Study of Space Cabin Atmospheres Status Report, 1 Jan. - 30 Jun. 1966
Performance characteristics of aerosol generator, and distribution sampling errors of electrostatic precipitator sampling device for space cabin atmosphere
AEROSOL↗
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Results for “Sampling error”
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.
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Distributionally Safe Path Planning: Wasserstein Safe RRT
In this paper, we propose a Wasserstein metric-based random path planning algorithm. Wasserstein Safe RRT (W-Safe RRT) provides finite-sample probabilistic guarantees on the safety of a returned path in an uncertain obstacle environment. Vehicle and obstacle states are modeled as distributions based upon state and model observations. Additionally, we define limits on distributional sampling error so the Wasserstein distance between a vehicle state distribution and obstacle distributions can be bounded. This enables the algorithm to return safe paths with a confidence bound through combining finite sampling error bounds with calculations of the Wasserstein distance between discrete distributions. W-Safe RRT is compared against a baseline minimum encompassing ball algorithm, which ensures balls that minimally encompass discrete state and obstacle distributions do not overlap. The improved performance is verified in a 3D environment using single, multi, and rotating non-convex obstacle cases, with and without forced obstacle error in adversarial directions, showing that W-Safe RRT can handle poorly modeled complex environments.
42 ENGINEERING↗
A Stochastic Covariance Shrinkage Approach in Ensemble Transform Kalman Filtering
The Ensemble Kalman Filters (EnKF) employ a Monte-Carlo approach to represent covariance information, and are affected by sampling errors in operational settings where the number of model realizations is much smaller than the model state dimension. To alleviate the effects of these errors EnKF relies on model-specific heuristics such as covariance localization, which takes advantage of the spatial locality of correlations among the model variables. This work proposes an approach to alleviate sampling errors that utilizes a locally averaged-in-time dynamics of the model, described in terms of a climatological covariance of the dynamical system. We use this covariance as the target matrix in covariance shrinkage methods, and develop a stochastic covariance shrinkage approach where synthetic ensemble members are drawn to enrich both the ensemble subspace and the ensemble transformation. We additionally provide for a way in which this methodology can be localized similar to the state-of-the-art LETKF method, and that for a certain model setup, our methodology significantly outperforms it.
54 ENVIRONMENTAL SCIENCES↗
Absorption of Solar Radiation by the Cloudy Atmosphere: Further Interpretations of Collocated Aircraft Measurements
We have extended the interpretations made in two prior studies of the aircraft shortwave radiation measurements that were obtained as part of the Atmospheric Radiation Measurements (ARM) Enhanced Shortwave Experiments (ARESE). These extended interpretations use the 500 nm (10 nm bandwidth) measurements to minimize sampling errors in the broadband measurements. It is indicated that the clouds present during this experiment absorb more shortwave radiation than predicted by clear skies and thus by theoretical models, that at least some (less than or equal to 20%) of this enhanced cloud absorption occurs at wavelengths less than 680 nm, and that the observed cloud absorption does not appear to be an artifact of sampling errors nor of instrument calibration errors.
Cess, R. D.↗
Analysis of Three Types of Collocated Disdrometer Measurements at the ARM Southern Great Plains Observatory
To better provide U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility’s disdrometer deployment strategy and support ARM precipitation-related projects, this study analyzes 18 months of drop size distribution (DSD) observations from six collocated disdrometers deployed at the ARM Southern Great Plains (SGP) site. Emphasis is placed on quantifying the uncertainties related to DSD and rainfall properties using different types of disdrometers and different pairing concepts. The instrument sampling errors are also discussed, which improves our understanding of instrument-specific impacts on rainfall measurements. The key findings are as follows: 1) All the disdrometers show consistent behaviors overall regarding accumulated precipitation, mean rainfall rate, DSD parameters, and radar reflectivity values; and 2) Strong agreement is shown between three disdrometer types in terms of their DSDs for mid-size drop range (D = 1 mm-4 mm). The two-dimensional video disdrometer performance meets expectations as the most accurate unit among all. The paired disdrometer shows a reduced statistical sampling error in terms of drop counts and other DSD properties. A concept of deploying two or more collocated disdrometers side by side is recommended for future ARM field campaigns.
54 ENVIRONMENTAL SCIENCES↗
Enabling phase quantification of anhydrous cements via Raman imaging
The phase composition of Portland cements is typically determined using conventional techniques like X-ray Diffraction (XRD) Rietveld analysis, optical microscopy point counting, and electron microscopy. However, these techniques have several limitations that may affect their accuracy in certain sample-specific scenarios. Here, we report a highly accurate phase quantification of 11 different types of commercial, anhydrous cements using a new and complementary technique: Raman imaging. Specifically, for the 4 principal phases, composition from our extensive data (250,000 Raman spectra per sample, error < 0.71%) and those obtained from XRD Rietveld and supplier data have high coefficients of determination (R{sup 2} > 0.98, mean deviation <2%). Additionally, we also quantify 8 secondary phases present in cement clinkers (gypsum, anhydrite, bassanite, syngenite, dolomite, calcite, quartz, and portlandite) with a high degree of confidence, thereby demonstrating that Raman imaging is a highly versatile tool for anhydrous phase quantification in a broad variety of cements.
36 MATERIALS SCIENCE↗
Unbiased estimation of oceanic mean rainfall from satellite borne radiometer measurements
The statistical properties of the radar derived rainfall obtained during the GARP Atlantic Tropical Experiment (GATE) are used to derive quantitative estimates of the spatial and temporal sampling errors associated with estimating rainfall from brightness temperature measurements such as would be obtained from a satelliteborne microwave radiometer employing a practical size antenna aperture. A basis for a method of correcting the so called beam filling problem, i.e., for the effect of nonuniformity of rainfall over the radiometer beamwidth is provided. The method presented employs the statistical properties of the observations themselves without need for physical assumptions beyond those associated with the radiative transfer model. The simulation results presented offer a validation of the estimated accuracy that can be achieved and the graphs included permit evaluation of the effect of the antenna resolution on both the temporal and spatial sampling errors.
Mittal, M. C.↗
Satellite Sampling and Retrieval Errors in Regional Monthly Rain Estimates from TMI AMSR-E, SSM/I, AMSU-B and the TRMM PR
Passive and active microwave rain sensors onboard earth-orbiting satellites estimate monthly rainfall from the instantaneous rain statistics collected during satellite overpasses. It is well known that climate-scale rain estimates from meteorological satellites incur sampling errors resulting from the process of discrete temporal sampling and statistical averaging. Sampling and retrieval errors ultimately become entangled in the estimation of the mean monthly rain rate. The sampling component of the error budget effectively introduces statistical noise into climate-scale rain estimates that obscure the error component associated with the instantaneous rain retrieval. Estimating the accuracy of the retrievals on monthly scales therefore necessitates a decomposition of the total error budget into sampling and retrieval error quantities. This paper presents results from a statistical evaluation of the sampling and retrieval errors for five different space-borne rain sensors on board nine orbiting satellites. Using an error decomposition methodology developed by one of the authors, sampling and retrieval errors were estimated at 0.25 resolution within 150 km of ground-based weather radars located at Kwajalein, Marshall Islands and Melbourne, Florida. Error and bias statistics were calculated according to the land, ocean and coast classifications of the surface terrain mask developed for the Goddard Profiling (GPROF) rain algorithm. Variations in the comparative error statistics are attributed to various factors related to differences in the swath geometry of each rain sensor, the orbital and instrument characteristics of the satellite and the regional climatology. The most significant result from this study found that each of the satellites incurred negative longterm oceanic retrieval biases of 10 to 30%.
Fisher, Brad↗
Method for Pre-Conditioning a Measured Surface Height Map for Model Validation
This software allows one to up-sample or down-sample a measured surface map for model validation, not only without introducing any re-sampling errors, but also eliminating the existing measurement noise and measurement errors. Because the re-sampling of a surface map is accomplished based on the analytical expressions of Zernike-polynomials and a power spectral density model, such re-sampling does not introduce any aliasing and interpolation errors as is done by the conventional interpolation and FFT-based (fast-Fourier-transform-based) spatial-filtering method. Also, this new method automatically eliminates the measurement noise and other measurement errors such as artificial discontinuity. The developmental cycle of an optical system, such as a space telescope, includes, but is not limited to, the following two steps: (1) deriving requirements or specs on the optical quality of individual optics before they are fabricated through optical modeling and simulations, and (2) validating the optical model using the measured surface height maps after all optics are fabricated. There are a number of computational issues related to model validation, one of which is the "pre-conditioning" or pre-processing of the measured surface maps before using them in a model validation software tool. This software addresses the following issues: (1) up- or down-sampling a measured surface map to match it with the gridded data format of a model validation tool, and (2) eliminating the surface measurement noise or measurement errors such that the resulted surface height map is continuous or smoothly-varying. So far, the preferred method used for re-sampling a surface map is two-dimensional interpolation. The main problem of this method is that the same pixel can take different values when the method of interpolation is changed among the different methods such as the "nearest," "linear," "cubic," and "spline" fitting in Matlab. The conventional, FFT-based spatial filtering method used to eliminate the surface measurement noise or measurement errors can also suffer from aliasing effects. During re-sampling of a surface map, this software preserves the low spatial-frequency characteristic of a given surface map through the use of Zernike-polynomial fit coefficients, and maintains mid- and high-spatial-frequency characteristics of the given surface map by the use of a PSD model derived from the two-dimensional PSD data of the mid- and high-spatial-frequency components of the original surface map. Because this new method creates the new surface map in the desired sampling format from analytical expressions only, it does not encounter any aliasing effects and does not cause any discontinuity in the resultant surface map.
Sidick, Erkin↗
Flow-based likelihoods for non-Gaussian inference
We investigate the use of data-driven likelihoods to bypass a key assumption made in many scientific analyses, which is that the true likelihood of the data is Gaussian. In particular, we suggest using the optimization targets of flow-based generative models, a class of models that can capture complex distributions by transforming a simple base distribution through layers of nonlinearities. We call these flow-based likelihoods (FBL). We analyze the accuracy and precision of the reconstructed likelihoods on mock Gaussian data, and show that simply gauging the quality of samples drawn from the trained model is not a sufficient indicator that the true likelihood has been learned. We nevertheless demonstrate that the likelihood can be reconstructed to a precision equal to that of sampling error due to a finite sample size. We then apply FBLs to mock weak lensing convergence power spectra, a cosmological observable that is significantly non-Gaussian (NG). We find that the FBL captures the NG signatures in the data extremely well, while other commonly used data-driven likelihoods, such as Gaussian mixture models and independent component analysis, fail to do so. This suggests that works that have found small posterior shifts in NG data with data-driven likelihoods such as these could be underestimating the impact of non-Gaussianity in parameter constraints. By introducing a suite of tests that can capture different levels of NG in the data, we show that the success or failure of traditional data-driven likelihoods can be tied back to the structure of the NG in the data. Here, unlike other methods, the flexibility of the FBL makes it successful at tackling different types of NG simultaneously. Because of this, and consequently their likely applicability across datasets and domains, we encourage their use for inference when sufficient mock data are available for training.
79 ASTRONOMY AND ASTROPHYSICS↗
Elemental abundances in stone meteorites.
Measurement of abundances of Na, Al, Sc, Cr, Mn, Fe, Co, and Cu by instrumental neutron activation analyses of 103 chondrites and 17 achondrites. Various sources of error in the method, including sampling errors, are discussed in detail. Examination of the patterns of coherence of the elements determined suggests the presence of effects of fractionation during condensation from the solar nebula of matter parental to chondrites. Such effects seem to be exhibited both in the abundances of lithophilic elements, perhaps being related to varied temperatures of accretion and in the abundances of those elements which would be affected by metal-silicate fractionation in the solar nebula. Atomic abundances relative to Si vary little in carbonaceous chondrites, suggesting that efficient mixing processes operated on these meteorites prior to or during their formation. It is suggested that, at present, no single class of carbonaceous chondrites is clearly more primitive than another. Carbonaceous and unequilibrated ordinary chondrites may represent aggregates of material accreted from the solar nebula at relatively low temperatures. The present data support a model of equilibration and minor mobilization of nonvolatile elements within small domains of chondrites after accretion. Such a model would be consistent with the petrologic types of Van Schmus and Wood (1967).
Schmitt, R. A.↗
Simulation of the earth's monthly average regional radiation balance derived from satellite measurements
Computer simulations of satellite-derived earth radiation parameters are used to determine the source and size of errors arising from measuring parameters over 1 month on a 2.5 deg x 2.5 deg longitude-latitude grid. November 1979 GOES data are used as a source of radiation parameter fields within each region. A mathematical model is given for the data-processing algorithms that are used to produce daily, monthly, and monthly hourly estimates of shortwave, longwave and net radiant exitance. Resulting retrieval errors are found to be due to insufficient knowledge of the temporal distribution of measurements. These errors, in combination with similar sampling errors produce biases in monthly net radiant exitance which are complex, regionally-dependent functions of the local time of the measurements. The 40 region standard errors of estimate for three satellites ranged from 2 to 3 W per sq. m., while single satellite errors ranged from 5 to 19 W per sq. m. A three-satellite system is therefore recommended to reduce biases.
Brooks, D. R.↗
Arctic sea ice leads from advanced very high resolution radiometer images
A large number of advanced very high resolution radiometer (AVHRR) images from throughout 1989 are analyzed to determine lead characteristics. The units of analysis are square 200-km cells, and there are 270 such cells in the data set. Clouds are masked manually. Leads determine from images of the potential open water delta, a scaled version of the surface temperature or albedo that weights thin ice by its thermal or brightness impact. The lead fraction is determined as the mean delta, the monthly mean lead fraction ranges from 0.02 in winter to 0.06 in summer in the central Arctic and is near 0.08 in the winter in the peripheral seas. A method of accounting for lead width sampling errors due to the finite sample areas is introduced. In the central Arctic the observed mean lead width for a threshold of delta = 0.1 ranges from 2 or 3 km (near the resolution of the instrument) in the winter to 6 km in the summer. In the peripheral seas it is about 5 km in the winter. Width distributions are often more heavily weighted in the tail than exponential distributions and are well approximated by a power law. The along-track, number density power law N = aw(exp -6) has a mean exponent of b = 1.60 (standard deviation 0.18) and shows some seasonal variability. Mean floe widths in the central Arctic are 40 to 50 km in the winter, dropping to about 10 km in the summer. For floes the power law has a mean exponent of 0.93 and exhibits a clearer annual cycle. Lead orientation is determined with a method based on the direction of maximum extent.
Lindsay, R. W.↗
Assessing the Impact of Pre-gpm Microwave Precipitation Observations in the Goddard WRF Ensemble Data Assimilation System
The forthcoming Global Precipitation Measurement (GPM) Mission will provide next generation precipitation observations from a constellation of satellites. Since precipitation by nature has large variability and low predictability at cloud-resolving scales, the impact of precipitation data on the skills of mesoscale numerical weather prediction (NWP) is largely affected by the characterization of background and observation errors and the representation of nonlinear cloud/precipitation physics in an NWP data assimilation system. We present a data impact study on the assimilation of precipitation-affected microwave (MW) radiances from a pre-GPM satellite constellation using the Goddard WRF Ensemble Data Assimilation System (Goddard WRF-EDAS). A series of assimilation experiments are carried out in a Weather Research Forecast (WRF) model domain of 9 km resolution in western Europe. Sensitivities to observation error specifications, background error covariance estimated from ensemble forecasts with different ensemble sizes, and MW channel selections are examined through single-observation assimilation experiments. An empirical bias correction for precipitation-affected MW radiances is developed based on the statistics of radiance innovations in rainy areas. The data impact is assessed by full data assimilation cycling experiments for a storm event that occurred in France in September 2010. Results show that the assimilation of MW precipitation observations from a satellite constellation mimicking GPM has a positive impact on the accumulated rain forecasts verified with surface radar rain estimates. The case-study on a convective storm also reveals that the accuracy of ensemble-based background error covariance is limited by sampling errors and model errors such as precipitation displacement and unresolved convective scale instability.
microwave observations↗
Sampling, aggregation, and variance estimation for area, yield, and production in LACIE
An approximately 2% sampling error was achieved in LACIE by sampling only approximately 2% of the sampling frame. The sample design in the yardstick region for which historical data were available down to a substratum level to support missing data resulting from cloud cover provided the most accurate estimate possible. The implemented strategy provided data of sufficient quality and quantity to support required performance levels and also to satisfy the existing constraints. The allocation scheme appeared to provide the most efficient usage of the available data and gave segment coverage of major producing areas and thus improved the probability of an accurate estimate.
Hallum, C. R.↗
Radiometric analysis of the longwave infrared channel of the Thematic Mapper on LANDSAT 4 and 5
The first objective was to evaluate the postlaunch radiometric calibration of the LANDSAT Thematic Mapper (TM) band 6 data. The second objective was to determine to what extent surface temperatures could be computed from the TM and 6 data using atmospheric propagation models. To accomplish this, ground truth data were compared to a single TM-4 band 6 data set. This comparison indicated satisfactory agreement over a narrow temperature range. The atmospheric propagation model (modified LOWTRAN 5A) was used to predict surface temperature values based on the radiance at the spacecraft. The aircraft data were calibrated using a multi-altitude profile calibration technique which had been extensively tested in previous studies. This aircraft calibration permitted measurement of surface temperatures based on the radiance reaching the aircraft. When these temperature values are evaluated, an error in the satellite's ability to predict surface temperatures can be estimated. This study indicated that by carefully accounting for various sensor calibration and atmospheric propagation effects, and expected error (1 standard deviation) in surface temperature would be 0.9 K. This assumes no error in surface emissivity and no sampling error due to target location. These results indicate that the satellite calibration is within nominal limits to within this study's ability to measure error.
Schott, John R.↗
Estimation of the diurnal cycle of oceanic precipitation from SSM/I data
A study of differences between the morning and evening monthly rainfall for 5 x 5 deg cells over the oceans from the SSM/I data has been conducted. The monthly rainfalls are estimated from the technique given by Wilheit et al. The difference between the morning and evening monthly rainfall arises due to the various random errors involved in the retrieval process, the sampling error in the observations, and the diurnal component of oceanic rainfall. The diurnal component is weak but clearly visible when averaged over large areas and for long time periods. The analysis shows that morning rainfall is consistently greater than evening rainfall. The Northern Hemisphere seem to have a larger diurnal variation than does the Southern Hemisphere. The maximum ratio between the morning and evening monthly rainfall is 1.7 while 1.2 is the more typical value.
Sharma, Awdhesh K.↗
Sampling data for OSSEs
An OSSE should for the sake of realism incorporate at least some of the high-frequency, small-scale phenomena that are suppressed by atmospheric models; these phenomena should be present in the realistic atmosphere sampled by all observing sensor systems whose data are being used. Errors are presently generated for an OSSE in a way that encompasses representational errors, sampling, geophysical local bias, random error, and sensor filtering.
Hoffman, Ross↗