Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “calibration estimator”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

High Latitude Considerations in the Latest GPCP monthly and daily products (V3.1)

The Global Precipitation Climatology Project (GPCP) product is a popular combined satellite-gauge precipitation data set in which the long-term standards of consistency and homogeneity is underlined. Here we discuss various high latitude analysis considered in the recently released GPCP V3.1 monthly and daily products. Satellite data are used over land and ocean and obtained from the Special Sensor Microwave Imager (SSMI), Special Sensor Microwave Imager/Sounder (SSMIS), geostationary imagers and polar orbiting infrared sounders. GPCP uses the Global Precipitation Climatology Centre (GPCC) over land, as its in situ component, but prior to combination with satellite data GPCC estimates are adjusted for gauge undercatch. Advanced sensors aboard the Tropical Rainfall Measuring Mission (TRMM), CloudSat, and Global Precipitation Measurement (GPM) mission have enabled more accurate estimation of rain and snowfall rates in recent years. Started with GPCP V3.1 these observations are integrated into GPCP through the development of the Tropical Combined Climatology (TCC) used at lower latitudes and the Merged CloudSat, TRMM, and GPM (MCTG) climatology used over the extra tropics and higher latitudes. Improved calibrations of Television-Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS) and Advanced Infrared Sounder (AIRS) precipitation are used outside 60ºN-S, where inside this zone the Goddard Profiling (GPROF) algorithm retrievals from SSMI/SSMIS is used to calibrate geostationary IR based precipitation estimate at monthly scale. The Gravity Recovery and Climate Experiment (GRACE) mass change observations are used to determine snowfall accumulations over frozen land and arctic basins and to assess gauge undercatch corrections. Observations of snow on sea ice from NASA’s Operation IceBridge (OIB) flights are utilized as an additional tool for snowfall assessment over sea ice. GPCP V3.1 has higher spatial resolution (0.5ox0.5o) than earlier versions (2.5ox2.5o) over both land and ocean, going back to 1983. Version 3 Daily product uses the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) Final Run V06 estimates, where available (initially restricted to 60°N-S), as well as rescaled TOVS/AIRS data in high-latitude areas, all calibrated to the GPCP V3.1 Monthly estimate. GPCP V3.1 shows about 6% increase in global oceanic precipitation and about 4.5% increase over global land and ocean compared to the previous version (V2.3), some major changes occur over the ocean and around 40oS and 60 oS. We will discuss other important changes of GPCP V3.1, compared to the earlier versions, and our future plans. Through this presentation we will also discuss that while ACCP will provide key information about precipitation, synergistic use of other Earth observing systems (e.g., mass change; recognized as a designated mission in 2017 decadal survey) can also help refine precipitation analysis, especially in high latitude and cold regions.

Ali Behrangi↗
DOE OSTI2020

Calibration of energy density functionals with deformed nuclei

Nuclear density functional theory is the prevalent theoretical framework for accurately describing nuclear properties at the scale of the entire chart of nuclides. Given an energy functional and a many-body scheme (e.g., single- or multireference level), the predictive power of the theory depends strongly on how the parameters of the energy functionals have been calibrated with experimental data. Expanded algorithms and computing power have enabled recent optimization protocols to include data in deformed nuclei in order to optimize the coupling constants of the energy functional. The primary motivation of this work is to test the robustness of such protocols with respect to some of the technical and numerical details of the underlying calculations, especially when the calibration explores a large parameter space. To this end, we quantify the effect of these uncertainties on both the optimization and statistical emulation of composite objective functions. We also emphasize that Bayesian calibration can provide better estimates of the theoretical errors used to define objective functions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1989

DSN 70-meter antenna microwave optics design and performance improvements. Part 2: Comparison with measurements

The measured Deep Space Network (DSN) 70-meter antenna performance at S- and X-bands is compared with the design expectations. A discussion of natural radio-source calibration standards is given. New estimates of DSN 64-meter antenna performance are given, based on improved values of calibration source flux and size correction. A comparison of the 64- and 70-meter performances shows that average S-band peak gain improvement is 1.94 dB, compared with a design expectation of 1.77 dB. At X-band, the average peak gain improvement is 2.12 dB, compared with the (coincidentally similar) design expectation of 1.77 dB. The average measured 70-meter S-band peak gain exceeds the nominal design-expected gain by 0.02 dB; the average measured 70-meter X-band peak gain is 0.14 dB below the nominal design-expected gain.

Bathker, D. A.↗
NASA NTRS2004

Hand-Eye Calibration of Robonaut

NASA's Human Space Flight program depends heavily on Extra-Vehicular Activities (EVA's) performed by human astronauts. EVA is a high risk environment that requires extensive training and ground support. In collaboration with the Defense Advanced Research Projects Agency (DARPA), NASA is conducting a ground development project to produce a robotic astronaut's assistant, called Robonaut, that could help reduce human EVA time and workload. The project described in this paper designed and implemented a hand-eye calibration scheme for Robonaut, Unit A. The intent of this calibration scheme is to improve hand-eye coordination of the robot. The basic approach is to use kinematic and stereo vision measurements, namely the joint angles self-reported by the right arm and 3-D positions of a calibration fixture as measured by vision, to estimate the transformation from Robonaut's base coordinate system to its hand coordinate system and to its vision coordinate system. Two methods of gathering data sets have been developed, along with software to support each. In the first, the system observes the robotic arm and neck angles as the robot is operated under external control, and measures the 3-D position of a calibration fixture using Robonaut's stereo cameras, and logs these data. In the second, the system drives the arm and neck through a set of pre-recorded configurations, and data are again logged. Two variants of the calibration scheme have been developed. The full calibration scheme is a batch procedure that estimates all relevant kinematic parameters of the arm and neck of the robot The daily calibration scheme estimates only joint offsets for each rotational joint on the arm and neck, which are assumed to change from day to day. The schemes have been designed to be automatic and easy to use so that the robot can be fully recalibrated when needed such as after repair, upgrade, etc, and can be partially recalibrated after each power cycle. The scheme has been implemented on Robonaut Unit A and has been shown to reduce mismatch between kinematically derived positions and visually derived positions from a mean of 13.75cm using the previous calibration to means of 1.85cm using a full calibration and 2.02cm using a suboptimal but faster daily calibration. This improved calibration has already enabled the robot to more accurately reach for and grasp objects that it sees within its workspace. The system has been used to support an autonomous wrench-grasping experiment and significantly improved the workspace positioning of the hand based on visually derived wrench position. estimates.

Nickels, Kevin↗
DOE OSTI2022

Quantifying subsurface parameter and transport uncertainty using surrogate modelling and environmental tracers

Here, we combine physics-based groundwater reactive transport modelling with machine-learning techniques to quantify hydrogeological model and solute transport predictive uncertainties. We train an artificial neural network (ANN) on a dataset of groundwater hydraulic heads and 3 H concentrations generated using a high-fidelity groundwater reactive transport model. Using the trained ANN as a surrogate model to reproduce the input–output response of the high-fidelity reactive transport model, we quantify the posterior distributions of hydrogeological parameters and hydraulic forcing conditions using Markov chain Monte Carlo calibration against field observations of groundwater hydraulic heads and 3 H concentrations. We demonstrate the methodology with a model application that predicts Chlorofluorocarbon-12 (CFC-12) solute transport at a contaminated field site in Wyoming, United States. Our results show that including 3 H observations in the calibration dataset reduced the uncertainty in the estimated permeability field and infiltration rates, compared to calibration against hydraulic heads alone. However, predictive uncertainty quantification shows that CFC-12 transport predictions conditioned to the parameter posterior distributions cannot reproduce the field measurements. We found that calibrating the model to hydraulic head and 3 H observations results in groundwater mean ages that are too large to explain the observed CFC-12 concentrations. The coupling of the physics-based reactive transport model with the machine-learning surrogate model allows us to efficiently quantify model parameter and predictive uncertainties, which is typically computationally intractable using reactive transport models alone.

58 GEOSCIENCES↗
NASA NTRS1988

Radiometric calibration analysis of SIR-B imagery

The second flight of the NASA Spaceborne Imaging Radar (SIR-B) collected nearly 8 hours of digital synthetic-aperture radar (SAR) data of the earth's surface. This paper analyzes the performance of the SAR to determine the accuracy of the relative radiometric calibration of these data. Procedures are presented for deriving accuracy estimates as well as limitations under which these estimates are valid. The impact on calibration of an RF breakdown in the primary antenna feed system cable is evaluated. An analysis of the platform stability based on the SAR echo data is also presented in conjunction with its resultant effect on the calibration accuracy. Finally, numerical error bounds are derived with guidelines on their utilization.

Wall, Stephen D.↗
DOE OSTI2025

Empirically-calibrated H100 node power models for accurate AI training energy estimation

Accurately quantifying the energy use of artificial intelligence (AI) training is critical for infrastructure planning, carbon accounting, and sustainable data center operation, but few studies have directly measured the power consumption of production workloads on contemporary hardware. By combining empirical measurements from Brookhaven National Laboratory during AI training on 8-graphics-processing-unit H100 systems with open-source benchmarking data, we develop statistical models relating computational intensity to node-level power consumption. We measure the gap between manufacturer-rated thermal design power (TDP) and actual power demand during AI training. Our analysis reveals that even computationally intensive workloads operate at only 76% of the 10.2 kW TDP rating. Our architecture-specific model, calibrated to floating-point operations, predicts energy consumption with 11.4% mean absolute percentage error, significantly outperforming TDP-based approaches (27%–37% error). We identified distinct power signatures between transformer and convolutional neural network architectures, with transformers showing characteristic fluctuations that may impact grid stability. These results provide a measurement-grounded basis for improving AI training energy estimates, enabling more reliable infrastructure sizing, cost projections, and environmental impact assessments.

Newkirk, Alex C↗
DOE OSTI2024

Measurement and calibration of non-linear shear terms in galaxy cluster fields

Introduction Galaxy cluster lensing is a powerful tool for measuring the mass of galaxy clusters, but accurate shear measurement and calibration are critical to obtaining reliable results. This study focuses on the measurement and calibration of weak lensing shears to improve mass estimates in cluster lensing. To deal with the problem, we first developed an image simulation pipeline, jedisim, which utilizes galaxy images extracted from the Hubble Space Telescope (HST) Ultra Deep Field (UDF) and the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS). Methods The simulations represent realistic galaxy distributions and morphologies as input sources. The foreground halo with a Navarro–Frenk–White (NFW) profile is constructed such that the lensing signals of background galaxies can be measured by the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) Science Pipelines. By comparing the measured reduced shear g meas and the true reduced shear g true , we observe non-linearity up to g ≲ 0.6 . We fit polynomials to the data with quadratic correction adequate to g ≲ 0.4 . Meanwhile, we conduct mass estimates using the pzmassfitter code on four different clusters. Results The mass estimate results are significantly improved after applying the shear calibration derived from the present work—from 4.954 ± 0.504 × 1 0 14 M ⊙ to 10.507 ± 0.498 × 1 0 14 M ⊙ after calibration for a simulated cluster with the mass of 10 × 1 0 14 M ⊙ . In multiple cases of validation, the estimated results are all consistent with true cluster mass. Discussion This study yields the first relationship between reality and shape measurement of the LSST Science Pipelines and serves as the first step toward the overall goal of mass calibration in cluster lensing. By addressing the challenges in shear measurement and calibration, we aim to enhance the accuracy and reliability of mass estimates in galaxy cluster lensing studies.

Astronomy & Astrophysics↗
NASA NTRS1984

Prelaunch absolute radiometric calibration of the reflective bands on the LANDSAT-4 protoflight Thematic Mapper

The results of the absolute radiometric calibration of the LANDSAT 4 thematic mapper, as determined during pre-launch tests with a 122 cm integrating sphere, are presented. Detailed results for the best calibration of the protoflight TM are given, as well as summaries of other tests performed on the sensor. The dynamic range of the TM is within a few per cent of that required in all bands, except bands 1 and 3. Three detectors failed to pass the minimum SNR specified for their respective bands: band 5, channel 3 (dead), band 2, and channels 2 and 4 (noisy or slow response). Estimates of the absolute calibration accuracy for the TM show that the detectors are typically calibrated to 5% absolute error for the reflective bands; 10% full-scale accuracy was specified. Ten tests performed to transfer the detector absolute calibration to the internal calibrator show a 5% range at full scale in the transfer calibration; however, in two cases band 5 showed a 10% and a 7% difference.

Barker, J. L.↗
DOE OSTI2020

A Bayesian Approach to Regional Decadal Predictability: Sparse Parameter Estimation in High-Dimensional Linear Inverse Models of High-Latitude Sea Surface Temperature Variability

Stochastic reduced models are an important tool in climate systems whose many spatial and temporal scales cannot be fully discretized or underlying physics may not be fully accounted for. One form of reduced model, the linear inverse model (LIM), has been widely used for regional climate predictability studies—typically focusing more on tropical or midlatitude studies. However, most LIM fitting techniques rely on point estimation techniques deriving from fluctuation–dissipation theory. In this methodological study we explore the use of Bayesian inference techniques for LIM parameter estimation of sea surface temperature (SST), to quantify the skillful decadal predictability of Bayesian LIM models at high latitudes. We show that Bayesian methods, when compared to traditional point estimation methods for LIM-type models, provide better calibrated probabilistic skill, while simultaneously providing better point estimates due to the regularization effect of the prior distribution in high-dimensional problems. We compare the effect of several priors, as well as maximum likelihood estimates, on 1) estimating parameter values on a perfect model experiment and 2) producing calibrated 1-yr SST anomaly forecast distributions using a preindustrial control run of the Community Earth System Model (CESM). Finally, we employ a host of probabilistic skill metrics to determine the extent to which an LIM can forecast SST anomalies at high latitudes. We find that the choice of prior distribution has an appreciable impact on estimation outcomes, and priors that emphasize physically relevant properties enhance the model’s ability to capture variability of SST anomalies.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1986

Calibrating an altimeter - How many tide gauges is enough?

The dominant contributor to the random error of an altimetric satellite system is the long wavelength uncertainty in the orbital radius. It is shown that calibration by a comparatively modest tide gauge system can drastically reduce the overall error in global estimates of large-scale oceanic variability. The procedure used is a form of optimal estimation. Absolute (time average) altimetric calibration is much more difficult because it requires absolute calibration of the tide gauge positions (in three dimensions) but the error reduction process would be the same.

Wunsch, Carl↗
NASA NTRS1989

Extracting ocean surface information from altimeter returns - The deconvolution method

An evaluation of the deconvolution method for estimating ocean surface parameters from ocean altimeter waveforms is presented. It is shown that this method presents a fast, accurate way of determining the ocean surface parameters from noisy altimeter data. Three parameters may be estimated by using this method, including the altimeter-height error, the ocean-surface standard deviation, and the ocean-surface skewness. By means of a Monte Carlo experiment, an 'optimum' deconvolution algorithm and the accuracies with which the above parameters may be estimated using this algorithm are determined. Then the influence of instrument effects, such as errors in calibration and pointing-angle estimation, on the estimated parameters is examined. Finally, the deconvolution algorithm is used to estimate height and ocean-surface parameters from Seasat data.

Rodriguez, Ernesto↗
NASA NTRSDate not supplied

Evaluating Current and Future Sensor-Specific Biomass Calibration in the Tallest Mangrove Forest on Earth

High-resolution global-scale estimates of aboveground biomass density will soon be available from a suite of spaceborne LiDAR and radar missions. The 2016-2017 AfriSAR campaign was specifically designed to evaluate a suite of sensors for estimating biomass in a range of tropical forest environments. Here, we compare the calibration and biomass estimates from 5 different active sensors - ALOS Global Digital Surface Model (DSM), Shuttle Radar Topography Mission (SRTM), Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), NASA Land Vegetation Ice Sensor (LVIS), and TanDEM-X (TDX) - in the tallest known mangrove forest on Earth - Pongara National Park, Gabon. We leverage this comparison to evaluate the implications for future satellite missions that are aimed at improving global estimates of forest carbon storage. Our findings are directly relevant for space-borne missions estimating terrestrial carbon storage - GEDI, ICESat-2, NISAR, BIOMASS, Tandem-X, and Tandem-L - highlighting the specific uncertainty and bias that can be expected in several global biomass products in mangrove ecosystems.

Atticus E L Stovall↗
NASA NTRS2018

Dither Gyro Scale Factor Calibration: GOES-16 Flight Experience

This poster is a sequel to a paper presented at the 34th Annual AAS Guidance and Control Conference in 2011, which first introduced dither-based calibration of gyro scale factors. The dither approach uses very small excitations, avoiding the need to take instruments offline during gyro scale factor calibration. In 2017, the dither calibration technique was successfully used to estimate gyro scale factors on the GOES-16 satellite. On-orbit dither calibration results were compared to more traditional methods using large angle spacecraft slews about each gyro axis, requiring interruption of science. The results demonstrate that the dither technique can estimate gyro scale factors to better than 2000 ppm during normal science observations.

calibration↗
NASA NTRS2004

The Precision and Accuracy of AIRS Level 1B Radiances for Climate Studies

We investigate uncertainties in the Atmospheric Infrared Sounder (AIRS) radiances based on in-flight and preflight calibration algorithms and observations. The global coverage and spectra1 resolution ((lamda)/(Delta)(lamda) ~ 1200) of AIRS enable it to produce a data set that can be used as a climate data record over the lifetime of the instrument. Therefore, we examine the effects of the uncertainties in the calibration and the detector stability on future climate studies. The uncertainties of the parameters that go into the AIRS radiometric calibration are propagated to estimate the accuracy of the radiances and any climate data record created from AIRS measurements. The calculated radiance uncertainties are consistent with observations. Algorithm enhancements may be able to reduce the radiance uncertainties by as much as 7%. We find that the orbital variation of the gain contributes a brightness temperature bias of < 0.01 K.

climate↗
DOE OSTI2024

On the uncertainty of long-period return values of extreme daily precipitation

Methods for calculating return values of extreme precipitation and their uncertainty are compared using daily precipitation rates over the Western U.S. and Southwestern Canada from a large ensemble of climate model simulations. The roles of return-value estimation procedures and sample size in uncertainty are evaluated for various return periods. We compare two different generalized extreme value (GEV) parameter estimation techniques, namely L-moments and maximum likelihood (MLE), as well as empirical techniques. Even for very large datasets, confidence intervals calculated using GEV techniques are narrower than those calculated using empirical methods. Furthermore, the more efficient L-moments parameter estimation techniques result in narrower confidence intervals than MLE parameter estimation techniques at small sample sizes, but similar best estimates. It should be noted that we do not claim that either parameter fitting technique is better calibrated than the other to estimate long period return values. While a non-stationary MLE methodology is readily available to estimate GEV parameters, it is not for the L-moments method. Comparison of uncertainty quantification methods are found to yield significantly different estimates for small sample sizes but converge to similar results as sample size increases. Finally, practical recommendations about the length and size of climate model ensemble simulations and the choice of statistical methods to robustly estimate long period return values of extreme daily precipitation statistics and quantify their uncertainty.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

Method and Apparatus for Spacecraft Gyroscope Scale Factor Calibration

A method and apparatus for estimating gyro scale factor during normal spacecraft operations, using small attitude motions that are compliant with mission pointing accuracy and stability requirements and a signal processing method that specifically detects the intentionally induced motions. This process increases operational availability by avoiding the need to take the spacecraft offline for large calibration maneuvers.

Reth, Alan D↗
NASA NTRS1993

Using AVIRIS for in-flight calibration of the spectral shifts of SPOT-HRV and of AVHRR?

The response of a satellite sensor varies during its lifetime; internal calibration devices can be used to follow the sensor degradation or in-flight calibrations are conducted from estimates of the radiance at satellite level for well predictable situations. Changes in gain are evaluated assuming that the spectral response of the sensor is stable with time; i.e., that the filter response as well as the optics or the electronics are not modified since the prelaunch determinations. Nevertheless, there is some evidence that the SPOT interferometer filters are affected by outgassing effects during the launch. Tests in vacuum chambers indicated a narrowing of the filters with a shift of the upper side towards the blue of about 10 nm which is more over consistant with the loss of gain observed during the launch. Also, during the lifetime of SPOT, the relationship between the loss of sensitivity and the filter bandwidth may correspond to this effect. On the other hand, the inconsistancy of the NOAA7 calibration between two methods (desert and ocean) having a different spectral sensitivity may indicate a spectral problem with a shift of the central wavelength of -20 nm. The basic idea here is to take advantage of the good spectral definition of AVIRIS to monitor these potential spectral degradations with an experimental opportunity provided by a field campaign held in La Crau (S.E. of France) in June 1991 which associated ground-based measurements and AVIRIS, SPOT2, NOAA-11 overpasses over both the calibration site of La Crau and an agricultural area.

Willart-Soufflet, Veronique↗