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The analysis of open-loop balanced flexible structures has been extended for closed-loop structures. The relationships between weights/ variances and characteristic values for the system as well as between weights/variances and plant/estimator pole location are derived in this paper. Thus, the weights can be determined in advance to meet the requirements of a closed-loop system.
Statistical mechanics of sequential randomization tests for two way variance emphasizing most recently acquired data and applications in quality control
Statistical mechanics of sequential randomization tests for one way variance emphasizing most recently acquired data and applications in quality control
On July 14, 1987 NASA's ER-2 high altitude aircraft flew a mission to measure the structure of stratocumulus clouds off the coast of California. A flight pattern was executed so that the two-dimensional variability of the clouds could be detected. The technique of analysis of the lidar data to measure cloud tops follows. First each signal is searched for its maximum in return strength. This maximum is caused by scattering of the laser light off cloud particles or from the ocean surface. Next the variance of the signal return above the level of maximum backscatter is determined. Cloud top is assigned to a level (above the level of maximum backscatter) where the backscatter exceeds the average variance. This two-step process is necessary because the level of maximum backscatter does not correspond to the cloud top. Ocean surface returns are easily separated from cloud returns in this process, described in detail by Boers, Spinhirne, and Hart (1988). Analysis of the data so far has shown that there were very few breaks in the clouds. Furthermore the layer top was very flat with local oscillations not exceeding 30 m. Such small cloud top variations are still well within the range of detectability, because the precision of this technique of cloud top detection has previously been established to be 13 to 15 m. Data are presently being analyzed to compute cloud top distributions and fractional cloudiness. The aim of this research is to relate the fractional cloudiness to the mean thermodynamic structure of the boundary layer. Researchers plan to compute spectral scales of the cloud top variability in two dimensions to determine the orientation of the clouds with respect to the mean wind. Furthermore the lidar derived cloud top distribution will be used in the computation of the thermodynamic and radiation budget of the boundary layer.
Two images were obtained from thematic mappers on Landsats 4 and 5 over the Washington, DC area during November 1982 and March 1984. Selected training areas containing different types of urban land use were examined,one area consisting entirely of forest. Mean digital radiance values for each bandpass in each image were examined, and variances, standard deviations, and covariances between bandpasses were calculated. It has been found that two bandpasses caused forested areas to stand out from other land use types, especially for the November 1982 image. In order to evaluate quantitatively the possible utility of the principal components analysis in selected feature extraction, the eigenvectors were evaluated for principal axes rotations which rendered each selected land use type most separable from all other land use types. The evaluated eigenvectors were plotted as a function of land use type, whose order was decided by considering anticipated shadow component and by examining the relative loadings indicative of vegetation for each of the principal components for the different features considered. The analysis was performed for each seven-band image separately and for the two combined images. It was found that by combining the two images, more dramatic land use type separation could be obtained.
The results of numerical models or of new observational programs are checked by comparing them with past observations. In view of the differing analysis techniques or differing data samples, the eddy diffusivities presented here agree remarkably well with past estimates. However, in the application of K-values to two-dimensional models, the actual magnitude of the diffusivities is no more important than their spatial patterns, i.e., their gradients with height and latitude. It should thus be noted that the present patterns are often much different from those of past results.
This document provides information on the testing and evaluation of thirteen dry rough vacuum pumps of various designs and from various manufacturers. Several types of rough vacuum pumps were evaluated, including scroll, roots, and diaphragm pumps. Tests included long term testing, speed curve generation, voltage variance, vibrations emissions and susceptibility, electromagnetic interference emissions and susceptibility, static leak rate, exhaust restriction, response/recovery time tests, and a contamination analysis for scroll pumps. Parameters were found for operation with helium, which often is not provided from the manufacturer
Studying the El Nino Southern Oscillation (ENSO) in the past can help us better understand its dynamics and improve its future projections. However, both paleoclimate reconstructions and model simulations of ENSO strength at the Last Glacial Maximum (LGM; 21 ka B.P.) have led to contradicting results. Here we perform model simulations using the recently developed water isotope-enabled Community Earth System Model (iCESM). For the first time, model-simulated oxygen isotopes are directly compared with those from ENSO reconstructions using the individual foraminifera analysis (IFA). We find that the LGM ENSO is most likely weaker comparing with the preindustrial. The iCESM suggests that total variance of the IFA records may only reflect changes in the annual cycle instead of ENSO variability as previously assumed. Furthermore, the interpretation of subsurface IFA records can be substantially complicated by the habitat depth of thermocline-dwelling foraminifera and their vertical migration with a temporally varying thermocline.
The 1979 - 1999 response of the climate system to variations in solar spectral irradiance is estimated by comparing the global averaged surface temperature anomalies simulated by a 2D (two dimensional) energy balance climate model to observed temperature anomalies. We perform a multiple regression of southern oscillation index and the individual model responses to solar irradiance variations, stratospheric and tropospheric aerosol loading, stratospheric ozone trends, and greenhouse gases onto each of five near-surface temperature anomaly data sets. We estimate the observed difference in global mean near surface air temperature attributable to the solar irradiance difference between solar maximum and solar minimum to be between 0.06 and 0.11 K, and that 1.1 - 3.8% of the total variance in monthly mean near-surface air temperature data is attributable to nations in solar spectral irradiance. For the five temperature data sets used in our analysis, the trends in raw monthly mean temperature anomaly data have a large range, spanning a factor of 3 from 0.06 to 0.17 K/decade. However. our analysis suggests that trends in monthly temperature anomalies attributable to the combination of greenhouse gas, stratospheric ozone, and tropospheric sulfate aerosol variations are much more consistent among data sets, ranging from 0.16 to 0.24 K/decade. Our model results suggest that roughly half of the warming from greenhouse gases is cancelled by the cooling from changes in stratospheric ozone. Tropospheric sulfate aerosol loading in the present day atmospheric contributes significantly to the net radiative forcing of the present day climate system. However, because the change in magnitude and latitudinal distribution of tropospheric sulfate aerosol has been small over the past 20 years, the change in the direct radiative forcing attributable to changes in aerosol loading over this time is also small.
The information content of the VAS radiances can be converted to meteorological parameters useful for analyzing a severe weather environment. The method by which the VAS variances are converted to vertical profiles of temperature, dewpoints, and equivalent potential temperature involves a basic regression technique using the most local radiosonde data available for establishing a correlation matrix. The results indicate that mesoscale features apparent within images of the radiances can be converted to usable temperature and moisture fields using regression when surface temperature and dewpoint observations are included within the total data base. In addition, results indicate that surface data are very important for better defining lower tropospheric structure that the VAS radiances alone cannot properly resolve. Analyses of these retrievals distinctly show mesoscale structure in the temperature and moisture fields derived with VAS radiances collected every 3 hours, and 0000 GMT. The retrievals capture the moisture structure. More important, convective instability is clearly detected immediately before the onset of convection. The results indicate that the VAS is capable of providing valuable mesoscale information suitable for analyzing a preconvective environment that is generally clear.
To improve radiative transfer calculations for inhomogeneous clouds, a consistent means of modeling inhomogeneity is needed. One current method of modeling cloud inhomogeneity is through the use of fractal parameters. This method is based on the supposition that cloud inhomogeneity over a large range of scales is related. An analysis technique named wavelet analysis provides a means of studying the multiscale nature of cloud inhomogeneity. In this paper, the authors discuss the analysis and modeling of cloud inhomogeneity through the use of wavelet analysis. Wavelet analysis as well as other windowed analysis techniques are used to study liquid water path (LWP) measurements obtained during the marine stratocumulus phase of the First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment. Statistics obtained using analysis windows, which are translated to span the LWP dataset, are used to study the local (small scale) properties of the cloud field as well as their time dependence. The LWP data are transformed onto an orthogonal wavelet basis that represents the data as a number of times series. Each of these time series lies within a frequency band and has a mean frequency that is half the frequency of the previous band. Wavelet analysis combined with translated analysis windows reveals that the local standard deviation of each frequency band is correlated with the local standard deviation of the other frequency bands. The ratio between the standard deviation of adjacent frequency bands is 0.9 and remains constant with respect to time. This ratio defined as the variance coupling parameter is applicable to all of the frequency bands studied and appears to be related to the slope of the data's power spectrum. Similar analyses are performed on two cloud inhomogeneity models, which use fractal-based concepts to introduce inhomogeneity into a uniform cloud field. The bounded cascade model does this by iteratively redistributing LWP at each scale using the value of the local mean. This model is reformulated into a wavelet multiresolution framework, thereby presenting a number of variants of the bounded cascade model. One variant introduced in this paper is the 'variance coupled model,' which redistributes LWP using the local standard deviation and the variance coupling parameter. While the bounded cascade model provides an elegant two- parameter model for generating cloud inhomogeneity, the multiresolution framework provides more flexibility at the expense of model complexity. Comparisons are made with the results from the LWP data analysis to demonstrate both the strengths and weaknesses of these models.
The Integrated Multi-satellitE Retrievals for GPM (IMERG) and the The Global Precipitation Climatology Project (GPCP) are two of the most popular precipitation products. IMERG is a relatively new dataset that targets the needs primarily of the hydrological community by resolving the hydrological cycle of precipitation at fine temporal (30-minutes) and spatial (10-km) scales. IMERG only recently exceeded the user base of the highly successful, but discontinued in 2019, TRMM Multi-satellite Precipitation Analysis (TMPA). GPCP, on the other hand, has traditionally been strong in the climate research community, and recently has been revised under the framework of NASA's Making Earth System Data records for Use in Research Environments (MEaSUREs) program. Both IMERG and GPCP are similar in the underlying approaches to achieve global coverage, in particular using satellite microwave and infrared observations, and adjusting the precipitation retrieval with rain gauge information. While there is a tendency to use both datasets interchangeably, differences remain and some of them limit the usage of IMERG as a climate data record at this point. By applying Principal Component Analysis, we identify the differences and similarities mode-by-mode, and further the guidance on suggested usage of IMERG as a climate data record. As an example in the attached figure, IMERG vs GPSP differences in the explained variance by the two leading seasonal modes can be identified, mainly in the extreme southern latitudes, and over western boundary currents (Gulfstream and Kuroshio). At the preparation time for this presentation, the new version "07"of IMERG was in the works that may resolve the issues presented here. Nevertheless, our analysis can help to gauge the uncertainties of the studies already done using the currently existing IMERG version "06", and evaluate the improvements in the upcoming version "07".
Diagrams of hail size parameters are presented and attempts are made to correlate size and mass/volume as indicators of integral quantities of hailstone fall. Radar cross-sections are presented, along with related radar parameters such as the equivalent radar reflectivity factor, the mean Doppler fallspeed, the variance of the Doppler spectrum, and the ratio of the reflectivity factors for the 3.21 and 10.0 cm radar wavelengths used. An analysis of the possible effects of natural or artificial modification of the hail size distribution are discussed.
We present the first results of a wide solid angle, moderately deep Chandra survey of the Lockman Hole North West Region. Our 9 ACIS-I fields cover an effective solid angle of 0.33 sq deg and reach a depth of 3 x 10(exp -16) erg/sq cm/s in the 0.4-2 keV band, and 3 x 10(exp -15) erg/sq cm/s in the 2-8 keV band. The best fit logN-logS for the entire field, the largest contiguous Chandra field yet observed, matches well onto that of the Chandra Deep Field North. This indicates that over 90% of the 2-8 keV X-ray background is resolved into point sources. We show that the full range of the 'cosmic variance' previously seen in different Chandra fields can be reproduced in a small region of the sky. The count-in-cells analysis shows that the 2-10 keV band sources have much larger angular correlation scale than that of the 0.4-2 keV band. We found the large difference is likely to be caused by a stronger spatial correlation in the hard band sources rather than the redshift effect.
Errors in a priori statistics in minimum variance estimation of regression parameters and effects on a posteriori estimates
The Climate Absolute Radiance and Refractivity Observatory (CLARREO) is a climate observation system designed to study Earth's climate variability with unprecedented absolute radiometric accuracy and SI traceability. Observation System Simulation Experiments (OSSEs) were developed using GCM output and MODTRAN to simulate CLARREO reflectance measurements during the 21st century as a design tool for the CLARREO hyperspectral shortwave imager. With OSSE simulations of hyperspectral reflectance, Feldman et al. [2011a,b] found that shortwave reflectance is able to detect changes in climate variables during the 21st century and improve time-to-detection compared to broadband measurements. The OSSE has been a powerful tool in the design of the CLARREO imager and for understanding the effect of climate change on the spectral variability of reflectance, but it is important to evaluate how well the OSSE simulates the Earth's present-day spectral variability. For this evaluation we have used hyperspectral reflectance measurements from the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY), a shortwave spectrometer that was operational between March 2002 and April 2012. To study the spectral variability of SCIAMACHY-measured and OSSE-simulated reflectance, we used principal component analysis (PCA), a spectral decomposition technique that identifies dominant modes of variability in a multivariate data set. Using quantitative comparisons of the OSSE and SCIAMACHY PCs, we have quantified how well the OSSE captures the spectral variability of Earth?s climate system at the beginning of the 21st century relative to SCIAMACHY measurements. These results showed that the OSSE and SCIAMACHY data sets share over 99% of their total variance in 2004. Using the PCs and the temporally distributed reflectance spectra projected onto the PCs (PC scores), we can study the temporal variability of the observed and simulated reflectance spectra. Multivariate time series analysis of the PC scores using techniques such as Singular Spectrum Analysis (SSA) and Multichannel SSA will provide information about the temporal variability of the dominant variables. Quantitative comparison techniques can evaluate how well the OSSE reproduces the temporal variability observed by SCIAMACHY spectral reflectance measurements during the first decade of the 21st century. PCA of OSSE-simulated reflectance can also be used to study how the dominant spectral variables change on centennial scales for forced and unforced climate change scenarios. To have confidence in OSSE predictions of the spectral variability of hyperspectral reflectance, it is first necessary for us to evaluate the degree to which the OSSE simulations are able to reproduce the Earth?s present-day spectral variability.
A statistical analysis was made on ground soils to define the general relationship and ranges of values of the field moisture relative to both the variance and coefficient of variation for a given test site and depth increment. The results of the variability study show that: (1) moisture variations within any given large field area are inherent and can either be controlled nor reduced; (2) neither a single value of the standard deviation nor coefficient of variation uniquely define the variability over the complete range of mean field moisture contents examined; and (3) using an upper bound standard deviation parameter clearly defines the maximum range of anticipated moisture variability. 87 percent of all large field moisture content standard deviations were less than 3 percent while about 96 percent of all the computed values had an upper bound of sigma=4 percent for these intensively sampled fields. The limit of accuracy curves of mean soil moisture measurements for large field sites relative to the required number of samples were determined.
Ground based observations by means of Cerenkov light detectors and air shower arrays have established that Cyngus X-3 is a powerful source of high energy particles. The detection of a 10 to the 15th power eV signal was first reported by the Kiel experiment. Air showers with large age parameter were accepted in order to select those generated by primary gamma rays. At variance with the expectation, the muon density associated with these events was found to be surprisingly high. This puzzling result stimulated a temporal analysis of the muons recorded in Nucleon Stability Experiment (NUSEX) coming from the region around the source. A positive signal was found suggesting the presentation of this result. The analysis of the data recorded during the 2.4 years of effective working time is presented with a fine tuning of the period and the energy spectrum of the muons from the Cygnus X-3 direction derived assuming consistency between NUSEX and SOUDAN results.