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At least 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Real-Time EDL Navigation Performance Using Spacecraft to Spacecraft Radiometric Data

A two-year task sponsored by NASA's Mars Technology Program's Advanced Entry, Descent and Landing (EDL) work area includes investigation of improvements to EDL navigation by processing spacecraft-to-spacecraft radiometric data. Spacecraft-to-spacecraft navigation will take advantage of the UHF link between two spacecraft (i.e. to an orbiter from an approaching lander for EDL telemetry relay) to build radiometric data, specifically the velocity between the two spacecraft along the radio beam, that are processed to determine position and velocity in real time. The improved onboard state knowledge provided by spacecraft-to-spacecraft navigation will improve the performance of entry guidance by providing a more accurate state estimate and ultimately reduce the landed position error. A previous paper documented the progress of the first year of this task, including the spacecraft definitions, selection and documentation of the required algorithms and analysis results used to define the algorithm set. The final year of this task is reported here. Topics include modifications to the previously selected algorithm set for implementation, and performance of the implemented algorithms in a stand-alone filter, on an emulator of the target processor and finally on a breadboard processing unit.

navigation↗
NASA NTRS2011

Lessons Learned from Assimilating Altimeter Data into a Coupled General Circulation Model with the GMAO Augmented Ensemble Kalman Filter

Satellite altimetry measurements have provided global, evenly distributed observations of the ocean surface since 1993. However, the difficulties introduced by the presence of model biases and the requirement that data assimilation systems extrapolate the sea surface height (SSH) information to the subsurface in order to estimate the temperature, salinity and currents make it difficult to optimally exploit these measurements. This talk investigates the potential of the altimetry data assimilation once the biases are accounted for with an ad hoc bias estimation scheme. Either steady-state or state-dependent multivariate background-error covariances from an ensemble of model integrations are used to address the problem of extrapolating the information to the sub-surface. The GMAO ocean data assimilation system applied to an ensemble of coupled model instances using the GEOS-5 AGCM coupled to MOM4 is used in the investigation. To model the background error covariances, the system relies on a hybrid ensemble approach in which a small number of dynamically evolved model trajectories is augmented on the one hand with past instances of the state vector along each trajectory and, on the other, with a steady state ensemble of error estimates from a time series of short-term model forecasts. A state-dependent adaptive error-covariance localization and inflation algorithm controls how the SSH information is extrapolated to the sub-surface. A two-step predictor corrector approach is used to assimilate future information. Independent (not-assimilated) temperature and salinity observations from Argo floats are used to validate the assimilation. A two-step projection method in which the system first calculates a SSH increment and then projects this increment vertically onto the temperature, salt and current fields is found to be most effective in reconstructing the sub-surface information. The performance of the system in reconstructing the sub-surface fields is particularly impressive for temperature, but not as satisfactory for salt.

Keppenne, Christian↗
DOE OSTI2021

Santa Barbara Desalination Digital Twin Technical Report

A digital twin is a computer model that has sufficient breadth and fidelity to enable diagnostic and predictive analyses for outputs of interest. We have constructed a digital twin of the SB desal plant using the ProteusLib software developed for NAWI under task 4.2.2, and specifically the ProteusLib models of pumps, filters, mixers, splitters, and reverse osmosis units and a seawater property model. The steps needed to configure this digital twin with the real conditions of the SB desal plant are: (1) Data reconciliation, which is gathering the data and plant connectivity and ensure that all relevant values are present and in the correct units and validating the data using material and energy balances, and correct for errors, gaps, or deviations; (2) Parameter estimation, which is matching model parameters with data from previous step, filling missing parameters with initial estimates, then iteratively refining estimated values. Once we completed the digital twin, we used the optimization capabilities of ProteusLib to compute outputs of interest over a chosen range of inputs. The primary output of interest to the owners of the plant is its specific energy consumption (kWh/acre-foot). The remainder of this report will provide more detail on the data reconciliation, parameter estimation, and analyses we performed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

A 1 km soil moisture dataset over eastern CONUS generated by assimilating SMAP data into the Noah-MP land surface model

An improved fine-scale soil moisture (SM) dataset at 1 km grid spacing, covering much of the eastern continental US, was generated by assimilating 9 km Soil Moisture Active Passive (SMAP) SM data into the v4.0.1 Noah-MP land surface model. With 12 ensemble members, the assimilation was carried out using the ensemble Kalman filter algorithm within NASA's Land Information System. The SM analysis for 2016 was fully validated against in situ observations from four different networks and compared with four other existing datasets. Results indicate that this SM analysis surpasses other datasets in top-layer SM distribution, including a machine-learning-based product, despite all SM estimates being less heterogeneous than observed. The analysis of anomalous errors suggests that large similarity in intrinsic errors is likely due to overlapping data sources among the selected SM datasets. More detailed evaluations were performed over two geographic areas. The observations collected by the Atmospheric Radiation Measurement facility in Oklahoma suggest that soil temperature and surface heat fluxes are concurrently simulated with good accuracy. Investigation into the 2016 southeastern US drought response further indicates drier conditions and higher evapotranspiration estimates compared to GLEAMv4.1. Notably, large errors are associated with grids having clay soil textures, underscoring the need for refined model treatments for specific soil types to further improve SM estimates. The dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.14370563 (Tai et al., 2024).

Tai, Sheng-Lun [Pacific Northwest National Laborat↗
NASA NTRS1975

Processing corrections for Skylab photographic imagery

Camera filters were inadvertently omitted from the six-channel multispectral photographic camera (S190A) during the exposure of the first roll of film from each channel on the final manned Skylab mission. Each of the films was overexposed and degraded as a result of the filter omissions. Explained are the techniques used by the NASA/JSC Photographic Technology Division to evaluate and process those films. These or similar techniques can be used in other photographic multispectral remote sensing applications in which black-and-white infrared and panchromatic or color infrared film are degraded due to loss of filtration or to overexposure. Results prove that data may be salvaged after camera exposure errors are made thus saving the expense of reacquiring data.

Lockwood, H. E.↗
NASA NTRS2012

An OSSE on Mesoscale Model Assimilation of Simulated HIRAD-Observed Hurricane Surface Winds

The hazards of landfalling hurricanes are well known, but progress on improving the intensity forecasts of these deadly storms at landfall has been slow. Many cite a lack of high-resolution data sets taken inside the core of a hurricane, and the lack of reliable measurements in extreme conditions near the surface of hurricanes, as possible reasons why even the most state-of-the-art forecasting models cannot seem to forecast intensity changes better. The Hurricane Imaging Radiometer (HIRAD) is a new airborne microwave remote sensor for observing hurricanes, and is operated and researched by NASA Marshall Space Flight Center in partnership with the NOAA Atlantic Oceanographic and Meteorological Laboratory/Hurricane Research Division, the University of Central Florida, the University of Michigan, and the University of Alabama in Huntsville. This instrument?s purpose is to study the wind field of a hurricane, specifically observing surface wind speeds and rain rates, in what has traditionally been the most difficult areas for other instruments to study; the high wind and heavy rain regions. Dr. T. N. Krishnamurti has studied various data assimilation techniques for hurricane and monsoon rain rates, and this study builds off of results obtained from utilizing his style of physical initializations of rainfall observations, but obtaining reliable observations in heavy rain regions has always presented trouble to our research of high-resolution rainfall forecasting. Reliable data from these regions at such a high resolution and wide swath as HIRAD provides is potentially very valuable to mesoscale forecasting of hurricane intensity. This study shows how the data assimilation technique of Ensemble Kalman Filtering (EnKF) in the Weather Research and Forecasting (WRF) model can be used to incorporate wind, and later rain rate, data into a mesoscale model forecast of hurricane intensity. The study makes use of an Observing System Simulation Experiment (OSSE) with a simulated HIRAD dataset sampled during a hurricane and uses EnKF to forecast the track and intensity prediction of the hurricane. Comparisons to truth and error metrics are used to assess the model?s forecast performance.

Albers, Cerese↗
NASA NTRS2019

Solar Activity Modeling: From Subgranular Dynamical Scales to the Solar Cycles

Dynamical effects of solar magnetoconvection span a wide range spatial and temporal scales that extends from the interior to the corona and from fast turbulent motions to the global-Sun magnetic activity. To study the solar activity on short temporal scales (from minutes to hours), we use 3D radiative MHD simulations that allow us to investigate complex turbulent interactions that drive various phenomena, such as plasma eruptions, spontaneous formation of magnetic structures, funnel-like structures and magnetic loops in the corona, and others. In particular, we focus on multi-scale processes of energy exchange across the different layers, which contribute to the corona heating and eruptive dynamics, as well as interlinks between different layers of the solar interior and atmosphere. For modeling the global-scale activity we use the data assimilation approach that has demonstrated great potential for building reliable long-term forecasts of solar activity. In particular, it has been shown that the Ensemble Kalman Filter (EnKF) method applied to the Parker-Kleeorin-Ruzmakin dynamo model is capable of predicting solar activity up to one sunspot cycle ahead in time, as well as estimating the properties of the next cycle a few years before it begins. In this presentation, using the available magnetogram data, we discuss development of the methodology and forecast quality criteria (including forecast uncertainties and sources of errors). We demonstrate the influence of observational limitation on the prediction accuracy. We present the EnKF predictions of the upcoming Solar Cycle 25 based on both the sunspot number series and observed magnetic fields, and discuss the uncertainties and potential of the data assimilation approach for modeling and forecasting the solar activity.

Kitiashvili, I. N.↗
NASA NTRS2019

Solar Activity Modeling: From Subgranular Dynamical Scales to the Solar Cycles

The dynamical effects of solar magnetoconvection span a wide range spatial and temporal scales that extend from the interior to the corona and from fast turbulent motions to global magnetic activity. To study the solar activity on short temporal scales (from minutes to hours), we use 3D radiative MHD simulations that allow us to investigate complex turbulent interactions that drive various phenomena, such as plasma eruptions, spontaneous formation of magnetic structures, funnel-like structures and magnetic loops in the corona, and others. In particular, we focus on multi-scale processes of energy exchange across layers of the solar interior and atmosphere, which contribute to coronal heating and eruptive dynamics. For modeling global-scale activity, we use a data assimilation approach that has demonstrated great potential for building reliable long-term forecasts of solar activity. In particular, it has been shown that the Ensemble Kalman Filter (EnKF) method applied to the Parker-Kleeorin-Ruzmakin dynamo model is capable of predicting solar activity up to one sunspot cycle ahead in time, as well as estimating the properties of the next cycle a few years before it begins. In this presentation, using the available magnetogram data, we discuss development of the methodology and forecast quality criteria (including forecast uncertainties and sources of errors). We demonstrate the influence of observational limitations on prediction accuracy, and we present the EnKF predictions of the upcoming Solar Cycle (25) based on both the sunspot number series and observed magnetic fields and discuss the uncertainties and potential of the data assimilation approach for modeling and forecasting solar activity.

Kitiashvili, I. N.↗
NASA NTRSDate not supplied

Sensing Small Uncrewed Aerial Vehicles with Distributed Radars for Advanced Air Mobility Surveillance

In the context of Advanced and Urban Air Mobility major attention is being reserved to the development of sensing strategies for small Uncrewed Aerial Vehicles (sUAVs) to enable their safe operations in and around urban areas. Such strategies should rely on non-cooperative distributed sensors to strengthen the surveillance solution towards the unreliability of Global Navigation Satellite System (GNSS) positioning information, which is typical for low-altitude-flying platforms in urban regions, and increase the monitored airspace volume. To this aim, this paper proposes a fusion solution for a network of distributed ground-based radars, which can be exploited to not only increase the coverage over large airspace volumes but also improve the overall detectability and traceability of sUAVs by leveraging on multiple views over the same area. The solution exploits a centralized fusion scheme in which measurements collected by each radar are shared with a Fusion Center where Kalman Filtering is exploited to build a unique, fused track. Tests conducted on experimental data collected using two sUAVs as flying targets and three distributed radars showed that the proposed solution can produce an increase in coverage from about 20 % (single radar configuration) to about 80 % of the targets’ flight path, as well as a finer accuracy yielding meter and meter-per-second root mean square error values on position and velocity components.

Federica Vitiello↗
NASA NTRS1972

The interstellar redding law in the ultraviolet deduced from filter photometry obtained by the OAO-2 satellite

Filter photometry has been obtained of 16 BO stars at ten effective wavelengths in the range 4250-1430 A. The wavelength dependence of the interstellar reddening law, deduced from a least squares fit of the observed values to the reddening line at each band, is found in satisfactory agreement with that derived by Bless and Savage (1972). Toward the shorter wavelengths the increase of the computed probable error of the slope of the mean reddening line suggests that large fluctuations in the law may occur from star to star. Similar computations, separating main-sequence stars and supergiants, indicate that the large fluctuations of the law appear to be well related to the luminosity of the stars; the supergiants show systematically less extinction, this deficiency becoming large toward the far UV. The small number in the sample however, does not allow a general conclusion to be drawn.

Laget, M.↗
NASA NTRS1986

Implementation and flight tests for the Digital Integrated Automatic Landing System (DIALS). Part 1: Flight software equations, flight test description and selected flight test data

Five flight tests of the Digital Automated Landing System (DIALS) were conducted on the Advanced Transport Operating Systems (ATOPS) Transportation Research Vehicle (TSRV) -- a modified Boeing 737 aircraft for advanced controls and displays research. These flight tests were conducted at NASA's Wallops Flight Center using the microwave landing system (MLS) installation on runway 22. This report describes the flight software equations of the DIALS which was designed using modern control theory direct-digital design methods and employed a constant gain Kalman filter. Selected flight test performance data is presented for localizer (runway centerline) capture and track at various intercept angles, for glideslope capture and track of 3, 4.5, and 5 degree glideslopes, for the decrab maneuver, and for the flare maneuver. Data is also presented to illustrate the system performance in the presence of cross, gust, and shear winds. The mean and standard deviation of the peak position errors for localizer capture were, respectively, 24 feet and 26 feet. For mild wind conditions, glideslope and localizer tracking position errors did not exceed, respectively, 5 and 20 feet. For gusty wind conditions (8 to 10 knots), these errors were, respectively, 10 and 30 feet. Ten hands off automatic lands were performed. The standard deviation of the touchdown position and velocity errors from the mean values were, respectively, 244 feet and 0.7 feet/sec.

Hueschen, R. M.↗
NASA NTRS1993

Optimal gains for a single polar orbiting satellite

Gains are the spatial weighting of an observation in its neighborhood versus the local values of a model prediction. They are the key to data assimilation, as they are the direct measure of how the data are used to guide the model. As derived in the broad context of data assimilation by Kalman and in the context of meteorology, for example, by Rutherford, the optimal gains are functions of the prediction error covariances between the observation and analysis points. Kalman introduced a very powerful technique that allows one to calculate these optimal gains at the time of each observation. Unfortunately, this technique is both computationally expensive and often numerically unstable for dynamical systems of the magnitude of meteorological models, and thus is unsuited for use in PMIRR data assimilation. However, the optimal gains as calculated by a Kalman filter do reach a steady state for regular observing patterns like that of a satellite. In this steady state, the gains are constants in time, and thus could conceivably be computed off-line. These steady-state Kalman gains (i.e., Wiener gains) would yield optimal performance without the computational burden of true Kalman filtering. We proposed to use this type of constant-in-time Wiener gain for the assimilation of data from PMIRR and Mars Observer.

Banfield, Don↗
DOE OSTI2025

Lanczos Algorithm, the Transfer Matrix, and the Signal-to-Noise Problem

This Letter introduces a method for determining the energy spectrum of lattice quantum chromodynamics by applying the Lanczos algorithm to the transfer matrix and using a bootstrap generalization of the Cullum-Willoughby method to filter out spurious eigenvalues. Proof-of-principle analyses of the simple harmonic oscillator and the lattice quantum chromodynamics proton mass demonstrate that this method provides faster ground-state convergence than the “effective mass,” which is related to the power-iteration algorithm. Lanczos provides more accurate energy estimates than multistate fits to correlation functions with small imaginary times while achieving comparable statistical precision. Two-sided error bounds are computed for Lanczos results and guarantee that excited-state effects cannot shift Lanczos results far outside their statistical uncertainties.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1975

Measurement updating using the U-D factorization

A new mechanization of the Kalman updating algorithm based on a U-D factorization of the estimate error covariance is introduced. Efficient and stable updating recursions are developed for the unit upper triangular factor U and the diagonal factor D, treating only the parameter estimation problem. Properties of the factorization update performed here include efficient one point at a time processing that requires little more computation than the optimal but numerically unstable conventional Kalman measurement update algorithm, and stability that compares with the square root filter.

Bierman, G. J.↗
NASA NTRS1981

Fiber optic transmission line stabilization apparatus and method

A reference signal of RF frequency modulates a 0.85 micrometer wavelength optical transmitter. The output of which passes through a first optical filter and a voltage-controller phase shifter. The output of the phase shifter is provided to the fiber optic transmission line. At the receiving end of the transmission line, the signal is demodulated and used to modulate a 1.06 micrometer optical transmitter. The signal from the transmitter is provided to the fiber optic transmission line and passes through the voltage-controlled phase shifter to a phase error detector. The phase of the modulation of the 1.06 micrometer wavelength signal is compared to the phase of the reference signal by the phase error detector. A phase control signal related to the phase difference is provided to the voltage controlled phase shifter which alters the phase of both optical signals until a predetermined phase relationship between modulation on the 1.06 micrometer signal and the reference signal is obtained.

Lutes, G. F., Jr.↗
NASA NTRS1984

Satellite tracking and Earth dynamics research programs

Following an upgrading program, ranging performance capabilities of a satellite-tracking pulsed laser system were assessed in terms of range accuracy, range noise, data yield, and reliability. With a shorter laser pulse duration (2.5 to 3.0 NSEC) and a new analog pulse processing system, the systematic range errors were reduced to 3 to 5 cm and range noise was reduced to 5 to 16 cm and range noise was reduced to 5 to 15 cm on Starlette and BE-C, and 10 to 18 cm on LAGEOS. Maximum pulse repetition rate was increased to 30 pulses per minute and significant improvement was made in signal to noise ratio by installing a 3 A interference filter and by reducing the range gate window to 200 to 400 nsec. The solution to a problem involving leakage of a fraction of the laser oscillator pulse through the pulse chopper was outlined.

Pearlman, M. R.↗
NASA NTRS1983

Initialization of mesoscale models - The possible impact of remotely sensed data

Little or no improvement appears to have been achieved in mesoscale numerical prediction. This has been blamed on inherent unpredictability, insufficient spatial resolution, inadequate parameterization of important physical processes, and errors in the numerical discretization of the governing hydrodynamic equations. Attention is presently given to the more fundamental difficulty of forecasting when present conditions are not known with sufficient accuracy, and to the matching of remotely sensed sounding-capability satellite and all-weather Doppler radar data with mesoscale numerical models. This matching is not straightforward; simple and variational forms of four-dimensional assimilation, and Kalman filtering, are suggested approaches but their practical testing remains to be accomplished. The most severe problem faced is that of the temperature and humidity profiles' vertical resolution.

Gal-Chen, T.↗
NASA NTRS1989

Shuttle relative navigation of a tethered satellite mission with current on board software

A Shuttle mission planned in 1991 will test the feasibility of tethers in space. This mission, a joint effort between Italy and the United States, will connect a satellite (built by the Italians) to the Shuttle with a 20 km long tether. This mission poses unique navigation problems. The flight software on the Shuttle was never designed to account for the low level acceleration that is generated by the gravity gradient. IMUs on the Shuttle was never designed to account for the low level acceleration that is generated by the gravity gradient. Inertial Maneuvering Units on the shuttle will sense the acceleration of the tether but it turns out that incorporating the continuous accelerometer noise also generates large error growth. Relative navigation is another important issue since the majority of the mission will be conducted while the satellite is out of the visual range of the crew. Some kind of feedback on the motion of the satellite will be desirable. Feedback of the satellite motion can be generated by using the rendezvous radar. To process the radar measurements, the flight software uses a 13 state Kalman Filter, but unfortunately with the filter currently tuned as it is, valid measurements tend to be ignored. This is due to the constraint of the tether on the satellite, which is an unmodeled force. Analysis shows that with proper tuning, relative navigation is possible.

Lee, Kevin A.↗