Arrival Time Research Articles

Retraction Note: Signal detection based on empirical mode decomposition and Teager–Kaiser energy operator and its application to P and S wave arrival time detection in seismic signal analysis

Retraction Note: Signal detection based on empirical mode decomposition and Teager–Kaiser energy operator and its application to P and S wave arrival time detection in seismic signal analysis

Automatic arrival-time picking of P- and S-waves of micro-seismic events based on relative standard generative adversarial network and GHRA

AbstractRapid, high-precision pickup of microseismic P- and S-waves is an important basis for microseismic monitoring and early warning. However, it is difficult to provide fast and highly accurate pickup of micro-seismic P- and S-waves arrival-time. To address this, the study proposes a lightweight and high-precision micro-seismic P- and S-waves arrival times picking model, lightweight adversarial U-shaped network (LAU-Net), based on the framework of the generative adversarial network, and successfully deployed in low-power devices. The pickup network constructs a lightweight feature extraction layer (GHRA) that focuses on extracting pertinent feature information, reducing model complexity and computation, and speeding up pickup. We propose a new adversarial learning strategy called application-aware loss function. By introducing the distribution difference between the predicted results and the artificial labels during the training process, we improve the training stability and further improve the pickup accuracy while ensuring the pickup speed. Finally, 8986 and 473 sets of micro-seismic events are used as training and testing sets to train and test the LAU-Net model, and compared with the STA/LTA algorithm, CNNDET+CGANet algorithm, and UNet++ algorithm, the speed of each pickup is faster than that of the other algorithms by 11.59ms, 15.19ms, and 7.79ms, respectively. The accuracy of the P-wave pickup is improved by 0.221, 0.01, and 0.029, respectively, and the S-wave pickup accuracy is improved by 0.233, 0.135, and 0.102, respectively. It is further applied in the actual project of the Shengli oilfield in Sichuan. The LAU-Net model can meet the needs of practical micro-seismic monitoring and early warning and provides a new way of thinking for accurate and fast on-time picking of micro-seismic P- and S-waves.

Fluorescence lifetime of plant leaves with sub-nanosecond resolution

Abstract The study aimed to develop a measurement apparatus for in vivo chlorophyll-a (Chl-a) fluorescence decay measurements of plants by means of Time Correlated Single Photon Counting (TCSPC). In this approach, sub-nanosecond laser pulses with a repetition rate of 10 MHz are applied to excite the sample, followed by the analysis of arrival times of the emitted fluorescence photons. Photon statistics are generated by iteratively fitting the sum of 2 exponential functions. The tool was tested on both plastid and in vivo leaf samples of Savoy cabbage (Brassica oleracea var. sabauda) with 3-4 subsequent leaves giving a complete sample coverage starting from the outermost. The Chl-a fluorescence lifetime exhibited a gradual increase in both the isolated plastid suspensions and the in vivo leaf samples towards the innermost leaf layers explained by an increase of natural absence of light (etiolation syndrome). Furthermore, cadmium stress and iron deficiency were investigated on treated sugar beet (Beta vulgaris) samples in vivo using TCSPS measurements. The reduced fluorescence quenching resulted in an increased fluorescence lifetime. Finally, a long-term (10 week) testing of the setup was carried out on Chl-retaining resurrection Haberlea rhodopensis plants protecting themselves by an elevated non-photochemical quenching yielding a decrease of fluorescence lifetime during their desiccation.

A Signal Matching Method of In-Orbit Calibration of Altimeter in Tracking Mode Based on Transponder

In this paper, a matching method for altimeter and transponder signals in Sub-optimal Maximum Likelihood Estimate (SMLE) tracking mode is proposed. In the in-orbit calibration of the altimeter in SMLE tracking mode using the reconstructive transponder, it is necessary to separate the forwarding signal from the ground echo signal. At the same time, the fluctuations in the received signal of the altimeter, which are caused by the forwarding signal of the transponder, can be eliminated. The transponder generates a bias when measuring the arrival time of the transmitting signal from the altimeter and embeds this bias in both the transponder-recorded data and the altimeter-recorded data. Therefore, the two sets of data have one-to-one correspondence, and they are superimposed using the sliding sum method. Moreover, the distance between the altimeter and the transponder is a parabolic geometric relationship, and the outliers are eliminated by the fitting error minimization decision, and the transponder signal is separated from the ground echo. The final altimeter transmitting–receiving signal path is obtained. Furthermore, the principles underlying this method can be used for any transponder that can adjust the response signal delay during calibration.

Differential diagnosis of thyroid nodules by DCE-MRI based on compressed sensing volumetric interpolated breath-hold examination: A feasibility study

ObjectivesTo explore the potential and performance of quantitative and semi-quantitative parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) based on compressed sensing volumetric interpolated breath-hold (CS-VIBE) examination in the differential diagnosis of thyroid nodules. Materials and methodsA total of 208 patients with 259 thyroid nodules scheduled for surgery operation were prospectively recruited. All participants underwent routine and DCE-MRI. DCE-MRI quantitative parameters [Ktrans, Kep, Ve], semi-quantitative parameters [wash-in, wash-out, time to peak (TTP), arrival time (AT), peak enhancement intensity (PEI), and initial area under curve in 60 s (iAUC)] and time-intensity curve (TIC) types were analyzed. Differential diagnostic performances were assessed using area under the receiver operating characteristic curve (AUC) and compared with the Delong test. ResultsKtrans, Kep, Ve, wash-in, wash-out, PEI and iAUC were statistically significantly different between malignant and benign nodules (P < 0.001). Among these parameters, ROC analysis revealed that Ktrans showed the highest diagnostic performance in the differentiation of benign and malignant nodules, followed by wash-in. ROC analysis also revealed that Ktrans achieved the best diagnostic performance for distinguishing papillary thyroid carcinoma (PTC) from non-PTC, follicular adenoma (FA) from non-FA, nodular goiter (NG) from non-NG, with AUC values of 0.854, 0.895 and 0.609, respectively. Type III curve is frequently observed in benign thyroid nodules, accounting for 77.4% (82/106). While malignant nodules are more common in type II, accounting for 57.5% (88/153). ConclusionThyroid examination using CS-VIBE based DCE-MRI is a feasible, non-invasive method to identify benign and malignant thyroid nodules and pathological types.

Assessment of radiological consequences to a hypothetical accident of the 3-MW TRIGA Mark-II nuclear research reactor of Bangladesh

The radiological consequences to a hypothetical accident are assessed for the 3-MW TRIGA Mark-II Nuclear Research Reactor of Bangladesh for both wet and dry seasons. Accident simulations are crucial for assessing the potentialrisks in the event of a nuclear accident. ORIGEN code was used to carry out the facility's radionuclide inventory. In the event of a hypothetical accident, the Total Effective Dose Equivalent (TEDE) is assessed using HotSpots. The findings indicate that for stability class A during the rainy season, the selected radionuclides arrived at a downwind distance of 0.03 km with an arrival time of less than one minute, and the highest TEDE, respiratory time-integrated air concentration, and ground deposition activity, respectively, were marked at 2.8E-12 Sv, 9.4E-01 Bq-sec/m3, and 2.4E-05 kBq/m2. In the case of I-131 at 0.03 km from the source, the thyroid gland is the most sensitive human organ, followed by the lung, esophagus, thymus, surface bone, skin, and breast. The estimated Committed Effective Dose Equivalent (CEDE) from I-131 is 9.1E-11 Sv. In contrast, the adrenal was the most sensitive organ with a maximum CEDE of 1.3E-13 Sv in the case of Cs-137 at a downwind distance of 0.03 km. The highest TEDE values for the general public and the vicinity around the TRIGA Mark II reactor facility are lower than the annual dose limits.

Scheduling of Container Transportation Vehicles in Surface Coal Mines Based on the GA–GWO Hybrid Algorithm

The coal loading operation of the coal preparation plant of an open pit coal mine causes chaos in coal mine vehicle scheduling due to the unreasonable arrival times of outgoing and container transportation vehicles. To further reduce the length of time that vehicle transportation equipment waits for each other and to reduce the total cost of container transportation, the optimisation model of container transportation vehicle scheduling in an open pit coal mine is constructed to minimise the minimum sum of the shortest time of container reversal and the lowest cost of container transportation. To accurately measure the total cost of container backward transportation, waiting time and unit waiting time cost parameters are introduced, and the total cost of container transportation is measured using the transportation cost and the waiting time cost transformation method. An improved grey wolf algorithm is proposed to speed up the convergence of the algorithm and improve the quality of the solution. When employing the genetic algorithm (GA) and grey wolf optimisation algorithm (GWO) for optimising the scheduling of container transport vehicles in coal mines, it is noted that while the GA can achieve the global optimum, its convergence speed is relatively slow. Conversely, the GWO converges more quickly, but it tends to be trapped in local optima. To accelerate the convergence speed of the algorithm and improve the solution quality, a hybrid GA−GWO algorithm is proposed, which introduces three genetic operations of selection, crossover, and mutation of GA into the GWO algorithm to prevent the algorithm from falling into the local optimum due to the fall; at the same time, it introduces hunting and attacking operations into the elite retention strategy of GA, which improves the stability of the algorithm’s global convergence. Analysis indicates that, compared to SA, GWO, and GA, the hybrid algorithm enhances optimisation speed by 43.1%, 46.2%, and 43.7%, increases optimisation accuracy by 4.12%, 6.1%, and 3.2%, respectively, and reduces the total container reversal time by 35.46, 22, and 31 h. The total cost of container transportation is reduced by 2437 RMB, 3512 RMB, and 1334 RMB, respectively.

Machine learning-based model for predicting arrival time of container ships

Forecasting container ship arrival times is challenging, requiring a thorough analysis for accuracy. This study investigates the effectiveness of machine learning (ML) techniques in maritime transportation. Using a dataset of 581 samples with 8 input variables and 1 output variable (arrival time), ML models are constructed. The Pearson correlation matrix reduces input variables to 7 key factors: freight forwarder, dispatch location, loading and discharge ports, post-discharge location, dispatch day of the week, and dispatch week. The ranking of ML performance for predicting the arrival time of container ships can be arranged in descending order as GB-PSO &gt; XGB &gt; RF &gt; RF-PSO &gt; GB &gt; KNN &gt; SVR. The best ML model, GB-PSO, demonstrates high accuracy in predicting the arrival time of container ships, with R2 = 0.7054, RMSE = 7.4081 days, MAE = 5.1891 days, and MAPE = 0.0993% for the testing dataset. This is a promising research outcome as it seems to be the first time that an approach involving the use of minimal and easily collectible input factors (such as freight forwarder, dispatch time and place, port of loading, post port of discharge, port of discharge) and the combination of a machine learning model has been introduced for predicting the arrival time of container ships.

Timescale of Groundwater Recharge in High Percolation Coastal Plain Soils

Understanding and modeling the timing and magnitude of groundwater recharge from rainfall infiltration through vadose-zone percolation is important for many reasons but especially because the flux is being acted on by root-zone evapotranspiration (ET), and very little rainfall infiltration ever becomes water-table recharge. This study elaborates on the considerable time of the wetting front’s arrival and ultimate bulk recharge of rainfall infiltration in the shallow water table with fine-sandy soil typical of coastal plain environments such as Florida. Calibrated Hydrus-1D modeling of Florida (Myakka) soil was evaluated at varying depths of the water table and hydraulic conductivities to bracket the timing of arrival of the wetting front and bulk fluxes. Useful normalized timing parameters are defined. In addition, this research further quantifies the concept of “wet equilibrium”, and the considerable vadose-zone storage potential over and above the hydrostatic pressure equilibrium that must be overcome to achieve any significant water-table recharge in typical seasonal hydrologic timescales. The results indicate recharge timescales for water-table depths of 1 m are approximately 1 day but are considerably longer for 2 m (2 weeks), 3 m (1 month), and 4 m (50 days) conditions. Given that daily vadose-zone potential ET demand can exceed 0.5 cm/day in this environment, estimating recharge from rainfall infiltration is likely unreliable unless this timescale and the plant-root-zone uptake processes are properly modeled in surface-groundwater models.

The Influence of Different Solar System Planetary Ephemeris on Pulsar Timing

Abstract The pulsar timing offers a comprehensive avenue for exploring diverse topics physics and astrophysics. High-precision solar system planetary ephemeris crucial for pulsar timing as it provides the positions and velocities of solar system planets include the Earth. However, it is inevitable that inherent inconsistencies exist in these ephemerides. Differences between various ephemerides can significantly impact pulsar timing and parameter estimations. Currently, pulsar timing highly depend on the JPL DE ephemeris, for instance, the Pulsar Timing Array (PTA) data analysis predominantly utilizes DE436. In this study, we examine inconsistencies across various ephemeris series, including JPL DE, EPM, and INPOP. Notably, discrepancies emerge particularly between the current ephemeris DE436 and the earliest released ephemeris DE200, as well as the most recent ephemerides, e.g. DE440, INPOP21A, and EPM2021. A further detailed analysis of the effects of ephemeris on geometric correction procedures for the conversion of measured topocentric Time of Arrivals (TOAs) is presented in this study. Our researches reveal that variations in the Roemer delays across different ephemerides lead to distinct differences. The timing residuals and the fact that these discrepancies can be readily incorporated into the subsequent pulsar parameters, leading to inconsistent fitting estimates, suggest that the influence of errors in the ephemeris on the timing process might currently be underappreciated.

On-Site Earthquake Early Warning Model for Selected Records in the NGA-West2 Dataset Using S- and P-Wave Spectral Ratios

ABSTRACT On-site earthquake early warning (EEW) requires the best estimates of earthquake magnitude and distance parameters within a few seconds after the P-wave arrival for estimating the subsequent S-wave parameters. The errors in the estimates of the earthquake P-wave parameters will propagate into the estimates of the S-wave parameters. To solve this problem, we used the methodology by Zhao and Zhao (2019), which uses the spectral ratio R3P between the response spectral values of the first 3 s of the S wave and that of the first 3 s of the P wave, referred to as the R3P model. The modeling presented here was based on strong-motion records from the Next Generation Attenuation (NGA)-West2 dataset. We also used the spectral ratio RFP between the response spectral values from the full records from the S-wave arrival time to the end of the record and that of the first 3 s P wave to develop a second EEW model (the RFP model). An advantage of these two models is that the magnitude and hypocentral distance are not required in considerable magnitude and distance ranges. This means that the errors in the estimated source and path parameters from the first 3 s of the P wave will not affect the model predictions. A theoretical justification for these results is that the magnitude and the distance scaling rates for the first 3 s of the P wave are similar to those of the first 3 s of the S wave. This may also apply to the full S-wave window within useful EEW magnitude and distance ranges. We also found that when the estimated magnitude and distance for a record are necessary, the effect of the corresponding errors would be smaller than using a ground-motion prediction equation (GMPE), because the magnitude and distance scaling rates from this study are smaller than those of many GMPEs.

Estimating Stage-Frequency Curves for Engineering Design in Small Ungauged Arctic Watersheds

The design of hydraulic structures in the Arctic is complicated by shallow relief, which cause unique runoff processes that promote snow-damming and refreeze of runoff. We discuss the challenges encountered in modeling snowmelt runoff into two coastal freshwater lagoons in Utqiaġvik, Alaska. Stage-frequency curves with quantified uncertainty were required to design two new discharge gates that would allow snowmelt runoff flows through a proposed coastal revetment. To estimate runoff hydrographs arriving at the lagoons, we modeled snowpack accumulation and ablation using SnowModel which in turn was used to force a physically-based hydraulic runoff model (HEC-RAS). Our results demonstrate the successful development of stage-frequency curves by incorporating a Monte Carlo simulation approach that quantifies the variability in runoff timing and volume. Our process highlights the complexities of Arctic hydrology by incorporating significant delays in runoff onset due to localized snow accumulation and melting processes. This methodology not only addresses the uncertainty in snow-damming and refreeze processes which affect the arrival time of snowmelt inflow peaks, but is also adaptable for application in other challenging environments where secondary runoff processes are predominant.

Active Fault-Locating Scheme for Hybrid Distribution Line Based on Mutation of Aerial-Mode Injected Pulse

Due to the overlap of initial traveling wave signals, the traveling wave propagation process in hybrid distribution lines is complicated to analyze. The most significant challenge posed by the traditional passive traveling wave-locating method for hybrid distribution lines lies in identifying the fault section and distinguishing the reflected wave from the fault point or the hybrid connection points. Based on this approach, with the application of the aerial-mode component of the pulse signal generated at the fault point, a fault-section-identification and fault-locating scheme for hybrid distribution feeders with active pulse injection is proposed. When power in a line is cut after a single-to-line ground (SLG) fault occurs, the same pulse is injected into the three phases from the neutral point of the coupling capacitor bank to construct the zero-mode component, which propagates to the SLG fault three-phase asymmetrical point, producing an aerial-mode component that is reflected back to the first end of the line. With the application of the arrival time of an aerial-mode wavefront, it is simple to locate the SLG fault for arbitrary forms of hybrid lines. The simulation results confirm the feasibility of the fault-locating scheme under different feeders, different fault locations, and fault resistances. The results of the experiments confirm the high practical value of the proposed method.

Measuring a Gravitomagnetic Effect with the Triple Pulsar PSR J0337+1715

To the first post-Newtonian order, the orbital angular momentum of the fast-revolving inner binary of the triple system PSR J0337+1715, made of a millisecond pulsar and a white dwarf, induces an annular gravitomagnetic field which displaces the line of apsides of the slower orbit of the other, distant white dwarf by −1.2 milliarcseconds per year. The current accuracy in determining the periastron of the outer orbit is 63.9 milliarcseconds after 1.38 years of data collection. By hypothesizing a constant rate of measurement of the pulsar’s times of arrivals over the next 10 years, assumed equal to the present one, it can be argued that the periastron will be finally known to a ≃0.15 milliarcseconds level, while its cumulative gravitomagnetic retrograde shift will be as large as −12 milliarcseconds. The competing post-Newtonian gravitolectric periastron advance due to the inner binary’s masses, nominally amounting to 74.3 milliarcseconds per year, can be presently modelled to an accuracy level as good as ≃0.04 milliarcseconds per year. The mismodeling in the much larger Newtonian periastron rate due to the quadrupolar term of the multipolar expansion of the gravitational potential of a massive ring representing the inner binary, whose nominal size for PSR J0337+1715 is 0.17 degrees per year, might be reduced down to the ≃0.5 milliarcseconds per year level over the next 10 years. Thus, a first measurement of such a novel form of gravitomagnetism, although undoubtedly challenging, might be, perhaps, feasible in a not too distant future.

Modeling and evaluating the impact of variable bus lane on isolated signal intersection performance

Dedicated Bus Lane (DBL) is a typical strategy to ensure priority for public transportation. However, when the frequency of bus departures is low, the DBL often remains vacant, reducing the efficiency of traffic utilization. Variable bus lane (VBL) strategy allows for the provision of service to social vehicles in specific scenarios, aiming to alleviate traffic congestion and improve emission efficiency at intersections. To analyze the impact of this strategy on dynamic queuing behavior and traffic efficiency at an isolated intersection, this paper proposes a multidimensional stochastic queuing model. The model incorporates various factors such as traffic demand, signal timing, bus arrival time, and the length of the variable area at intersections. By examining the steady-state distribution of queue length and calculating performance indicators, the effectiveness of VBL in improving intersection performance is evaluated. Numerical simulations have demonstrated that the proposed model accurately describes the vehicle queuing process at intersections under different conditions. Theoretical analysis confirms that the implementation of VBL strategy can effectively improve traffic efficiency at intersections and minimize the queue length of vehicles.

Parallel Algorithm on Multicore Processor and Graphics Processing Unit for the Optimization of Electric Vehicle Recharge Scheduling

Parallel Algorithm on Multicore Processor and Graphics Processing Unit for the Optimization of Electric Vehicle Recharge Scheduling

Enhancing vessel arrival time prediction: A fusion-based deep learning approach

The logistic community of shippers has struggled to predict the precise arrival time of the seagoing vessels with reliable certainty. While deep-learning approaches are promising, the existing methods fail to provide desirable results due to a shallow prediction architecture. This research work proposes a method to predict vessel arrival time that could eventually be incorporated into an intelligent decision support system that we call Vessel Arrival Time Prediction (VATP). VATP presents a hybrid architecture of Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Attention mechanism, Dropout, and Dense layers to take advantage of the coarse-grained local features produced by CNN, the longitudinal nature of AIS (long-time dependencies) via LSTM and paying attention to the influence feature on the arrival time. While many prior approaches have relied solely on AIS data, and some incorporated a combination of AIS and vessel information, our method also integrates diverse data sources, including Automatic Identification System (AIS), Augmented information (Ain), and Maritime Weather Data (MWD). Furthermore, only a few methods have considered weather information, often using minimal weather-related features. In contrast, our approach involves a comprehensive range of weather-related features, and besides these data sources, we extracted specially crafted features.To verify the effectiveness of VATP, we conducted our experiment on large-scale datasets. The VATP model obtained a Root Mean Square Error (RMSE) of 10.63 and a Mean Absolute Percentage Error (MAPE) of 35.11 %. Our results demonstrate that VATP achieves significant performance. Furthermore, these positive results demonstrate that i) the accuracy of the VATP approach can be improved using the AIS, MWD, and Ain information; ii) learning from a unified feature set can result in a significant performance improvement compared to learning from a subset of the features; iii) we also obtained a superior performance in comparison with other well-known methods in the literature and various state-of-the-art baseline methods. Finally, our results illustrate the consistent performance of the VATP across different datasets.

Enhanced Moving Source Localization with Time and Frequency Difference of Arrival: Motion-Assisted Method for Sub-Dimensional Sensor Networks

Localizing a moving source by Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) commonly requires at least N+1 sensors in N-dimensional space to obtain more than N pairs of TDOAs and FDOAs, thereby establishing more than 2N equations to solve for 2N unknowns. However, if there are insufficient sensors, the localization problem will become underdetermined, leading to non-unique solutions or inaccuracies in the minimum norm solution. This paper proposes a localization method using TDOAs and FDOAs while incorporating the motion model. The motion between the source and sensors increases the equivalent length of the baseline, thereby improving observability even when using the minimum number of sensors. The problem is formulated as a Maximum Likelihood Estimation (MLE) and solved through Gauss–Newton (GN) iteration. Since GN requires an initialization close to the true value, the MLE is transformed into a semidefinite programming problem using Semidefinite Relaxation (SDR) technology, while SDR results in a suboptimal estimate, it is sufficient as an initialization to guarantee the convergence of GN iteration. The proposed method is analytically shown to reach the Cramér–Rao Lower Bound (CRLB) accuracy under mild noise conditions. Simulation results confirm that it achieves CRLB-level performance when the number of sensors is lower than N+1, thereby corroborating the theoretical analysis.

Method for the P-wave arrival pickup of rock fracture acoustic emission signals under strong noise

This research aimed to investigate the accuracy of picking of P-wave arrival times in rock fracture acoustic emission signals. In order to simulate the mining scenario, Gaussian white noise and pulse noise were added to the data collected in the laboratory. Complete ensemble empirical mode decomposition with adaptive noise + Wavelet (CEEMDAN + Wavelet) was improved in this paper, where the Spearman rank correlation coefficient was adopted to effectively select intrinsic mode functions for denoising which retained the inherent characteristics of the rock fracture signal. The absolute amplitude and energy change rate of the envelope signal, calculated based on the Hilbert transform, were used as the input of the short term average/long term average (STA/LTA) normalization algorithm to pickup the P-wave arrival time. The reliability of this method was tested on 30 groups of recorded rock fracture laboratory data and 60 groups of added noise data. Taking the manual pickup results as the standard, the errors of CEEMDAN + Wavelet + STA/LTA + AIC (Akaike information criterion) method with the absolute amplitude of the signal as the input are all within 10 ms, and 86.67% of the results are within 5 ms. The method proposed in this paper effectively addressing the issue of false pickup caused by the sensitivity of AIC and traditional STA/LTA method for strong noise, and achieving relatively high accuracy and stability in processing low signal-to-noise ratio signals. This work contributes to monitor microscopic changes in rock bodies and is of great significance for the prediction and monitoring of geological disasters.

Spatial variation in spring arrival patterns of Afro‐Palaearctic bird migration across Europe

AbstractAimGeographical patterns of migrant species arrival have been little studied, despite their relevance to global change responses. Here, we quantify continent‐wide interspecific variation in spatiotemporal patterns of spring arrival of 30 common migrant bird species and relate these to species characteristics and environmental conditions.LocationEurope.Time Period2010–2019.Major Taxa StudiedBirds, 30 species.MethodsUsing citizen science data from EuroBirdPortal, we modelled arrival phenology for 30 Afro‐Palaearctic migrant species across Europe to extract start and duration of species arrival at a 400 km square resolution. We related inter and intraspecific variation in arrival and duration to species characteristics and temperature at the start of the growing season (green‐up).ResultsSpatial variation in start of arrival times indicates that it took, on average, 1.6 days for the leading migratory front to move northwards by 100 km (range: 0.6–2.5 days). There was a major gradient in arrival phenology, from species which arrived earlier, least synchronously, in colder temperatures and progressed slowly northwards to species which arrived later, most synchronously and in warmer temperatures and advanced quickly through Europe. The slow progress of early arrivers suggests that temperature limits their northward advance; this group included Aerial Insectivores and species wintering north of the Sahel. For the late arrivers, which included species wintering further south, seasonal resource availability in Africa may delay their arrival into Europe.Main ConclusionsWe found support for the green‐wave hypothesis applying widely to migratory landbirds. Species arrival phenologies are linked to ecological differences between taxa, such as diet, and wintering location. Understanding these differences informs predictions of species' sensitivity to global change. Publishing these arrival phenologies will facilitate further research and have additional conservation benefits such as informing designation of hunting seasons. Our methods are applicable to any taxa with repeated occurrence data across large scales.