction skill, and predictability of tropical cyclones (TCs) in NASA’s Goddard Earth Observing System Subseasonal to Seasonal (GEOS-S2S) forecast system version 2. GEOS reasonably simulates the number and spatial distribution of TCs compared to observations except in the Atlantic where the model simulates too few TCs due to low genesis rates in the Caribbean Sea and Gulf of Mexico. The environmental conditions, diagnosed through a genesis potential index, do not clearly explain model biases in the genesis rates, especially in the Atlantic. At the storm scale, GEOS reforecasts replicate several key aspects of the thermodynamic and dynamic structures of observed TCs, such as a warm core and the secondary circulation. The model, however, fails to simulate an off-center eyewall when evaluating vertical velocity, precipitation, and moisture. The analysis of the prediction skill of TC genesis and occurrence shows that GEOS has comparable skill to other global models in WMO S2S archive and that its skill could be further improved by increasing the ensemble size. After calibration, GEOS forecasts are skillful in the western North Pacific and southern Indian Ocean up to 20 days in advance. A model-based predictability analysis demonstrates the importance of the Madden–Julian oscillation (MJO) as a source of predictability of TC occurrence beyond the 14-day lead time. Forecasts initialized under strong MJO conditions show evidence of predictability beyond week 3. However, due to model biases in the forecast distribution, there are notable gaps between the MJO-related prediction skill and predictability, which require further study.