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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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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1977

Sampling errors in the measurement of rain and hail parameters

Attention is given to a general derivation of the fractional standard deviation (FSD) of any integrated property X such that X(D) = cD to the n. This work extends that of Joss and Waldvogel (1969). The equation is applicable to measuring integrated properties of cloud, rain or hail populations (such as water content, precipitation rate, kinetic energy, or radar reflectivity) which are subject to statistical sampling errors due to the Poisson distributed fluctuations of particles sampled in each particle size interval and the weighted sum of the associated variances in proportion to their contribution to the integral parameter to be measured. Universal curves are presented which are applicable to the exponential size distribution permitting FSD estimation of any parameters from n = 0 to n = 6. The equations and curves also permit corrections for finite upper limits in the size spectrum and a realistic fall speed law.

Gertzman, H. S.↗
NASA NTRS1993

Evaluation of sampling errors of precipitation from spaceborne and ground sensors

The spatial and temporal characteristics of rainfall over Oklahoma and Kansas are analyzed using the raingage data collected during the Preliminary Regional Experiment for STORM-Central (PRE-STORM). The spectra obtained are compared with those obtained from the oceanic precipitation in the GARP and with that obtained from analyzing raingage records in east Texas. In addition, the spectra are used to evaluate the sampling errors that are due to the spatial gaps in measurements. It was found that the temporal spectra from PRE-STORM had a spectral shape similar to that obtained in GARP, except that the tail of the PRE-STORM spectra has a lower slope than in GARP spectra, suggesting that the largest difference between tropical oceanic rainfall and convective land precipitation is at the highest frequencies.

Graves, Charles E.↗
NASA NTRS1994

Evaluating the design of an earth radiation budget instrument with system simulations. Part 2: Minimization of instantaneous sampling errors for CERES-I

Much of the new record of broadband earth radiation budget satellite measurements to be obtained during the late 1990s and early twenty-first century will come from the dual-radiometer Clouds and Earth's Radiant Energy System Instrument (CERES-I) flown aboard sun-synchronous polar orbiters. Simulation studies conducted in this work for an early afternoon satellite orbit indicate that spatial root-mean-square (rms) sampling errors of instantaneous CERES-I shortwave flux estimates will range from about 8.5 to 14.0 W/m on a 2.5 deg latitude and longitude grid resolution. Rms errors in longwave flux estimates are only about 20% as large and range from 1.5 to 3.5 W/sq m. These results are based on an optimal cross-track scanner design that includes 50% footprint overlap to eliminate gaps in the top-of-the-atmosphere coverage, and a 'smallest' footprint size to increase the ratio in the number of observations lying within to the number of observations lying on grid area boundaries. Total instantaneous measurement error also depends on the variability of anisotropic reflectance and emission patterns and on retrieval methods used to generate target area fluxes. Three retrieval procedures from both CERES-I scanners (cross-track and rotating azimuth plane) are used. (1) The baseline Earth Radiaton Budget Experiment (ERBE) procedure, which assumes that errors due to the use of mean angular dependence models (ADMs) in the radiance-to-flux inversion process nearly cancel when averaged over grid areas. (2) To estimate N, instantaneous ADMs are estimated from the multiangular, collocated observations of the two scanners. These observed models replace the mean models in computation of satellite flux estimates. (3) The scene flux approach, conducts separate target-area retrievals for each ERBE scene category and combines their results using area weighting by scene type. The ERBE retrieval performs best when the simulated radiance field departs from the ERBE mean models by less than 10%. For larger perturbations, both the scene flux and collocation methods produce less error than the ERBE retrieval. The scene flux technique is preferable, however, because it involves fewer restrictive assumptions.

Stowe, Larry↗
NASA NTRS1983

Statistical theory for estimating sampling errors of regional radiation averages based on satellite measurements

The processes which determine the weather and climate are driven by the radiation received by the earth and the radiation subsequently emitted. A knowledge of the absorbed and emitted components of radiation is thus fundamental for the study of these processes. In connection with the desire to improve the quality of long-range forecasting, NASA is developing the Earth Radiation Budget Experiment (ERBE), consisting of a three-channel scanning radiometer and a package of nonscanning radiometers. A set of these instruments is to be flown on both the NOAA-F and NOAA-G spacecraft, in sun-synchronous orbits, and on an Earth Radiation Budget Satellite. The purpose of the scanning radiometer is to obtain measurements from which the average reflected solar radiant exitance and the average earth-emitted radiant exitance at a reference level can be established. The estimate of regional average exitance obtained will not exactly equal the true value of the regional average exitance, but will differ due to spatial sampling. A method is presented for evaluating this spatial sampling error.

Smith, G. L.↗
NASA NTRS1979

Delay compensation - Its effect in reducing sampling errors in Fourier spectroscopy

An approximate formula is derived for the spectrum ghosts caused by periodic drive speed variations in a Michelson interferometer. The solution represents the case of fringe-controlled sampling and is applicable when the reference fringes are delayed to compensate for the delay introduced by the electrical filter in the signal channel. Numerical results are worked out for several common low-pass filters. It is shown that the maximum relative ghost amplitude over the range of frequencies corresponding to the lower half of the filter band is typically 20 times smaller than the relative zero-to-peak velocity error, when delayed sampling is used. In the lowest quarter of the filter band it is more than 100 times smaller than the relative velocity error. These values are ten and forty times smaller, respectively, than they would be without delay compensation if the filter is a 6-pole Butterworth.

Zachor, A. S.↗
NASA NTRS1993

Sampling errors in rainfall estimates by multiple satellites

This paper examines the sampling characteristics of combining data collected by several low-orbiting satellites attempting to estimate the space-time average of rain rates. The several satellites can have different orbital and swath-width parameters. The satellite overpasses are allowed to make partial coverage snapshots of the grid box with each overpass. Such partial visits are considered in an approximate way, letting each intersection area fraction of the grid box by a particular satellite swath be a random variable with mean and variance parameters computed from exact orbit calculations. The derivation procedure is based upon the spectral minimum mean-square error formalism introduced by North and Nakamoto. By using a simple parametric form for the spacetime spectral density, simple formulas are derived for a large number of examples, including the combination of the Tropical Rainfall Measuring Mission with an operational sun-synchronous orbiter. The approximations and results are discussed and directions for future research are summarized.

North, Gerald R.↗
NASA NTRS1988

A model study of potential sampling errors due to data scatter around expendable bathythermograph transects in the tropical Pacific

A linear multiple vertical-mode model described by McPhaden et al. (1988) is used to examine potential errors due to data scatter around expendable bathythermograph (XBT) transects in the tropical Pacific. Two methods of sampling are compared. In the first, the model was sampled along approximately straight lines of grid points corresponding to the mean positions of XBT tracks in the eastern, central, and western Pacific; in the second, the model was sampled again at the dates and locations of actual XTB casts for 1979-1983. The model indicates that the data scattered zonally around XBT transects in general can lead to about 2 dyn cm error in dynamic height in composite sections of XBT data. Errors larger than 2 dyn cm occurred in regions where XBT sample spacing in the zonal direction was insufficient to resolve Rossby wave variations in the model.

Mcphaden, Michael J.↗
NASA NTRS1976

Sample size and sampling errors as the source of dispersion in chemical analyses

The paper describes a Monte Carlo model for simulation of two-dimensional representations of thin sections of some of the more common igneous rock textures. These representations are extrapolated to three dimensions to develop a volume of 'rock'. The model (here applied to a medium-grained high-Ti basalt) can be used to determine a statistically significant sample for a lunar rock or to predict the probable errors in the oxide contents that can occur during the analysis of a sample that is not representative of the parent rock.

Clanton, U. S.↗
NASA NTRS1997

Correction of Sampling Errors in Ocean Surface Cross-Sectional Estimates from Nadir-Looking Weather Radar

The return from the ocean surface has a number of uses for airborne meteorological radar. The normalized surface cross section has been used for radar system calibration, estimation of surface winds, and in algorithms for estimating the path-integrated attenuation in rain. However, meteorological radars are normally optimized for observation of distributed targets that fill the resolution volume, and so a point target such as the surface can be poorly sampled, particularly at near-nadir look angles. Sampling the nadir surface return at an insufficient rate results in a negative bias of the estimated cross section. This error is found to be as large as 4 dB using observations from a high-altitude airborne radar. An algorithm for mitigating the error is developed that is based upon the shape of the surface echo and uses the returned signal at the three range gates nearest the peak surface echo.

Caylor, I. Jeff↗
NASA NTRS1981

Time-variable Earth's albedo model characteristics and applications to satellite sampling errors

Characteristics of the time variable Earth albedo model are described. With the cloud cover multiplying factor adjusted to produce a global annual average albedo of 30.3, the global annual average cloud cover is 45.5 percent. Global annual average sunlit cloud cover is 48.5 percent; nighttime cloud cover is 42.7 percent. Month-to-month global average albedo is almost sinusoidal with maxima in June and December and minima in April and October. Month-to-month variation of sunlit cloud cover is similar, but not in all details. The diurnal variation of global albedo is greatest from November to March; the corresponding variation of sunlit cloud cover is greatest from May to October. Annual average zonal albedos and monthly average zonal albedos are in good agreement with satellite-measured values, with notable differences in the polar regions in some months and at 15 S. The albedo of some 10 deg by 10 deg. areas of the Earth versus zenith angle are described. Satellite albedo measurement sampling effects are described in local time and in Greenwich mean time.

Bartman, F. L.↗
NASA NTRS1992

Sampling errors in the vertical fluxes of potential temperature and moisture measured by aircraft during FIFE

This paper contributes to understanding the particular problems encountered during FIFE 87 as a result of utilizing relatively short (15 km) averaging lengths for the flux estimates. The implications of employing data that had been high-pass filtered with a five-km cutoff wavelength for flux estimates are also investigated. It is found that a 15-km run length is just adequate for low-level flux estimates as long as the runs are crosswind.

Grossman, Robert L.↗
NASA NTRS2002

Sampling Errors of Monthly-mean Radiative Fluxes from the Earth Radiation Budget Satellite

The Earth Radiation Experiment (ERBE) consisted of scanning and non-scanning radiometers on the dedicated Earth Radiation Budget Satellite ERBS) and also on the NOAA-9 and -10 operational spacecraft. The non-scanning radiometers included a pair of wide field-of-view (WFOV) radiometers for measuring outgoing longwave radiation and reflected solar radiation (Luther et al., 1986). The ERBS was placed into an orbit with 57 deg. inclination and 620 km altitude on 16 October 1984. The instruments began collecting data in November 1984 and the non-scanning radiometers provided data until June 2002, providing a 17-year data set.

Bess, T. Dale↗
NASA NTRSDate not supplied

Assessing Risk Due to Small Sample Size in Probability of Detection Analysis Using Tolerance Intervals

Small sample size (e.g.6-30) poses risk in results of probability of detection (POD) analysis using tolerance intervals. This method is also called as the limited sample or LS POD. The analysis is performed either during NDE procedure qualification or for assessment of reliability of an NDE procedure. The risk is primarily due to sampling error. Smaller samples are not likely to be random to the population or representative of the population. The small samples are likely to be biased. Biased samples have smaller standard deviation compared to the population. POD analysis with small biased sample can lead to overestimation of POD. Many sampling schemes are available in statistics to mitigate sampling risk. Primary objective of POD analysis is to determine a decision threshold from signal response measurements of a sample such that it is less than or equal to population decision threshold for 90% POD. Sampling error implies that this NDE reliability condition is violated. One of sampling types is called a representative sample. Representative samples reduce variance in POD estimates but also reduce magnitude of the error. Sampling sensitivity analysis for some sampling types is performed here using repetitive random sampling or Monte Carlo method. Six sampling types are considered for comparison. Some of the sampling types are similar to drawing a representative sample. LS POD model assumes random sampling. Therefore, random sampling is used as a basis for comparison with each sampling type. The sampling types used in the analysis are, A. Nominal and worst-case sampling, B. Worst-case sampling, C. Nominal case sampling, D. Random sampling, E. Random target, and sub-target sampling. F. Nominal target and sub-target sampling. Results of Monte Carlo simulation indicate that type F sampling can mitigate sampling risk and is also more practical to implement. Type A sampling may also mitigate the sampling risk, but it may be less practical to implement.

Ajay M Koshti↗
NASA NTRS1986

Assessment of measurement error due to sampling perspective in the space-based Doppler lidar wind profiler

A Multipair Algorithm (MPA) has been developed to minimize the contribution of the sampling error in the simulated Doppler lidar wind profiler measurements (due to angular and spatial separation between shots in a shot pair) to the total measurement uncertainty. Idealized wind fields are used as input to the profiling model, and radial wind estimates are passed through the MPA to yield a wind measurement for 300 x 300 sq km areas. The derived divergence fields illustrate the gradient patterns that are particular to the Doppler lidar sampling strategy and perspective.

Houston, S. H.↗
NASA NTRS2004

Physical Validation of TRMM TMI and PR Monthly Rain Products Over Oklahoma

The Tropical Rainfall Measuring Mission (TRMM) provides monthly rainfall estimates using data collected by the TRMM satellite. These estimates cover a substantial fraction of the earth's surface. The physical validation of TRMM estimates involves corroborating the accuracy of spaceborne estimates of areal rainfall by inferring errors and biases from ground-based rain estimates. The TRMM error budget consists of two major sources of error: retrieval and sampling. Sampling errors are intrinsic to the process of estimating monthly rainfall and occur because the satellite extrapolates monthly rainfall from a small subset of measurements collected only during satellite overpasses. Retrieval errors, on the other hand, are related to the process of collecting measurements while the satellite is overhead. One of the big challenges confronting the TRMM validation effort is how to best estimate these two main components of the TRMM error budget, which are not easily decoupled. This four-year study computed bulk sampling and retrieval errors for the TRMM microwave imager (TMI) and the precipitation radar (PR) by applying a technique that sub-samples gauge data at TRMM overpass times. Gridded monthly rain estimates are then computed from the monthly bulk statistics of the collected samples, providing a sensor-dependent gauge rain estimate that is assumed to include a TRMM equivalent sampling error. The sub-sampled gauge rain estimates are then used in conjunction with the monthly satellite and gauge (without sub- sampling) estimates to decouple retrieval and sampling errors. The computed mean sampling errors for the TMI and PR were 5.9% and 7.796, respectively, in good agreement with theoretical predictions. The PR year-to-year retrieval biases exceeded corresponding TMI biases, but it was found that these differences were partially due to negative TMI biases during cold months and positive TMI biases during warm months.

Fisher, Brad L.↗
NASA NTRS2002

Estimation of Rainfall Sampling Uncertainty: A Comparison of Two Diverse Approaches

The spatial and temporal intermittence of rainfall causes the averages of satellite observations of rain rate to differ from the "true" average rain rate over any given area and time period, even if the satellite observations are perfectly accurate. The difference of satellite averages based on occasional observation by satellite systems and the continuous-time average of rain rate is referred to as sampling error. In this study, rms sampling error estimates are obtained for average rain rates over boxes 100 km, 200 km, and 500 km on a side, for averaging periods of 1 day, 5 days, and 30 days. The study uses a multi-year, merged radar data product provided by Weather Services International Corp. at a resolution of 2 km in space and 15 min in time, over an area of the central U.S. extending from 35N to 45N in latitude and 100W to 80W in longitude. The intervals between satellite observations are assumed to be equal, and similar In size to what present and future satellite systems are able to provide (from 1 h to 12 h). The sampling error estimates are obtained using a resampling method called "resampling by shifts," and are compared to sampling error estimates proposed by Bell based on earlier work by Laughlin. The resampling estimates are found to scale with areal size and time period as the theory predicts. The dependence on average rain rate and time interval between observations is also similar to what the simple theory suggests.

Steiner, Matthias↗