Historical Calibration Research Articles

Calibrating Ground-Based Radars against TRMM and GPM

AbstractCalibration error represents a significant source of uncertainty in quantitative applications of ground-based radar (GR) reflectivity data. Correcting it requires knowledge of the true reflectivity at well-defined locations and times during a volume scan. Previous work has demonstrated that observations from certain spaceborne radar (SR) platforms may be suitable for this purpose. Specifically, the Ku-band precipitation radars on board the Tropical Rainfall Measuring Mission (TRMM) satellite and its successor, the Global Precipitation Measurement (GPM) missionCore Observatorysatellite together provide nearly two decades of well-calibrated reflectivity measurements over low-latitude regions (±35°). However, when comparing SR and GR reflectivities, great care must be taken to account for differences in instrument sensitivity and frequency, and to ensure that the observations are spatially and temporally coincident. Here, a volume-matching method, developed as part of the ground validation network for GPM, is adapted and used to quantify historical calibration errors for three S-band radars in the vicinity of Sydney, Australia. Volume-matched GR–SR sample pairs are identified over a 7-yr period and carefully filtered to isolate reflectivity differences associated with GR calibration error. These are then used in combination with radar engineering work records to derive a piecewise-constant time series of calibration error for each site. The efficacy of this approach is verified through comparisons between GR reflectivities in regions of overlapping coverage, with improved agreement when the estimated errors are removed.

Robust Portfolio Insurance: Maximum Protection with Minimum Assumptions

Protecting portfolio against extreme losses is a fundamentally difficult task since past experience provides a poor guidance for the future. This paper focuses on a robust approach to the portfolio insurance, which does not require historical calibration, and therefore avoids the hazards of data mining. We make a non-technical introduction to this methodology and compare it to traditional strategies such as Option Based Portfolio Insurance (OBPI) and Constant Proportion Portfolio Insurance (CPPI). We further propose its extension to the case when the investment horizon is not defined. As an illustration, we show how to apply this methodology to managing active risk of a smart beta equity portfolio.

Earthquake Early Warning ShakeAlert System: Testing and Certification Platform

Earthquake early warning systems provide warnings to end users of incoming moderate to strong ground shaking from earthquakes. An earthquake early warning system, ShakeAlert, is providing alerts to beta end users in the western United States, specifically California, Oregon, and Washington. An essential aspect of the earthquake early warning system is the development of a framework to test modifications to code to ensure functionality and assess performance. In 2016, a Testing and Certification Platform (TCP) was included in the development of the Production Prototype version of ShakeAlert. The purpose of the TCP is to evaluate the robustness of candidate code that is proposed for deployment on ShakeAlert Production Prototype servers. TCP consists of two main components: a real‐time in situ test that replicates the real‐time production system and an offline playback system to replay test suites. The real‐time tests of system performance assess code optimization and stability. The offline tests comprise a stress test of candidate code to assess if the code is production ready. The test suite includes over 120 events including local, regional, and teleseismic historic earthquakes, recentering and calibration events, and other anomalous and potentially problematic signals. Two assessments of alert performance are conducted. First, point‐source assessments are undertaken to compare magnitude, epicentral location, and origin time with the Advanced National Seismic System Comprehensive Catalog, as well as to evaluate alert latency. Second, we describe assessment of the quality of ground‐motion predictions at end‐user sites by comparing predicted shaking intensities to ShakeMaps for historic events and implement a threshold‐based approach that assesses how often end users initiate the appropriate action, based on their ground‐shaking threshold. TCP has been developed to be a convenient streamlined procedure for objectively testing algorithms, and it has been designed with flexibility to accommodate significant changes in development of new or modified system code. It is expected that the TCP will continue to evolve along with the ShakeAlert system, and the framework we describe here provides one example of how earthquake early warning systems can be evaluated.

Automated Sample Preparation Enables LC-MS/MS as a Routine Diagnostic Analysis for Serum Testosterone.

The advantage of LC-MS/MS for analyzing serum testosterone in female, pediatric, and hypogonadal male patient samples is well accepted (J Clin Endocrinol Metab 2010;95:4542-8). However, many clinical laboratories still use testosterone immunoassays because of the technical challenges of LC-MS/MS (Clin Chem 2010;56:1234-44). Although LC-MS/MS has been shown to have better accuracy and specificity than immunoassay, better reproducibility has been more elusive because of the complexities of sample preparation (Clin Chem 2008;54:1290-7; Rev Endocr Metab Disord 2013;14:185-205). We evaluated replacing automated immunoassay with LC-MS/MS for all testosterone analyses at the University of California San Diego Health System Center for Advanced Laboratory Medicine. We used a novel extraction media, AC Extraction Plate™ (AC Plate), from Tecan Schweiz with automated liquid handling for LC-MS/MS sample preparation. We modified the existing vendor application and validated the method for matrix effect, recovery, precision, trueness [accuracy relative to certified reference material (CRM)], specificity, reportable range, sample stability, and correlation with other methods. Method performance was excellent, with a reportable range of 4-1560 ng/dL (0.14-54.13 nmol/L), between-day CV <6%, mean accuracy for CRM of <4.0% bias, no interference from hemolysis, icterus, lipemia, serum separator tube gel, or common steroids/metabolites, and mean bias of 1.3% vs 4 other LC-MS/MS testosterone methods. A retrospective analysis of calibration stability suggests that sparse and/or historical calibration is feasible for routine use. We conclude that automated extraction with the AC Plate and a focus on robustness during method development delivered ease of use and method performance consistent with adopting LC-MS/MS for all testosterone testing.

Temporal and spatial evaluation of satellite-based rainfall estimates across the complex topographical and climatic gradients of Chile

Abstract. Accurate representation of the real spatio-temporal variability of catchment rainfall inputs is currently severely limited. Moreover, spatially interpolated catchment precipitation is subject to large uncertainties, particularly in developing countries and regions which are difficult to access. Recently, satellite-based rainfall estimates (SREs) provide an unprecedented opportunity for a wide range of hydrological applications, from water resources modelling to monitoring of extreme events such as droughts and floods.This study attempts to exhaustively evaluate – for the first time – the suitability of seven state-of-the-art SRE products (TMPA 3B42v7, CHIRPSv2, CMORPH, PERSIANN-CDR, PERSIAN-CCS-Adj, MSWEPv1.1, and PGFv3) over the complex topography and diverse climatic gradients of Chile. Different temporal scales (daily, monthly, seasonal, annual) are used in a point-to-pixel comparison between precipitation time series measured at 366 stations (from sea level to 4600 m a.s.l. in the Andean Plateau) and the corresponding grid cell of each SRE (rescaled to a 0.25° grid if necessary). The modified Kling–Gupta efficiency was used to identify possible sources of systematic errors in each SRE. In addition, five categorical indices (PC, POD, FAR, ETS, fBIAS) were used to assess the ability of each SRE to correctly identify different precipitation intensities.Results revealed that most SRE products performed better for the humid South (36.4–43.7° S) and Central Chile (32.18–36.4° S), in particular at low- and mid-elevation zones (0–1000 m a.s.l.) compared to the arid northern regions and the Far South. Seasonally, all products performed best during the wet seasons (autumn and winter; MAM–JJA) compared to summer (DJF) and spring (SON). In addition, all SREs were able to correctly identify the occurrence of no-rain events, but they presented a low skill in classifying precipitation intensities during rainy days. Overall, PGFv3 exhibited the best performance everywhere and for all timescales, which can be clearly attributed to its bias-correction procedure using 217 stations from Chile. Good results were also obtained by the research products CHIRPSv2, TMPA 3B42v7 and MSWEPv1.1, while CMORPH, PERSIANN-CDR, and the real-time PERSIANN-CCS-Adj were less skillful in representing observed rainfall. While PGFv3 (currently available up to 2010) might be used in Chile for historical analyses and calibration of hydrological models, the high spatial resolution, low latency and long data records of CHIRPS and TMPA 3B42v7 (in transition to IMERG) show promising potential to be used in meteorological studies and water resource assessments. We finally conclude that despite improvements of most SRE products, a site-specific assessment is still needed before any use in catchment-scale hydrological studies.

Uncertainty back-propagation in PLS model inversion for design space determination in pharmaceutical product development

Abstract The inversion of latent-variable models is an effective tool to assist the determination of the design space (DS) of a new pharmaceutical product. A challenging issue in partial least-square (PLS) regression model inversion is to describe how the uncertainty on the model outputs (product quality) relates to the uncertainty on the model inputs (raw material properties and process parameters). In this study, a methodology to relate the uncertainty on the output of a PLS model to the uncertainty on the model inputs is proposed. Two uncertainty back-propagation models are formulated and critically compared. Frequentist confidence regions (CRs) for the solution of the inversion problem are built. These CRs represent a subspace of the historical knowledge space within which the DS of the product to be developed is likely to lie with assigned confidence level. The input combinations that belong to these CRs (and that are consistent with the historical calibration data set) should be primarily investigated when an experimental campaign is to be performed to determine the DS. The proposed methodology is tested on three different case studies, two of which involve experimental data taken from the literature, respectively, on a roller compactor and on a wet granulator. It is shown that both uncertainty back-propagation models are effective in bracketing the DS, with the second model outperforming the first one in terms of shrinkage of the space within which experiments should be carried out to identify the DS.

Downdating high-resolution population density maps using sealed surface cover time series

Many countries in Europe and North America see their natural and agricultural landscapes being replaced by a fragmented, sprawled landscape. Spatially detailed modelling of changes in land use, population and transport could help to forecast the impact of scenarios aimed at mitigating the process of urban sprawl. A common problem with land-use change models however, is the lack of historical data for proper model calibration. In this paper we describe an approach for developing historical population density maps by downdating a recent high-resolution population density raster, using a time series of sealed surface data and historical census data as an input. In the proposed approach, we hypothesise a local relationship between increasing population densities and increasing sealed surface fraction estimates, the latter obtained from remote sensing imagery. We apply the method to Flanders, Belgium, a region where population growth and improved transport networks led to a diffuse urban expansion, with ribbon development along many roads and a strong fragmentation of open space. The resulting population and sealed surface maps provide interesting data on the urban sprawl phenomenon in the past decades. By computing a densification index we observe that most urban areas witness a recent population density increase while in several rural areas the built-up area per inhabitant is still growing. The downdated time series of population maps obtained in this study will be used to set up a historical calibration for an activity-based cellular automata model for Flanders and Brussels which, among other data, needs high-resolution population maps.

Counterparty Credit Risk in OTC Derivatives under Basel III

Recent financial crises were the root of many changes in regulatory implementations in the banking sector. Basel previously covered the default capital charge for counterparty exposures however, the crisis showed that more than two third of the losses related to this risk emerged from the exposure to the movement of the counterparty’s credit quality and not its actual default therefore, Basel III divided the required counterparty risk capital into two categories: The traditional default capital charge and an additional counter-party credit valuation adjustment (CVA) capital charge. In this article, we explain the new methodologies to compute these capital charges on the OTC market: The standardized approach for default capital charge (SA-CCR) and the basic approach for CVA (BA-CVA). Based on historical calibration and future estimations, we built internal models in order to compare them with the amended standardized approach. Up till June 2015, interest rate and FX derivatives constituted more than 90% of the traded total OTC notional amount; we constructed our application on such portfolios containing and computed their total counterparty capital charge. The analysis reflected different impacts of the netting and collateral agreements on the regulatory capital depending on the instruments’ typologies. Moreover, results showed an important increase in the capital charge due to the CVA addition doubling it in some cases.

Simulation and Calibration of Options Prices under a Levy-Type Stochastic Dynamic and Semi Markov Market Switching Regimes Processes

This work mainly highlights the benefits of derivative pricing in a semi Markov switching market. We explore the main differences between Markov and Semi Markov regime switching models. The three main problems we deal with are, (1) historical parameter calibration through the recently developed LLGMM method, (2) effects of semi Markov parameters on option prices and (3) comparison of Heston model, semi Markov regime model and Markov regime model calibration performances over both sequential option price calibration and the overall implied volatility surface of the market. Employing the LLGMM method, parameters of the spot price process described by a linear Levy-type stochastic differential equation under semi-Markov structural perturbations are calibrated to observed prices. From Fourier space time stepping and Carr and Madan methods, risk neutral parameters of the spot price are calibrated and interpreted. American and European style vanilla and exotic option prices are simulated. The presented results are shown in the context of a piecewise constant semi-Markov intensity matrix approximating a Weibull intensity matrix. We obtain that under the conditional minimum entropy martingale measure, option prices predictably increase as the regime risk increases through the intensity matrix of the semi Markov process. Calibration and simulation results demonstrate noticeable effects of semi-Markov parameters on option prices and a demonstrably better calibration fit of the Black Scholes model over the entire volatility surface, in a market with semi Markov regimes.

Evaluation of dental therapists undertaking dental examinations in a school setting in Scotland.

To measure agreement between dental therapists and the Scottish gold-standard dentist undertaking National Dental Inspection Programme (NDIP) examinations. A study of interexaminer agreement between 19 dental therapists and the national gold-standard dentist was carried out. Pre-calibration training used the caries diagnostic criteria and examination techniques agreed by the British Association for the Study of Community Dentistry (BASCD). Twenty-three 5-year-old children (Primary 1) and 17 11-year-old children (Primary 7) children were examined. Agreement was assessed using kappa statistics on d3 mft and D3 MFT for P1 and P7 children, sensitivity and specificity values, and kappa statistics on d3 t/D3 T and ft/FT. Calibration data on P1 and P7 children from 2009-2012 involving dentists as examiners were used for comparison. Economic evaluation was undertaken using a cost minimization analysis approach. The mean kappa score was 0.84 (SD 0.07) ranging from 0.69 to 0.94. All dental therapists scored good or very good agreement with the gold-standard dentist. This compares with historic NDIP calibration data with dentists, against the same gold-standard dentist, where the mean kappa value was 0.68 (SD 0.22) with a range of 0.35-1.00. The mean sensitivity score was 0.98 (SD 0.04) (range 0.88-1.0) and mean specificity score was 0.90 (SD 0.06) (range 0.78-0.96). Health economic analysis estimated that salary costs would be 33.6% lower if dental therapists were substituted for dentists in the year 2013, with an estimated saving of approximately £103 646 per annum on the national budget. We conclude that dental therapists show a high level of interexaminer agreement, and with the appropriate annual training and calibration, they could undertake dental examinations as part of the NDIP programme.

Guidelines on the use of inverse velocity method as a tool for setting alarm thresholds and forecasting landslides and structure collapses

Predicting the time of failure is a topic of major concern in the field of geological risk management. Several approaches, based on the analysis of displacement monitoring data, have been proposed in recent years to deal with the issue. Among these, the inverse velocity method surely demonstrated its effectiveness in anticipating the time of collapse of rock slopes displaying accelerating trends of deformation rate. However, inferring suitable linear trend lines and deducing reliable failure predictions from inverse velocity plots are processes that may be hampered by the noise present in the measurements; data smoothing is therefore a very important phase of inverse velocity analyses. In this study, different filters are tested on velocity time series from four case studies of geomechanical failure in order to improve, in retrospect, the reliability of failure predictions: Specifically, three major landslides and the collapse of an historical city wall in Italy have been examined. The effects of noise on the interpretation of inverse velocity graphs are also assessed. General guidelines to conveniently perform data smoothing, in relation to the specific characteristics of the acceleration phase, are deduced. Finally, with the aim of improving the practical use of the method and supporting the definition of emergency response plans, some standard procedures to automatically setup failure alarm levels are proposed. The thresholds which separate the alarm levels would be established without needing a long period of neither reference historical data nor calibration on past failure events.

Long-term stability and zero drift of digital barometric pressure gauges

Several digital pressure gauges at the National Metrology Institute of Japan (NMIJ) have been calibrated in the barometric pressure range on a regular basis for over ten years. The long-term stability of the zero and span readings for these pressure gauges was evaluated using their historical calibration data. The evaluation showed that most of the gauges have quite good long-term stabilities for the span readings, but some have large zero drifts with rates of about (10 to 50) Pa yr−1. This paper discusses the causes for this drift: it can be explained by the combination of a small leak and gas emissions from the sensor volume, which are estimated from the typical drift rates. The zero drift of a particular gauge is well-approximated by an exponential function of time; the fitting function may give a good estimation of the zero drift in the future. This indicates that continuous characterization of a pressure gauge may enable appropriate correction of the indication and provide users more reliable data with less calibration work.

Looking forward by looking back: using historical calibration to improve forecasts of human disease vector distributions.

Arthropod disease vectors, most notably mosquitoes, ticks, tsetse flies, and sandflies, are strongly influenced by environmental conditions and responsible for the vast majority of global vector-borne human diseases. The most widely used statistical models to predict future vector distributions model species niches and project the models forward under future climate scenarios. Although these methods address variations in vector distributions through space, their capacity to predict changing distributions through time is far less certain. Here, we review modeling methods used to validate and forecast future distributions of arthropod vectors under the effects of climate change and outline the uses or limitations of these techniques. We then suggest a validation approach specific to temporal extrapolation models that is gaining momentum in macroecological modeling and has great potential for epidemiological modeling of disease vectors. We performed systematic searches in the Web of Science, ScienceDirect, and Google Scholar to identify peer-reviewed English journal articles that model arthropod disease vector distributions under future environment scenarios. We included studies published up to and including June, 2014. We identified 29 relevant articles for our review. The majority of these studies predicted current species niches and projected the models forward under future climate scenarios without temporal validation. Historically calibrated forecast models improve predictions of changing vector distributions by tracking known shifts through recently observed time periods. With accelerating climate change, accurate predictions of shifts in disease vectors are crucial to target vector control interventions where needs are greatest.

Photodiode array to charged aerosol detector response ratio enables comprehensive quantitative monitoring of basic drugs in blood by ultra-high performance liquid chromatography

Quantitative screening for a broad range of drugs in blood is regularly required to assess drug abuse and poisoning within analytical toxicology. Mass spectrometry-based procedures suffer from the large amount of work required to maintain quantitative calibration in extensive multi-compound methods. In this study, a quantitative drug screening method for blood samples was developed based on ultra-high performance liquid chromatography with two consecutive detectors: a photodiode array detector and a corona charged aerosol detector (UHPLC–DAD–CAD). The 2.1mm×150mm UHPLC column contained a high-strength silica C18 bonded phase material with a particle size of 1.8μm, and the mobile phase consisted of methanol/0.1% trifluoroacetic acid in gradient mode. Identification was based on retention time, UV spectrum and the response ratio from the two detectors. Using historic calibration over a one-month period, the median precision (RSD) of retention times was 0.04% and the median accuracy (bias) of quantification 6.75%. The median precision of the detector response ratio over two orders of magnitude was 12%. The applicable linear ranges were generally 0.05–5mgL−1. The method was validated for 161 compounds, including antipsychotics, antidepressants, antihistamines, opioid analgesics, and adrenergic beta blocking drugs, among others. The main novelty of the method was the proven utility of the response ratio of DAD to CAD, which provided the additional identification efficiency required. Unlike with mass spectrometry, the high stability of identification and quantification allowed the use of facile historic calibration.

Techniques and Review of Absolute Flux Calibration from the Ultraviolet to the Mid-Infrared

The measurement of precise absolute fluxes for stellar sources has been pursued with increased vigor since the discovery of dark energy and the realization that its detailed understanding requires accurate spectral energy distributions (SEDs) of redshifted Ia supernovae in the rest frame. The flux distributions of spectrophotometric standard stars were initially derived from the comparison of stars to laboratory sources of known flux but are now mostly based on calculated model atmospheres. For example, pure hydrogen white dwarf (WD) models provide the basis for the HST CALSPEC archive of flux standards. The basic equations for quantitative spectrophotometry and photometry are explained in detail. Several historical lab-based flux calibrations are reviewed; and the SEDs of stars in the major online astronomical databases are compared to the CALSPEC reference standard spectrophotometry. There is good evidence that relative fluxes from the visible to the near-IR wavelength of ∼2.5 μm are currently accurate to 1% for the primary reference standards, and new comparisons with lab flux standards show promise for improving that precision.

O31: Comprehensive non-MS drug screening by UPLC-PDA-CAD with durable quantitative calibration

Introduction Mass spectrometric (MS) methods are today widely used in analytical toxicology, but in comprehensive quantitative drug screening it is laborious to maintain quantitative calibration and use of historic calibration is not feasible. In non-MS gas chromatographic (GC) and liquid chromatographic (LC) methods, historic calibration is widely used. However, GC is not amenable to analysis of current polar drugs and traditional LC-UV methods do not have high enough identification power to meet the requirements of screening. Ultra-performance LC (UPLC) with two consecutive detectors appears to possess high potential for simultaneous multi-component screening and quantification. In this study, a comprehensive quantitative screening method for 170 basic drugs in blood samples was developed and validated using UPLC coupled with photodiode array (PDA) and corona charged aerosol detector (CAD). Methods Blood samples were extracted with organic solvent (ethyl acetate: butyl acetate, 25:75) in basic conditions. After extraction, the organic solvent was evaporated to dryness and samples were reconstituted with the UPLC mobile phase. Dibenzepine was used as an internal standard. Chromatographic separation was performed at 60 °C using a HSS C18 column (150 mm × 2,1 mm, particle size 1,8 μm), and the mobile phase consisted of 0.1% trifluoroacetic acid and methanol. After a three minute isocratic phase, a linear gradient from 5% to 95% methanol in 15 minutes followed. The flow rate was 0.4 mL/min. UV spectra were collected in the range of 210–400 nm, and wavelength of 230 nm was used for quantification. Substance identification was based on the UV spectrum, retention times on both detectors, and the response ratio of CAD and PDA at wavelength 230 nm. Calibration was carried out using a single calibration point at the vicinity of the upper limit of therapeutic range of each drug. Individual calibration curves were created for both detectors and the average result was used for quantification. Results The retention times were found be very stable, the relative standard deviation of retention times being Conclusion The developed method allows the detection and quantification of 170 basic drugs in therapeutic and toxic concentrations in a single run. Due to the stability of calibration and good linearity, historic one-point calibration can be utilized. Adding the CAD detector with universal response after the PDA detector increases the reliability of both identification and quantification. In addition, drugs with poor UV absorption can be detected and quantitated. This method provides a tool for comprehensive quantitative screening for ordinary basic drugs in blood and leaves LC-MS target analysis to be applied to low-dose compounds.

Quantifying uncertainty in remote sensing-based urban land-use mapping

Land-use/land-cover information constitutes an important component in the calibration of many urban growth models. Typically, the model building involves a process of historic calibration based on time series of land-use maps. Medium-resolution satellite imagery is an interesting source for obtaining data on land-use change, yet inferring information on the use of urbanised spaces from these images is a challenging task that is subject to different types of uncertainty. Quantifying and reducing the uncertainties in land-use mapping and land-use change model parameter assessment are therefore crucial to improve the reliability of urban growth models relying on these data. In this paper, a remote sensing-based land-use mapping approach is adopted, consisting of two stages: (i) estimating impervious surface cover at sub-pixel level through linear regression unmixing and (ii) inferring urban land use from urban form using metrics describing the spatial structure of the built-up area, together with address data. The focus lies on quantifying the uncertainty involved in this approach. Both stages of the land-use mapping process are subjected to Monte Carlo simulation to assess their relative contribution to and their combined impact on the uncertainty in the derived land-use maps. The robustness to uncertainty of the land-use mapping strategy is addressed by comparing the most likely land-use maps obtained from the simulation with the original land-use map, obtained without taking uncertainty into account. The approach was applied on the Brussels-Capital Region and the central part of the Flanders region (Belgium), covering the city of Antwerp, using a time series of SPOT data for 1996, 2005 and 2012. Although the most likely land-use map obtained from the simulation is very similar to the original land-use map – indicating absence of bias in the mapping process – it is shown that the errors related to the impervious surface sub-pixel fraction estimation have a strong impact on the land-use map's uncertainty. Hence, uncertainties observed in the derived land-use maps should be taken into account when using these maps as an input for modelling of urban growth.

An integrated stress testing framework via Markov switching simulation

Capturing tail events, especially those that include the rare possibility of severe loss, is one of the important objectives of modern risk analysis. However, the past behavior of financial data is not necessarily an accurate predictor of possible scenarios in the future. The economic turmoil of recent years has resulted in calls for a more forward-looking approach to financial risk management that integrates expert knowledge on plausible future scenarios with classical risk management models calibrated on past behavior. As a complementary risk analysis tool, stress testing is receiving more and more attention, both from regulators and from practitioners. Nevertheless, classical risk analysis models, such as value-at-risk models, that are based on historical data, are often disconnected from stress testing. This disconnection can prevent a comprehensive view of the risk profile of a financial institution. This paper proposes a multiperiod switching simulation based method for integrated stress testing risk analysis that incorporates plausible events that are not necessarily captured in historical data or in historical stressed calibration of risk models. Our application focuses on the integration of rare stress events and model stress into a market risk portfolio. However, the proposed method is also applicable to other financial risk models, eg, portfolio credit risk models. An integrated risk model and stress testing framework not only leads to forward-looking tail risk measurement, which mitigates the black swan effect, but also takes stress testing into advanced risk management decision making analysis, such as scenario-based portfolio optimization.

INCORPORATING LAND-USE MAPPING UNCERTAINTY IN REMOTE SENSING BASED CALIBRATION OF LAND-USE CHANGE MODELS

Abstract. Building urban growth models typically involves a process of historic calibration based on historic time series of land-use maps, usually obtained from satellite imagery. Both the remote sensing data analysis to infer land use and the subsequent modelling of land-use change are subject to uncertainties, which may have an impact on the accuracy of future land-use predictions. Our research aims to quantify and reduce these uncertainties by means of a particle filter data assimilation approach that incorporates uncertainty in land-use mapping and land-use model parameter assessment into the calibration process. This paper focuses on part of this work, more in particular the modelling of uncertainties associated with the impervious surface cover estimation and urban land-use classification adopted in the land-use mapping approach. Both stages are submitted to a Monte Carlo simulation to assess their relative contribution to and their combined impact on the uncertainty in the derived land-use maps. The approach was applied on the central part of the Flanders region (Belgium), using a time-series of Landsat/SPOT-HRV data covering the years 1987, 1996, 2005 and 2012. Although the most likely land-use map obtained from the simulation is very similar to the original classification, it is shown that the errors related to the impervious surface sub-pixel fraction estimation have a strong impact on the land-use map’s uncertainty. Hence, incorporating uncertainty in the land-use change model calibration through particle filter data assimilation is proposed to address the uncertainty observed in the derived land-use maps and to reduce uncertainty in future land-use predictions.

Flow measurement of liquid hydrocarbons with positive displacement meters: the correction for slippage

In the oil industry, the economical and fiscal impact of the measurements accuracy on the custody transfer operations implies fulfilling strict requirements of legal metrology. In this work, we focus on the positive displacement meters (PD meters) for refined liquid hydrocarbons. The state of the art of the lack of accuracy due to slippage flow in these meters is revised. The slippage flow due to the pressure drop across the device has been calculated analytically by applying the Navier–Stokes equation. No friction with any wall of the slippage channel has been neglected and a more accurate formula than the one found in the literature has been obtained. PD meters are calibrated against a bidirectional prover in order to obtain their meter factor which allows correction of their indications. Instead of the analytical model, an empirical one is proposed to explain the variation of the meter factor of the PD meters with flow rate and temperature for a certain hydrocarbon. The empirical model is based on the historical calibration data, of 9 years on average, of 25 m with four types of refined hydrocarbon. This model has been statistically validated by linear least-squares fitting. By using the model parameters, we can obtain the meter factor corresponding to different conditions of temperature and flow rate from the conditions in which the devices were calibrated. The flow parameter is such that a 10% flow rate variation implies a meter factor variation lower than 0.01%. A rule of thumb value for the temperature parameter is 0.005% per degree Celsius. The model residuals allow surveillance of the device drift and quantifying its contribution to the meter factor uncertainty. The observed drift is 0.09% at 95% confidence level in the analyzed population of meters.