Multiple Stations Research Articles

Characterization of the skill of the CORDEX-Africa regional climate models to simulate regional climate setting in the East African Transboundary Omo Gibe River Basin, Ethiopia

Regional climate models (RCMs) that produce good outputs in one region or for specific variables may underperform for others. Thereby, assessing the performance of various model simulations and their corresponding mean ensemble is critical in identifying the most suitable models. In this regard, a study was conducted to evaluate the performance of ten RCMs against observations from multiple ground-based stations in the East African Transboundary Omo Gibe River Basin, Ethiopia, during the baseline period of 1986–2005. The study evaluated the models' ability to replicate various aspects of climatic variables and their corresponding statistical indicators. The results confirmed that RCMs have varying abilities to reproduce climatic conditions across the basin. The ensembles and RACMO22T (EC-EARTH) were better at replicating the average annual precipitation distribution. Meanwhile, the CCLM4-8-17 (MPI) together with the ensembles better captured the measured precipitation annually, despite the discrepancies in the actual magnitudes. All RCMs were able to simulate the seasonal precipitation patterns effectively, with RACMO22T (EC-EARTH), CCLM4-8-17 (CNRM), RCA4 (CNRM), CCLM4-8-17 (MPI), and REMO2009 (MPI) models captured superior, excluding the maximum value. Interannual and seasonal rainfall pattern variations were more significant than variations in air temperature. Additionally, a better correlation was observed between actual and simulated precipitation at multiple separate monitoring places. The RCA4 (MPI) and CCLM4-8-17 (MPI) demonstrated reasonable minimum and maximum temperatures. The RCA4 (MIROC5) model was more effective in reproducing extreme precipitation events. However, all RCMs and their ensembles tended to overestimate the return periods of these events. In general, the research highlights the importance of selecting reliable RCMs that better replicate observed climatic settings and employing the ensemble mean of top-performing models following systematic bias adjustment for a specific application.

Review of electric vehicle (EV) charging using renewable solar photovoltaic (PV) nano grid

This review article gives a comprehensive review of existing research on renewable solar photovoltaic (PV) nanogrid, which is described from small-scale power system with a single domain for reliability, control, and power quality (PQ) for electric vehicle (EV) charging. A primary feeder on the Microgrid is connected to a nanogrid test bed that includes PV as power source, a battery energy storage system (BESS), smart-inverter multiple and EV charging stations (EVCS). The control algorithms are graded on four metrics: (1) voltage profiles, (2) renewable penetration, (3) PV curtailed and (4) net power flows. To investigate the local power quality, a steady-state power flow model of the nano-grid is created. The control algorithms successfully employ the battery to shift the nano-grid peak load while limiting the nano-grid demand to set level. Furthermore, an increasing emphasis is being placed on commonly used strategies for addressing the characteristics of each renewable system. This review paper characterizes the dynamic operation of 4 distinct BESS control algorithms for solar EV charging nanogrid: (1) peak load shifting, (2) reduce peak period impact, (3) cap demand, and (4) photovoltaic capture. These control modes are executed and analyzed on real-world nano-grid site, and optimal BESS control modes are assessed in terms of (1) solar electric vehicle charging, (2) power quality, (3) grid net demand, (4) photovoltaic curtailment, and (5) solar penetration. Finally, the problems highlight research gaps, and discussions on future trends are critical for enhancing the general technology of the renewable solar photovoltaic nano-grid for EV charging.

Prediction of the soil water content in the Luanhe river basin based on CMIP6

A hydrological model (Soil and Water Assessment Tool, SWAT) of the Luanhe River Basin was built based on collected hydrological and meteorological data to better understand how the soil water content varies under climate change. To assess the applicability of the eight selected global climate models (GCMs), four single indicators and one comprehensive indicator were calculated at multiple stations. According to the evaluation results, a weighted multimode ensemble was established. To downscale and correct the deviation from the meteorological data while maintaining the trend signal, the enhanced Delta-DCSI technique was utilized. Eventually, the corrected GCM data were used to drive the SWAT model to simulate the spatial and temporal changes in the soil water content in the Luanhe River Basin under future scenarios. The results indicated that (1) the SWAT model was suitable for and capable of simulating hydrological processes in the basin; (2) the SWAT model can be accurately driven by multimodel ensemble data after model optimization; (3) the soil water content in the basin slowly increased and fluctuated over time; and (4) in the future, lower soil water contents will occur in a few northern subbasins, and consistently higher water contents will occur in the remaining areas. The findings could provide a scientific basis for water resource management and ecological environmental protection in the Luanhe River Basin.

Relating steep REE patterns in modern volcanism and the development of an amphibole-rich middle to lower crust at the Colombian magmatic arc: A geochemical and receiver functions approach

In this study, I integrated geochemical modeling and receiver functions to test for magmatic fractionation above a garnet pyroxenite arc root structure beneath the Colombian magmatic arc (Northern Andes), as a feasible mechanism to explain the record of steep rare earth element (REE) patterns in modern volcanism and the development of an amphibole-rich middle to lower crust. Magmatic fractionation was evaluated through the geochemical modeling of volcanic rocks that show steep REE patterns with high Sr/Y and La/Yb ratios. A well-defined REE geochemical trend was accomplished for all samples at temperature and pressure estimates that are consistent with middle to lower crustal conditions (∼825-935 °C, ∼10.0–14.7 kbar). From the integration of receiver functions, after analyzing the middle to lower crustal signals at multiple stations on top of active volcanoes, the deep crust of the arc is interpreted as a seismically fast (Vs: ≥4.5 km/s) and dense arc root (≥3.3 g/cm3), above which, lies a low-velocity zone with a horizontal to tilted anisotropy axis. This anisotropic low-velocity region is proposed as a partially molten domain related to the evolution of the arc system. The proposed magmatic fractionating model explains satisfactorily the genesis of rocks with steep REE patterns and high Sr/Y (>40) and La/Yb (>20) ratios as a counterpart for a clinopyroxene amphibolite residuum composing deep levels of the arc crust. I suggest that these fractionating processes are playing an important role in the construction of the Colombian magmatic arc, enabling the formation of a dense garnet pyroxenite arc root, a genetically related amphibole-rich middle to lower crust, and the derivation of steep REE patterns with high Sr/Y and La/Yb ratios in the modern Colombian volcanism.

Hierarchical Bayesian calibration of regional moment magnitude predictive models

Onsite Earthquake Early Warning (EEW) applications require robust predictive models that can relate the early part of the recorded seismic waves to seismicity and/or ground shaking characteristics, the latter representing the variables used to make decisions about raising or not an alarm. The use of limited information to establish these models within a real-time estimation setting, typically corresponding to the first few seconds after the identification of the seismic wave arrival, introduces significant uncertainty (variability) in the predictions. To increase the number of historical observations available for the model calibration, data from multiple seismic stations within the same geographic region are utilized for the latter task. Unfortunately, this introduces additional sources of variability in the predictive model development, originating from local geographical effects and site conditions that affect differently the underlying physics at each station. This paper introduces a hierarchical Bayesian model updating framework to address this challenge. To better focus the research effort, the calibration of earthquake magnitude nonlinear regression models based on the seismic maximum predominant period is investigated, though underlying principles can be extended to other type of decision variables and intensity measures used in EEW applications. Within the hierarchical updating framework, the predictive models for each station are separately calibrated (first level), while sharing information across the stations by selecting common calibration hyper-parameters (second level). This facilitates a reduction of the variability of the estimates originating from regional wave propagation effects while simultaneously providing sufficient amount of data for calibrating the predictive models even for stations with scarce available records. To support computational efficiency in the hierarchical calibration, conjugate priors are utilized, while for accommodating nonlinear regression relationships samples from the posterior distribution, representing the model calibration, are obtained using Metropolis-within-Gibbs sampling. As illustrative case study, the application to the Sichuan region of Southwestern China is examined, with extensive comparisons performed for the accuracy and robustness of the resultant probabilistic predictive models.

Strategies analysis for improving SWAT model accuracy and representativeness of calibrated parameters in sediment simulation for various land use and climate conditions

Sediment yields in a watershed are usually affected by various natural disturbances and anthropogenic activities, and can be simulated by using hydrological models. However, due to the availability and type of data (i.e., continuous or discrete) of measured sediment data, model calibration and validation performances could be significantly affected and the calibrated parameters may not be representative for the area. In this study, the sediment rating curves (SRCs) were developed to increase measurements of sediments for model calibration, and five strategies of using measured and estimated sediment data on improving a hydrological model (Soil and Water Assessment Tool, SWAT) calibration and validation for multiple sediment stations were analyzed. The five strategies were: using measured sediment data (S1), using measured data and different portions of estimated sediment during typhoon events (S2 to S4), and using estimated sediment data during entire simulation period (S5). The results showed that although the S1 mostly performed better than other sediment strategies, S4 and S5 were also suitable for improving sediment simulation at the downstream stations (STN1, 2, and 3) and upstream station (STN4), respectively. Moreover, the impact of incorporating different portions of typhoon-induced sediment data (S2, S3, and S4) on the model performance showed differently at stations. In sum, the proposed analytical procedure is expected to be useful for calibrating sediment parameters of hydrological models with limited or highly unevenly distributed measured sediment data.

Graph Machine Learning for Improved Imputation of Missing Tropospheric Ozone Data.

Gaps in the measurement series of atmospheric pollutants can impede the reliable assessment of their impacts and trends. We propose a new method for missing data imputation of the air pollutant tropospheric ozone by using the graph machine learning algorithm "correct and smooth". This algorithm uses auxiliary data that characterize the measurement location and, in addition, ozone observations at neighboring sites to improve the imputations of simple statistical and machine learning models. We apply our method to data from 278 stations of the year 2011 of the German Environment Agency (Umweltbundesamt - UBA) monitoring network. The preliminary version of these data exhibits three gap patterns: shorter gaps in the range of hours, longer gaps of up to several months in length, and gaps occurring at multiple stations at once. For short gaps of up to 5 h, linear interpolation is most accurate. Longer gaps at single stations are most effectively imputed by a random forest in connection with the correct and smooth. For longer gaps at multiple stations, the correct and smooth algorithm improved the random forest despite a lack of data in the neighborhood of the missing values. We therefore suggest a hybrid of linear interpolation and graph machine learning for the imputation of tropospheric ozone time series.

Robust Beamforming for IRS-Aided SWIPT in Cognitive Satellite and Terrestrial Networks

This paper proposes a robust beamforming (BF) scheme for intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) in a cognitive satellite and terrestrial network (CSTN). The satellite network serves multiple earth stations through the multicast transmission, while the terrestrial network operating at the same spectrum implements the SWIPT through IRS-aided multicast technology. Assuming that the imperfect channel state information (CSI) is available, we formulate an optimization problem to maximize the minimum achievable rate of the information receivers (IRs), subject to the transmit power budget, achievable rate and harvesting energy requirements. To address this nonconvex problem, we propose a tight bound robust BF algorithm based on Lagrange duality and alternating optimization (AO) to jointly optimize the active and passive beamformers for the base station, satellite and IRS, respectively. Simulation results confirm the robustness and superiority of our proposed BF scheme over other related works.

Multi-Port Hybrid DC Circuit Breaker Based on Bridge Commutation Branch

Flexible DC grids have become an important means to support large-scale new energy integration utilization due to their advantages of flexible power control and high efficiency. Flexible DC grids generally consist of multiple converter stations and DC lines. Therefore, there are multiple line intersection points, and DC circuit breakers (DCCBs) need to be installed at the intersection to isolate faults. Multi-port hybrid DCCBs (MPHCBs) based on topology optimization can greatly reduce the cost by replacing multiple two-port DCCBs with one multi-port DCCB at the intersection. However, currently researched multi-port DCCBs still have a high cost and large volume while lacking engineering verification. This paper proposes a hybrid multi-port DCCB based on a bridge commutation branch that greatly reduces the number of power electronic devices used and the cost and volume of the DCCB. First, the topology and control method of the proposed MPHCB are studied, and its cost and reliability are analyzed. Then, the breaking test of the proposed MPHCB based on the bridge commutation branch is carried out at a 15 kV voltage level. The results show that the proposed MPHCB can interrupt a 15 kA current in 3 ms, which verifies the feasibility of the scheme.

Anthropogenic trace metals in Setiu Wetland: Spatial and seasonal distribution and implications for environmental health

The growing urban wastewater volume poses a major global environmental challenge, especially in developing nations where inadequate treatment and discharge impact clean water availability. This study focused on Setiu Wetland, aiming to analyze seasonal and spatial variations of trace metals in particulate form from anthropogenic and pathogenic sources. Surface water samples were collected from multiple stations, measuring physical parameters like salinity, temperature, pH, dissolved oxygen (DO), and dissolved organic carbon (DOC). Samples were filtered using ultrafiltration and analyzed with inductively coupled plasma mass spectrometry (ICP-MS). Correlations between trace metals and other parameters were examined to understand their dynamics in the area. The study found that particulate trace metals were influenced by anthropogenic sources, including municipal, industrial, agricultural, and domestic wastewater discharge. The measured concentrations for Cd, Cu, Pb, Fe, and Zn were 10.9 ± 5.1, 31.8 ± 16.8, 72.5 ± 28.5, 20,206 ± 17,929, and 46,742 ± 30,898 μg/g, respectively, indicating their significant presence. To gain deeper insights, a model incorporating principal component analysis (PCA) analyzed the relationships between trace metals, dissolved oxygen levels, and factors like pH, temperature, salinity, and DOC. Regression techniques predicted Cd, Zn, and Fe concentrations based on salinity, with pH serving as an additional predictor for Cu. Moreover, Pb concentration was influenced by temperature, dissolved oxygen, and dissolved organic carbon. These findings emphasize the importance of regular monitoring and assessment of trace metals in protected tropical areas and wetlands, helping mitigate environmental risks associated with wastewater discharge and preserving these ecosystems.

Multiple stations in an integrated design of cardiovascular system morphology for medical undergraduate

The morphology knowledge is essential for clinical, diagnostic and surgical practice in medicine. However, it is a great challenge teaching this science in an integrated curriculum, since it has the need of active methods associated with technology, in a total impaired workload. Therefore, this work described an educational design of multiple practice stations in order to teach morphology of the cardiovascular system in the undergraduate medical education. This activity was conducted in the Multicampi School of Medical Sciences of Federal University of Rio Grande do Norte (UFRN) in Caicó/Brazil. Four anatomy stations and four embryology and histology stations of heart and blood vessels were drawn up. Anatomy stations approached cardiac cycle and cardiac valves (1 A); mediastinum and pericardium (2 A); internal morphology and heart conduction system (3 A) and external morphology of heart (4 A). Whereas embryology and histology stations approached embryogenesis of the heart (1B); cardiac microanatomy and fetal circulation (2B); and vascular microanatomy (3B;4B). All the stations emphasized cognitive and psychomotor fields associated with clinical correlations to active application of morphology knowledge. The multiple stations were considered as an organized, productive, enlightening and riveting alternative to medical students and teaching staff. This innovative experience integrated and energized the three areas of morphological sciences, resignifying the teaching and learning of cardiovascular system morphology.

A pre-seismic anomaly detection approach based on graph attention isomorphism network

Pre-seismic anomaly detection plays a crucial role in reducing economic losses and casualties caused by earthquakes. This paper proposes a novel four-step approach for pre-seismic anomaly detection. In the first step, a series of pre-seismic features are extracted by analyzing the earthquake catalog and geomagnetic signals. In the second step, the multi-view learning strategy is employed to obtain fusion features. In the third step, multiple seismic stations in one seismic zone are treated as a seismic station network, and a pre-seismic anomaly detection model is constructed based on the station network. In the final step, four evaluation indicators are introduced to comprehensively evaluate the detection results. Verification results show that the proposed method is effective and achieves better performance than other existing methods.

How do walking environments along the connecting path affect the disparity in public bicycle usage among multiple docking stations around tube stations?

The integration of public bicycles with tube systems has the potential to enhance urban transportation sustainability and efficiency. However, the utilization of public bicycles can significantly differ among various docking stations, even when they fall within the coverage area of the same tube station. Understanding the factors that attract or discourage individuals from using public bicycles in different locations is essential for informed decision-making regarding infrastructure and policy enhancements. This study examines the disparity in public bicycle usage, with a specific focus on the micro-level walking environments along connecting paths between docking stations and tube stations. Transaction data collected from London public bicycle and tube systems are utilized, alongside micro-level walking environment and macro-level built environment and socio-economic factors. A generalized additive mixed model is employed to capture nonlinear effects and spatial autocorrelations in docking station usage.The results reveal significant effects of walking environments along connecting paths on docking station usage. Specifically, positive associations are observed between bicycle station usage and footpaths quality, presence of pedestrian crossing, and points of interests (e.g., green space) along the connecting path, as well as tube ridership, cycle facilities, and land-use mixture. Conversely, negative relationships are observed with walking distance, physical barriers, road crossed, and bus stops. Moreover, proximity to transit, distance to the city center, and deprivation level demonstrate threshold effects on bicycle station usage. This study provides several practical implications for improving the multi-mode transportation system and the planning of docking stations around tube station, aiming to create cycling-friendly and transit-oriented environments.

HiSTGNN: Hierarchical spatio-temporal graph neural network for weather forecasting

Weather forecasting is an attractive yet challenging task due to its significant impacts on human life and the intricate nature of atmospheric motion. Deep learning-based techniques, utilizing abundant observations, have gained popularity in recent years. However, many existing methods mainly explore temporal patterns of meteorological variables while neglecting the interactions between variables across regions. To address this limitation, we propose HiSTGNN, a novel Hierarchical Spatio-temporal Graph Neural Network, which enables accurate predictions of multiple variables and stations over multiple time steps. HiSTGNN incorporates an adaptive graph learning module that constructs a self-learning hierarchical graph, comprising a global graph representing regions and a local graph capturing meteorological variables for each region. By leveraging graph convolution and gated temporal convolution with a dilated inception as the backbone, we effectively capture hidden spatial dependencies and diverse long-term meteorological trends. Additionally, we introduce dynamic interactive learning to facilitate bidirectional information between the two-level graphs. Experiments on three real-world meteorological datasets demonstrate the superiority of our proposed method compared to seven well-known baselines, including convolutional neural networks, recurrent neural networks, and spatio-temporal forecasting methods. Notably, HiSTGNN achieves remarkable improvements over the state-of-the-art weather forecasting method on the WD_BJ dataset, with a 4.25% decrease in MAE and a 5.34% decrease in RMSE.

Crowdsourcing air temperature data for the evaluation of the urban microscale model PALM—A case study in central Europe

In summertime and during heat events the urban heat island can negatively impact human health in urban areas. In the context of climate change, climate adaptation receives more attention in urban planning. Microscale urban climate modelling can identify risk areas and evaluate adaptation strategies. Concurrently, evaluating the model results with observational data is essential. So far, model evaluation is mostly limited to short-term field campaigns or a small number of stations. This study uses novel crowdsourcing data from Netatmo citizen weather stations (CWS) to evaluate the urban microscale model PALM for a hot day (Tmax ≥ 30°C) in Bochum in western Germany with anticyclonic atmospheric conditions. Urban-rural air temperature differences are represented by the model. A quality control procedure is applied to the crowdsourced data prior to evaluation. The comparison between the model and the crowdsourced air temperature data reveals a good model performance with a high coefficient of determination (R2) of 0.86 to 0.88 and a root mean squared error (RMSE) around 2 K. Model accuracy shows a temporal pattern and night-time air temperatures during the night are underestimated by the model, likely due to unresolved cloud cover. The crowdsourced air temperature data proved valuable for model evaluation due to the high number of stations within urban areas. Nevertheless, weaknesses related to data quality such as radiation errors must be considered during model evaluation and only the information derived from multiple stations is suitable for model evaluation. The procedure presented here can easily be transferred to planning processes as the model and the crowdsourced air temperature data are freely available. This can contribute to making informed decisions for climate adaptation in urban areas.

Assessing spatiotemporal trends of total and extreme precipitation in a subtropical highland region: A climateperspective.

This study used a dataset of 30 years (1990-2020) of daily observations from 24 meteorological stations in the northern highlands of Pakistan to assess trends in extreme precipitation indices. The RClimDex model was used to analyze the indices, and the Modified Mann-Kendal test and the Theil-Sen slope estimator were applied to determine trends and slopes, respectively. The results showed a significant decrease in total annual precipitation amount (PRCPTOT) with varying rates of negative trend from -4.44 mm/year to -19.63 mm/year. The total winter and monsoon precipitation amounts were also decreased during the past three decades. The intensity-based precipitation indices (RX1Day, RX5Day, R95p, R99p, and SDII) showed a significant decrease in extreme intensity events over time, while the count of consecutive dry days (CDD) and consecutive wet days (CWD) indicated a significant decrease in duration at multiple stations. The annual counts of days with precipitation more than or equal to 10 mm (R10), 20 mm (R20), and 25 mm (R25) exhibited a significant decrease in frequency of extreme precipitation events, with the decrease more pronounced in the northern parts of the study domain. The findings of this study indicate a significant decline in the intensity, frequency, and extent of precipitation extremes across the northern highlands of Pakistan over the past 30 years.

A non-parametric panel model for climate data with seasonal and spatial variation

Abstract We consider a panel data model that allows for heterogeneous time trends at different locations. The model is well suited to identifying trends in climate data recorded at multiple stations. We propose a new estimation method for the model and derive an asymptotic theory for the proposed estimation method. For inferential purposes, we develop a bootstrap method for the case where weak correlation presents in both dimensions of the error terms. We examine the finite-sample properties of the proposed model and estimation method through extensive simulated studies. Finally, we use the newly proposed model and method to investigate monthly rainfall, temperature, and sunshine data of the UK, respectively. Overall, we find spring and winter have changed significantly over the past 50 years. Changes vary with respect to locations for the other seasons.

An Efficient Hybrid Multi-Station TDOA and Single-Station AOA Localization Method

Multi-station passive localization algorithms based on hybrid Time Difference of Arrival (TDOA) and Angle of Arrival (AOA) have been thoroughly studied. But it usually requires each station to obtain the AOA of the source and relies on the precise station position. These conditions are generally not satisfied in practical applications. In addition, the geometry between the source and these stations also affects the localization accuracy. This paper studies the passive source localization problem based on multi-station TDOA and single-station AOA measurements. First, we propose a closed-form solution source localization method based on multi-station TDOA and single-station AOA measurements, which requires only the reference station used to calculate TDOA to be able to observe the AOA measurements of the source. It solves the problem of hybrid localization of different numbers of TDOA and AOA measurements. Theoretical analysis proves that the performance of the proposed method can reach the Cramer-Rao Lower Bound (CRLB) under small noise conditions. Then, we model the relationship between the position of each station relative to the source and the localization accuracy. Through the D-optimality criterion, we obtain the optimal geometry between multiple stations and the source Simulation results confirm the validity of these theories.

Harmonics Interaction Mechanism and Impact on Extinction Angles in Multi-Infeed HVDC Systems

Harmonics interaction is an important factor in causing concurrent commutation failures (CCF) of multiple converter stations in multi-infeed HVDC (MI-HVDC) systems, but it was not studied sufficiently in existing literature. In this paper, the mechanism analysis and quantitative calculation of harmonic interaction are investigated. Firstly, the generation and interaction process of harmonic during AC fault events in the MI-HVDC systems with single-terminal connection and hierarchical connection on receiving-end are illustrated, respectively. Then, the common characteristics of harmonic interaction in generalized MI-HVDC systems are concluded. Moreover, to evaluate the impact of harmonic interaction on commutation process quantitatively, an equivalent circuit model of harmonic transfer in MI-HVDC system is established. Based on this equivalent model, the harmonic voltage transfer coefficient is deduced, and the extinction angle of inverters considering harmonic interaction in MI-HVDC systems can be calculated. Finally, the calculation accuracy based on equivalent model is verified via the electromagnetic transient simulation of MI-HVDC system. Simulation results show that harmonic interaction will eventually cause the formation of harmonic source in each converter station of MI-HVDC systems. These harmonic components will change the extinction angle of remote inverters to increase the risk of CCFs.

High-resolution VLBI astrometry of pulsar scintillation screens with the θ - θ transform

ABSTRACT The recent development of $\theta - \theta$ techniques in pulsar scintillometry has opened the door for new high-resolution imaging techniques of the scattering medium. By solving the phase retrieval problem and recovering the wavefield from a pulsar dynamic spectrum, the Doppler shift, time delay, and phase offset of individual images can be determined. However, the results of phase retrieval from a single dish are only known up to a constant phase rotation, which introduces extra parameters when doing astrometry using very long baseline interferometry. We present an extension to previous $\theta - \theta$ methods using the interferometric visibilities between multiple stations to calibrate the wavefields. When applied to existing data for PSR B0834+06, we measure the effective screen distance and lens orientation with five times greater precision than was possible in previous work.