Calibration Events Research Articles

Indoor Direct Comparison between High-Accuracy Pyrheliometer and Room Temperature Absolute Radiometer

Abstract This article proposes a practicable indoor method by directly comparing the pyrheliometer with the room temperature absolute radiometer (RTAR) to overcome the inconvenience of outdoor pyrheliometer calibration caused by the limitation of weather conditions. The RTAR serves as the primary standard for radiant power, irradiance, illuminance, and candela at the Center for Measurement Standards/ITRI. Given that the RTAR and pyrheliometer exhibit different sensitivity curves, the key to a successful comparison lies in designing an incident beam that enables both the RTAR and pyrheliometer to operate within their respective uniform sensitivity regions. While the indoor incident beam’s irradiance is considerably lower than solar radiation, the comparison results demonstrate a quite well measurement consistence between the RTAR and the pyrheliometer. As the indoor method is insusceptible to weather, the complex data acquisition procedures for the outdoor method can be simplified due to the stable incident beam indoors. Despite current documentary standards recommending outdoor calibration and comparison for pyrheliometer, the indoor method presented in this article can serve as a supplementary approach for assessing the accuracy of pyrheliometers during the intervals between two calibration or comparison events.

Development of a Practical Procedure for Data-Driven Weigh-in-Motion Equipment Calibration Scheduling to Assure Data Accuracy and Consistency over Time

Weigh-in-motion (WIM) technology is a traffic monitoring technology that highway agencies use to obtain information about the weight, axle loading, and configuration of heavy vehicles moving at operational speed. To ensure high-quality WIM data, the Federal Highway Administration (FHWA) recommends regular calibration of WIM equipment. This study addresses the need to optimize the allocation of the limited resources that agencies have for WIM equipment calibration by developing a procedure for data-driven calibration scheduling. This was accomplished through an analysis of WIM measurement errors from test truck data collected during field performance validation and calibration events and an analysis of monthly changes in truck weight and axle loading characteristics, based on WIM data collected between calibration events. The analysis results were used to draw conclusions on the functional performance of different WIM sites. The study also demonstrates how the newly developed National Cooperative Highway Research Program (NCHRP) WIM Data Quality Assurance Analysis Tool can be used to compute truck weight and axle loading parameters and visualize data analysis results using four case studies: two WIM sites with piezo quartz sensors in asphalt pavements and two WIM sites with bending plate sensors in concrete pavements. This paper provides a practical procedure and recommendations that highway agencies can use to develop data-driven WIM calibration schedules that will ensure consistent high-quality WIM data for sites managed by an agency with the aid of the NCHRP WIM Data Quality Assurance Analysis Tool.

Evaluation of the number of events’ influence on model performance and uncertainty in urban data-scarce areas based on behavioral parameter ranking method

Rainfall-runoff data in drainage systems in urban areas is the essential input variable for urban hydrological modeling. Obtaining high quality and sufficient size of urban flood events is always difficult due to the inconvenient underground observation and the untimely capture of the rapid rising and recession stages of urban runoff. It largely constrains the efficiency of model calibration and brings large uncertainty in urban rainfall-runoff simulation. Therefore, the evaluation of the number of events’ influence on model performance and uncertainty is of great significance, which can provide useful information to help the decision makers select sufficient useful observation data for model calibration with relatively fewer events. In this study, we constructed a comprehensive method of behavioral parameter ranking of multi-events (BPROME) based on coupling the Generalized Likelihood Uncertainty Estimation (GLUE) algorithm and the Storm Water Management Model (SWMM). The results in the two small demonstration areas (named Case #A and Case #B) in Shenzhen city of China proved the good performance of the BPROME in uncertainty assessment in urban areas. We found the model uncertainty (average bandwidth (B) and average deviation amplitude (D)) and model performance (containing Ratio (CR) and the maximum value of behavioral samples’ NSE (NSEmax)) became stable at a certain number of events in both two case areas. The B and D decrease and the CR and NSEmax increase as the number of event increases. In particular, the model performance and uncertainty reach their appropriate state concerning the limited observations at a certain range of numbers of events (both three to five in our two case areas). Our results demonstrate the potential influence of the numbers of events for the urban rainfall-runoff modeling calibration which can balance the model efficiency and data collection cost (the number of input rainfall-runoff data). The findings could help decision-makers seek a trade-off between data investment and acceptable model performance.

Impact of observation thresholds in the assessment of field-scale soil saturated hydraulic conductivity

The soil saturated hydraulic conductivity (Ks) plays a key role in the partitioning of rainfall into runoff and infiltration. Field-scale variability of Ks is often represented as a lognormal random field, and its parameters are frequently estimated by calibrating areal-average infiltration models with infiltration observations obtained under natural/artificial rainfall conditions. Such observations are obtained in terms of censored moments as infiltration is controlled by only a fraction of the Ks random field during a rainfall event. In this study, the impact of such observations on the maximum likelihood estimates (MLE) of the Ks distribution parameters is evaluated. Based on the data from several rainfall-runoff events observed under natural conditions over an experimental field, the results demonstrate the role of temporal variation of rainfall in resolving the Ks field for a rainfall event. Validation results show that the performance of the MLEs is impacted by both the fraction of the Ks field resolved by a rainfall event and its capacity to capture the variability of Ks. Finally, recommendations are provided as to what constitutes a good calibration event. The impact of incorporating reliability of the calibration events in consolidating information was tested using an information-theoretic measure.

Evaluation of WIM data consistency based on temporal axle load spectra

It is crucial to evaluate the consistency in weigh-in-motion (WIM) loading over time and quantify the relative accuracy of axle loads based on axle load spectra (ALS) data for different sensor types. This paper presents the temporal evaluation of the ALS from 51 WIM sites and 128 records available in the long-term pavement performance data. Analysis of ALS data over time shows that for single ALS, there is a significant difference in peak loads between the bending plate, and quartz piezo sensor measurements. Also, 100% of the bending plate 86% of the quartz piezo, and 66% of the piezo cables sites exhibited consistent single axle peak loads after 1 year of calibration event. For tandem ALS, significant differences were observed between loaded peaks of bending plate sensor from both quartz piezo and piezo cable sensors. Also, 83% of the bending plate, 78% of the quartz piezo, and 50% of the piezo cable sites exhibited loaded peaks consistently 1 year after calibration. The results show that calibration frequencies longer than 1 year may be acceptable for the bending plate sensors. However, calibration frequencies of at least 1 year for quartz piezo and less than a year for piezo cable sensors are recommended. Pavement designers and analysts should be aware of the changes in WIM data and calibration frequency over time.

Wavelet Analysis and the Information Cost Function Index for Selection of Calibration Events for Flood Simulation

Globally, floods are a prevalent type of natural disaster. Simulating floods is a critical component in the successful implementation of flood management and mitigation strategies within a river basin or catchment area. Selecting appropriate calibration data to establish a reliable hydrological model is of great importance for flood simulation. Usually, hydrologists select the number of flood events used for calibration depending on the catchment size. Currently, there is no numerical index to help hydrologists quantitatively select flood events for calibrating the hydrological models. The question is, what is the necessary and sufficient amount (e.g., 10 events) of calibration flood events that must be selected? This study analyses the spectral characteristics of flood data in Sequences before model calibration. The absolute best set of calibration data is selected using an entropy-like function called the information cost function (ICF), which is calculated from the discrete wavelet transform (DWT) decomposition results. Given that the validation flood events have already been identified, we presume that the greater the similarity between the calibration dataset and the validation dataset, the higher the performance of the hydrological model should be after calibration. The calibration datasets for the Tunxi catchment in southeast China were derived from 21 hourly flood events, and the calibration datasets were generated by arranging 14 flood events in sequences from 3 to 14 (i.e., a Sequence of 3 with 12 sets (set 1 = flood events 1, 2, 3; set 2 = flood events 2, 3, 4, …, and so on)), resulting in a total of 12 sequences and 78 sets. With a predetermined validation set of 7 flood events and the hydrological model chosen as the Hydrologic Engineering Center (HEC–HMS) model, the absolute best calibration flood set was selected. The best set from the Sequence of 10 (set 4 = S10′) was found to be the absolute best calibration set of flood events. The potential of the percentile energy entropy was also analyzed for the best calibration sets, but the ICF was the most consistent index to reveal the ranking based on similarity with model performance. The proposed ICF index in this study is helpful for hydrologists to use data efficiently with more hydrological data obtained in the new era of big data. This study also demonstrates the possibility of improving the effectiveness of utilizing calibration data, particularly in catchments with limited data.

Performance and outlook for the SADAR array network at the Newell County facility

Since installation in November 2021, a sparse network of SADAR compact volumetric arrays has been continuously and persistently acquiring data for passive microseismic monitoring of the Newell County Facility (NCF) CO2 storage pilot site. This report summarises several fundamental results from a year of continuous monitoring of the NCF using the SADAR permanent array network, including an updated seismic velocity model determined from ground truth calibration events, the observed seismicity derived from the curated year-long bulletin, and quantified estimates of system performance. In particular, the bulletin includes more than 560 analyst-verified located events excluding those at the surface, with at least 200 considered to be well-located. We have documented noise reduction provided by coherent processing of SADAR array data in excess of 30 dB compared to surface sensors and signal-to-noise ratio (SNR) improvements up to 20 dB compared to single channels. Combining the bulletin information and performance measurements, we estimate magnitude of completeness Mc ≈ −2.5 Mw, and an event-to-receiver range for locatable events of at least 600 m from individual arrays. We conclude with a look at continuing and planned projects using the SADAR network at the NCF including tests of active source acquisition.

A novel ensemble algorithm based on hydrological event diversity for urban rainfall–runoff model calibration and validation

Hydrological models such as the stormwater management model (SWMM) are essential tools for managing water resources. Model performance typically relies on calibration to historic rainfall–runoff events. However, traditional calibration techniques suffer many issues, such as ad-hoc event selection, ignoring model parameter uncertainty, and poor validation techniques. We introduce a novel ensemble framework that relies on data-driven event selection of calibration and validation events to achieve robust model generalisation.The proposed framework is applied to SWMM models for two urbanised catchments in Southern Ontario, Canada. The framework uses unsupervised machine learning to cluster rainfall–runoff events. Events in different clusters have diverse characteristics; sampling events from each cluster ensures a diverse set of events for calibration. Validation events can be sampled in the same manner, which ensures similarity between calibration and validation data. We study the effects that the calibration event cluster has on model sensitivity and generalisation. Only minor differences in model parameter sensitivity were observed between different clusters. However, the cluster(s) used for calibration were found to be very important for model generalisation. Next, we generate ensemble predictions, which use multiple SWMM models, each calibrated to different events. Ensemble performance is compared to the single-model baseline and across increasing numbers of ensemble members. Results demonstrate improvements in generalisation and decrease in prediction variance across increasing ensemble size. Finally, we compare several different techniques for ensemble combination, which increase in complexity from simple averaging to ANN-based stacking augmented with exogenous input features. The ANN-based stacking performs the best according to two out of three evaluation criteria. Simple averaging, while inferior in performance to stacking, consistently outperforms the single model baseline. The novel framework proposed in this work provides a data-driven, adaptable alternative to conventional calibration frameworks that have been proven to improve model performance.

Calibration frequency and analytical variability of laboratory measurements

BackgroundThere is uncertainty whether increased frequency of calibrations may affect the overall analytical variability of a measurement procedure as reflected in quality control (QC) performance. In this simulation study, we examined the impact of calibration frequencies on the variability of laboratory measurements. MethodsA 5-point calibration curve was modeled with simulated concentrations ranging from 10 to 10,000 mmol/l, and signal intensities with CVs of 3 % around the mean, under a Gaussian distribution. 3 levels of QC (20, 150, 600 mmol/l) interspersed within the analytical measurement range were also simulated. ResultsThe CV of the 3 QC levels remained stable across the different calibration frequencies simulated (5, 10, 15 and 30 QC measurements per recalibration episode). The imprecision was greatest (18 %) at the lowest concentration of 20 mmol/l, when the calibration curve was derived using ordinary least squares regression, reducing to 3.5 % and 3.8 % at 150 and 600 mmol/l, respectively. The CV of all 3 QC concentrations remained constant at 3.4 % and close the predefined CV (3 %) when weighted least squares regression was used to derive the calibration model. Similar findings were observed with 2-point calibrations using WLS models at narrower concentration ranges (50 and 100 mmol/l as well as 50 and 500 mmol/l). DiscussionWithin the parameters of the simulation study, an increased frequency of calibration events does not adversely impact the overall analytical performance of a measurement procedure under most circumstances.

Web-based prototype system for flood simulation and forecasting based on the HEC-HMS model

This study addresses Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model simulation issues in web environments and identifies key model parameters to facilitate practical flood forecasting. Jython scripts serving as an intermedium layer between the web-based forecast system and HEC-HMS model were created to enable HEC-DSS database writing and reading and HEC-HMS model execution as a background process. Empirical relationships between model parameters and time series characteristics were established to mitigate the difficulty in determining event-dependent model parameters. An open-source web-based prototype system for flood simulation and forecasting (WSFF) based on the HEC-HMS was built by incorporating these methods and parameter knowledge into the model and knowledge subsystem. The WSFF performance was evaluated via a case study involving 12 historical flood events in the Chuanchang watershed, southeastern China. Among these 12 events, nine events served as calibration events to establish WSFF parameter knowledge, and three events were used to verify the WSFF performance. A consistent and satisfactory performance in terms of peak flow, total flood volume, peak flow timing and overall hydrograph fitting effect was found. The average Nash–Sutcliffe efficiency (NSE) for the validation events reached 0.81 versus 0.82 for the calibration events. The relative error in peak flow (REP) and relative error in flood volume (REV) indicated a reasonable flood forecasting accuracy, with an REP within 15% and RET within 1 h. Developed as open-source software, the WSFF could serve as a useful basis for the hydrological community regarding HEC-HMS adoption in flood forecasting.

Source scaling comparison and validation in Central Italy: data intensive direct Swaves versus the sparse data coda envelope methodology

SUMMARY Robustness of source parameter estimates is a fundamental issue in understanding the relationships between small and large events; however, it is difficult to assess how much of the variability of the source parameters can be attributed to the physical source characteristics or to the uncertainties of the methods and data used to estimate the values. In this study, we apply the coda method by Mayeda et al. using the coda calibration tool (CCT), a freely available Java-based code (https://github.com/LLNL/coda-calibration-tool) to obtain a regional calibration for Central Italy for estimating stable source parameters. We demonstrate the power of the coda technique in this region and show that it provides the same robustness in source parameter estimation as a data-driven methodology [generalized inversion technique (GIT)], but with much fewer calibration events and stations. The Central Italy region is ideal for both GIT and coda approaches as it is characterized by high-quality data, including recent well-recorded seismic sequences such as L'Aquila (2009) and Amatrice–Norcia–Visso (2016–2017). This allows us to apply data-driven methods such as GIT and coda-based methods that require few, but high-quality data. The data set for GIT analysis includes ∼5000 earthquakes and more than 600 stations, while for coda analysis we used a small subset of 39 events spanning 3.5 < Mw < 6.33 and 14 well-distributed broad-band stations. For the common calibration events, as well as an additional 247 events (∼1.7 < Mw < ∼5.0) not used in either calibration, we find excellent agreement between GIT-derived and CCT-derived source spectra. This confirms the ability of the coda approach to obtain stable source parameters even with few calibration events and stations. Even reducing the coda calibration data set by 75 per cent, we found no appreciable degradation in performance. This validation of the coda calibration approach over a broad range of event size demonstrates that this procedure, once extended to other regions, represents a powerful tool for future routine applications to homogeneously evaluate robust source parameters on a national scale. Furthermore, the coda calibration procedure can homogenize the Mw estimates for small and large events without the necessity of introducing any conversion scale between narrow-band measures such as local magnitude (ML) and Mw, which has been shown to introduce significant bias.

Validation of CrIS Radiometric Performance through Its Comparison to ABI

Radiometric intercomparison between satellite remote sensing instruments has become an increasingly common practice to monitor the stability and even the accuracy of their radiance products. The assessment also enables the evaluation of calibration improvements made to these products, as well as the identification and resolution of remaining calibration inadequacies. In this paper, the radiance products of the Cross-track Infrared Sounder (CrIS), an interferometer-based hyperspectral IR sounder in low Earth orbit (LEO), is compared with the level-1b (L1b) radiance products of the infrared (IR) bands of the Advanced Baseline Imager (ABI), an imaging radiometer in geostationary (GEO) orbit. Two CrIS instruments are currently in operation on S-NPP and NOAA-20 satellites, respectively, and two ABI instruments are in operation on GOES-16 and GOES-17 satellites, respectively. Radiometric intercomparisons are performed between each CrIS-ABI pair. An established procedure by GSICS for such GEO-LEO instrument comparison is principally followed to emulate the radiance of ABI IR bands from CrIS spectra of the collocated pixels to be compared with the actual ABI radiance. Results show that the long-term time series of CrIS-ABI radiance bias have been stable within 0.2 K for nearly all ABI IR bands within a spectral range from 3.7 μm to 13.3 μm, except those with known calibration issues. Miscellaneous calibration events that had occurred to either instrument and altered the biases are identified and explained. While the main goal of the work is to support the on-orbit Cal/Val of CrIS, including the future JPSS-2/3/4 CrIS, such observations can also be referenced to further improve the calibration of ABI.

Comparison Between Two Methodologies for Assessing Historical Earthquake Parameters and Their Impact on Seismicity Rates in the Western Alps

We investigate the differences in seismicity rate estimates from two historical earthquake catalogues obtained with two methodologies (Boxer and QUake-MD) calibrated on a common dataset of macroseismic intensities and calibration events. The two methodologies were then applied to a test data set of historical earthquakes covering the France, Italy and Switzerland Alpine region. Differences between the resulting magnitude estimates and instrumental magnitudes show a standard deviation of 0.4 for both methodologies, with a mean residual of 0.01 for Boxer and − 0.04 for Quake-MD. A systematic difference in magnitude estimates between the two methodologies that correlates with the depth estimated by Quake-MD has been observed. This is attributed to the difference in the treatment of the depth parameter between Boxer and QUake-MD. Nevertheless, differences in magnitude estimates between the two methodologies show a mean residual of 0.006 and a standard deviation of 0.35 resulting in seismicity rates that are not significantly different considering the associated uncertainties. Such results made us believe that the European community could gain in the reduction of epistemic uncertainties associated with the estimate of historical earthquake parameters by agreeing on a common macroseismic and calibration dataset across borders. These efforts should be strongly encouraged. On the other hand, we show that even in the ideal conditions of this benchmark (same calibration events and same macroseismic intensity dataset), methodological differences can lead to systematic differences in magnitude estimates. It is therefore paramount to explore different methodologies for a more realistic quantification of the epistemic uncertainties in estimates of maximum magnitudes and seismic activity rates.

Daily monitoring algorithms to detect geostationary imager visible radiance anomalies

The NASA Clouds and the Earth’s Radiant Energy System (CERES) project provides observed flux and cloud products for the climate science community. Geostationary satellite (GEO) imager measured clouds and broadband derived fluxes are incorporated in the CERES SYN1deg product to provide regional diurnal information in between Sun-synchronous Terra and Aqua CERES measurements. The recently launched GEO imagers with onboard calibration systems have active calibration teams that incrementally update the calibration in order to mitigate calibration drifts. However, short-term L1B radiance anomalies and calibration adjustment discontinuities may still exist in the record. To avoid any GEO cloud and flux artifacts in the CERES SYN1deg product, these calibration events must be addressed while scaling the GEO imagers to the Aqua-moderate resolution imaging spectroradiometer (MODIS) calibration reference. All-sky tropical ocean ray-matching (ATO-RM) and deep convective cloud invariant target (DCC-IT)-based monitoring algorithms are presented to detect calibration-driven daily anomalies in the GOES-16 Advanced Baseline Imager L1B visible (0.65 μm) radiance measurements. Sufficient daily ATO-RM sampling was obtained both by ray-matching GOES-16 with multiple MODIS and visible-infrared imaging radiometer suite imagers as well as by increasing the grid resolution. Optimized angular matching and outlier filtering were most effective in reducing the ATO-RM daily gain algorithm noise. The DCC-IT daily calibration algorithm utilized a larger domain and included more GOES-16 scan times. The DCC-IT daily gain uncertainty was reduced by normalizing the DCC regional reflectance on a regional, seasonal, and diurnal basis. The combination of ATO-RM and DCC-IT daily monitoring algorithms is shown to detect, with a high degree of confidence, daily GOES-16 L1B calibration-driven radiance anomalies >2.4 % , while keeping false positives at a minimum. Remarkably, the ATO-RM and DCC-IT daily gains are mostly within 0.5%. The ATO-RM and DCC-IT daily monitoring algorithms can be easily adapted to other GEO imagers and visible channels.

3D Gaze in Virtual Reality: Vergence, Calibration, Event Detection

Eye movement analysis in modern 3D rendering systems is reviewed and three new techniques are derived inspired by work developed in early Virtual Reality so-called 2.5D implementations, namely (a) gaze depth (i.e., vergence) estimation, (b) vergence calibration, and (c) real-time 3D event detection that considers eye- and head-coupling. The new 3D calibration shows excellent error reduction in terms of Mean Squared Error (MSE).

Radiometric calibration accuracy and stability of GOES-16 ABI Infrared radiance

The Advanced Baseline Imager (ABI) is the primary instrument onboard NOAA’s current generation of Geostationary Operational Environment Satellites R-series (GOES-R) satellites, measuring the reflected and emitted energy from the Earth. It consists of 16 channels, 10 in the thermal infrared (IR) and 6 in the solar reflective spectrum. Being the first in the GOES-R series satellites, GOES-16 was launched on November 19, 2016, and became operational as GOES-East at 75.2°W since December 18, 2017. We examine the radiometric calibration accuracy and stability of GOES-16 ABI IR radiance since its first light in January 2017, including the effects of two major updates of the GOES-R Ground Segment processing for the IR channels in October 2017, and June 2018. Using measurements by multiple hyperspectral radiometers from low Earth orbit satellites as references, it is found that, when converted to scene brightness temperature of 300 K, the calibrated ABI IR radiance is accurate within 0.13 K for Ch16 (13.3 μm), within 0.06 K for channel 12 (9.6 μm), and within 0.05 K for the other IR channels. This is an order of magnitude better than the requirement of 1 K. Since June, 2018, the radiometric calibration of GOES-16 ABI IR channels has been temporally stable, spatially uniform within the ABI full-disk field of view, absent of diurnal and seasonal variations, and invariant within various timelines. Other than short term disruptions as noted in the Calibration Event Log, GOES-16 ABI IR Level 1b products since June 19, 2018, is a reliable reference for satellite intercomparison or intercalibration studies.

The electric field dependence of single electron emission in the PIXeY two-phase xenon detector

Dual phase xenon detectors are widely used in experimental searches for galactic dark matter particles. The origin of single electron backgrounds following prompt scintillation and proportional scintillation signals in these detectors is not fully understood, although there has been progress in recent years. In this paper, we describe single electron backgrounds in 83mKr calibration events and their correlation with drift and extraction fields, using the Particle Identification in Xenon at Yale (PIXeY) dual-phase xenon time projection chamber. The single electron background induced by the Fowler-Nordheim (FN) effect is measured, and its electric field dependence is quantified. The photoionization of grids and impurities by prompt scintillation and proportional scintillation also contributes to the single electron background.

Simplified simulation of rock avalanches and subsequent debris flows with a single thin-layer model: Application to the Prêcheur river (Martinique, Lesser Antilles)

High discharge debris flows in mountainous and volcanic areas are major threats to populations and infrastructures. Modeling such events is challenging because the associated processes are complex, and because we often lack data to constrain rheological parameters. In this work, we show how extensive field data can help model a rock avalanche, and the subsequent remobilization of the deposits as a high discharge debris flow, with a single one-phase thin-layer numerical code, SHALTOP, and up to two rheological parameters. With the Prêcheur river catchment (Martinique, Lesser Antilles) as a case study, we use geological and geomorphological data, topographic surveys, seismic recordings and granulometric analyses to define realistic simulation scenarios and determine the main characteristics of documented events for model calibration. Then, we model a possible 1.9×106 m3 rock avalanche. The resulting deposits are remobilized instantaneously as a high discharge debris flow. We show that, for a given unstable volume, successive collapses allow to better reproduce the dynamics of the rock avalanche, but do not change the geometry of the final deposits, and thus the initial conditions of the subsequent debris flow simulation. The location of the debris flow initiation has also little influence on simulation results. However, progressive remobilization of materials slows down the debris flow and limits overflows, in comparison to an instantaneous release. Nevertheless, high discharge debris flows are well reproduced with an instantaneous initiation. Besides, the range of travel times measured for other significant debris flows in the Prêcheur river is consistent with our simulation results.

Horizontal position reconstruction in PandaX-II

Dual-phase noble-gas time projection chambers (TPCs) have improved the sensitivities for dark matter direct search in past decades. The capability of TPCs to reconstruct 3-D vertexes of keV scale recoilings is one of the most advantageous features. In this work, we develop two horizontal position reconstruction algorithms for the PandaX-II dark matter search experiment using the dual-phase liquid xenon TPC. Both algorithms are optimized by the 83mKr calibration events and use photon distribution of ionization signals among photomultiplier tubes to infer the positions. According to the events coming from the gate electrode, the uncertainties in the horizontal positions are 3.4 mm (3.9 mm) in the analytical (simulation-based) algorithm for an ionization signal with several thousand photon electrons in the center of the TPC.

Investigating model performance and parameter sensitivity for runoff simulation across multiple events for a large green roof

AbstractGreen roofs are a form of green infrastructure aimed at retaining or slowing the movement of precipitation as stormwater runoff to sewer systems. To determine total runoff versus retention from green roofs, researchers and practitioners alike employ hydrologic models that are calibrated to one or more observed events. However, questions still remain regarding how event size may impact parameter sensitivity, how best to constrain initial soil moisture (ISM), and whether limited observations (i.e., a single event) can be used within a calibration‐validation framework. We explored these questions by applying the storm water management model to simulate a large green roof located in Syracuse, NY. We found that model performance was very high (e.g., Nash Sutcliffe efficiency index > 0.8 and Kling‐Gupta efficiency index > 0.8) for many events. We initially compared model performance across two parameterizations of ISM. For some events, we found similar performance when ISM was varied versus set to zero; for others, varying ISM yielded higher performance as well as greater water balance closure. Within a calibration‐validation framework, we found that calibrating to larger events tended to produce moderate to high performance for other non‐calibration events. However, very small storms were notoriously difficult to simulate, regardless of calibration event size, as these events are likely fully retained on the roof. Using regional sensitivity analysis, we confirmed that only a subset of model parameters was sensitive across 16 events. Interestingly, many parameters were sensitive regardless of event size, though some parameters were more sensitive when simulating smaller events. This emphasizes that storm size likely influences parameter sensitivity. Overall, we show that while calibrating to a single event can achieve high performance, exploring simulations across multiple events can yield important insight regarding the hydrologic performance of green roofs that can be used to guide the gathering of in situ properties and observations for refining model frameworks.