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NASA NTRS1991

The in-flight calibration of the Hubble Space Telescope attitude sensors

A detailed review of the in-flight calibration of the Hubble Space Telescope attitude sensors is presented. The review, which covers the period from the April 24, 1990, launch of the spacecraft until the time of this writing (June 1991), describes the calibrations required and accuracies achieved for the four principal attitude sensing systems on the spacecraft: the magnetometers, the fixed head star trackers, the gyroscopes, and the fine guidance sensors (FGS's). In contrast to the other three sensor groups, the Hubble Telecope's FGS's are unique in the precision and performance levels being attempted; spacecraft control and astrometric research at the near-milliarcsecond level are the ultimate goals. FGS calibration accuracies at the 20-milliarcsecond level have already been achieved, and plans for new data acquisitions and reductions that should substantially improve these results are in progress. A summary of the basic attributes of each of the four sensor groups with respect to its usage as an attitude measuring system is presented, followed by a discussion of the calibration items of interest for that group. The calibration items are as follows: for the magnetometers, the corrections for the spacecraft's static and time-varying magnetic fields; for the fixed-head star trackers, their relative alignments and use in performing onboard attitude updates; for the gyroscopes, their scale factors, alignments, and drift rate biases; and for the FGS's, their magnifications, optical distortions, and alignments. The discussion covers the procedures used for each calibration, as well as the order of the calibrations within the general flow of orbital verification activities. It also includes a synopsis of current plans for the eventual calibration of the FGS's to achieve their near-milliarcsecond design accuracy. The conclusions include a table indicating the current and predicted ultimate accuracies for each of the calibration items.

Welter, Gary L.↗

NASA NTRS1983

A rotationally biased upwind difference scheme for the Euler equations

The upwind difference schemes of Godunov, Osher, Roe and van Leer are able to resolve one dimensional steady shocks for the Euler equations within one or two mesh intervals. Unfortunately, this resolution is lost in two dimensions when the shock crosses the computing grid at an oblique angle. To correct this problem, a numerical scheme was developed which automatically locates the angle at which a shock might be expected to cross the computing grid and then constructs separate finite difference formulas for the flux components normal and tangential to this direction. Numerical results which illustrate the ability of this method to resolve steady oblique shocks are presented.

Davis, S. F.↗

NASA NTRS1984

A rotationally biased upwind difference scheme for the Euler equations

Davis, S. F.↗

NASA NTRS1974

Surface-state spectra from thick-oxide MOS tunnel junctions

The conductance and capacitance of thick-oxide MOS tunnel junctions (SiO2 thickness 40 to 65 A) have been measured from 35 Hz to 210 kHz. It is demonstrated that the use of a thick-oxide MOS tunnel junction makes it possible to obtain the surface-state data throughout the whole silicon band gap with better resolution and better sensitivity than the conventional MOS capacitance techniques. A slight departure from equilibrium may occur in the voltage range where large tunnel current flows. Corrections to the energy scale must be made in this voltage range. A method for the evaluation of the junction quality is discussed. The simplified equivalent circuits necessary for the calculation of surface-state data are constructed under various bias conditions by an approach different from that used in a previously published work. The present work supports the model that at least some of the observed surface states are a consequence of the diffusion of contact metals into the oxide.

Ma, T. P.↗

NASA NTRS1992

Viscous flow past a nacelle isolated and in proximity of a flat plate

Linearized-theory design procedures have proven to be useful in preliminary design stages of supersonic aircraft configurations. These procedures are impaired, however, by their inability to account for certain nonlinear effects inherent in complicated flows. The present computations are aimed at providing necessary information for correction and improvement of a particular linearized design method. Three-dimensional, viscous, supersonic flows past nacelle and nacelle-flat plate configurations are investigated. The thin-layer Navier-Stokes equations are solved using an implicit, upwind-biased, finite-volume method. A hybrid domain decomposition technique is utilized to ease the grid generation task. Computations were made for an unit Reynolds number of 2.0 million per foot and Freestream Mach numbers of 1.6, 2.0, and 2.3.

Fouladi, Kamran↗

NASA NTRS1992

Calculation of area-averaged vertical profiles of the horizontal wind velocity from volume-imaging lidar data

Area-averaged horizontal wind measurements are derived from the motion of spatial inhomogeneities in aerosol backscattering observed with a volume-imaging lidar. Spatial averaging provides high precision, reducing sample variations of wind measurements well below the level of turbulent fluctuations, even under conditions of very light mean winds and strong convection or under the difficult conditions represented by roll convection. Wind velocities are measured using the two-dimensional spatial cross correlation computed between successive horizontal plane maps of aerosol backscattering, assembled from three-dimensional lidar scans. Prior to calculation of the correlation function, three crucial steps are performed: (1) the scans are corrected for image distortion by the wind during a finite scan time; (2) a temporal high pass median filtering is applied to eliminate structures that do not move with the wind; and (3) a histogram equalization is employed to reduce biases to the brightest features.

Schols, J. L.↗

NASA NTRS2021

Validation of Satellite-Based Precipitation Products from TRMM to GPM

The global precipitation measurement mission (GPM) has been in operation for sevenyears and continues to provide a vast quantity of global precipitation data at finer temporospatialresolutions with improved accuracy and coverage. GPM’s signature algorithm, the integratedmultisatellite retrievals for GPM (IMERG) is a next-generation of precipitation product expectedfor wide variety of research and operational applications. This study evaluates the latest version(V06B) of IMERG and its predecessor, the tropical rainfall measuring mission (TRMM) multisatelliteprecipitation (TMPA) 3B42 (V7) using ground-based and gauge-corrected multiradar multisensorsystem (MRMS) precipitation products over the conterminous United States (CONUS). The spatialdistributions of all products are analyzed. The error characteristics are further examined for 3B42 andIMERG in winter and summer by an error decomposition approach, which partitions total bias intohit bias, biases due to missed precipitation and false precipitation. The volumetric and categoricalstatistical metrics are used to quantitatively evaluate the performance of the two satellite-basedproducts. All products show a similar precipitation climatology with some regional differences.The two satellite-based products perform better in the eastern CONUS than in the mountainousWestern CONUS. The evaluation demonstrates the clear improvement in IMERG precipitationproduct in comparison with its predecessor 3B42, especially in reducing missed precipitation inwinter and summer, and hit bias in winter, resulting in better performance in capturing lighter andheavier precipitation.

Jianxin Wang↗

NASA NTRS2011

Reconciling Satellite-Derived Atmospheric Properties with Fine-Resolution Land Imagery: Insights for Atmospheric Correction

This paper investigates to what extent satellite measurements of atmospheric properties can be reconciled with fine-resolution land imagery, in order to improve the estimates of surface reflectance through physically based atmospheric correction. The analysis deals with mountainous area (Landsat scene of Peruvian Amazon/Andes, 72 E and 13 S), where the atmosphere is highly variable. Data from satellite sensors were used for characterization of the key atmospheric constituents: total water vapor (TWV), aerosol optical depth (AOD), and total ozone. Constituent time series revealed the season-dependent mean state of the atmosphere and its variability. Discrepancies between AOD from the Advanced Along-Track Scanning Radiometer (AATSR) and Moderate Resolution Imaging Spectroradiometer (MODIS) highlighted substantial uncertainty of atmospheric aerosol properties. The distribution of TWV and AOD over a Landsat scene was found to be exponentially related to ground elevation (mean R(sup 2) of 0.82 and 0.29, respectively). In consequence, the atmosphere-induced and seasonally varying bias of the top-of-atmosphere signal was also elevation dependent (e.g., mean Normalized Difference Vegetation Index bias at 500 m was 0.06 and at 4000 m was 0.01). We demonstrate that satellite measurements of key atmospheric constituents can be downscaled and gap filled with the proposed "background + anomalies" approach, to allow for a better compatibility with fine-resolution land surface imagery. Older images (i.e., predating the MODIS/ATSR era), without coincident atmospheric data, can be corrected using climatologies derived from time series of satellite retrievals. Averaging such climatologies over space compromises the quality of correction result to a much greater degree than averaging them over time. We conclude that the quality of both recent and older fine-resolution land surface imagery can be improved with satellite-based atmospheric data acquired to date.

Zelazowski, Przemyslaw↗

NASA NTRS2023

Validity of the Landsat Surface Reflectance Archive for Aquatic Science: Implications for Cloud-Based Analysis

Originally developed for terrestrial science and applications, the US Geological Survey Landsat surface reflectance (SR) archive spanning ~ 40 yr of observations has been increasingly utilized in large‐scale water‐quality studies. These products, however, have not been rigorously validated using in situ measured reflectance. This letter quantifies and demonstrates the quality of the SR products by harnessing a sizeable global dataset ( N = 1100). We found that the Landsat 8/9 SR in the green and red bands marginally meet the targeted accuracy requirements (30%), whereas the uncertainties in the blue and coastal‐aerosol bands ranged from 48% to 110%. We further observed > +25% biases in the visible bands of Landsat 5/7 SR, which can introduce an apparent downward trend when applied in time‐series analyses combined with Landsat 8/9. Users must exercise caution when using this archive for trend analyses, and progress in atmospheric correction is required to foster advanced applications of the Landsat archive for aquatic science.

Landsat, lakes, harmful algal blooms↗

NASA NTRS2011

Uncertainty Assessment of the SeaWiFS On-Orbit Calibration

Ocean color climate data records require water-leaving radiances with 5% absolute and 1% relative accuracies as input. Because of the amplification of any sensor calibration errors by the atmospheric correction, the 1% relative accuracy requirement translates into a 0.1% long-term radiometric stability requirement for top-of-the atmosphere radiances. The rigorous on-orbit calibration program developed and implemented for SeaWiFS by the NASA Ocean Biology Processing Group (OBPG) Calibration and Validation Team (CVT) has allowed the CVT to maintain the stability of the radiometric calibration of SeaWiFS at 0.13% or better over the mission. The uncertainties in the resulting calibrated top-of-the-atmosphere (TOA) radiances can be addressed in terms of accuracy (biases in the measurements), precision (scatter in the measurements), and stability (repeatability of the measurements). The calibration biases of lunar observations relative to the USGS RObotic Lunar Observatory (ROLO) photometric model of the Moon are 2-3%. The biases from the vicarious calibration against the Marine Optical Buoy (MOBY) are 1-2%. The precision of the calibration derived from the solar calibration signal-tonoise ratios are 0.16%, from the lunar residuals are 0.13%, and from the vicarious gains are 0.10%. The long-term stability of the TOA radiances, derived from the lunar time series, is 0.13%. The stability of the vicariouslycalibrated TOA radiances, incorporating the uncertainties in the MOBY measurements and the atmospheric correction, is 0.30%. These results allow the OBPG to produce climate data records from the SeaWiFS ocean color data.

Eplee, Robert E., Jr.↗

NASA NTRS2024

Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM2.5 Monitoring in Accra, Ghana

Particulate matter air pollution is a leading cause of global mortality, particularly in Asia and Africa. Addressing the high and wide-ranging air pollution levels requires ambient monitoring, but many low- and middle-income countries (LMICs) remain scarcely monitored. To address these data gaps, recent studies have utilized low-cost sensors. These sensors have varied performance, and little literature exists about sensor intercomparison in Africa. By colocating 2 QuantAQ Modulair-PM, 2 PurpleAir PA-II SD, and 16 Clarity Node-S Generation II monitors with a reference-grade Teledyne monitor in Accra, Ghana, we present the first intercomparisons of different brands of low-cost sensors in Africa, demonstrating that each type of low-cost sensor PM2.5 is strongly correlated with reference PM2.5, but biased high for ambient mixture of sources found in Accra. When compared to a reference monitor, the QuantAQ Modulair-PM has the lowest mean absolute error at 3.04 μg/m3, followed by PurpleAir PA-II (4.54 μg/m3) and Clarity Node-S (13.68 μg/m3). We also compare the usage of 4 statistical or machine learning models (Multiple Linear Regression, Random Forest, Gaussian Mixture Regression, and XGBoost) to correct low-cost sensors data, and find that XGBoost performs the best in testing (R2: 0.97, 0.94, 0.96; mean absolute error: 0.56, 0.80, and 0.68 μg/m3 for PurpleAir PA-II, Clarity Node-S, and Modulair-PM, respectively), but tree-based models do not perform well when correcting data outside the range of the colocation training. Therefore, we used Gaussian Mixture Regression to correct data from the network of 17 Clarity Node-S monitors deployed around Accra, Ghana, from 2018 to 2021. We find that the network daily average PM2.5 concentration in Accra is 23.4 μg/m3, which is 1.6 times the World Health Organization Daily PM2.5 guideline of 15 μg/m3. While this level is lower than those seen in some larger African cities (such as Kinshasa, Democratic Republic of the Congo), mitigation strategies should be developed soon to prevent further impairment to air quality as Accra, and Ghana as a whole, rapidly grow.

Humidity↗

NASA NTRS1988

DSS 14 antenna calibrations for GSSR/VLA Saturn radar experiments

The DSS 14 pointing and gain were calibrated to support X-band bistatic radar observations of Saturn's rings. The observations used the Goldstone Solar System Radar and the National Radio Astronomy Observatory's Very Large Array (VLA) in Socorro, New Mexico. The pointing calibrations were based on conscan offset data collected during Voyager 1 and 2 support passes. The conscan data show angle-of-arrival sensing with no bias and 0.3 mdeg 1-sigma error. Using the calibrations, demonstrated blind pointing performance on Saturn was less than 3 mdeg 1-sigma error. Meteorological observations at the site were used to reduce elevation errors caused by atmospheric refraction. The techniques used corrected about one-third of the error-poorer than expected performance.

Guiar, C. N.↗

NASA NTRS1997

Tests of a Semi-Analytical Case 1 and Gelbstoff Case 2 SeaWiFS Algorithm with a Global Data Set

A semi-analytical algorithm was tested with a total of 733 points of either unpackaged or packaged-pigment data, with corresponding algorithm parameters for each data type. The 'unpackaged' type consisted of data sets that were generally consistent with the Case 1 CZCS algorithm and other well calibrated data sets. The 'packaged' type consisted of data sets apparently containing somewhat more packaged pigments, requiring modification of the absorption parameters of the model consistent with the CalCOFI study area. This resulted in two equally divided data sets. A more thorough scrutiny of these and other data sets using a semianalytical model requires improved knowledge of the phytoplankton and gelbstoff of the specific environment studied. Since the semi-analytical algorithm is dependent upon 4 spectral channels including the 412 nm channel, while most other algorithms are not, a means of testing data sets for consistency was sought. A numerical filter was developed to classify data sets into the above classes. The filter uses reflectance ratios, which can be determined from space. The sensitivity of such numerical filters to measurement resulting from atmospheric correction and sensor noise errors requires further study. The semi-analytical algorithm performed superbly on each of the data sets after classification, resulting in RMS1 errors of 0.107 and 0.121, respectively, for the unpackaged and packaged data-set classes, with little bias and slopes near 1.0. In combination, the RMS1 performance was 0.114. While these numbers appear rather sterling, one must bear in mind what mis-classification does to the results. Using an average or compromise parameterization on the modified global data set yielded an RMS1 error of 0.171, while using the unpackaged parameterization on the global evaluation data set yielded an RMS1 error of 0.284. So, without classification, the algorithm performs better globally using the average parameters than it does using the unpackaged parameters. Finally, the effects of even more extreme pigment packaging must be examined in order to improve algorithm performance at high latitudes. Note, however, that the North Sea and Mississippi River plume studies contributed data to the packaged and unpackaged classess, respectively, with little effect on algorithm performance. This suggests that gelbstoff-rich Case 2 waters do not seriously degrade performance of the semi-analytical algorithm.

Carder, Kendall L.↗

NASA NTRS2010

Validation of TRMM Precipitation Radar Through Comparison of its Multi-Year Measurements to Ground-Based Radar

A procedure to accurately resample spaceborne and ground-based radar data is described, and then applied to the measurements taken from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and the ground-based Weather Surveillance Radar-1988 Doppler (WSR-88D or WSR) for the validation of the PR measurements and estimates. Through comparisons with the well-calibrated, non-attenuated WSR at Melbourne, Florida for the period 1998-2007, the calibration of the Precipitation Radar (PR) aboard the TRMM satellite is checked using measurements near the storm top. Analysis of the results indicates that the PR, after taking into account differences in radar reflectivity factors between the PR and WSR, has a small positive bias of 0.8 dB relative to the WSR, implying a soundness of the PR calibration in view of the uncertainties involved in the comparisons. Comparisons between the PR and WSR reflectivities are also made near the surface for evaluation of the attenuation-correction procedures used in the PR algorithms. It is found that the PR attenuation is accurately corrected in stratiform rain but is underestimated in convective rain, particularly in heavy rain. Tests of the PR estimates of rainfall rate are conducted through comparisons in the overlap area between the TRMM overpass and WSR scan. Analyses of the data are made both on a conditional basis, in which the instantaneous rain rates are compared only at those pixels where both the PR and WSR detect rain, and an unconditional basis, in which the area-averaged rain rates are estimated independently for the PR and WSR. Results of the conditional rain comparisons show that the PR-derived rain is about 9% greater and 19% less than the WSR estimates for stratiform and convective storms, respectively. Overall, the PR tends to underestimate the conditional mean rain rate by 8% for all rain categories, a finding that conforms to the results of the area-averaged rain (unconditional) comparisons.

Liao, Liang↗

NASA NTRS2012

A Full-Envelope Air Data Calibration and Three-Dimensional Wind Estimation Method Using Global Output-Error Optimization and Flight-Test Techniques

A novel, efficient air data calibration method is proposed for aircraft with limited envelopes. This method uses output-error optimization on three-dimensional inertial velocities to estimate calibration and wind parameters. Calibration parameters are based on assumed calibration models for static pressure, angle of attack, and flank angle. Estimated wind parameters are the north, east, and down components. The only assumptions needed for this method are that the inertial velocities and Euler angles are accurate, the calibration models are correct, and that the steady-state component of wind is constant throughout the maneuver. A two-minute maneuver was designed to excite the aircraft over the range of air data calibration parameters and de-correlate the angle-of-attack bias from the vertical component of wind. Simulation of the X-48B (The Boeing Company, Chicago, Illinois) aircraft was used to validate the method, ultimately using data derived from wind-tunnel testing to simulate the un-calibrated air data measurements. Results from the simulation were accurate and robust to turbulence levels comparable to those observed in flight. Future experiments are planned to evaluate the proposed air data calibration in a flight environment.

Taylor, Brian R.↗

NASA NTRS2015

CORRIGENDUM of the MJO Transition from Shallow to Deep Convection in Cloudsat-Calipso Data and GISS GCM Simulations

We have identified several errors in the calculations that were performed to create Fig. 3 of Del Genio et al. (2012). These errors affect the composite evolution of precipitation and column water vapor versus lag relative to the Madden-Julian oscillation (MJO) peak presented in that figure. The precipitation and column water vapor data for the April and November 2009 MJO events were composited incorrectly because the date of the MJO peak at a given longitude was assigned to the incorrect longitude band. In addition, the precipitation data for all MJO events were first accumulated daily and the daily accumulations averaged at each lag to create the composite, rather than the averaging of instantaneous values that was used for other composite figures in the paper. One poorly sampled day in the west Pacific therefore biases the composite precipitation in that region at several lags after the MJO peak. Finally, a 4-day running mean was mistakenly applied to the precipitation and column water vapor data rather than the intended 5-day running mean. The results of the corrections are that an anomalous west Pacific precipitation maximum510 days after the MJO peak is removed and the maximum in west Pacific precipitation one pentad before the MJO peak is now more evident; there is now a clear maximum in precipitation for the entire warm pool one pentad before the MJO peak; west Pacific column water vapor now varies more strongly as a function of lag relative to the peak; and precipitation, and to a lesser extent column water vapor, in general vary more smoothly with time. The corrections do not affect any other parts of the paper nor do they change the scientific conclusions we reached. The 4-day running mean error also affects Figs. 1 and 2 therein, with almost imperceptible impacts that do not affect any results or necessitate major changes to the text.

correction↗

NASA NTRS2021

Validation of Satellite-Based Precipitation Products from TRMM to GPM

The global precipitation measurement mission (GPM) has been in operation for seven years and continues to provide a vast quantity of global precipitation data at finer temporospatial resolutions with improved accuracy and coverage. GPM’s signature algorithm, the integrated multisatellite retrievals for GPM (IMERG) is a next-generation of precipitation product expected for wide variety of research and operational applications. This study evaluates the latest version (V06B) of IMERG and its predecessor, the tropical rainfall measuring mission (TRMM) multisatellite precipitation (TMPA) 3B42 (V7) using ground-based and gauge-corrected multiradar multisensor system (MRMS) precipitation products over the conterminous United States (CONUS). The spatial distributions of all products are analyzed. The error characteristics are further examined for 3B42 and IMERG in winter and summer by an error decomposition approach, which partitions total bias into hit bias, biases due to missed precipitation and false precipitation. The volumetric and categorical statistical metrics are used to quantitatively evaluate the performance of the two satellite-based products. All products show a similar precipitation climatology with some regional differences. The two satellite-based products perform better in the eastern CONUS than in the mountainous Western CONUS. The evaluation demonstrates the clear improvement in IMERG precipitation product in comparison with its predecessor 3B42, especially in reducing missed precipitation in winter and summer, and hit bias in winter, resulting in better performance in capturing lighter and heavier precipitation.

MRMS↗

NASA NTRS2012

The Aquarius Salinity Retrieval Algorithm: Early Results

The Aquarius L-band radiometer/scatterometer system is designed to provide monthly salinity maps at 150 km spatial scale to a 0.2 psu accuracy. The sensor was launched on June 10, 2011, aboard the Argentine CONAE SAC-D spacecraft. The L-band radiometers and the scatterometer have been taking science data observations since August 25, 2011. The first part of this presentation gives an overview over the Aquarius salinity retrieval algorithm. The instrument calibration converts Aquarius radiometer counts into antenna temperatures (TA). The salinity retrieval algorithm converts those TA into brightness temperatures (TB) at a flat ocean surface. As a first step, contributions arising from the intrusion of solar, lunar and galactic radiation are subtracted. The antenna pattern correction (APC) removes the effects of cross-polarization contamination and spillover. The Aquarius radiometer measures the 3rd Stokes parameter in addition to vertical (v) and horizontal (h) polarizations, which allows for an easy removal of ionospheric Faraday rotation. The atmospheric absorption at L-band is almost entirely due to O2, which can be calculated based on auxiliary input fields from numerical weather prediction models and then successively removed from the TB. The final step in the TA to TB conversion is the correction for the roughness of the sea surface due to wind. This is based on the radar backscatter measurements by the scatterometer. The TB of the flat ocean surface can now be matched to a salinity value using a surface emission model that is based on a model for the dielectric constant of sea water and an auxiliary field for the sea surface temperature. In the current processing (as of writing this abstract) only v-pol TB are used for this last process and NCEP winds are used for the roughness correction. Before the salinity algorithm can be operationally implemented and its accuracy assessed by comparing versus in situ measurements, an extensive calibration and validation (cal/val) activity needs to be completed. This is necessary in order to tune the inputs to the algorithm and remove biases that arise due to the instrument calibration, foremost the values of the noise diode injection temperatures and the losses that occur in the feedhorns. This is the subject of the second part of our presentation. The basic tool is to analyze the observed difference between the Aquarius measured TA and an expected TA that is computed from a reference salinity field. It is also necessary to derive a relation between the scatterometer backscatter measurements and the radiometer emissivity that is induced by surface winds. In order to do this we collocate Aquarius radiometer and scatterometer measurements with wind speed retrievals from the WindSat and SSMIS F17 microwave radiometers. Both of these satellites fly in orbits that have the same equatorial ascending crossing time (6 pm) as the Aquarius/SAC-D observatory. Rain retrievals from WindSat and SSMIS F 17 can be used to remove Aquarius observations that are rain contaminated. A byproduct of this analysis is a prediction for the wind-induced sea surface emissivity at L-band.

Meissner, Thomas↗