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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

A Geomagnetic Estimate of Mean Paleointensity

To test a statistical hypothesis about Earth's magnetic field against paleomagnetism, the present field is used to estimate time averaged paleointensity. The estimate uses the modem magnetic multipole spectrum R(n), which gives the mean square induction represented by spherical harmonics of degree n averaged over the sphere of radius a = 6371.2 km. The hypothesis asserts that the low degree multipole powers of the core-source field are distributed as chi-squared with 2n+l degrees of freedom and expectation values {R(n)} = K[(n+l/2)/n(n+l](c/a)(sup 2n+4), where c is the 3480 km radius of Earth's core. (This is compatible with a usually mainly geocentric axial dipolar field). Amplitude K is estimated by fitting theoretical to observational spectra through degree 12. The resulting calibrated expectation spectrum is summed through degree 12 to estimate expected square intensity {F(sup 2)}. The sum also estimates {F(sup 2)} averaged over geologic time, in so far as the present magnetic spectrum is a fair sample of that generated in the past by core geodynamic processes.

Voorhies, Coerte↗
NASA NTRS2004

A Geomagnetic Estimate of Mean Paleointensity

To test a statistical hypothesis about Earth's magnetic field against paleomagnetism, the present field is used to estimate time averaged paleointensity. The estimate used the modern magnetic multipole spectrum R(n), which gives the mean square induction represented by spherical harmonics of degree n averaged over the sphere of radius a = 6371.2 km. The hypothesis asserts that low degree multi-pole powers of the coresource field are distributed as chi-squared with 2n+1 degrees of freedom and expectation values, where c is the 3480 km radius of the Earth's core. (This is compatible with a usually mainly geocentric axial dipolar field). Amplitude K is estimated by fitting theoretical to observational spectra through degree 12. The resulting calibrated expectation spectrum is summed through degree 12 to estimate expected square intensity F(exp 2). The sum also estimates F(exp 2) averaged over geologic time, in so far as the present magnetic spectrum is a fair sample of that generated in the past by core geodynamic processes. Additional information is included in the original extended abstract.

Voorhies, Coerte V.↗
NASA NTRS2020

Calibration and Performance of Juno Radio Science Data

Juno Radio Science measures the frequency of X- and Ka-band radio links between the Juno spacecraft and the Earth-based observing stations of NASA’s Deep Space Network (DSN) in order to determine the gravitational field of Jupiter. The received frequency contains information on the gravitational field and is also perturbed by the propagation environment, including Earth troposphere and ionosphere, electrons in the solar plasma, electrons in the Io Plasma Torus around Jupiter, and instrumental effects on both the spacecraft and the ground electronics. Each of these effects must be calibrated out of the data to ensure an accurate estimation of the Jupiter gravitational field. This work discusses the data processing, calibration, and performance of the frequency measurements. The precision of the frequency measurements average 1.1 mHz (1σ standard deviation at 60 second integration time), or 5.3 microns/sec in units of velocity. The remaining noise sources are primarily from residual troposphere and charged particles. Further improvement could be made in future radio science experiments with the addition of a cross-link and stiffer ground antennas.

Buccino, Dustin R↗
NASA NTRS2021

Calibration and Performance of Juno Radio Science Data

Juno Radio Science measures the frequency of X- and Ka-band radio links between the Juno spacecraft and the Earth-based observing stations of NASA’s Deep Space Network (DSN) in order to determine the gravitational field of Jupiter. The received frequency contains information on the gravitational field and is also perturbed by the propagation environment, including Earth troposphere and ionosphere, electrons in the solar plasma, electrons in the Io Plasma Torus around Jupiter, and instrumental effects on both the spacecraft and the ground electronics. Each of these effects must be calibrated out of the data to ensure an accurate estimation of the Jupiter gravitational field. This work discusses the data processing, calibration, and performance of the frequency measurements. The precision of the frequency measurements average 1.1 mHz (1σ standard deviation at 60 second integration time), or 5.3 microns/sec in units of velocity. The remaining noise sources are primarily from residual troposphere and charged particles. Further improvement could be made in future radio science experiments with the addition of a cross-link and stiffer ground antennas.

Oudrhiri, Kamal↗
NASA NTRS2020

Creating and Using Sensors That Tell Us About Precipitation

Precipitation is deceptively simple to measure - just put a container in the back yard - and this was the only technology available until radar was discovered to be capable of sensing precipitation in the 1940's,leading to quantitative estimates by the 1970's.Meanwhile, satellite-based sensors started advancing.The first precipitation estimates from space re purposed the existing geosynchronous satellite infrared (GEO-IR) data, but purpose-built passive microwave (PMW) sensors soon became a reality. In 1987, the launch of the first Special Sensor Microwave/Imager on the Defense Meteorological Satellite Program F08 by the U.S. Department of Defense, and their decision to open the dataset top public use, created a boom in precipitation algorithms that continues to this day. Experimental work to create a global multi-satellite product by Global Precipitation Climatology Project, and then a "virtual constellation"of PMW sensors from satellite agencies around the globe by the NASA/JAXA Tropical Rainfall Measuring Mission (TRMM) and by NOAA/NWS Climate Prediction Center, resulted in a new generation of quasi-global multi-satellite precipitation estimates at increasingly fine time and space scales. TRMM and the NASA/JAXA Global Precipitation Measurement mission have hosted precipitation radars in space, providing critical new quasi-global information about 3-D precipitation structures and enabling improved calibration of the PMW constellation's estimates.

Huffman, George J.↗
NASA NTRS2001

(Almost) Featureless Stereo: Calibration and Dense 3D Reconstruction Using Whole Image Operations

The conventional approach to shape from stereo is via feature extraction and correspondences. This results in estimates of the camera parameters and a typically spare estimate of the surface. Given a set of calibrated images, a dense surface reconstruction is possible by minimizing the error between the observed image and the image rendered from the estimated surface with respect to the surface model parameters. Given an uncalibrated image and an estimated surface, the camera parameters can be estimated by minimizing the error between the observed and rendered images a function of the camera parameters. We use a very small dense set of matched features to provide camera parameter estimates for the initial dense surface estimate. We then re-estimate the camera parameters as described above, and then re-estimate the surface. This process is iterated. Whilst it can not be proven to converge, we have found that around three iterations results in excellent surface and camera parameters estimates.

Smelyanskiy, V. N.↗
NASA NTRS2017

Effective Assimilation of SMAP Observations Using Statistical Techniques

Statistical techniques permit the retrieval of soil moisture estimates in a model climatology while retaining the spatial and temporal signatures of the satellite observations. As a consequence, the need for bias correction prior to an assimilation of these estimates is reduced, which could result in a more effective use of the independent information provided by the satellite observations. In this study, a statistical neural network (NN) retrieval algorithm is calibrated using SMAP brightness temperature observations and modeled soil moisture estimates (similar to those used to calibrate the SMAP Level 4 DA system). Daily values of surface soil moisture are estimated using the NN and then assimilated into the NASA Catchment model. The skill of the assimilation estimates is assessed based on a comprehensive comparison to in situ measurements from the SMAP core and sparse network sites as well as the International Soil Moisture Network. The NN retrieval assimilation is found to significantly improve the model skill, particularly in areas where the model does not represent processes related to agricultural practices. Additionally, the NN method is compared to assimilation experiments using traditional bias correction techniques. The NN retrieval assimilation is found to more effectively use the independent information provided by SMAP resulting in larger model skill improvements than assimilation experiments using traditional bias correction techniques.

Kolassa, J.↗
DOE OSTI2020

Quantifying concordance of correlated cosmological data sets

We develop estimators of agreement and disagreement between correlated cosmological data sets. These account for data correlations when computing the significance of both tensions and excess confirmation while remaining statistically optimal. We discuss and thoroughly characterize different approaches commenting on the ones that have the best behavior in practical applications. We complement the calculation of their statistical distribution within the Gaussian model with one estimator that takes non-Gaussianities fully into account. To illustrate the use of our techniques, we apply these estimators to supernovae measurements of the distance-redshift relation, absolutely calibrated by the local distance ladder. The suite of best estimators that we discuss finds results that are in excellent agreement between estimators and find no indications of significant internal inconsistencies in this data set above the 1% probability threshold. Furthermore, this shows the robustness of local determinations of the Hubble constant to features in the distance-redshift relation.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2001

A TRMM-Calibrated Infrared Rainfall Algorithm Applied Over Brazil

A satellite infrared (IR) technique for estimating rainfall over northern South America is presented. The objectives are to examine the diurnal variability of rainfall and to investigate the relative contributions from the convective and stratiform components. In this study, we apply the Convective-Stratiform Technique (CST) of Adler and Negri (1988). The parameters of the original technique were re-calibrated using coincident rainfall estimates (Olson et W., 2000) derived from the Tropical Rain Measuring Mission (TRMM) Microwave Imager (TMI) and GOES IR (11 micrometer) observations. Local circulations were found to play a major role in modulating the rainfall and its diurnal cycle. These included land/sea circulations (notably along the northeast Brazilian coast and in the Gulf of Panama), mountain/valley circulations (along the Andes Mountains), and circulations associated with the presence of rivers. This last category was examined in detail along the Amazon R. east of Manaus. There we found an early morning rainfall maximum along the river (5 LT at 58W, 3 LT at 56W). Rainfall avoids the river in the afternoon (12 LT and later), notably at 56 W. The width of the river seems to be generating a land/river circulation which enhances early morning rainfall but inhibits afternoon rainfall. Results are compared to ground-based radar data collected during the Large-Scale Biosphere-Atmosphere (LBA) experiment in southwest Brazil, to monthly raingages in northeastern Brazil, and to data from the TRMM Precipitation Radar.

Negri, Andrew J.↗
NASA NTRS2010

Use of GPS TEC Maps for Calibrating Single Band VLBI Sessions

GPS TEC ionosphere maps were first applied to a series of K and Q band VLBA astrometry sessions to try to eliminate a declination bias in estimated source positions. Their usage has been expanded to calibrate X-band only VLBI observations as well. At K-band, approx.60% of the declination bias appears to be removed with the application of GPS ionosphere calibrations. At X-band however, it appears that up to 90% or more of the declination bias is removed, with a corresponding increase in RA and declination uncertainties of approx.0.5 mas. GPS ionosphere calibrations may be very useful for improving the estimated positions of the X-only and S-only sources in the VCS and RDV sessions.

Gordon, David↗
NASA NTRS2015

Attitude Ground System (AGS) For The Magnetospheric Multi-Scale (MMS) Mission

The Magnetospheric Multiscale (MMS) mission is a Solar-Terrestrial Probe mission consisting of four identically instrumented spin-stabilized spacecraft flying in an adjustable pyramid-like formation around the Earth. The formation of the MMS spacecraft allows for three-dimensional study of the phenomenon of magnetic reconnection, which is the primary objective of the mission. The MMS spacecraft were launched early on March 13, 2015 GMT. Due to the challenging and very constricted attitude and orbit requirements for performing the science, as well as the need to maintain the spacecraft formation, multiple ground functionalities were designed to support the mission. These functionalities were incorporated into a ground system known as the Attitude Ground System (AGS). Various AGS configurations have been used widely to support a variety of three-axis-stabilized and spin-stabilized spacecraft missions within the NASA Goddard Space Flight Center (GSFC). The original MMS operational concept required the AGS to perform highly accurate predictions of the effects of environmental disturbances on the spacecraft orientation and to plan the attitude maneuvers necessary to stay within the science attitude tolerance. The orbit adjustment requirements for formation control drove the need also to perform calibrations that have never been done before in support of NASA GSFC missions. The MMS mission required support analysts to provide fast and accurately calibrated values of the inertia tensor, center of mass, and accelerometer bias for each MMS spacecraft. During early design of the AGS functionalities, a Kalman filter for estimating the attitude, body rates, center of mass, and accelerometer bias, using only star tracker and accelerometer measurements, was heavily analyzed. A set of six distinct filters was evaluated and considered for estimating the spacecraft attitude and body rates using star tracker data only. Four of the six filters are closely related and were compared during support of the Time History of Events and Macroscale Interactions during Substorms (THEMIS) and Space Technology-5 (ST-5) missions. These analyses exposed high dependency and sensitivity on the knowledge of the spacecraft inertia tensor for both body rates and accelerometer bias estimation. The conclusion of the analysis led to the design of an inertia tensor calibration technique using only star tracker data. The second most important result of the analysis was the design of two separate Kalman filters to estimate the spacecraft attitude and body rates and the accelerometer bias instead of a single combined filter. In this paper, the calibration results of the mass properties, as well as the performance of the spacecraft attitude and body rates filters using flight data are presented and compared against the mission requirements.

Estimation↗
NASA NTRSDate not supplied

Probabilistic Calibration of Expensive Models using Efficiently Trained Surrogates

Calibration of computational models in the presence of uncertainty is often cast as a Bayesian inference problem and solved via sampling methods, e.g., Markov chain Monte Carlo. When the computational model is expensive, this task becomes intractable due to the large number of samples required to accurately estimate the posterior distribution of the calibration parameters. A popular solution to this problem is to use machine learning to develop a faster-to-evaluate, lower-fidelity substitute for the original model to serve as a surrogate while solving the inference problem. Although considered an offline cost, generating training data to construct this surrogate model can still be an expensive task in practice. An active learning algorithm is presented that focuses training on improving surrogate accuracy specifically in and around the bulk of the posterior distribution, as this is where the model is exercised during calibration. Candidate samples are drawn from families of distributions related to an approximation of the posterior. The sample maximizing predictive variance is then selected for evaluation by the original computational model, yielding a label for the training point. Iterating this approach increases efficiency relative to space filling designs (e.g., Latin hypercube sampling) by avoiding low probability points. Practical considerations are discussed, including the benefits of using a sequential Monte Carlo sampling approach, convergence heuristics, and the importance of both exploration and exploitation given that the true posterior is unknown a priori.

uncertainty quantification↗
NASA NTRS2001

Calibration of Gyros with Temperature Dependent Scale Factors

The general problem of gyro calibration can be stated as the estimation of the scale factors, misalignments, and drift-rate biases of the gyro using the on-orbit sensor measurements. These gyro parameters have been traditionally treated as temperature-independent in the operational flight dynamics ground systems at NASA Goddard Space Flight Center (GSFC), a scenario which has been successfully applied in the gyro calibration of a large number of missions. A significant departure from this is the Microwave Anisotropy Probe (MAP) mission where, due to the high thermal variations expected during the mission phase, it is necessary to model the scale factors as functions of temperature. This paper addresses the issue of gyro calibration for the MAP gyro model using a manufacturer-supplied model of the variation of scale factors with temperature. The problem is formulated as a least squares problem and solved using the Levenberg-Marquardt algorithm in the MATLAB(R) library function NLSQ. The algorithm was tested on simulated data with Gaussian noise for the quaternions as well as the gyro rates and was found to consistently converge close to the true values. Significant improvement in accuracy was noticed due to the estimation of the temperature-dependent scale factors as against constant scale factors.

Belur, Sheela V.↗
NASA NTRS1993

New error calibration tests for gravity models using subset solutions and independent data - Applied to GEM-T3

A new method has been developed to provide a direct test of the error calibrations of gravity models based on actual satellite observations. The basic approach projects the error estimates of the gravity model parameters onto satellite observations, and the results of these projections are then compared with data residual computed from the orbital fits. To allow specific testing of the gravity error calibrations, subset solutions are computed based on the data set and data weighting of the gravity model. The approach is demonstrated using GEM-T3 to show that the gravity error estimates are well calibrated and that reliable predictions of orbit accuracies can be achieved for independent orbits.

Lerch, F. J.↗
DOE OSTI2023

Toward Improved Regional Hydrological Model Performance Using State-Of-The-Science Data-Informed Soil Parameters

Accurate soil moisture and streamflow data are an aspirational need of many hydrologically relevant fields. Model simulated soil moisture and streamflow hold promise but models require validation prior to application. Calibration methods are commonly used to improve model fidelity but misrepresentation of the true dynamics remains a challenge. In this study, we leverage soil parameter estimates from the Soil Survey Geographic (SSURGO) database and the probability mapping of SSURGO (POLARIS) to improve the representation of hydrologic processes in the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) over a central California domain. Our results show WRF-Hydro soil moisture exhibits increased correlation coefficients ( r ), reduced biases, and increased Kling-Gupta Efficiencies (KGEs) across seven in situ soil moisture observing stations after updating the model's soil parameters according to POLARIS. Compared to four well-established soil moisture data sets including Soil Moisture Active Passive data and three Phase 2 North American Land Data Assimilation System land surface models, our POLARIS-adjusted WRF-Hydro simulations produce the highest mean KGE (0.69) across the seven stations. More importantly, WRF-Hydro streamflow fidelity also increases, especially in the case where the model domain is set up with SSURGO-informed total soil thickness. The magnitude and timing of peak flow events are better captured, r increases across nine United States Geological Survey stream gages, and the mean KGE across seven of the nine gages increases from 0.12 to 0.66. Our pre-calibration parameter estimate approach, which is transferable to other spatially distributed hydrological models, can substantially improve a model's performance, helping reduce calibration efforts and computational costs.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2006

JMR Noise Diode Stability and Recalibration Methodology after Three Years On-Orbit

The Jason Microwave Radiometer (JMR) is included on the Jason-1 ocean altimeter satellite to measure the wet tropospheric path delay (PD) experienced by the radar altimeter signal. JMR is nadir pointing and measures the radiometric brightness temperature (T(sub B)) at 18.7, 23.8 and 34.0 GHz. JMR is a Dicke radiometer and it is the first radiometer to be flown in space that uses noise diodes for calibration. Therefore, monitoring the long term stability of the noise diodes is essential. Each channel has three redundant noise diodes which are individually coupled into the antenna signal to provide an estimate of the gain. Two significant jumps in the JMR path delays, relative to ground truth, were observed around 300 and 700 days into the mission. Slow drifts in the retrieved products were also evident over the entire mission. During a recalibration effort, it was determined that a single set of calibration coefficients was not able to remove the calibration jumps and drifts, suggesting that there was a change in the hardware and time dependent coefficients would be required. To facilitate the derivation of time dependent coefficients, an optimal estimation based calibration system was developed which iteratively determines that set of calibration coefficients which minimize the RMS difference between the JMR TBs and on-Earth hot and cold absolute references. This optimal calibration algorithm was used to fine tune the front end path loss coefficients and derive a time series of the JMR noise diode brightness temperatures for each of the nine diodes. Jumps and drifts, on the order of 1% to 2%, are observed among the noise diodes in the first three years on-orbit.

Jason 1↗
NASA NTRS1981

Calibration validation for the GEOS 3 altimeter

An absolute calibration of the altitude data is needed for some applications of the large quantity of altimeter data taken by the GEOS 3 intensive mode altimeter. The considered calibration technique is based on the use of a gravimetric geoid model and satellite passes which are nearly overhead at island laser tracking sites. Near overhead passes of GEOS 3 were achieved at Bermuda. Two of these passes were tracked by the Bermuda laser. The reported investigation has the objective to obtain a best estimate of the GEOS 3 altimeter calibration bias, using the two available passes and reconciling differences between them. Assuming that orbits with accurate altitudes over Bermuda are obtained, discrepancies between the two passes could be due to incorrect time tagging of the altimeter data. To assist in the resolution of the timing question, additional GEOS 3 passes through the calibration area were selected. On the basis of the obtained results, it is recommended to add 10.24 ms to GEOS 3 altimeter time tags before using the data.

Martin, C. F.↗
DOE OSTI2020

Application of Neutron Multiplicity Counting Experiments to Optimal Cross-Section Adjustments

This paper presents the first application of model calibration to neutron multiplicity counting (NMC) experiments for cross-section optimization that is informed by adjoint-based sensitivity analysis (SA) and first-order uncertainty quantification (UQ). We summarize previous work on SA applied to NMC and describe notable modifications and additions. We give the procedure for first-order UQ and Bayesian-inference-based parameter estimation (PE). We then discuss model calibration applied to NMC of a 4.5-kg sphere of weapons-grade, alpha-phase plutonium metal (the BeRP ball) with the nPod neutron multiplicity counter. For the BeRP ball in bare and polyethylene-reflected configurations, we discuss the sensitivity of the first- and second-moment detector responses (i.e., first and second moments of the NMC distribution, respectively) to the cross sections. We describe the sources of uncertainty in the measured and simulated responses. Specifically, uncertainty in the measured responses is due to both random and systematic sources. Uncertainty in the simulated responses is due to the cross-section covariances. We describe in detail the adjustment to the cross sections and cross-section covariances due to the optimization. Due to the contribution of systematic uncertainties to the measured response uncertainties, the adjustment to the cross sections is similar in trend but larger in magnitude compared to that recommended by previous work. We compare the measured responses to responses simulated with nominal and optimized cross sections, demonstrating that the best-estimate cross sections produce simulations of NMC experiments that are more accurate with reduced uncertainty.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗