Instrumental Errors Research Articles

Calibration and Validation of NOAA-21 Ozone Mapping and Profiler Suite (OMPS) Nadir Mapper Sensor Data Record Data

The Ozone Mapping and Profiler Suites (OMPS) Nadir Mapper (NM) is a grating spectrometer within the OMPS nadir instruments onboard the SNPP, NOAA-20, and NOAA-21 satellites. It is designed to measure Earth radiance and solar irradiance spectra in wavelengths from 300 nm to 380 nm for operational retrievals of the nadir total column ozone. This study presents calibration and validation analysis results for the NOAA-21 OMPS NM SDR data to meet the JPSS scientific requirements. The NOAA-21 OMPS SDR calibration derives updates of several previous OMPS algorithms, including the dark current correction algorithm, one-time wavelength registration from ground to on-orbit, daily intra-orbit wavelength shift correction, and stray light correction. Additionally, this study derives an empirical scale factor to remove 2.2% of systematic biases in solar flux data, which were caused by pre-launch solar calibration errors of the OMPS nadir instruments. The validation of the NOAA-21 OMPS SDR data is conducted using various methods. For example, the 32-day average method and radiative transfer model are employed to estimate inter-sensor radiometric calibration differences from either the SNPP or NOAA-20 data. The quality of the NOAA-21 OMPS NM SDR data is largely consistent with that of the SNPP and NOAA-20 OMPS data, with differences generally within ±2%. This meets the scientific requirements, except for some deviations mainly in the dichroic range between 300 nm and 303 nm. The deep convective cloud target approach is used to monitor the stability of NOAA-21 OMPS reflectance above 330 nm, showing a variation of 0.5% over the observed period. Data from the NOAA-21 VIIRS M1 band are used to estimate OMPS NM data geolocation errors, revealing that along-track errors can reach up to 3 km, while cross-track errors are generally within ±1 km.

Replicability of paleotemperature records in the northern Okinawa Trough and its implications for paleoceanographic reconstructions

Geochemical proxies are frequently utilized in the reconstruction of past ocean temperatures. Due to resource constraints, these reconstructions typically rely on a single sediment core, raising questions about the local and regional representativeness of paleotemperature records. To address this, we analyzed four sediment cores located within a 10-km radius in the northern Okinawa Trough (OT), which share the same climatic forcing and thus should reflect similar climate variations. We compiled published data and generated new paleotemperature estimates based on three widely used geochemical proxies (foraminiferal Mg/Ca, U37K′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{U}}_{37}^{{{\ ext{K}}^{\\prime}}}$$\\end{document}, TEX86\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{TEX}}_{86}$$\\end{document}). Analysis of the mean absolute deviations for nearby records based on the same proxy revealed that U37K′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{U}}_{37}^{{{\ ext{K}}^{\\prime}}}$$\\end{document} has the highest reproducibility, followed by Mg/Ca and TEX86\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{TEX}}_{86}$$\\end{document}. However, inconsistencies in inter-proxy offsets among nearby sites suggest the presence of noise in the proxy records, likely stemming from instrumental errors and sediment heterogeneity. Furthermore, the Mg/Ca and U37K′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{U}}_{37}^{{{\ ext{K}}^{\\prime}}}$$\\end{document} paleotemperature records agree within uncertainty when accounting for inter-site variability and calibration uncertainties, challenging previous interpretations of temperature signals from different seasons. All proxies indicate similar glacial-interglacial trends, albeit with varying magnitudes of temperature change. Both Mg/Ca and U37K′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{U}}_{37}^{{{\ ext{K}}^{\\prime}}}$$\\end{document} records suggest a glacial cooling of ~ 3 °C, whereas TEX86\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{TEX}}_{86}$$\\end{document} sea surface temperature (SST) data indicate a stronger glacial cooling of approximately ~ 6–8 °C. Modern observations indicate a subsurface TEX86\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{TEX}}_{86}$$\\end{document} recording depth of 50–100 m, coinciding with the thermocline. However, the TEX86\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{TEX}}_{86}$$\\end{document} subsurface temperature (subT) record does not resemble the Mg/Ca records of thermocline-dwelling foraminifera species. Instead, there is a better agreement with benthic foraminiferal Mg/Ca records of Uvigerina spp. (~ 700 m) and the intermediate temperature record derived from radiolarian assemblages (~ 500 m), pointing to a TEX86\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{TEX}}_{86}$$\\end{document} recording depth that is deeper than the thermocline. In summary, our findings show that proxy noise can impact inter-proxy comparisons of paleotemperature records, but not the direction of glacial-interglacial shifts. Future research should prioritize constraining the recording depth of paleotemperature proxies and reducing calibration uncertainty for more precise and reliable quantitative paleotemperature reconstruction.

Microbiome-based correction for random errors in nutrient profiles derived from self-reported dietary assessments

Since dietary intake is challenging to directly measure in large-scale cohort studies, we often rely on self-reported instruments (e.g., food frequency questionnaires, 24-hour recalls, and diet records) developed in nutritional epidemiology. Those self-reported instruments are prone to measurement errors, which can lead to inaccuracies in the calculation of nutrient profiles. Currently, few computational methods exist to address this problem. In the present study, we introduce a deep-learning approach—Microbiome-based nutrient profile corrector (METRIC), which leverages gut microbial compositions to correct random errors in self-reported dietary assessments using 24-hour recalls or diet records. We demonstrate the excellent performance of METRIC in minimizing the simulated random errors, particularly for nutrients metabolized by gut bacteria in both synthetic and three real-world datasets. Additionally, we find that METRIC can still correct the random errors well even without including gut microbial compositions. Further research is warranted to examine the utility of METRIC to correct actual measurement errors in self-reported dietary assessment instruments.

Limb Temperature Observations in the Stratosphere and Mesosphere Derived from the OMPS Sensor

Molecular scattering (Rayleigh scattering) has been extensively used from the ground with lidars and from space to observe the limb, thereby deriving vertical temperature profiles between 30 and 80 km. In this study, we investigate how temperature can be measured using the new Ozone Mapping and Profiler Suite (OMPS) sensor, aboard the Suomi NPP and NOAA-21 satellites. The OMPS consists of three instruments whose main purpose is to study the composition of the stratosphere. One of these, the Limb Profiler (LP), measures the radiance of the limb of the middle atmosphere (stratosphere and mesosphere, 12 to 90 km altitude) at wavelengths from 290 to 1020 nm. This new data set has been used with a New Simplified Radiative Transfer Model (NSRTM) to derive temperature profiles with a vertical resolution of 1 km. To validate the method, the OMPS-derived temperature profiles were compared with data from four ground-based lidars and the ERA5 and MSIS models. The results show that OMPS and the lidars are in agreement within a range of about 5 K from 30 to 80 km. Comparisons with the models also show similar results, except for ERA5 beyond 50 km. We investigated various sources of bias, such as different attenuation sources, which can produce errors of up to 120 K in the UV range, instrumental errors around 0.8 K and noise problems of up to 150 K in the visible range for OMPS. This study also highlighted the interest in developing a new miniaturised instrument that could provide real-time observation of atmospheric vertical temperature profiles using a constellation of CubeSats with our NSRTM.

Reliability Analysis of Steam Turbine Instrumentation Using the Failure Mode and Effect Analysis (FMEA) Method at PT. PLN Nusantara Power UP Tenayan

PT. PLN Nusantara Power Unit Generation Tenayan is a company operating in the Steam Power Plant sector in Pekanbaru, Indonesia. One of the main components of a steam power plant (PLTU) is a steam turbine. A steam turbine is a machine that functions to convert kinetic energy into electrical current. Operational failures are often caused by less than optimal instrumentation in the steam turbine. This research uses the failure mode and effect analysis (FMEA) method with the aim of identifying the type of failure, the cause of the failure, the effect of the failure, and determining the RPN value. The analysis results from this research show that the turbine instrumentation components still meet performance standards because the risk priority number (RPN) value is less than 200. The conclusion from this research is that errors in steam turbine instrumentation are inaccurate sensor readings, switches cannot deactivate the equipment, and readings control room does not match local readings, the instrumentation component with the highest risk priority number (RPN) is the solenoid valve with a value of 140, and the instrumentation component with the lowest risk priority number (RPN) is the pressure indicator with a value of 30. The component given top priority in action The recommendation is for the solenoid valve component, namely to carry out maintenance every 6 months and add an overspeed protection system

A-245 Evaluation of an Automated MALDI-TOF Target and Antimicrobial Susceptibility Suspension Preparation Instrument

Abstract Background Several studies have shown that the implementation of automation in microbiology laboratories may allow for increased volume growth without the need for additional staffing. One of the more recently available pieces of microbiology automation, the COPAN Colibri, automates the manual steps of colony spotting and preparation of targets for use on MALDI-TOF instruments for bacterial identification. This same instrument can also prepare a diluted bacterial inoculum for immediate use on automated susceptibility testing platforms. We wanted to evaluate the performance of both of these functions in real-time with clinical cultures, to determine if implementation would be beneficial over current workflows. Methods The COPAN Colibri was operated following the manufacturer’s recommendations with the exception of the identification of gram-positive organisms. The Colibri is FDA cleared for MALDI-TOF target spotting, however not with the use of formic acid, nor is it FDA cleared for susceptibility suspension preparation. Preliminary data suggested that addition of formic acid was necessary for accurate identification. A subset of cultures were digitally read in real-time as part of clinical workflows, selected for Colibri MALDI-TOF target spotting and Vitek2 suspension preparation using the COPAN WebApp software. Targets were run on the MALDI Bioptyper Sirius (Bruker) and suspensions were run on Vitek2 (BioMerieux) using the pre-dilution mode using GN-99 and GN-801 cards for appropriate organisms. All results were compared to standard manual workflows. Results A total of 145 gram-negative and 60 gram-positive organisms were picked for MALDI-target spotting on Colibri. Compared to standard testing, 144 (99%) gram-negative and 57 (95%) gram-positive Colibri spotted isolates matched expected species-level identifications. 97 Gram negative bacilli were run on Vitek 2 generating 95.52% categorical agreement across all drugs but with 13 major errors (MEs) and 2 very major errors (VMEs), which exceeded the acceptable percentage for certain drugs. The total number of failed runs was 13 (13.34% of total GNB isolates run), and all required complete resetting of MALDI-spotting and suspension preparation. Conclusions The Colibri performed well for MALDI-TOF target spotting with the addition of formic acid for gram-positive organisms. It did not perform within the acceptable range for susceptibility suspension preparation as it exceeded the recommended cutoffs for MEs and VMEs for certain drugs. The proportion of failed runs due to instrument errors may offset any advantages in labor savings due to the need for repeat set up.

The fault in our nature: Error rates in a human observation study on surgical instrument errors in the OR

BackgroundErrors in sterile processing of surgical instruments result in wasted chargeable operating room minutes. Data delineating this problem have been generated primarily through human observation and reporting. Given the inherent error rate in human tasks, we hypothesized that the observed rate of surgical instrument errors per case per day would increase over a six-week longitudinal study as observers became more familiar with their environment and more comfortable identifying errors. MethodsA previously published dataset on surgical instrument errors was analyzed for the average errors per case per day over six weeks. Errors per case per day were compared to the percentage of inpatient cases for each respective date since the error rate in inpatient cases is twice that of outpatient cases. ResultsWhile the average errors per case per day increases from 0.28 to 0.62, indicating a potential increase over time, no statistically significant trend was found (p = 0.157). A positive but modest correlation was observed between inpatient percentage and error rates (Pearson correlation = 0.344), nearing statistical significance (p = 0.068). The inpatient case percentage remained stable over time, with no significant trend detected (p = 0.284). ConclusionsHuman observation is a critical tool for defining waste arising from sterile processing errors. While the gradual increase in errors per case per day increases, the variability cannot be attributed to the initial adaptation the observer's environment. Future studies should assess inter-rated reliability and explore alternative automated observation methods to have a more accurate measurement of the number of errors observed.

Understanding error rejection of differential impedance spectroscopy for the in situ characterization of highly conductive fluids

Abstract Differential impedance spectroscopy (DIS) can offer distinct advantages over conventional impedance spectroscopy (IS), particularly for the in situ characterization of highly conductive, corrosive fluids. To enhance the understanding of DIS and determine the conditions under which this method is preferable over conventional IS with fixed electrode positions, error calculations were performed. Two scenarios were analyzed, one accounting solely for random instrument errors and the other including random errors as well as systematic errors from a serial offset impedance. Our analyses reveal that while permittivity measurements of highly conductive media remain challenging regardless of whether a conventional or differential impedance approach is used, DIS enables highly accurate conductivity measurements across a wide frequency and conductivity range. Studying different cell dimensions with equal cell constants revealed that despite the increased influence of systematic errors at small scales, conductivity can still be accurately measured, underscoring the applicability of DIS for microfluidic applications. Other potential parasitic effects, such as those arising from the electromagnetic skin effect, stray capacitances, electrode size, and temperature gradients, are discussed, providing a comprehensive framework for sample characterization with DIS.

An efficient electrochemical biosensor based on double-conductive hydrogel as antifouling interface for ultrasensitive analysis of biomarkers in complex serum medium

Electrochemical biosensors are suitable for trace detection of biomarkers in serum due to their high sensitivity and fast response time. However, the extensive adsorption of non-specific biomolecules may affect detection results and reduce the operating life of sensors. Herein, an efficient electrochemical biosensor based on double-conductive antifouling hydrogel was developed for ultrasensitive detection of carcinoembryonic antigen (CEA) in serum. Specifically, the antifouling hydrogel was designed with MXene as the conductive framework, and its inherent surface hydrophilicity made it have good antifouling ability. Meanwhile, the introduction of conductive poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) further improved the conductivity and stability and of antifouling hydrogel. Furthermore, the prepared MXene nanosheets exhibited large surface area, which could load a large amount of [Ru(NH3)6]3+ as internal standards. Importantly, the encapsulation of [Ru(NH3)6]3+ in hydrogel can not only eliminate the environmental and instrument errors, but also realize the integration of antifouling and internal standards, thus simplifying the construction process and improving the detection accuracy of the biosensor. Based on this, the proposed signal “on-off” electrochemical antifouling biosensor realized trace detection of CEA in a wide linear range (1 pg/mL ∼ 1 μg/mL), with a detection limit as low as 0.41 pg/mL (3δ/k). This study broadens the application of hydrogels in electrochemical antifouling systems, providing a positive reference for sensitive and accurate determination of biomarkers in serum samples.

A Postlaunch Update on the Effects of Instrumental Measurement Errors on SWOT Estimates of Sea Surface Height, Velocity, and Vorticity

Abstract The Ka-band radar interferometer (KaRIn) on the Surface Water and Ocean Topography (SWOT) satellite that was launched in December 2022 is providing the first two-dimensional altimetric views of sea surface height (SSH). Measurements are made across two parallel swaths of 50-km width separated by a 20-km gap. In the data product that will be used for most oceanographic applications, SSH estimates with a footprint diameter of about 3 km are provided on a 2 km × 2 km grid. Early analyses of in-flight KaRIn data conclude that the instrumental noise for this footprint diameter has a standard deviation less than σ3km = 0.40 cm for conditions of 2-m significant wave height. This is a factor of 2.3 better than the prelaunch expectation based on the science requirement specification. The SSH fields measured by KaRIn allow the first satellite estimates of essentially instantaneous surface current velocity and vorticity computed geostrophically from SSH. The effects of instrumental noise on smoothed estimates of velocity and vorticity based on early postlaunch assessments are quantified here as functions of the half-power filter cutoff wavelength of the smoothing. Signal-to-noise ratios for smoothed estimates of velocity and vorticity are determined from simulated noisy KaRIn data derived from a high-resolution numerical model of the California Current System. The wavelength resolution capabilities for σ3km = 0.40 cm are found to be about 17 and 35 km for velocity and vorticity, respectively, which correspond to feature diameters of about 8.5 and 17.5 km, and are better than the prelaunch expectations by about 45% and 35%.

Water budgets in an arid and alpine permafrost basin: Observations from the High Mountain Asia

Ground freeze‒thaw processes have significant impacts on infiltration, runoff and evapotranspiration. However, there are still critical knowledge gaps in understanding of hydrological processes in permafrost regions, especially of the interactions among permafrost, ecology, and hydrology. In this study, an alpine permafrost basin on the northeastern Qinghai‒Tibet Plateau was selected to conduct hydrological and meteorological observations. We analyzed the annual variations in runoff, precipitation, evapotranspiration, and changes in water storage, as well as the mechanisms for runoff generation in the basin from May 2014 to December 2015. The annual flow curve in the basin exhibited peaks both in spring and autumn floods. The high ratio of evapotranspiration to annual precipitation (>1.0) in the investigated wetland is mainly due to the considerably underestimated ‘observed’ precipitation caused by the wind-induced instrumental error and the neglect of snow sublimation. The stream flow from early May to late October probably came from the lateral discharge of subsurface flow in alpine wetlands. This study can provide data support and validation for hydrological model simulation and prediction, as well as water resource assessment, in the upper Yellow River Basin, especially for the headwater area. The results also provide case support for permafrost hydrology modeling in ungauged or poorly gauged watersheds in the High Mountain Asia.

On the capability of high redshift kSZ measurement with galaxy surveys

The kinematic Sunyaev-Zel'dovich (kSZ) effect has been detected at z < 1 using various techniques and data sets. The ongoing and upcoming spectroscopic galaxy surveys such as DESI (Dark Energy Spectroscopic Instrument) and PFS (Prime Focus Spectrograph) will push the detection beyond z = 1, and therefore map the baryon distribution at high redshifts. Such detection can be achieved by both the kSZ stacking and tomography methods. While the two methods are theoretically equivalent, they differ significantly in the probed physics and scales, and required data sets. Taking the combination of PFS and ACT (Atacama Cosmology Telescope) as an example, we build mocks of kSZ and galaxies, quantify the kSZ detection S/N, and compare between the two methods. We segment the PFS galaxies into three redshift bins: 0.6 < z < 1.0, 1.0 < z < 1.6, and 1.6 < z < 2.4. For tomography method, our analysis reveals that the two higher redshift bins exhibit significantly higher S/N ratios, with values of 32 and 28, respectively, compared to the first redshift bin, which yielded an S/N of 8. This is attributed to not only the increasing of electron density with redshifts, but also the larger survey volume and the reduced non-linearity, facilitating velocity reconstruction at higher redshifts. Therefore, the capability of the PFS survey to measure high redshift kSZ effect stands as a substantial advantage over other spectroscopic surveys at lower redshift. The S/N of kSZ stacking largely depends on the number of galaxy groups available from another photometric survey. But in general, its S/N is lower than that of kSZ tomography, largely due to CMB instrument noise and error in galaxy group redshift. Incorporating next-generation CMB surveys like CMB-S4, characterized by significantly reduced instrument noise and improved angular resolution, is expected to enhance tomographic detection by a factor of ten and stacking detection by fivefold. This future high S/N detection holds the promise of not only providing precise constraints on the overall baryon abundance but also initiating a new insight into baryon distribution.

Constellation design and performance of future quantum satellite gravity missions

Temporal aliasing is currently the largest error contributor to time-variable satellite gravity field models. Therefore, the evolution of sensor technologies has to be complemented by strategies to reduce temporal aliasing errors. The most straightforward way to improve temporal aliasing is through extended satellite constellations because they improve the observation geometry and increase the achievable temporal resolution. Therefore, strategies to optimize the design of larger satellite constellations are investigated in this contribution. A complete constellation modeling procedure is presented, starting from primary design variables (such as the required targeted resolutions) and concluding with concrete orbital elements for the individual satellites. In parallel, it is evaluated if improved instrument sensitivities based on quantum technologies (cold atom interferometry) can be fully exploited in the case of larger constellations. For this, future quantum satellite gravity missions adopting the gradiometry concept (similar to the GOCE mission) and the low-low satellite-to-satellite tracking concept (similar to GRACE/-FO) are simulated on optimized constellations with up to 6 satellites/pairs. The retrieval performance of a 6-pair mission in terms of the global equivalent water height RMS can be improved by a factor of roughly 3 compared to an inclined double-pair mission. 3D-gradiometry intrinsically has a better de-aliasing behavior but has extremely high accuracy requirements for the gradiometer (about 10 µEotvos) and the attitude reconstruction to be of any benefit. All simulations show that when incorporating improved sensor technologies, such as future quantum sensing instruments in extended constellations, temporal aliasing will remain the dominant error source by far, up to five orders of magnitude larger than the instrument errors. Therefore, improving sensor technologies has to go hand in hand with larger satellite constellations and improved space–time parameterization strategies to further reduce temporal aliasing effects.Graphical

Bayesian inversion of HFC-134a emissions in southern China from a new AGAGE site: Results from an observing system simulation experiment

The abundance of hydrofluorocarbons (HFCs) in the atmosphere is increasing and is of significant importance to the Earth's system. It is thus crucial to investigate the spatial distribution of HFC emissions. However, inversion modeling, a commonly used approach, is susceptible to errors from a-priori emissions, inversion grids, observations, and other parameters. In this study, we conduct Observing System Simulation Experiment (OSSE) to evaluate the impact of inversion parameters on HFC emission estimates, focusing on HFC-134a at the newly established Xichong (XCG) AGAGE site in Shenzhen, China. We regard the EDGAR (v7) dataset as a reference for “true” emissions and simulate the “true” atmosphere using the GEOS-Chem model. Our OSSE indicates that conducting inversions in the medium-sensitivity region with source-receptor sensitivity larger than 6.0 (nmol mol−1)/(mol m−2 s−1) minimizes the bias between a-posteriori and “true” total emissions to 0.58 %–1.88 %. In the high-sensitivity region, enhancing the spatial resolution of inversion grids lowers this error from −27.07 % to +0.36 %. The accuracy of a-priori spatial distribution crucially influences that of the a-posteriori: the higher correlation coefficient between a-priori and “true” emissions, the better a-posteriori agree with the “true” emissions, as evidenced by a reduced root mean square error (RMSE) of the a-posteriori vs. “true” emissions from 1.44 × 10−9 g m−2 s−1 (GDP-based) to 8.78 × 10−10 g m−2 s−1 (VIIRS-based). Furthermore, selecting regularization parameters and balancing instrumental and a-priori errors are also important. Our OSSE setups allow for parameter testing during the inversion, offering a framework to assess the regional representativeness of future HFC measurement sites.

Measurement Biases in Ocean Temperature Profiles from Marine Mammal Dataloggers

Abstract The study focuses on biases in ocean temperature profiles obtained by means of Satellite Relay Data Loggers (SRDL recorders) and time–depth recorder (TDR) attached to marine mammals. Quasi-collocated profiles from Argo floats and from ship-based conductivity–temperature–depth (CTD) profilers are used as reference. SRDL temperature biases depend on the sensor type and vary with depth. For the most numerous group of Valeport 3 (VP3) and conductivity–temperature–fluorescence (CTF) sensors, the bias is negative except for the layer 100–200 m. The vertical bias structure suggests a link to the upper-ocean thermal structure within the upper 200-m layer. Accounting for a time lag which might remain in the postprocessed data reduces the bias variability throughout the water column. Below 200-m depth, the bias remains negative with the overall mean of −0.027° ± 0.07°C. The suggested depth and thermal corrections for biases in SRDL data are within the uncertainty limits declared by the manufacturer. TDR recorders exhibit a different bias pattern, showing the predominantly positive bias of 0.08°–0.14°C below 100 m primarily due to the systematic error in pressure. Significance Statement The purpose of this work is to improve the consistency of the data from the specific instrumentation type used to measure ocean water temperature, namely, the data from miniature temperature sensors attached to marine mammals. As mammals dive during their route to and from their feeding areas, these sensors measure water temperature and dataloggers send the measured temperature data to oceanographic data centers via satellites as soon as the mammals return to the sea surface. We have shown that these data exhibit small systematic instrumental errors and suggested the respective corrections. Taking these corrections into account is important for the assessment of the ocean climate change.

Uncertainty estimates in the NISAR high-resolution soil moisture retrievals from multi-scale algorithm

It is important to know the amount of systematic and random uncertainties in any state variable to improve its geophysical application potential. The expected high-resolution (200 [m]) soil moisture product from the NASA-ISRO Synthetic Aperture Radar (NISAR) mission is no exception. Thus, knowing the quality of the soil moisture retrievals through the estimation of various error sources is imperative. The estimation error sources in soil moisture retrievals can be obtained by various methods. In situ measurements provide a reliable estimate of the uncertainty of soil moisture retrievals. However, in situ measurements are available only for limited locations, as they are typically very tedious and expensive to obtain. Thus, an analytical approach has been developed to obtain an estimate of the uncertainty in the soil moisture retrievals that vary in space and time across grid-cells. This uncertainty estimation is specifically developed for the multi-scale algorithm of the upcoming NISAR mission, which will provide soil moisture retrievals at 200 [m] resolution. The multi-scale algorithm for the NISAR mission disaggregates the coarser resolution soil moisture (∼9 [km]) to high-resolution (∼200 [m]) using NISAR L-band SAR measurements. However, uncertainty in high-resolution soil moisture retrievals might be introduced due to errors in input datasets (e.g., coarse resolution soil moisture, instrument error of SAR, etc.) and multi-scale algorithm parameters. Therefore, this study carried out a detailed sensitivity analysis of input datasets and algorithm parameters using the proposed approach. The sensitivity analysis shows that error in the input coarse resolution soil moisture is one of the primary drivers of uncertainty in the high-resolution soil moisture retrievals. The other portion of the uncertainty comes from errors in the algorithm parameters, and noise in SAR co-pol and cross-pol backscatter observations. Furthermore, the approach was tested on the UAVSAR L-band data time-series that had been simulated to closely match the expected characteristics of NISAR (e.g., spatial resolution and noise). The uncertainty estimates in UAVSAR-based high-resolution retrievals were compared with the SMAPVEX-12 in situ measurements. The uncertainties estimated for different crops were found to be close to the ubRMSE metric, which is also lower than the NISAR mission accuracy goal (0.06 [m3/m3]).

Thermal and visual comforts of occupants for a naturally ventilated educational building in low-income economies: A machine learning approach

Thermal and visual comfort (TVC) significantly impact the health and productivity of occupants as well as the indoor environmental quality in educational buildings. Machine learning approaches have currently gained popularity for TVC prediction; however, accurately predicting TVC faces challenges due to instrumental errors, environmental noise, and imbalanced data. Moreover, studies have revealed the unreliability of TVC predictions, particularly in naturally ventilated buildings. Therefore, this study develops three machine learning models, random forest (RF), decision tree (DT), and XGBoost, to accurately predict TVC using 1310 sets of field survey data collected from a naturally ventilated educational building (NVEB) in Dhaka, Bangladesh. Recursive feature elimination with cross-validation is employed to identify optimal features, while two resampling methods are applied to manage imbalanced classification. The RF model achieves the highest accuracy of 96 % for thermal comfort prediction, while the XGBoost model attains the highest accuracy of 95 % for visual comfort prediction. Meanwhile, the DT model achieves an accuracy of 94 % and 93 % for TVC prediction, respectively. Moreover, both RF and XGBoost outperform DT in class prediction performance, with an average area under the curve (AUC) scores above 0.95, while DT achieves an average AUC score above 0.80. In terms of two resampling methods, the results show that the models developed using the synthetic minority over-sampling technique (SMOTE) + edited nearest neighbour approach leads to less misclassification compared to the SMOTE + Tomek link. These findings demonstrate the capability of machine learning models in accurately predicting TVC in NVEBs and handling imbalanced datasets.

Research on Calculation Method of On-Orbit Instrumental Line Shape Function for the Greenhouse Gases Monitoring Instrument on the GaoFen-5B Satellite

The Greenhouse Gases Monitoring Instrument is based on the spectroscopic principle of spatial heterodyne spectroscopy technology and has the characteristics of no moving parts, a hyperspectral resolution, and a large luminous flux. The instrumental line shape function is one of the most important parameters characterizing the features of the instrument, and it plays a vital role in the system error analysis of the instrument’s measurements. To accurately obtain the instrumental line shape function of a spatial heterodyne spectrometer during the on-orbit period and improve the accuracy of gas concentration retrieval, this study develops a method to model and characterize the characteristics of the instrumental line shape function, including modulation loss and phase error. This study employs the solar calibration spectrum in the 1.568–1.583 μm bands to conduct iterative calculations of the instrumental line shape function error model. After the instrumental line function is updated, the average relative deviation is reduced from 1.83% to 0.756% between the theoretical and measured solar spectra. Additionally, the average relative deviation is reduced from 7.049% to 2.106% between the GMI nadir and theoretical nadir spectra. The findings demonstrate that updating the instrumental line shape function mitigates the impact of variations in the spectrometer’s instrumental line shape due to alterations in the orbital environment. This study offers a dependable reference for both the enhancement and oversight of a spectrometer’s instrumental line shape function, along with an investigation of shifts in instrument parameters.

The C1s core levels of polycyclic aromatic hydrocarbons and styrenic polymers: A first-principles study.

Understanding core level shifts in aromatic compounds is crucial for the correct interpretation of x-ray photoelectron spectroscopy (XPS) of polycyclic aromatic hydrocarbons (PAHs), including acenes, as well as of styrenic polymers, which are increasingly relevant for the microelectronic industry, among other applications. The effect of delocalization through π aromatic systems on the stabilization of valence molecular orbitals has been widely investigated in the past. However, little has been reported on the impact on the deeper C1s core energy levels. In this work, we use first-principles calculations at the level of many body perturbation theory to compute the C1s binding energies of several aromatic systems. We report a C1s red shift in PAHs and acenes of increasing size, both in the gas phase and in the molecular crystal. C1s red shifts are also calculated for stacked benzene and naphthalene pairs at decreasing intermolecular distances. A C1s red shift is in addition found between oligomers of poly(p-hydroxystyrene) and polystyrene of increasing length, which we attribute to ring-ring interactions between the side-chains. The predicted shifts are larger than common instrumental errors and could, therefore, be detected in XPS experiments.

Resolving nonuniform flow in gas turbines: challenges, progress, and moving forward

Abstract The flow in gas turbines exhibits a highly unsteady, complex and nonuniform manner, which presents two main challenges. Firstly, it introduces instrumentation errors, contributing to uncertainties when calculating one-dimensional performance metrics during rig or engine tests using fixed-location rakes. Secondly, it raises mechanical concerns, including high-cycle fatigue due to blade row interactions in turbomachines and thermal fatigue caused by hot-streaks at the combustor exit. Experimental characterisation of the flow nonuniformity in gas turbines is highly challenging due to the confined space and harsh environment for instrumentation. This paper presents recent efforts to address this issue by resolving the nonuniform flow in gas turbines using spatially under-sampled measurements. The proposed approach utilises discrete probe data and leverages a ‘Fourier-based approximation’ method developed by the author. The technique has undergone preliminary experimental validation involving reconstruction of the total pressure distribution in a multi-stage axial compressor and the total temperature field at the exit of the combustor and high-pressure turbine. Results show that, in the multi-stage axial compressor environment, reconstruction of inter-stage total pressure is achieved using a reduced dataset covering less than 20% of the annulus with reasonably good accuracy. The reconstructed total pressure yields almost identical mean total pressure values at all spanwise locations, with a maximum deviation of less than 0.02%. Additionally, reconstruction of the total temperature distribution at an engine-representative full annulus combustor is achieved using measurements at ten carefully selected circumferential locations. Results show that the reconstructed temperature field successfully captures the primary features associated with combustor exit temperature flow. The reconstructed temperature field yields excellent agreement in the magnitude of radial temperature distribution factor (RTDF) and overall temperature distribution factor (OTDF) predictions to the experiment, with a deviation of less than 0.5% for RTDF and less than 2.5% for OTDF. Lastly, reconstruction of the total temperature distribution at the exit of the GE E3 high-pressure turbine (HPT) is achieved using measurements at eight carefully selected circumferential locations. Results demonstrate remarkable robustness in resolving the temperature profile at the HPT exit with high fidelity, irrespective of the HPT inlet conditions. The initial validation results are promising, demonstrating that the new probe layout scheme and the Fourier-based approximation method enable effective characterisation of flow nonuniformity in gas turbines, thereby providing valuable insights into the complex flow of gas turbine engines.