Localization Radius Research Articles

Scale‐ and Variable‐Dependent Localization for 3DEnVar Data Assimilation in the Rapid Refresh Forecast System

AbstractThis study demonstrates the advantages of scale‐ and variable‐dependent localization (SDL and VDL) on three‐dimensional ensemble variational data assimilation of the hourly‐updated high‐resolution regional forecast system, the Rapid Refresh Forecast System (RRFS). SDL and VDL apply different localization radii for each spatial scale and variable, respectively, by extended control vectors. Single‐observation assimilation tests and cycling experiments with RRFS indicated that SDL can enlarge the localization radius without increasing the sampling error caused by the small ensemble size and decreased associated imbalance of the analysis field, which was effective at decreasing the bias of temperature and humidity forecasts. Moreover, simultaneous assimilation of conventional and radar reflectivity data with VDL, where a smaller localization radius was applied only for hydrometeors and vertical wind, improved precipitation forecasts without introducing noisy analysis increments. Statistical verification showed that these impacts contributed to forecast error reduction, especially for low‐level temperature and heavy precipitation.

Tuning the soft bandgap in the density of the states: the measurement of a "magnetogap" effect in carbon-black samples

We investigated the magnetic field dependence on the variable range hopping electron conduction in carbon-black samples with different volumetric densities. By applying the concepts and equations of hopping regimes when the magnetic field is applied, we were able to establish a connection between the variation of the hopping temperatures, TES and TM, with the localization radius of electron conduction in carbon-black, as well as with the density of states of this material. The volumetric density dependence of all these physical quantities is also discussed. Finally, based on the magnetic field effects on the hopping parameters, we introduced the idea of a "Magnetogap" effect, indicating how the Coulomb gap changes with the magnetic field.

Discovery of the Optical and Radio Counterpart to the Fast X-Ray Transient EP 240315a

Fast X-ray Transients (FXTs) are extragalactic bursts of soft X-rays first identified ≳10 yr ago. Since then, nearly 40 events have been discovered, although almost all of these have been recovered from archival Chandra and XMM-Newton data. To date, optical sky surveys and follow-up searches have not revealed any multiwavelength counterparts. The Einstein Probe, launched in 2024 January, has started surveying the sky in the soft X-ray regime (0.5–4 keV) and will rapidly increase the sample of FXTs discovered in real time. Here we report the first discovery of both an optical and radio counterpart to a distant FXT, the fourth source publicly released by the Einstein Probe. We discovered a fast-fading optical transient within the 3′ localization radius of EP 240315a with the all-sky optical survey ATLAS, and our follow-up Gemini spectrum provides a redshift, z = 4.859 ± 0.002. Furthermore, we uncovered a radio counterpart in the S band (3.0 GHz) with the MeerKAT radio interferometer. The optical (rest-frame UV) and radio luminosities indicate that the FXT most likely originates from either a long gamma-ray burst or a relativistic tidal disruption event. This may be a fortuitous early mission detection by the Einstein Probe or may signpost a mode of discovery for high-redshift, high-energy transients through soft X-ray surveys, combined with locating multiwavelength counterparts.

A New Approach to Carbon Nanotube Filament Nanostructuring for Additive Manufacturing.

A new technique of additive prototyping filament volumetric nanostructuring based on the high-speed mechanical mixing of acrylonitrile-butadiene-styrene (ABS) copolymer granules and single-walled carbon nanotube (CNT) powder (without prior dispersion in solvents) is considered. The morphological spectra of scanning electron microscopy (SEM) images of nanostructured filament slice surfaces were obtained and characterized with the original mathematical simulation. The relations of structural changes in the "ingredient-matrix" polymer system with dielectric and mechanical properties of the ABS-based filaments were established. The supplementation of 1.5 mass.% of CNT powder to the ABS filament composition leads to the tensile strength increasing from 36 ± 2 to 42 ± 2 MPa. It is shown that the greater the average biharmonic amplitude and the morphological spectrum localization radius of the slice surfaces' SEM images, the lower the electrical resistance of the corresponding nanostructured filaments. The possibility of carbon nanotube-modified filament functional layers forming using the extrusion additive prototyping technique (FFF) on the surface of plasma-chemically modified PET substrates (for the creation of load cell elements) is experimentally demonstrated.

Ensemble-based assimilation of wave model predictions: Contrasting the impact of assimilation in nearshore and offshore forecasting at different distances from assimilated data

Accurate wave predictions are crucial for many marine-related activities, including offshore operations, navigation, and marine renewable energy. To improve the forecast performance, this study presents a data assimilation system that utilizes the WAVEWATCH III wave model and the local Ensemble Optimal Interpolation scheme to assimilate significant wave heights data from HY-2B altimeter in the China Seas. The effects of assimilation are analyzed in both coastal and offshore regions at different distances from assimilated satellite data points during a forecast period. The results show that systematic bias in wave forecasting for both offshore and nearshore locations can be effectively reduced through the use of data assimilation, with localization employed to minimize the influence of random errors. Nearshore forecasting benefits from assimilation to a greater extent than offshore forecasting, with a more enduring improvement observed. Additionally, the decline in forecast improvement with increasing distance from assimilated data highlights the necessity of assimilating a greater quantity of observational satellite data. Furthermore, careful consideration should be given to the size of the ensemble when using a specific localization radius, as the improvement in skill may stagnate with larger ensembles. Finally, assimilating additional nearshore satellite data does not negatively impact the predictive precision.

Lifshitz model in the presence of spin–orbit coupling

Wave function of a localized state created by a short-range impurity in two dimensions falls off with distance, r, from the impurity as 1r1/2exp(−ra), where a is the localization radius. With randomly positioned identical impurities with low concentration, n≪a−2, the level smears into a band due to the overlap of the impurity wave functions. This is the essence of the Lifshitz model. We demonstrate that, upon incorporation of the spin–orbit coupling, the impurity wave functions acquire oscillating factors which, subsequently, modify their overlap. As a result of such modification, the density of states develops singularities at certain energies.

Dependence of hopping electrical resistivity on concentration and localization radius, simulation study

The exponential dependence of resistivity on concentration and on electron localization radius in hopping electron transport is of special significance. It is the main clue in favor of hopping conductance mechanism. This dependence was studied experimentally, theoretically and in simulations. So far, the simulations were carried out in the Miller–Abrahams resistor network approximation. In this work, a more precise simulation is presented, which takes into account electron correlations in a fully connected network of sites, and achieves averaging by simulation of supercells. Although occasionally a quantitative agreement with existing reports is achieved, the excellent agreement between the percolation theory and some existing simulations, is a coincidence. The new results are interpreted using percolation theory. There are indications on the important role of the prefactor of resistivity in interpreting the slope of the semi-logarithmic plot of resistivity vs average separation of localization sites. The influence of the finite temperature is investigated. The simulations are carried out assuming a doped crystalline semiconductor as the hopping medium, however the results are applicable to other systems with the identical dependencies of the hopping rate on hopping distance.

Water table depth assimilation in integrated terrestrial system models at the larger catchment scale

As an important source of water for human beings, groundwater plays a significant role in human production and life. However, different sources of uncertainty may lead to unsatisfactory simulations of groundwater hydrodynamics with hydrological models. The goal of this study is to investigate the impact of assimilating groundwater data into the Terrestrial System Modeling Platform (TSMP) for improving hydrological modeling in a real-world case. Daily groundwater table depth (WTD) measurements from the year 2018 for the Rur catchment in Germany were assimilated by the Localized Ensemble Kalman Filter (LEnKF) into TSMP. The LEnKF is used with a localization radius so that the assimilated measurements only update model states in a limited radius around the measurements, in order to avoid unphysical updates related to spurious correlations. Due to the mismatch between groundwater measurements and the coarse model resolution (500 m), the measurements need careful screening before data assimilation (DA). Based on the spatial autocorrelation of the WTD deduced from the measurements, three different filter localization radii (2.5, 5, and 10 km) were evaluated for assimilation. The bias in the simulated water table and the root mean square error (RMSE) are reduced after DA, compared with runs without DA [i.e., open loop (OL) runs]. The best results at the assimilated locations are obtained for a localization radius of 10 km, with an 81% reduction of RMSE at the measurement locations, and slightly smaller RMSE reductions for the 5 and 2.5 km radius. The validation with independent WTD data showed the best results for a localization radius of 10 km, but groundwater table characterization could only be improved for sites <2.5 km from measurement locations. In case of a localization radius of 10 km the RMSE-reduction was 30% for those nearby sites. Simulated soil moisture was validated against soil moisture measured by cosmic-ray neutron sensors (CRNS), but no RMSE reduction was observed for DA-runs compared to OL-run. However, in both cases, the correlation between measured and simulated soil moisture content was high (between 0.70 and 0.89, except for the Wuestebach site).

Modeling of narrow beams of electromagnetic waves with axial symmetry on a plane

A mathematical model of narrow beams of monochromatic electromagnetic waves propagating in vacuum is developed. Beams of electromagnetic waves are constructed with the order of axial symmetry m m( 0,1, 2, ), = K characterized by the beam localization radius Rпуч, the beam narrowness parameter n n( 2), ≥ and the beam attenuation coefficient N N( 2). ≥ The structure of the electric field for a TE-polarized wave beam is studied numerically.

Direct Assimilation of All-Sky GOES-R ABI Radiances in GSI EnKF for the Analysis and Forecasting of a Mesoscale Convective System

Abstract In this study, all-sky GOES-R ABI infrared radiances at their native resolution are assimilated using an enhanced GSI ensemble Kalman filter (EnKF) data assimilation (DA) system, and the impacts of the data on the analysis and forecast of a mesoscale convective system (MCS) are explored. Results show that all-sky ABI BT data can correctly build up observed storms within the model and effectively remove spurious storms in model background through frequent DA cycles. Both bias and root-mean-squared innovation of the background and analysis are significantly reduced during the DA cycles, and free forecasts are improved when verified subjectively and objectively against observed ABI BTs and independent radar reflectivity observations. A horizontal localization radius of 30 km is found to produce the best results while 5-min DA cycles improve the storm analyses over 15-min cycles, but the differences in forecasts are small. Further analyses show that the clearing of spurious clouds by ABI radiance is correctly accompanied by reduction in moisture through background error cross covariance, but overdrying often occurs, which can cause spurious storm decay in the forecast. The problem is reduced when the ensemble mean of observation prior instead of observation prior of the ensemble mean state is used in the ensemble mean state update equation of EnKF. The significant difference between the two ways that the ensemble mean of observation prior is calculated when the observational operator is very nonlinear has not been recognized in earlier cloudy radiance DA studies. Significance Statement Satellite observations in cloudy regions are not used in most current operational weather prediction systems due to complex nonlinear relations between satellite-observed quantities, the radiances, and model state in such regions. The models also must predict clouds reasonably well for cloudy observations to be effectively assimilated. The latest NOAA geostationary satellites can provide radiance observations at high spatial and temporal resolutions and such data in both cloudy- and clear-air regions are assimilated using an advanced data assimilation method into a model that explicitly represents convection. Forecasts up to 4 h are improved by the assimilation while several issues associated with the assimilation are discussed. The study contributes to the eventual use of all-sky satellite radiance data in operational models.

Experimental observations on metal-like carrier transport and Mott hopping conduction behaviours in boron-doped Si nanocrystal multilayers

Studies on the carrier transport characteristics of semiconductor nanomaterials are the important and interesting issues which are helpful for developing the next generation of optoelectronic devices. In this work, we fabricate B-doped Si nanocrystals/SiO2 multilayers by plasma enhanced chemical vapor deposition with subsequent high temperature annealing. The electronic transport behaviors are studied via Hall measurements within a wide temperature range (30–660 K). It is found that when the temperature is above 300 K, all the B-doped Si nanocrystals with the size near 4.0 nm exhibit the semiconductor-like conduction characteristics, while the conduction of Si nanocrystals with large size near 7.0 nm transforms from semiconductor-like to metal-like at high B-doping ratios. The critical carrier concentration of conduction transition can reach as high as 2.2 × 1020 cm−3, which is significantly higher than that of bulk counterpart and may be even higher for the smaller Si nanocrystals. Meanwhile, the Mott variable-range hopping dominates the carrier transport when the temperature is below 100 K. The localization radius of carriers can be regulated by the B-doping ratios and Si NCs size, which is contributed to the metallic insulator transition.

Benefit of vertical localization for sea surface temperature assimilation in isopycnal coordinate model

Sea surface temperature (SST) observations are a critical data set for long-term climate reconstruction. However, their assimilation with an ensemble-based data assimilation method can degrade performance in the ocean interior due to spurious covariances. Assimilation in isopycnal coordinates can delay the degradation, but it remains problematic for long reanalysis. We introduce vertical localization for SST assimilation in the isopycnal coordinate. The tapering functions are formulated empirically from a large pre-industrial ensemble. We propose three schemes: 1) a step function with a small localization radius that updates layers from the surface down to the first layer for which insignificant correlation with SST is found, 2) a step function with a large localization radius that updates layers down to the last layer for which significant correlation with SST is found, and 3) a flattop smooth tapering function. These tapering functions vary spatially and with the calendar month and are applied to isopycnal temperature and salinity. The impact of vertical localization on reanalysis performance is tested in identical twin experiments within the Norwegian Climate Prediction Model (NorCPM) with SST assimilation over the period 1980–2010. The SST assimilation without vertical localization greatly enhances performance in the whole water column but introduces a weak degradation at intermediate depths (e.g., 2,000–4,000 m). Vertical localization greatly reduces the degradation and improves the overall accuracy of the reanalysis, in particular in the North Pacific and the North Atlantic. A weak degradation remains in some regions below 2,000 m in the Southern Ocean. Among the three schemes, scheme 2) outperforms schemes 1) and 3) for temperature and salinity.

DC hopping photoconductivity via three-charge-state point defects in partially disordered semiconductors

The stationary (DC) hopping photoconductivity caused by the migration of electrons via intrinsic point t-defects of the same type with three charge states (−1, 0, and +1 in units of elementary charge) is theoretically studied. It is assumed that t-defects are randomly (Poissonian) distributed over a crystal and hops of single electrons occur only via t-defects in the charge states (−1), (0) and (0), (+1). Under the influence of intercenter illumination nonequilibrium charge states (−1) and (+1) of defects are generated due to photostimulated electron transitions between pairs of defects in the charge states (0). During the recombination of nonequilibrium charge states (−1) and (+1) of defects, pairs of defects in the charge states (0) are formed. It is assumed that illumination does not heat the crystal, i.e. does not increase the coefficient of thermal ionization of t-defects. The dependence of the ratio of photoconductivity to dark hopping electrical conductivity on the ratio of photoionization coefficient (γ) of neutral t-defects to coefficient of ‘capture’ (α) of an electron from a negatively charged to a positively charged t-defect is calculated. The calculations of hopping photoconductivity were carried out for the partially disordered silicon crystal with total concentration of t-defects of 3·1019 cm−3, compensated by shallow hydrogen-like donors. The ratios of donor concentration to t-defect concentration (compensation ratios) are 0.25, 0.5, and 0.75. It is taken into account that an electron localization radius on t-defect in the charge state (−1) is greater that on t-defect in the charge state (0). The calculated value of the dark hopping electrical conductivity is consistent with the known experimental data. A negative DC photoconduction at γ > α is predicted, due to a decrease in the concentration of electrons hopping via states (−1), (0) and (0), (+1).

A local data assimilation method (Local DA v1.0) and its application in a simulated typhoon case

Abstract. Integrating the hybrid and multiscale analyses and the parallel computation is necessary for current data assimilation schemes. A local data assimilation method, Local DA, is designed to fulfill these needs. This algorithm follows the grid-independent framework of the local ensemble transform Kalman filter (LETKF) and is more flexible in hybrid analysis than the LETKF. Local DA employs an explicitly computed background error correlation matrix of model variables mapped to observed grid points/columns. This matrix allows Local DA to calculate static covariance with a preset correlation function. It also allows the conjugate gradient (CG) method to be used to solve the cost function and allows localization to be performed in model space, observation space, or both spaces (double-space localization). The Local DA performance is evaluated with a simulated multiscale observation network that includes sounding, wind profiler, precipitable water vapor, and radar observations. In the presence of a small-size time-lagged ensemble, Local DA can produce a small analysis error by combining multiscale hybrid covariance and double-space localization. The multiscale covariance is computed using error samples decomposed into several scales and independently assigning the localization radius for each scale. Multiscale covariance is conducive to error reduction, especially at a small scale. The results further indicate that applying the CG method for each local analysis does not result in a discontinuity issue. The wall clock time of Local DA implemented in parallel is halved as the number of cores doubles, indicating a reasonable parallel computational efficiency of Local DA.

Assessment of the data assimilation framework for the Rapid Refresh Forecast System v0.1 and impacts on forecasts of a convective storm case study

Abstract. The Rapid Refresh Forecast System (RRFS) is currently under development and aims to replace the National Centers for Environmental Prediction (NCEP) operational suite of regional- and convective-scale modeling systems in the next upgrade. In order to achieve skillful forecasts comparable to the current operational suite, each component of the RRFS needs to be configured through exhaustive testing and evaluation. The current data assimilation component uses the hybrid three-dimensional ensemble–variational data assimilation (3DEnVar) algorithm in the Gridpoint Statistical Interpolation (GSI) system. In this study, various data assimilation algorithms and configurations in GSI are assessed for their impacts on RRFS analyses and forecasts of a squall line over Oklahoma on 4 May 2020. A domain of 3 km horizontal grid spacing is configured, and hourly update cycles are performed using initial and lateral boundary conditions from the 3 km grid High-Resolution Rapid Refresh (HRRR). Results show that a baseline RRFS run is able to represent the observed convection, although with stronger cells and large location errors. With data assimilation, these errors are reduced, especially in the 4 and 6 h forecasts using 75 % of the ensemble background error covariance (BEC) and 25 % of the static BEC with the supersaturation removal function activated in GSI. Decreasing the vertical ensemble localization radius from 3 layers to 1 layer in the first 10 layers of the hybrid analysis results in overall less skillful forecasts. Convection is greatly improved when using planetary boundary layer pseudo-observations, especially at 4 h forecast, and the bias of the 2 h forecast of temperature is reduced below 800 hPa. Lighter hourly accumulated precipitation is predicted better when using 100 % ensemble BEC in the first 4 h forecast, but heavier hourly accumulated precipitation is better predicted with 75 % ensemble BEC. Our results provide insight into the current capabilities of the RRFS data assimilation system and identify configurations that should be considered as candidates for the first version of RRFS.

Electronic properties of Cu2(Zn, Cd)SnS4 determined by the high-field magnetotransport

Resistivity, ρ(T), and magnetoresistance (MR) are investigated in the Cu2Zn1−x Cd x SnS4 single crystals for compositions x ≡ Cd/(Zn + Cd) = 0.15–0.24, in the temperature range of T ∼ 50–300 K in pulsed magnetic fields of B up to 20 T. The Mott variable-range hopping (VRH) conductivity is established within wide temperature intervals lying inside ΔT M ∼ 60–190 K for different x. The deviations from the VRH conduction, observable above and below ΔT M, are connected to the nearest-neighbor hopping regime and to the activation on the mobility threshold of the acceptor band (AB) with width W ≈ 16–46 meV. The joint analysis of ρ(T) and positive MR permitted determination of other important electronic parameters. These include the localization radius, α ≈ 19–30 Å, the density of the localized states, g(μ) ≈ (1.6–21) × 1017 meV−1 cm−3 at the Fermi level μ, and the acceptor concentration, N A ∼ (6–8) × 1019 cm−3, for various x and in conditions of different vicinity of the investigated samples to the metal–insulator transition. In addition, details of the AB structure, including positions of μ and of the mobility threshold, E c, are found depending on the alloy composition.

A Mini‐Batch Stochastic Optimization‐Based Adaptive Localization Scheme and Its Implementation in NLS‐i4DVar

AbstractThis paper proposes a mini‐batch stochastic optimization‐based adaptive localization scheme for computing the “optimal” localization radius in data assimilation (DA) applications. After constructing a cost function of the localization radius by estimating forecast and observation error statistics, a mini‐batch stochastic gradient descent method with a novel sampling strategy is proposed to minimize the cost function. The proposed stochastic optimization algorithm is further incorporated into the DA method NLS‐i4DVar (the nonlinear least squares integral correcting four‐dimensional variational DA method), which was developed by the authors in Tian et al. (2021, https://doi.org/10.1029/2021EA001767). It is utilized to compute the “optimal” covariance localization radii adaptively and flow‐dependently inside NLS‐i4DVar. The computational cost of NLS‐i4DVar with the proposed adaptive localization scheme only increases slightly due to the use of the mini‐batch stochastic optimization algorithm. Numerical experimental results using the shallow‐water equations demonstrate that NLS‐i4DVar with the proposed adaptive localization scheme shows substantial performance improvement over the standard NLS‐i4DVar method.

Multivariate ensemble sensitivity analysis applied for an extreme rainfall over Indian subcontinent

Ensemble sensitivity analysis (ESA) uses sample statistics of ensemble forecasts to estimate relationships between forecast metrics and initial conditions. The ensemble sensitivity analysis is often considered as a simple univariate regression as it includes an approximation of analysis covariance matrix with corresponding diagonal elements. In this work, univariate ensemble sensitivity is extended to multivariate ensemble sensitivity that incorporates the contribution from the full covariance matrix in the sensitivity calculations. The performance of multivariate ensemble sensitivity over univariate is examined for meso- and convective scale ensemble forecasts of a heavy rainfall event that happened over the Chennai city in India in December 2015. The ensemble forecasts and analyses are generated using the Advanced Research - Weather Research and Forecasting (WRF) model Data Assimilation Research Testbed (DART) based Ensemble Kalman Filter (EnKF). Multivariate ensemble sensitivity shows organized sensitivity patterns, while the sensitivity values are found to be broadly distributed in univariate ensemble sensitivity. Both the methods are validated using a perturbed initial condition approach, and the results indicate that the multivariate ensemble sensitivity method is effective in predicting the forecast response closest to the actual model response compared to the univariate ensemble sensitivity. The impact of model error on sensitivity calculations is examined by generating a new set of ensembles that uses the Stochastic Kinetic Energy Backscatter Scheme (SKEBS). In the presence of added model error, the forecast response estimated by multivariate using SKEBS ensembles compares better with the actual response. It is found that the performance of the multivariate approach depends on the optimal choice of localization radius, and if insufficient localization is applied, the spurious long-distance correlation contaminates the performance of the multivariate ensemble sensitivity method. The impact of various forecast lead times on the univariate and multivariate ensemble sensitivity analysis indicates that responses using multivariate ensemble sensitivity are more accurate than univariate, especially at longer lead times when nonlinearity becomes significant. The performance of univariate and multivariate methods in convection-permitting scale is examined by using the high-resolution ensemble forecasts, and it is found that the multivariate sensitivity with localization substantially improves the estimates in finer scales.

Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network

Abstract. The mountainous snow cover is highly variable at all temporal and spatial scales. Snowpack models only imperfectly represent this variability, because of uncertain meteorological inputs, physical parameterizations, and unresolved terrain features. In situ observations of the height of snow (HS), despite their limited representativeness, could help constrain intermediate and large-scale modeling errors by means of data assimilation. In this work, we assimilate HS observations from an in situ network of 295 stations covering the French Alps, Pyrenees, and Andorra, over the period 2009–2019. In view of assimilating such observations into a spatialized snow cover modeling framework, we investigate whether such observations can be used to correct neighboring snowpack simulations. We use CrocO, an ensemble data assimilation framework of snow cover modeling, based on a particle filter suited to the propagation of information from observed to unobserved areas. This ensemble system already benefits from meteorological observations, assimilated within SAFRAN analysis scheme. CrocO also proposes various localization strategies to assimilate snow observations. These approaches are evaluated in a leave-one-out setup against the operational deterministic model and its ensemble open-loop counterpart, both running without HS assimilation. Results show that an intermediate localization radius of 35–50 km yields a slightly lower root mean square error (RMSE), and a better spread–skill than the strategy of assimilating all the observations from a whole mountain range. Significant continuous ranked probability score (CRPS) improvements of about 13 % are obtained in the areas where the open-loop modeling errors are the largest, e.g., the Haute-Ariège, Andorra, and the extreme southern Alps. Over these areas, weather station observations are generally sparser, resulting in more uncertain meteorological analyses and, therefore, snow simulations. In situ HS observations thus show an interesting complementarity with meteorological observations to better constrain snow cover simulations over large areas.

Structural Properties and Hopping Conduction in the Normal State of Electron-Doped Superconductor Cuprate Eu2-x Ce x CuO4+α-δ.

Electron-doped superconducting cuprate of Eu2–xCexCuO4+α–δ has been studied in the whole doping regime from x = 0.10–0.20 with reducing oxygen content to investigate the relation between the crystal structure and the hopping conduction in the normal state. Parameter of the crystal structure has been extracted from the X-ray diffraction (XRD) measurement while hopping conduction parameters have been obtained from resistivity measurements. The Eu–O bond length decreases with the increasing doping concentration, indicating the successful doping by the partial replacing of Eu3+ with Ce4+. The resistivity increases with decreasing temperature in all measured samples. This is an indication of bad metal-like behavior in the whole regime in the normal state of electron-doped superconducting cuprate of Eu2–xCexCuO4+α–δ. The temperature dependence of resistivity was analyzed by the Arrhenius law and the variable range hopping model. It is found that the hopping conduction mechanism more likely follows the variable range hopping rather than the Arrhenius law, indicating that the hopping mechanism occurs in three dimensions. The Cu–O bond length probably plays an important role in decreasing the activation energy. The decreasing value of the activation energy correlates with the increase in the localization radius.