Grid Point Locations Research Articles

Global ionospheric total electron content short-term forecast based on Light Gradient Boosting Machine, Extreme Gradient Boosting, and Gradient Boost Regression

Total Electron Content (TEC) forecasting using machine learning has been extensively preferred in characterizing the spatio-temporal variability of the ionosphere, to support space communication and navigation applications. Although, a variety of machine learning methods have been evolved, still, there is a greater persistence in the prediction of ionospheric TEC due to adverse space weather impacts and the complex behavior of ionosphere. And hence, more sophisticated short-term ionospheric TEC forecasting models should be developed for better prediction accuracy. The present study investigated and evaluated the performance of three models – Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Gradient Boost Regression (GBR) algorithms using the data set for 25 years (1997–2021) GNSS Earth Observation Network System (GEONET) Global mean TEC time-series data. For better prediction accuracy, the data of geomagnetic storm indices Dst and Mg-II index were also utilized in training the three models. One year of data for both high (2015) and low (2020) solar activity were used to test the three models. The prediction plots are used to visualize the level of prediction error for the three algorithms. The statistical results illustrate that the LightGBM model performed better in predicting TEC with an RMSE value of 2.25 TECU (2015) and 0.52 TECU (2020) for high and low solar activity years respectively for global mean TEC data and with an RMSE of 2.34 TECU at Japan grid point location of (34.95°N and 134.05°E). In turn the XGBoost and GBR models provide an absolute TEC forecasting with an RMSE of 2.76 and 2.59 TECU (2015) and 0.60 and 0.55 TECU (2020) correspondingly for global data and as 2.39 and 2.93 TECU for grid point location.

Automation and optimization in daylight analysis and life cycle assessment for automated daylight monitoring device for a metro rail station box in Ahmedabad, India

Daylight analysis and optimization are pre-requisites for energy-efficient and sustainable infrastructure. Conventional practices of designing and providing artificial lighting often ignore the consideration of the availability of natural daylight for the infrastructure. This paper aims to analyze the availability and potential of natural daylight reviewing the sky component type at a designated location, real-time sensor-based illuminance measurement on-site and BIM-based daylight simulation. Consequently, a sensor prototype was deployed to the metro rail station for automated optimization. Firstly, the sky component type of Ahmedabad was determined based on the literature review. Subsequently, sensors were integrated at the strategic grid point location covering the entire metro rail station to collect real-time Illuminance for daylight factor calculation. Furthermore, BIM-based sun path and shadow analysis and daylight simulation were carried out to suggest the possibility and potential of daylight savings. Finally, based on all the analysis and results, a newly designed sensor-based prototype was developed and equipped to enhance automated optimization for daylight savings, which resulted in 29% energy savings monthly. This approach significantly helps the project stakeholders to enhance building energy performance and lead a step closer towards sustainable infrastructure.

Addressing the Spatiotemporal Patterns of Heatwaves in Portugal with a Validated ERA5-Land Dataset (1980–2021)

This study presents a comprehensive analysis of heatwaves in mainland Portugal from 1 October 1980 to 30 September 2021 (41 hydrological years). It addresses a research gap by providing an updated assessment using high-resolution reanalysis daily minimum and maximum temperature data (Tmin and Tmax) from the gridded ERA5-Land dataset, overcoming the lack of publicly available daily temperature records. To assess the representation of the previous dataset, nine different grid-point locations across the country were considered. By comparing monthly ERA5-Land temperature data to ground-based records from the Portuguese Met Office, a monthly validation of the data was conducted for the longest common period, demonstrating good agreement between the two datasets. The heatwave magnitude index (HWMI) was employed to establish the temperature thresholds and thus identify heatwaves (defined as three or more consecutive days above the threshold). With over 640 Tmin heatwave days recorded at each of the nine ERA5-Land grid-points, data analysis revealed a discernible upward trend in Tmin heatwaves. The grid-point situated in the capital city’s urban area, i.e., Lisbon, exhibited the highest number of Tmin heatwave days. With an average of more than 800 Tmax heatwave days over the 41-year period, the northern and interior regions of Portugal had the greatest number of occurrences, reaching up to 916. A kernel rate estimation method was applied to further investigate the annual frequency of Tmin and Tmax heatwave occurrences. Results exhibited clear temperature changes, with a widespread increase in the number of heatwave days over the past two decades, particularly for Tmax. In summary, the occurrence of this phenomenon displayed significant spatial variations, with the southern interior and coastal grid-points experiencing a greater increase in annual Tmax heatwave days, rising from 10 to 30 between 2018 and 2019.

Development of high-resolution gridded data for water availability identification through GRACE data downscaling: Development of machine learning models

Estimation of total water availability has paramount importance in planning sustainable development of a region, particularly in arid water-scarce areas. Coarse-resolution of existing total water availability or terrestrial water storage anomaly (TWSA) data is the major limitation of their applications in different sectors. An attempt has been made to downscale Gravity Recovery and Climate Experiment (GRACE) TWSA data to develop a high-resolution gridded data product of the total water availability of Iraq. European reanalysis (ERA5) precipitation, evapotranspiration, surface runoff, subsurface runoff and soil water contents data were used to downscale GRACE 1.0° spatial resolution monthly TWSA to 0.1° spatial resolution for the period 2002–2020. A machine learning (ML)-based recursive feature elimination algorithm was used to identify the optimum input combination according to the nonlinear relationship of ERA5 variables with GRACE water equivalence data. The selected subset of inputs was used to develop the downscaling models using three classical ML algorithms for the available GRACE measurement points over Iraq. The models were calibrated at 70% of GRACE grid point locations and validated in the rest of the points. Finally, the model was used to predict TWSA at each ERA5 grid point to generate Iraq's high-resolution water availability dataset. The results showed higher performance of random forest in downscaling TWSA compared to other algorithms. The model estimated the TWSA at validation points with Kling-Gupta Efficiency (KGE) in the range of 0.5–0.91 and Nash-Sutcliff Efficiency (NSE) between 0.54 and 0.88. The modelled high-resolution TWSA data shows higher availability of water resources in the north, particularly northeast of Iraq, and the least in the southeast. The technique developed in this study can be implemented in developing a high-resolution gridded water availability dataset from satellite GRACE data in the region where in-situ estimation is very limited.

An Extreme Value Analysis of Long-Term GNSS Ionospheric Total Electron Content Data Observed at Japan Grid Point Location (34.95° N and 134.05° E)

An Extreme Value Analysis of Long-Term GNSS Ionospheric Total Electron Content Data Observed at Japan Grid Point Location (34.95° N and 134.05° E)

A Novel Method for Modeling Lowest-Level Vertical Motion

Abstract A new method for modeling the lowest model level vertical motion is described and validated. Instead of smoothing terrain heights, the new method calculates the terrain gradient on a high-resolution grid and averages the gradient values around a gridpoint location. In essence, the method provides a way to achieve some of the impact of very steep terrain on the flow without the computational overhead associated with the very high grid resolution needed to fully resolve complex terrain. The more accurate depiction of the terrain gradient leads to an increase in orographic vertical motion and causes rainfall to occur more often over the windward-facing mountain slopes, consistent with observations. Model results are compared with rain gauge data during the month of January 2016 as well as radar data from a case study on 9 March 2012. When implemented in the Weather Research and Forecasting (WRF) Model over the island of Oahu and compared with the current WRF method, the model precipitation forecast skill is improved. The new method produces more precipitation over the island during January 2016, which is closer to the observed value. On 9 March 2012, the new method clearly focuses the precipitation over the Ko‘olau Mountains, reducing the number of false alarm forecasts by nearly one-half. Although the changes to model precipitation skill were small, they were generally positive.

A New Global Total Electron Content Empirical Model

Research on total electron content (TEC) empirical models is one of the important topics in the field of space weather services. Global TEC empirical models based on Global Ionospheric Maps (GIMs) TEC data released by the International GNSS Service (IGS) have developed rapidly in recent years. However, the accuracy of such global empirical models has a crucial restriction arising from the non-uniform accuracy of IGS TEC data in the global scope. Specifically, IGS TEC data accuracy is higher on land and lower over the ocean due to the lack of stations in the latter. Using uneven precision GIMs TEC data as a whole for model fitting is unreasonable. Aiming at the limitation of global ionospheric TEC modelling, this paper proposes a new global ionospheric TEC empirical model named the TECM-GRID model. The model consists of 5183 sections, corresponding to 5183 grid points (longitude 5°, latitude 2.5°) of GIM. Two kinds of single point empirical TEC models, SSM-T1 and SSM-T2, are used for TECM-GRID. According to the locations of grid points, the SSM-T2 model is selected as the sub-model in the Mid-Latitude Summer Night Anomaly (MSNA) region, and SSM-T1 is selected as the sub-model in other regions. The fitting ability of the TECM-GRID model for modelling data was tested in accordance with root mean square (RMS) and relative RMS values. Then, the TECM-GRID model was validated and compared with the NTCM-GL model and Center for Orbit Determination in Europe (CODE) GIMs at time points other than modelling time. Results show that TECM-GRID can effectively describe the Equatorial Ionization Anomaly (EIA) and the MSNA phenomena of the ionosphere, which puts it in good agreement with CODE GIMs and means that it has better prediction ability than the NTCM-GL model.

Sparse Bayesian Approach for DOD and DOA Estimation With Bistatic MIMO Radar

This study addresses the problem of joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation with bistatic multiple-input multiple-output (MIMO) radar. To the best of our knowledge, a limited number of sparse Bayesian learning (SBL)-based methods exist that can be applied to joint DOD and DOA estimation. This is because of the heavy computational load and strong correlation between the nearby basis. To overcome these challenges, we present a new coarse non-uniformly sampled 2D grid and propose an improved SBL-based method for joint estimation of the DOD and DOA in MIMO radar. With the new grid, the computational load can be significantly reduced, and the nearby 2D grid points can provide a low correlation basis. To handle the modeling error derived from the coarse grid, we also introduce a modified linear approximation method into the SBL framework in which the locations of grid points are considered as adjustable parameters, and the grid points can be updated recursively. Finally, a block majorization-minimization algorithm is applied to perform Bayesian inference. Experimental results indicate that our method can improve the joint DOD and DOA estimation performance, particularly in the case of low signal-noise-ratio, limited snapshots, or correlated signals.

A Block Alternating Optimization Method for Direction-of-Arrival Estimation With Nested Array

In this paper, direction-of-arrival estimation using nested array is studied in the framework of sparse signal representation. With the vectorization operator, a new real-valued non-negative sparse signal recovery model, which has a wider virtual array aperture, is built. To leverage celebrated compressive sensing algorithms, the continuous parameter space has to be discretized to a number of fixed grid points, which inevitably incurs modeling error caused by off-grid gap. To remedy this issue, a block alternating optimization method is put forth that jointly estimates the sparse signal and refines the locations of grid points. Specifically, inspired by the majorization minimization, the proposed method iteratively minimizes a surrogate function majorizing the given objective function, where only a single block of variables are updated per iteration while the remaining ones are kept fixed. The proposed method features affordable computational complexity, and numerical tests corroborate its superior performance relative to existing alternatives in both overdetermined and underdetermined scenarios.

Sparse Bayesian learning for off-grid DOA estimation with nested arrays

The existing off-grid sparse Bayesian learning (SBL) DOA estimation method for nested arrays suffers from two major drawbacks: reduced array aperture and high modeling error. To solve these issues, a new data model formulation is first presented in this paper, in which we take the noise variance as a part of the unknown signal of interest, so as to learn its value by the Bayesian inference inherently. Then, we provide a novel grid refining procedure to eliminate the modeling error caused by off-grid gap, where we consider the locations of grid points as adjustable parameters and proceed to refine the grid point iteratively. Simulation results demonstrate that our method significantly improves the DOA estimation performance especially using a coarse grid.

Grid Evolution Method for DOA Estimation

Off-grid direction of arrival (OGDOA) estimation methods deal with the situations where true direction of arrivals (DOAs) are not on the discretized sampling grid. However, existing OGDOA estimation methods are faced with a tradeoff between density of initial grid and computational workload. Furthermore, these methods fail if more than one true DOA is located in a same grid interval. In order to speed up the OGDOA estimation methods and solve the problem of more than one DOA in one initial grid interval, a grid evolution direction of arrival (GEDOA) estimation method is proposed. Based on the combination of OGDOA estimation methods and grid refinement, this new approach makes the grid nonuniformly evolve from coarse to dense. The proposed method contains two subprocesses, i.e., learning process and fission process. The learning process aims to update locations of grid points and estimate DOAs. The fission process aims to generate new grid points and guarantees that there is only one DOA in a grid interval. The two processes iterate alternatively. Finally, an adaptive and nonuniform grid and an estimated spatial spectrum based on this grid are achieved. Compared with the previous methods, GEDOA has better computational efficiency because fewer grid points are used at each iteration. Furthermore, GEDOA has better resolution and lower MSE at relative high SNR. This is because that the evolved grid obtained by this new approach is information adaptive. Numerical simulations validate the effectiveness of the proposed method.

Off-Grid Localization in MIMO Radars Using Sparsity

In this letter, we propose a new accurate approach for target localization in multiple-input multiple-output (MIMO) radars, which exploits the sparse spatial distribution of targets to reduce the sampling rate. We express the received signal of a MIMO radar in terms of the deviations of target parameters from the grid points in the form of a block sparse signal using the expansion around all the neighbor points. Applying a block sparse recovery method, we can estimate both the grid-point locations of targets and these deviations. The proposed approach can yield more accurate localization with higher detection probability compared with its counterparts. Moreover, the proposed approach can reduce the computational complexity.

Reconstruction Algorithm of Calibration Map for RPT Techniques in Quadrilateral Bubble Column Reactor Using MCNPX Code

Radioactive Particle Tracking (RPT) is non-invasive evaluation technique capable of visualising and tracking the motion of the identified phase in bubble column reactor. The single radioactive particle emits γ-ray, and its movement in the column is tracked with the aid of arrays radiation detectors. In this study, Monte Carlo approach was programmed to reconstruct the particle tracer position so-called calibration map. The iterative reconstruction algorithms is used to generate and calculate the 2600 coordinates of calibration map from the number of photon counts from the ten NaI scintillation detectors. To validate the simulation precision, a spiral trajectory of radioactive particle inside the bubble column region consist of 84 grid point locations of the particle were applied. Calibration algorithm was developed for radioactive isotopes Au-198 and Sc-46 particle position verification and determination of statistical uncertainty from the introduction of a various number of primary photon emission. The result of the studies proved that higher number of particles used in the algorithm for position reconstruction gives more accuracy and we found that the Sc-46 obtain more accurate calculation and shows low statistical level than Au-198 particle. The outcome of this simulation based on random sampling from Monte Carlo N-Particle Extended (MCNPX) demonstrated that the calibration map could successfully be implemented in RPT technique to observe the dynamic movement of radioactive particle which represents the tracked media in the quadrilateral bubble column.

Reconstruction Algorithm of Calibration Map for RPT Techniques in Quadrilateral Bubble Column Reactor Using MCNPX Code

Radioactive Particle Tracking (RPT) is non-invasive evaluation technique capable of visualising and tracking the motion of the identified phase in bubble column reactor. The single radioactive particle emits ?-ray, and its movement in the column is tracked with the aid of arrays radiation detectors. In this study, Monte Carlo approach was programmed to reconstruct the particle tracer position so-called calibration map. The iterative reconstruction algorithms is used to generate and calculate the 2600 coordinates of calibration map from the number of photon counts from the ten NaI scintillation detectors. To validate the simulation precision, a spiral trajectory of radioactive particle inside the bubble column region consist of 84 grid point locations of the particle were applied. Calibration algorithm was developed for radioactive isotopes Au-198 and Sc-46 particle position verification and determination of statistical uncertainty from the introduction of a various number of primary photon emission. The result of the studies proved that higher number of particles used in the algorithm for position reconstruction gives more accuracy and we found that the Sc-46 obtain more accurate calculation and shows low statistical level than Au-198 particle. The outcome of this simulation based on random sampling from Monte Carlo N-Particle Extended (MCNPX) demonstrated that the calibration map could successfully be implemented in RPT technique to observe the dynamic movement of radioactive particle which represents the tracked media in the quadrilateral bubble column.

Seasonal Analysis of Cloud Objects in the High-Resolution Rapid Refresh (HRRR) Model Using Object-Based Verification

AbstractIn this study, object-based verification using the method for object-based diagnostic evaluation (MODE) is used to assess the accuracy of cloud-cover forecasts from the experimental High-Resolution Rapid Refresh (HRRRx) model during the warm and cool seasons. This is accomplished by comparing cloud objects identified by MODE in observed and simulated Geostationary Operational Environmental Satellite 10.7-μm brightness temperatures for August 2015 and January 2016. The analysis revealed that more cloud objects and a more pronounced diurnal cycle occurred during August, with larger object sizes observed in January because of the prevalence of synoptic-scale cloud features. With the exception of the 0-h analyses, the forecasts contained fewer cloud objects than were observed. HRRRx forecast accuracy is assessed using two methods: traditional verification, which compares the locations of grid points identified as observation and forecast objects, and the MODE composite score, an area-weighted calculation using the object-pair interest values computed by MODE. The 1-h forecasts for both August and January were the most accurate for their respective months. Inspection of the individual MODE attribute interest scores showed that, even though displacement errors between the forecast and observation objects increased between the 0-h analyses and 1-h forecasts, the forecasts were more accurate than the analyses because the sizes of the largest cloud objects more closely matched the observations. The 1-h forecasts from August were found to be more accurate than those during January because the spatial displacement between the cloud objects was smaller and the forecast objects better represented the size of the observation objects.

Implementation of TF/SF boundary for unconditionally stable five-step LOD-FDTD method

The implementation of total-field/scattered-field (TF/SF) boundary for the unconditionally stable five-step locally one-dimensional finite-difference time-domain (LOD5-FDTD) method is proposed in this paper. There are 12 cases to be considered for each sub step, among them 8 cases for electric field grid points, based on the computational formulations and the particular location of grid points at the TF/SF face. Numerical results show that the plane wave can be successfully introduced into the TF region and maintains unconditional stabilization. Finally, the radar cross section (RCS) of a metal plate is computed, which further verifies the effectiveness of the proposed TF/SF connecting boundary.

Pre‐flood inundation mapping for flood early warning

AbstractIn this study the results of two rainfall‐run‐off simulations were used as input into a MIKE11 GIS and hydrological modelling process for flood inundation mapping based on the flood event (27 September to 8 October 2000) in Malaysia of the Langat River Basin area. Separate inundation maps were generated for the recorded observed rainfall and from a developed quantitative precipitation forecast (QPF), which was based on top of the cloud reflectance and brightness temperature (TB) derive from Advanced Very High Resolution Radiometer (AVHRR) and Geostationary Meteorological Satellite (GMS) satellite data sets. The QPF had rain rates between 3 and 12 mm/h for the 264 h rainfall duration. While the actual recorded rainfall for the same duration was used for the observed. The objective of the study was to compare the similarities of the flood inundation generated from the QPF run‐off with that generated from the rainfall‐run‐off of the actual flood event. The accuracies of the maps were verified using grid point locations of flooded areas taken during the event. The selected sampled point of the verification showed an accuracy of 70% of the QPF on the observed flood map. Sampled points measured flood extent, coverage and depth of flood in the basin area.

Predictability of the Performance of an Ensemble Forecast System: Predictability of the Space of Uncertainties

Abstract The performance of an ensemble prediction system is inherently flow dependent. This paper investigates the flow dependence of the ensemble performance with the help of linear diagnostics applied to the ensemble perturbations in a small local neighborhood of each model gridpoint location ℓ. A local error covariance matrix 𝗣ℓ is defined for each local region, and the diagnostics are applied to the linear space defined by the range of the ensemble-based estimate of 𝗣ℓ. The particular diagnostics are chosen to help investigate the efficiency of in capturing the space of analysis and forecast uncertainties. Numerical experiments are carried out with an implementation of the local ensemble transform Kalman filter (LETKF) data assimilation system on a reduced-resolution [T62 and 28 vertical levels (T62L28)] version of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). Both simulated observations under the perfect model scenario and observations of the real atmosphere in a realistic setting are used in these experiments. It is found that (i) paradoxically, the linear space provides an increasingly better estimate of the space of forecast uncertainties as the time evolution of the ensemble perturbations becomes more nonlinear with increasing forecast time; (ii) provides a more reliable linear representation of the space of forecast uncertainties for cases of more rapid error growth (i.e., for cases of lower predictability); and (iii) the ensemble dimension (E dimension) is a reliable predictor of the performance of in predicting the space of forecast uncertainties.

An efficient dynamic system for real-time robot-path planning

This paper presents a simple yet efficient dynamic-programming (DP) shortest path algorithm for real-time collision-free robot-path planning applicable to situations in which targets and barriers are permitted to move. The algorithm works in real time and requires no prior knowledge of target or barrier movements. In the case that the barriers are stationary, this paper proves that this algorithm always results in the robot catching the target, provided it moves at a greater speed than the target, and the dynamic-system update frequency is sufficiently large. Like most robot-path-planning approaches, the environment is represented by a topologically organized map. Each grid point on the map has only local connections to its neighboring grid points from which it receives information in real time. The information stored at each point is a current estimate of the distance to the nearest target and the neighbor from which this distance was determined. Updating the distance estimate at each grid point is done using only the information gathered from the point's neighbors, that is, each point can be considered an independent processor, and the order in which grid points are updated is not determined based on global knowledge of the current distances at each point or the previous history of each point. The robot path is determined in real time completely from the information at the robot's current grid-point location. The computational effort to update each point is minimal, allowing for rapid propagation of the distance information outward along the grid from the target locations. In the static situation, where both the targets and the barriers do not move, this algorithm is a DP solution to the shortest path problem, but is restricted by lack of global knowledge. In this case, this paper proves that the dynamic system converges in a small number of iterations to a state where the minimal distance to a target is recorded at each grid point and shows that this robot-path-planning algorithm can be made to always choose an optimal path. The effectiveness of this algorithm is demonstrated through a number of simulations.

Dose reconstruction in deforming lung anatomy: Dose grid size effects and clinical implications

In this study we investigated the accumulation of dose to a deforming anatomy (such as lung) based on voxel tracking and by using time weighting factors derived from a breathing probability distribution function (p.d.f.). A mutual information registration scheme (using thin-plate spline warping) provided a transformation that allows the tracking of points between exhale and inhale treatment planning datasets (and/or intermediate state scans). The dose distributions were computed at the same resolution on each dataset using the Dose Planning Method (DPM) Monte Carlo code. Two accumulation/interpolation approaches were assessed. The first maps exhale dose grid points onto the inhale scan, estimates the doses at the "tracked" locations by trilinear interpolation and scores the accumulated doses (via the p.d.f.) on the original exhale data set. In the second approach, the "volume" associated with each exhale dose grid point (exhale dose voxel) is first subdivided into octants, the center of each octant is mapped to locations on the inhale dose grid and doses are estimated by trilinear interpolation. The octant doses are then averaged to form the inhale voxel dose and scored at the original exhale dose grid point location. Differences between the interpolation schemes are voxel size and tissue density dependent, but in general appear primarily only in regions with steep dose gradients (e.g., penumbra). Their magnitude (small regions of few percent differences) is less than the alterations in dose due to positional and shape changes from breathing in the first place. Thus, for sufficiently small dose grid point spacing, and relative to organ motion and deformation, differences due solely to the interpolation are unlikely to result in clinically significant differences to volume-based evaluation metrics such as mean lung dose (MLD) and tumor equivalent uniform dose (gEUD). The overall effects of deformation vary among patients. They depend on the tumor location, field size, volume expansion, tissue heterogeneity, and direction of tumor displacement with respect to the beam, and are more likely to have an impact on serial organs (such as esophagus), rather than on large parallel organs (such as lung).