Sources Of Uncertainty Research Articles

A hybrid model coupling process-driven and data-driven models for improved real-time flood forecasting

Accurate and reliable incoming flood forecasting is an important prerequisite for flood warning, flood risk analysis and reservoir flood control operation. This paper proposes a hybrid model for real-time flood forecasting that couples process-driven hydrological models (HMs) with data-driven models (DDMs). The generic hybrid model framework adds DDMs as the post-processing procedure for residual correction to the original results of HMs, and considers multiple uncertainties in input data, parameter and model structure simultaneously. The performance of the hybrid model is evaluated comprehensively in terms of deterministic forecast accuracy, interval forecast reliability, and the reliability and sharpness of probabilistic forecast. Taking the multireservoir system at the east Pi River as a study case, the results indicate that: (1) Compared to the benchmark model (ensemble XAJ model), the hybrid model with additional residual analysis show a significant improvement. The average continuous ranked probability score (CRPS) metric values calculated by the Stacking-Hybrid model improved by 71.5 %, 67.0 % and 38.1 % in the three data samples. Furthermore, the adaptability of the Stacking-Hybrid model for residual correction during short-duration intense rainfall events has been validated, with the relative error of the peak discharge improved to within ± 10 %. (2) The Stacking-Hybrid model, which also takes into account structure uncertainty, is able to better exploit the combined advantages and improve the stability of the model performance compared to those that only apply a single DDM. (3) When the number of iterations within the BOA reaches 300, the parameter optimization process is capable to search for the hyperparameters that bring out the best performance of the DDMs. (4) When the ensemble size reaches 200, the uncertainty of HM parameters can be fully defined, and the consumed computational resources can be controlled within an acceptable bound while ensuring stable model performance. Overall, the hybrid model that takes into account multiple sources of uncertainty generates both interval and probabilistic forecast in addition to deterministic forecast, which can provide richer risk information for subsequent flood warning and reservoir operation, making the flood prevention decisions more reliable and scientific.

The Heat Flux Method for hybrid iron–methane–air flames

Recently, a cyclic energy storage concept was proposed involving the use of metal powder as CO2-free energy carrier, known as the metal fuel cycle. In this cycle, the burning of iron powder is considered as the discharge agent of the energy carrier. However, for this cycle to be an efficient one, a better understanding of the laminar burning velocity of iron powder is required. This study presents a newly developed burner based on the Heat Flux Method (HFM), which can measure the burning velocities of flat hybrid iron–methane–air flames. Since laminar iron flames are difficult to stabilize and have – even for micron-sized particles – burning velocities in close proximity to their terminal velocity, methane is used as a stabilizing agent. In this paper, the design of the new burner system is presented with results for burning velocities of iron–methane–air flames. The results show a steady decrease in burning velocity when iron is added to a stoichiometric methane–air flame down to 16 cm/s. It is hypothesized that in the case of relatively low iron concentrations, the iron acts as a heat sink within these flames, consequently reducing the flame temperature and laminar burning velocity. For these low concentrations, methane is the governing fuel. At concentrations above 250 g/m3, the reduction of the burning velocity comes to a halt, and the iron becomes the governing fuel in the flame. A comprehensive error analysis reveals that the primary sources of uncertainty stem from fluctuations in the iron content and parabolic fitting of the thermocouple measurements in the HFM.Novelty and Significance statementThe novelty of this study is a newly developed burner based on the well-known Heat Flux Method (HFM). In accordance with the HFM, a new burner was designed to facilitate stable flat adiabatic hybrid iron–methane–air flames for the first time and measure its key parameter, the adiabatic burning velocity. Further, a metered iron powder dispersion system was developed for accurate iron mass flow measurements. The results show a steady decrease in burning velocity when iron is added to a stoichiometric methane–air flame in relatively low concentrations and show a very weak dependence of the burning velocity on the iron content at iron concentrations above 250 g/m3. These findings enable the validation of 1D simulations for iron-laden flames. A detailed assessment of the measurement uncertainties reveals that the largest source of uncertainty can be derived back to the stability of iron mass flow, leading to small flame propagation fluctuations on relatively short intervals.

Laying the ground for future cross-organizational process mining research and application: a literature review

Purpose Process mining (PM) has emerged as a leading technology for gaining data-based insights into organizations’ business processes. As processes increasingly cross-organizational boundaries, firms need to conduct PM jointly with multiple organizations to optimize their operations. However, current knowledge on cross-organizational process mining (coPM) is widely dispersed. Therefore, we synthesize current knowledge on coPM, identify challenges and enablers of coPM, and build a socio-technical framework and agenda for future research.Design/methodology/approach We conducted a literature review of 66 articles and summarized the findings according to the framework for Information Technology (IT)-enabled inter-organizational coordination (IOC) and the refined PM framework. The former states that within inter-organizational relationships, uncertainty sources determine information processing needs and coordination mechanisms determine information processing capabilities, while the fit between needs and capabilities determines the relationships’ performance. The latter distinguishes three categories of PM activities: cartography, auditing and navigation.Findings Past literature focused on coPM techniques, for example, algorithms for ensuring privacy and PM for cartography. Future research should focus on socio-technical aspects and follow four steps: First, determine uncertainty sources within coPM. Second, design, develop and evaluate coordination mechanisms. Third, investigate how the mechanisms assist with handling uncertainty. Fourth, analyze the impact on coPM performance. In addition, we present 18 challenges (e.g. integrating distributed data) and 9 enablers (e.g. aligning different strategies) for coPM application.Originality/valueThis is the first article to systematically investigate the status quo of coPM research and lay out a socio-technical research agenda building upon the well-established framework for IT-enabled IOC.

Laser spectroscopy of highly charged ions at the storage ring CSRe: Schottky-based spectroscopic investigation and XUV fluorescence detector development

Laser spectroscopy stands out as a powerful tool to investigate atomic and nuclear properties and also test fundamental theories by precisely measuring the energy levels in highly charged ions. In this work, the fine-structure splitting 1s22s2S1/2−1s22p2P1/2 transition in lithium-like 16O5+ has been investigated in a laser spectroscopy experiment at the heavy-ion storage ring CSRe. The excitation resonance of the tunable narrowband UV laser and the ions was observed using a Schottky resonator with very high sensitivity. The experimental uncertainties including the ion beam velocity determination, the space charge effects, laser wavelength calibration, and angular misalignment between the laser and ion beam were analyzed, and the 2S1/2−2P1/2 transition wavelength was determined to be λ0= 103.45(38) nm. The primary source of experimental uncertainty arises from the inaccurate measurement of the high voltage applied on the electron cooler and therewith the ion beam velocity at the CSRe. In order to solve this problem, a high-precision divider to precisely measure the high-voltage of the electron cooler is under construction, which is expected to improve the present experimental accuracy by three orders of magnitude. In addition, an XUV optical detector equipped with an off-axis parabolic mirror has been developed and installed at the CSRe for the investigation of slow dipole-forbidden transitions in highly charged ions, such as hyperfine splitting and M1 transitions. The experimental studies presented here pave the way for future laser spectroscopy experiments at the CSRe as well as at the future larger-scale scientific facility HIAF in China.

Rb-Sr constraints on the age of Moon formation

Determining the age of the Moon, which is commonly considered as the termination of Earth accretion has been a complex challenge for geochronology. A number of methods have been used to delineate the age of the Moon based either on absolute chronology of lunar rocks or have relied on more indirect methods using short-lived nuclides such as 182Hf that was present in the early history of the Solar System. Model ages usually require some assumptions that are sometimes controversial or harder to verify.In this study, new high precision Sr isotope data (2.4 ppm, 2SD) were obtained for a well-dated lunar anorthosite (60025) in order to better constrain the initial 87Sr/86Sr of the bulk silicate Moon. This new data is then used to model the Sr isotope evolution of the Earth-Moon starting from the beginning of the Solar System. To comply with the HfW and stable isotope constraints, we then assume that the Earth and Moon were equilibrated at the time of Moon formation. By investigating systematically all the sources of uncertainties in our model, we show that compared with previous work on anorthosites, one can tighten the constraints on the youngest age of Moon formation to no >79 Ma after the beginning of the Solar System, i.e. the Moon cannot be younger than 4488 Ma.

Buffering of Aerosol‐Cloud Adjustments by Coupling Between Radiative Susceptibility and Precipitation Efficiency

AbstractAerosol‐cloud interactions (ACI) in warm clouds are the primary source of uncertainty in effective radiative forcing (ERF) during the historical period and, by extension, inferred climate sensitivity. The ERF due to ACI (ERFaci) is composed of the radiative forcing due to changes in cloud microphysics and cloud adjustments to microphysics. Here, we examine the processes that drive ERFaci using a perturbed parameter ensemble (PPE) hosted in CAM6. Observational constraints on the PPE result in substantial constraints in the response of cloud microphysics and macrophysics to anthropogenic aerosol, but only minimal constraint on ERFaci. Examination of cloud and radiation processes in the PPE reveal buffering of ERFaci by the interaction of precipitation efficiency and radiative susceptibility.

Biomass supply chain network design under uncertainty, risk and resilience: A systematic literature review

Biomass supply chain faces numerous challenges, uncertainties and risks. Resilience holds the key for effectively addressing these dynamic challenges and uncertainties and risks inherent in biomass supply chain management. This paper aims to highlight the uncertainty, risk, and resilience, which are significant concepts of biomass supply chain network design, with a systematic literature review. An innovative classification scheme is developed to combine these concepts from an operational research perspective. First, the articles are investigated according to the sources of uncertainty, risk management, resilient network designs, and detailed subcategories. Then, the problem characteristics, modeling uncertainty, and solution methods are analyzed. This systematic literature review serves as a guide for further studies by analyzing uncertainty, risk, and resilience in the biomass supply chain network design, and identifies a list of potential issues for future research directions.

Does high-latitude ionospheric electrodynamics exhibit hemispheric mirror symmetry?

Abstract. Ionospheric electrodynamics is a problem of mechanical stress balance mediated by electromagnetic forces. Joule heating (the total rate of frictional heating of thermospheric gases and ionospheric plasma) and ionospheric Hall and Pedersen conductances comprise three of the most basic descriptors of this problem. More than half a century after identification of their central role in ionospheric electrodynamics, several important questions about these quantities, including the degree to which they exhibit hemispheric symmetry under reversal of the sign of dipole tilt and the sign of the y component of the interplanetary magnetic field (so-called “mirror symmetry”), remain unanswered. While global estimates of these key parameters can be obtained by combining existing empirical models, one often encounters some frustrating sources of uncertainty: the measurements from which such models are derived, usually magnetic field and electric field or ion drift measurements, are typically measured separately and do not necessarily align. The models to be combined moreover often use different input parameters, different assumptions about hemispheric symmetry, and/or different coordinate systems. We eliminate these sources of uncertainty in model predictions of electromagnetic work J⋅E (in general not equal to Joule heating ηJ2) and ionospheric conductances by combining two new empirical models of the high-latitude ionospheric electric potential and ionospheric currents that are derived in a mutually consistent fashion: these models do not assume any form of symmetry between the two hemispheres; are based on Apex magnetic coordinates (denoted Apex), spherical harmonics, and the same model input parameters; and are derived exclusively from convection and magnetic field measurements made by the Swarm and CHAMP satellites. The model source code is open source and publicly available. Comparison of high-latitude distributions of electromagnetic work in each hemisphere as functions of dipole tilt and interplanetary magnetic field clock angle indicates that the typical assumption of mirror symmetry is largely justified. Model predictions of ionospheric Hall and Pedersen conductances exhibit a degree of symmetry, but clearly asymmetric responses to dipole tilt and solar wind driving conditions are also identified. The distinction between electromagnetic work and Joule heating allows us to identify where and under what conditions the assumption that the neutral wind corotates with the Earth is not likely to be physically consistent with predicted Hall and Pedersen conductances.

Structuring and Development of Poultry Sectors in Algeria: Limits of Modernization Policies: The Case of the “Turkey” Sector (2000-2020)

Background: The Algerian poultry industry has had the biggest growth in the country. The purpose of this study is to demonstrate that the modernization of the poultry industry, driven by the policies pertaining to poultry that Algeria enacted between 2000 and 2020, did not result in the creation of governance structures that were in line with the demands of improving the performance of these industries. This theory is supported by the “Turkey” sector example. Methods: In order to explain Algeria’s poor governance of the poultry industry, the new institutional economy (NIE) invests within the framework of the “Sector” approach. The 1,288 units of turkey farms dispersed among 38 wilayas, or 80% of all the wilayats in the nation representing all possible production regions, were the subject of surveys conducted on turkey farms between 2015 and 2017. Result: The “atomized” breeding structures, the importance of “independent” breeding, the lack of economies of scale, the low level of development of integrated production and the absence of hybrid institutional arrangements are what set the “Turkey” sector apart, according to an analysis of its organizational structures. All of these things contribute to high production and transaction costs. Nonetheless, the “turkey” sector’s lack of coordination mechanisms continues to be a serious problem that causes abrupt changes and volatility in producer pricing, which in turn causes cyclical crises. These crises are a source of uncertainty and dangerous hazards, especially for small farms.

Ice Thickness Measurement and Volume Modeling of Muztagh Ata Glacier No.16, Eastern Pamir

As a heavily glaciated region, the Eastern Pamir plays a crucial role in regional water supply. However, considerable ambiguity surrounds the distribution of glacier ice thickness and the details of ice volume. Accurate data at the local scale are largely insufficient. In this study, ground-penetrating radar (GPR) was applied to assess the ice thickness at Muztagh Glacier No.16 (MG16) in Muztagh Ata, Eastern Pamir, for the first time, detailing findings from four distinct profiles, bridging the gap in regional measurements. We utilized a total of five different methods based on basic shear stress, surface velocity, and mass conservation, aimed at accurately delineating the ice volume and distribution for MG16. Verification was conducted using measured data, and an aggregated model outcome provided a unified view of ice distribution. The different models showed good agreement with the measurements, but there were differences in the unmeasured areas. The composite findings indicated the maximum ice thickness of MG16 stands at 115.87 ± 4.55 m, with an ice volume calculated at 0.27 ± 0.04 km3. This result is relatively low compared to the findings of other studies, which lies in the fact that the GPR measurements somewhat constrain the model. However, the model parameters remain the primary source of uncertainty. The results from this study can be used to enhance water resource assessments for future glacier change models.

Reconstruction of environmental site conditions by the integration of SCADA and reanalysis data

For the operational optimization of wind farms, AEP estimation and other tasks, high quality data of environmental conditions at the site are necessary. However, such data is often not available or has insufficient quality. This work tries to fill this gap, by integrating two data sources: the (usually available) operational data from the SCADA (Supervisory Control and Data Acquisition) system, and reanalysis data. SCADA data streams contain measurements from each wind turbine in the farm, but they are affected by various sources of uncertainty (including local flow effects, miscalibration, etc.), and might contain gaps. Meteorological reanalysis datasets can be used to fill gaps and complement SCADA data. However, modelled data can contain a wide range of biases and errors, due to limited model fidelity, coarse spatial and temporal resolution, inaccuracies in the input data feeding the model, etc. This study considers various methods to extract and merge wind speed and direction information from these diverse data sources. The analysis is based on field data measured at two experimental test sites, an offshore site equipped with 111 multi-MW turbines and a lidar buoy, and an onshore site equipped with 14 multi-MW wind turbines and a lidar. The methods are evaluated in the spectral and temporal domains by comparing the reconstructed wind characteristics with measurements from the lidars.

On the Challenges of Simulating Streamflow in Glacierized Catchments of the Himalayas Using Satellite and Reanalysis Forcing Data

Abstract Hydrologic assessment of climate change impacts on complex terrains and data-sparse regions like High Mountain Asia is a major challenge. Combining hydrological models with satellite and reanalysis data for evaluating changes in hydrological variables is often the only available approach. However, uncertainties associated with the forcing dataset, coupled with model parameter uncertainties, can have significant impacts on hydrologic simulations. This work aims to understand and quantify how the uncertainty in precipitation and its interaction with the model uncertainty affect streamflow estimation in glacierized catchments. Simulations for four precipitation datasets [Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), Climate Hazards Group Infrared Precipitation with Station (CHIRPS), ERA5-Land, and Asian Precipitation–Highly Resolved Observational Data Integration Toward Evaluation (APHRODITE)] and two glacio-hydrological models [Glacio-Hydrological Degree-Day Model (GDM) and Hydrological Model for Distributed Systems (HYMOD_DS)] are evaluated for the Marsyangdi and Budhigandaki River basins in Nepal. Temperature sensitivity of streamflow simulations is also investigated. Relative to APHRODITE, which compared well with ground stations, ERA5-Land overestimates the catchment average precipitation for both basins by more than 70%; IMERG and CHIRPS overestimate by ∼20%. Precipitation uncertainty propagation to streamflow exhibits strong dependencies to model structure and streamflow components (snowmelt, ice melt, and rainfall-runoff), but overall uncertainty dampens through precipitation-to-streamflow transformation. Temperature exerts a significant additional source of uncertainty in hydrologic simulations of such environments. GDM was found to be more sensitive to temperature variations, with >50% increase in total flow for 20% increase in actual temperature, emphasizing that models that rely on lapse rates for the spatial distribution of temperature have much higher sensitivity. Results from this study provide critical insight into the challenges of utilizing satellite and reanalysis products for simulating streamflow in glacierized catchments. Significance Statement This work investigates the uncertainty of streamflow simulations due to climate forcing and model parameter/structure uncertainty and quantifies the relative importance of each source of uncertainty and its impact on simulating different streamflow components in glacierized catchments of High Mountain Asia. Results highlight that in high mountain regions, temperature uncertainty exerts a major control on hydrologic simulations and models that do not adequately represent the spatial variability of temperature are more sensitive to bias in the forcing data. These findings provide guidance on important aspects to be considered when modeling glacio-hydrological response of catchments in such areas and are thus expected to impact both research and operation practice related to hydrologic modeling of glacierized catchments.

On the retrieval of cloud optical thickness from spectral radiances - A sensitivity study with high albedo surfaces

Measurements of spectral zenith radiance in the 320–950 nm wavelength range have been carried out since 2021 at the Thule High Arctic Atmospheric Observatory (THAAO, https://www.thuleatmos-it.it/, 76.5° N, 68.8° W, 225 m a.s.l) located in Pituffik, northern Greenland. This study evaluates whether such observations could provide, in principle, scientifically meaningful cloud optical depth (τ) estimates, what would be the limitations, which are the necessary auxiliary measurements, and which radiance wavelengths would be better suited for the goal. Although clouds play a critical role in the Arctic, a climatology of τ in high albedo conditions is particularly difficult to obtain. THAAO might have the instrument capabilities to provide such a long-term dataset. We use a radiative transfer package to simulate visible spectra with different cloud and surface conditions, assuming homogeneous overcast sky and liquid water clouds of fixed geometrical thickness. Simulations are run to reproduce typical conditions encountered at THAAO when measurements are carried out. We find that the assumption of a broadband albedo instead of a spectrally-resolved one is the source of the largest uncertainties. Tests on size and phase of cloud particles showed that a 50% uncertainty in reff leads to a ∼10% error in τ, and that a 10% contamination of ice crystals in a low-level liquid water cloud leads to an error in τ estimates of less than 5%. All the tests showed that the most critical τ range is between the thin and thick cloud regimes (τ ∼ 7–15), where the retrievals can be less reliable. Otherwise, tests suggest that in the environmental conditions that characterize late spring and summer at THAAO, and given the observatory measurements capabilities, estimates of τ for low-level clouds could be accurately retrieved both in high and low surface albedo conditions by means of zenith radiance measurements in the UV-Vis-NIR range.

Landslide susceptibility assessment using different rainfall event-based landslide inventories: advantages and limitations

AbstractThe present work aims to evaluate potential sources of uncertainty associated with rainfall-triggered event-based landslide inventories within the framework of landslide susceptibility assessment. Therefore, this study addresses the following questions: (i) How representative is an event-based landslide inventory map of the total landslide activity and distribution in a study area?; (ii) How reliable is an event-based landslide susceptibility map?; (iii) How appropriate is an event-based landslide inventory map for independently validating a landslide susceptibility map? To address these questions, two independent and contrasting rainfall event-based landslide inventories were used, together with a historical landslide inventory, to assess landslide susceptibility for different types of landslides in a study area located north of Lisbon, Portugal. The results revealed the following findings: (i) contrasting rainfall critical conditions for failure can trigger similar landslide types, although they may vary in size and be spatially constrained by different predisposing conditions, particularly lithology and soil type; (ii) landslide susceptibility models using event-based landslide inventories are not reliable in the study area, regardless of the landslide inventory map used for training and validation; and (iii) complementary sources of uncertainty results from using incomplete historical landslide inventories to assess landslide susceptibility and non-totally independent landslide inventories for modeling validation. The present study enhances the understanding of regional landslide susceptibility patterns based on contrasting rainfall-trigger conditions, providing valuable information to minimize exposure; to design regional landslide early warning systems for specific rainfall-trigger landslide events; and to improve the response and preparedness of civil protection services.

Urban flood prediction using ensemble artificial neural network: an investigation on improving model uncertainty

Reducing the impact of artificial neural networks (ANN) affected by sources of uncertainty is crucial to improving the reliability of the flood prediction model. This study proposes an ensemble artificial neural network (EANN) model to predict the degree of flooding in coastal cities. Combined methods are used to reduce the model’s uncertainty, heuristic neural pathway strength feature selection is used to select inputs, the coupling method is used to optimize network architecture and parameters, and the integration method which paralleling three ANN models with different predicted lead periods ensemble together is used to capture output uncertainty. The EANN model has successfully predicted flooding in the Chinese coastal city of Macao during a typhoon, with convincing accuracy. The study also analyzed the impacts of both long and short training datasets with appropriate time intervals on ANN modeling performance. It was found that the performance of short training datasets, with appropriate time intervals, was similar to or better than models with long training datasets.

A real‐time decision support system to improve operations in electric bus networks

ABSTRACTElectrifying transit bus networks (TBNs) has recently become a challenging problem that many public transport operators around the world are facing. Due to the limited driving range of electric buses, electric TBNs are more sensitive to operational delays and uncertainties. Moreover, the impact on sustainability is most profound when the buses are powered by renewable energy resources, which are often subject to intermittency and uncertainty. In this work, we tackle the complicated problem of planning charging schedules amid these various sources of uncertainty. We develop a real‐time decision support system that uses real‐time data, predictions, and mathematical optimization to update the charging schedules and mitigate the impact of operational uncertainties. Our results show that the online strategy can maintain higher reliability and renewable energy utilization levels compared to other charging strategies. The study has been carried out in cooperation with the public transport operator in Rotterdam in the Netherlands to assist them in their TBN transition process.

Technical note: Exploring parameter and meteorological uncertainty via emulation in volcanic ash atmospheric dispersion modelling

Abstract. ​​​​​​​Consideration of uncertainty in volcanic ash cloud forecasts is increasingly of interest, with an industry goal to provide probabilistic forecasts alongside deterministic forecasts. Simulations of volcanic clouds via dispersion modelling are subject to a number of uncertainties relating to the eruption itself (mass of ash emitted and when), parameterisations of physical processes, and the meteorological conditions. To fully explore these uncertainties through atmospheric dispersion model simulations alone may be expensive, and instead, an emulator can be used to increase understanding of uncertainties in the model inputs and outputs, going beyond combinations of source, physical, and meteorological inputs that were simulated by the dispersion model. We emulate the NAME (Numerical Atmospheric-dispersion Modelling Environment) dispersion model for simulations of the Raikoke 2019 eruption and use these emulators to compare simulated ash clouds to observations derived from satellites, constraining NAME source and internal parameters via history matching. We demonstrate that the effect of varying both meteorological scenarios and model parameters can be captured in this way with accurate emulation and using only a small number of runs per meteorological scenario. We show that accounting for meteorological uncertainty simultaneously with other uncertainties may lead to the identification of different sensitive model parameters and may lead to less constrained source and internal NAME parameters; however, through idealised experiments, we argue that this is a reasonable result and is properly accounting for all sources of uncertainty in the model inputs.

Evaluating intra-fractional tumor motion in lung stereotactic radiotherapy with deep inspiration breath-hold.

To evaluate the intra-fractional tumor motion in lung stereotactic body radiotherapy (SBRT) with deep inspiration breath-hold (DIBH), and to investigate the adequacy of the current planning target volume (PTV) margins. Twenty-eight lung SBRT patients with DIBH were selected in this study. Among the lesions, twenty-three were at right or left lower lobe, two at right middle lobe, and three at right or left upper lobe. Post-treatment gated cone-beam computed tomography (CBCT) was acquired to quantify the intra-fractional tumor shift at each treatment. These obtained shifts were then used to calculate the required PTV margin, which was compared with the current applied margin of 5mm margin in anterior-posterior (AP) and right-left (RL) directions and 8mm in superior-inferior (SI) direction. The beam delivery time was prolonged with DIBH. The actual beam delivery time with DIBH (Tbeam_DIBH) was compared with the beam delivery time without DIBH (Tbeam_wo_DIBH) for the corresponding SBRT plan. A total of 113 treatments were analyzed. At six treatments (5.3%), the shifts exceeded the tolerance defined by the current PTV margin. The average shifts were 0.0±1.9mm, 0.1±1.5mm, and -0.5±3.7mm in AP, RL, and SI directions, respectively. The required PTV margins were determined to be 4.5, 3.9, and 7.4mm in AP, RL, and SI directions, respectively. The average Tbeam_wo_DIBH and Tbeam_DIBH were 2.4±0.4 min and 3.6±1.5 min, respectively. The average treatment slot for lung SBRT with DIBH was 25.3±7.9 min. Intra-fractional tumor motion is the predominant source of treatment uncertainties in CBCT-guided lung SBRT with DIBH. The required PTV margin should be determined based on data specific to each institute, considering different techniques and populations. Our data indicate that our current applied PTV margin is adequate, and it is possible to reduce further in the RL direction. The time increase of Tbeam_DIBH, relative to the treatment slot, is not clinically significant.

Urban influence on convective precipitation in the Paris region: Hectometric ensemble simulations in a case study

AbstractThe purpose of this study is to investigate the influence of the urban environment of the Paris region on an isolated convective cell that formed downwind of the city on May 7, 2022, using the Meso‐NH research atmospheric model at a horizontal scale of 300 m. To account for all sources of forecast uncertainty, the initial and lateral boundary conditions of the simulations are provided by an ensemble prediction system. A multi‐layer urban scheme is used to represent the influence of buildings on the airflow accurately. Two sets of ensemble simulations are performed: the first set (URB) uses a fine‐scale surface description of the city, while the second set (NOURB) replaces urban surfaces with vegetation. This sensitivity test shows that, despite the high variability of simulated precipitation within the ensemble, the city of Paris plays a statistically significant role in the initiation of convection in this case. Convective cells are initiated over the city for several members of the URB ensemble, while almost no precipitation is simulated for the same members of the NOURB ensemble. The mean 6‐h rainfall accumulation of the URB ensemble is increased by 70% over Paris (compared with the NOURB ensemble) and no statistically significant trend is found around the city. The analysis reveals that the capital experiences a higher sensible heat flux due to drier and warmer air, resulting in enhanced vertical velocities and an increase in boundary‐layer height in the URB ensemble. Additionally, the total water content and cloud fraction over Paris are intensified, leading to more precipitation. These findings suggest that urbanisation has a notable impact on convection and precipitation processes in this case.

Evaluating the impact of anatomical and physiological variability on human equivalent doses using PBPK models.

Addressing human anatomical and physiological variability is a crucial component of human health risk assessment of chemicals. Experts have recommended probabilistic chemical risk assessment paradigms in which distributional adjustment factors are used to account for various sources of uncertainty and variability, including variability in the pharmacokinetic behavior of a given substance in different humans. In practice, convenient assumptions about the distribution forms of adjustment factors and human equivalent doses (HEDs) are often used. Parameters such as tissue volumes and blood flows are likewise often assumed to be lognormally or normally distributed without evaluating empirical data for consistency with these forms. In this work, we performed dosimetric extrapolations using physiologically based pharmacokinetic (PBPK) models for dichloromethane (DCM) and chloroform that incorporate uncertainty and variability to determine if the HEDs associated with such extrapolations are approximately lognormal and how they depend on the underlying distribution shapes chosen to represent model parameters. We accounted for uncertainty and variability in PBPK model parameters by randomly drawing their values from a variety of distribution types. We then performed reverse dosimetry to calculate HEDs based on animal points of departure for each set of sampled parameters. Corresponding samples of HEDs were tested to determine the impact of input parameter distributions on their central tendencies, extreme percentiles, and degree of conformance to lognormality. This work demonstrates that the measurable attributes of human variability should be considered more carefully and that generalized assumptions about parameter distribution shapes may lead to inaccurate estimates of extreme percentiles of HEDs.