Achievement of 0.005 % combined transfer uncertainties in the NIST detector calibration facility

Improvements in a lamp-monochromator-based facility at the National Institute of Standards and Technology (NIST), the Visible near-infrared Spectral Comparator Facility (VisSCF) which is used to calibrate optical detectors for spectral radiant power responsivity from 300 nm to 1100 nm, are described. These changes include extending the VisSCF operational range down to 300 nm from 350 nm, thereby fully covering the ultraviolet-A (UVA) spectral region and partially covering the UVB range. These improvements have lowered the magnitudes of most of the components in the uncertainty budget and have led to combined 0.005 % transfer (k=1) uncertainties in the spectral power responsivity calibrations over most of the spectral range. Redevelopment of the uncertainty budget results in total expanded uncertainties of spectral responsivities of less than 0.1 % (k=2) over the spectral range from 380 nm to 980 nm, with the greatest uncertainty term coming from the calibrations of the transfer standards.

Geometric system analysis of ILMD-based LID measurement systems using Monte-Carlo simulation

Imaging Luminance Measuring Device (ILMD) based luminous intensity distribution measurement systems are an established method for measuring the luminous intensity distribution (LID) of light sources in the far field. The advantage of this system is the high-resolution acquisition of a large angular range with one image. For the uncertainty budget, the mathematical description of the system can be divided into photometric and geometric contributions. In the following, we will present a Monte-Carlo approach to analyse the geometric contributions which are the uncertainty of measurement direction and measurement distance. Therefore, we set up a geometric system description based on kinematic transformations that describes the connection between detector and light source position. To consider all relevant input quantities we simulate the adjustment and measurement process. Finally, an analysis of the geometric input parameters is shown.

New facility for primary calibration of differential spectral responsivity of solar cells using LDLS-based monochromatic source

A newly established setup for primary calibration and characterization of solar cells at NMCC/SASO is presented. This differential spectral responsivity (DSR) measurement instrument uses laser-driven light source (LDLS)-based modulated (AC) source to measure the spectral responsivity of photovoltaic (PV) detectors and solar cells. The setup is intended for measuring the spectral responsivity in the wavelength range from 250 nm to 2000 nm, bias level up to 1.5 kW/m2, with which a measurement uncertainty of 1.06 % (k = 2, in the range of 300 nm to 900 nm) could be achieved. We present validation measurements as well as spectral responsivity and external quantum efficiency (EQE) measurements of reference solar cells to demonstrate the objective of the setup. We present a preliminary evaluation of the associated uncertainty components as well as an uncertainty budget for validation, optimization and standardization of our setup.

From Validation Statistics to Uncertainty Estimates: Application to VIIRS Ocean Color Radiometric Products at European Coastal Locations

Uncertainty estimates are needed to assess ocean color products and qualify the agreement between missions. Comparison between field observations and satellite data, a process defined as validation, has been the traditional way to assess satellite products. However validation statistics can provide only an approximation for satellite data uncertainties as field measurements have their own uncertainties and as the validation process is imperfect, comparing data potentially differing in temporal, spatial or spectral characteristics. This study describes a method to interpret in terms of uncertainties the validation statistics obtained for ocean color remote sensing reflectance RRS knowing the uncertainties associated with field data. This approach is applied to observations collected at sites part of the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) located in coastal regions of the European seas, and to RRS data from the VIIRS sensors on-board the SNPP and JPSS1 platforms. Similar estimates of uncertainties σVRS (term accounting for non-systematic contributions to the uncertainty budget) are obtained for both missions, decreasing with wavelength from the interval 0.8–1.4 10−3 sr−1 in the blue to a maximum of 0.24 10−3 sr−1 in the red, values that are at least twice (but up to 8 times) the uncertainties reported for the field data. These uncertainty estimates are then used to qualify the agreement between the VIIRS products, defining the extent to which they agree within their stated uncertainty. Despite significant biases between the two missions, their RRS products appear fairly compatible.

Effects of rotation errors on goniometric measurements

Goniometric measurements are essential for the determination of many optical quantities, and quantifying the effects of errors in the rotation axes on these quantities is a complex task. In this paper, we show how a measurement model for a four-axis goniometric system can be developed to allow the effects of alignment and rotation errors to be included in the uncertainty of the measurement. We use three different computational methods to propagate the uncertainties due to several error sources through the model to the rotation angles and then to the measurement of bidirectional reflectance and integrated diffuse reflectance, a task that would otherwise be intractable. While all three methods give the same result, the GTC Python package is the simplest and intrinsically provides a full uncertainty budget, including all correlations between measurement parameters. We then demonstrate how the development of a measurement model and the use of GTC has improved our understanding of the system. As a consequence, taking advantage of negative correlations between measurements in different geometries allows us to minimise the total uncertainty in integrated diffuse reflectance, lowering the standard uncertainty from 0.0029 to 0.0015.

Uncertainty of Thermographic Temperature Measurement of Electric Units Contained in Switchgear

The result of the works presented is the uncertainty budget of a thermographic temperature measurement taken through an IR window. The type B uncertainty determination method has been employed. Publication of European Accreditation EA-4/02 has been patterned. Conditions prevailing in course of the thermographic temperature measurement of low-voltage electric units contained in the switchgear were recreated as part of the works. The measurement system has been presented. Components of the infrared radiation reaching the camera lens in case when an IR window was used and when an IR window was not used have been discussed. Uncertainties estimated for the measurement done with an IR window and without an IR window have been compared.

UNFILTERED TRAP-BASED PHOTOMETER CALIBRATION

LED-based light sources have replaced massively traditional sources. The metrology traceability chains realised in leading European NMIs utilise the absolutely calibrated broadband radiometers (three-element silicon trap detectors) for calibrating primary photometers. Specific spectral properties of white LED allow to apply the trap detectors directly as new primary photometers. The unfiltered technique (Dӧnsberg at al., 2014) is used and the calibration of spectral irradiance responsivity is needed. We have a long experience in detector-based spectral irradiance responsivity calibrations declared by particular CMC’s published in BIPM KCDB. The aim of this work was to revise the uncertainty budget in order to reduce the measurement uncertainties for specific application of calibration of the trap-based unfiltered primary photometers UPP. The two calibration methods were used to analyse the occasional back-reflection effect of the UPP front aperture. The measurement was performed using our reference spectral responsivity facility in spectral range 350 nm – 900 nm.

Calibration of On-Board Energy Measurement Systems Installed in Locomotives for AC Distorted Current and High Voltage Waveforms and Determination of Its Uncertainty Budget

Periodic calibrations of Energy Measurement Systems (EMS) installed in locomotives must be carried out to demonstrate the required accuracy established in the EN 50463-2 standard according to European Parliament and Council Directive 2008/57/EC on the interoperability of rail systems within the Community. As a result of the work performed in the “MyRailS” EURAMET project an AC calibration facility was developed consisting of a fictive power source was developed. This fictive power source can generate distorted sinusoidal voltages up to 25 kV-50 Hz and 15 kV-16.7 Hz as well as distorted sinusoidal currents up to 500 A with harmonic content up to 5 kHz or phase-fired current waveform stated in EN50463-2 standard. These waveforms are representative of those that appear during periods of acceleration and breaking of the train. Reference measuring systems have been designed and built consisting of high voltage and high current transducers adapted to multimeters, which function as digital recorders to acquire synchronized voltage and current signals. An approved procedure has been developed and an in-depth uncertainty analysis has been performed to achieve a set of uncertainty formulas considering the influence parameters. Different influence parameters have been analyzed to evaluate uncertainty contributions for each quantity to be measured: rms voltage, rms current, active power, apparent power and non-active power of distorted voltage and current waveforms. The resulting calculated global expanded uncertainty for the developed Energy Measuring Function calibration set up has been better than 0.5% for distorted waveforms. This paper is focused on presenting the complete set of expressions and formulas developed for the different influence parameters, necessary for uncertainty budget calculation of an Energy Measuring Function calibration.