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DOE OSTI2013

Estimating Bacteria Emissions from Inversion of Atmospheric Transport: Sensitivity to Modelled Particle Characteristics

Model-simulated transport of atmospheric trace components can be combined with observed concentrations to obtain estimates of ground-based sources using various inversion techniques. These approaches have been applied in the past primarily to obtain source estimates for long-lived trace gases such as CO2. We consider the application of similar techniques to source estimation for atmospheric aerosols, by using as a case study the estimation of bacteria emissions from different ecosystem regions in the global atmospheric chemistry and climate model ECHAM5/MESSy-Atmospheric Chemistry (EMAC). Simulated particle concentrations in the tropopause region and at high latitudes, as well as transport of particles to tundra and land ice regions are shown to be highly sensitive to scavenging in mixed-phase clouds, which is poorly characterized in most global climate models. This may be a critical uncertainty in correctly simulating the transport of aerosol particles to the Arctic. Source estimation via Monte Carlo Markov Chain is applied to a suite of sensitivity simulations and the global mean emissions are estimated. We present an analysis of the partitioning of uncertainties in the global mean emissions that are attributable to particle size, CCN activity, the ice nucleation scavenging ratios for mixed-phase and cold clouds, and measurement error. Uncertainty due to CCN activity or to a 1 um error in particle size is typically between 10% and 40% of the uncertainty due to data uncertainty, as measured by the 5%-ile to 95%-ile range of the Monte Carlo ensemble. Uncertainty attributable to the ice nucleation scavenging ratio in mized-phase clouds is as high as 10% to 20% of the data uncertainty. Taken together, the four model 20 parameters examined contribute about half as much to the uncertainty in the estimated emissions as do the measurements. This was a surprisingly large contribution from model uncertainty in light of the substantial data uncertainty, which ranges from 81% to 870% for each of ten ecosystems for this case study. The effects of these and other model parameters in contributing to the uncertainties in the transport of atmospheric aerosol particles should be treated explicitly and systematically in both forward and inverse modelling studies.

characteristics↗

DOE OSTI2015

Emissions of nitrogen oxides from US urban areas: estimation from Ozone Monitoring Instrument retrievals for 2005–2014

Satellite remote sensing of tropospheric nitrogen dioxide (NO 2 ) can provide valuable information for estimating surface nitrogen oxides (NO x ) emissions. Using an exponentially modified Gaussian (EMG) method and taking into account the effect of wind on observed NO 2 distributions, we estimate 3-year moving-average emissions of summertime NO x from 35 US (United States) urban areas directly from NO 2 retrievals of the Ozone Monitoring Instrument (OMI) during 2005–2014. Following conclusions of previous studies that the EMG method provides robust and accurate emission estimates under strong-wind conditions, we derive top-down NO x emissions from each urban area by applying the EMG method to OMI data with wind speeds greater than 3–5 m s -1 . Meanwhile, we find that OMI NO 2 observations under weak-wind conditions (i.e., < 3 m s −1 ) are qualitatively better correlated to the surface NO x source strength in comparison to all-wind OMI maps; therefore, we use them to calculate the satellite-observed NO 2 burdens of urban areas and compare with NO x emission estimates. The EMG results show that OMI-derived NO x emissions are highly correlated ( R > 0.93) with weak-wind OMI NO 2 burdens as well as with bottom-up NO x emission estimates over 35 urban areas, implying a linear response of the OMI observations to surface emissions under weak-wind conditions. The simultaneous EMG-obtained effective NO 2 lifetimes (~ 3.5 ± 1.3 h), however, are biased low in comparison to the summertime NO 2 chemical lifetimes. In general, isolated urban areas with NO x emission intensities greater than ~ 2 Mg h -1 produce statistically significant weak-wind signals in 3-year average OMI data. From 2005 to 2014, we estimate that total OMI-derived NO x emissions over all selected US urban areas decreased by 49 %, consistent with reductions of 43, 47, 49, and 44 % in the total bottom-up NO x emissions, the sum of weak-wind OMI NO 2 columns, the total weak-wind OMI NO 2 burdens, and the averaged NO 2 concentrations, respectively, reflecting the success of NO x control programs for both mobile sources and power plants. The decrease rates of these NO x -related quantities are found to be faster (i.e., -6.8 to -9.3 % yr −1 ) before 2010 and slower (i.e., -3.4 to -4.9 % yr −1 ) after 2010. For individual urban areas, we calculate the R values of pair-wise trends among the OMI-derived and bottom-up NO x emissions, the weak-wind OMI NO 2 burdens, and ground-based NO 2 measurements, and high correlations are found for all urban areas (median R = 0.8), particularly large ones ( R up to 0.97). The results of the current work indicate that using the EMG method and considering the wind effect, the OMI data allow for the estimation of NO x emissions from urban areas and the direct constraint of emission trends with reasonable accuracy.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Emissions of nitrogen oxides from US urban areas: estimation from Ozone Monitoring Instrument retrievals for 2005-2014

Satellite remote sensing of tropospheric nitrogen dioxide (NO 2 ) can provide valuable information for estimating surface nitrogen oxides (NO x ) emissions. Using an exponentially-modified Gaussian (EMG) method and taking into account the effect of wind on observed NO 2 distributions, we estimate three-year moving-average emissions of summertime NO x from 35 US urban areas directly from NO 2 retrievals of the Ozone Monitoring Instrument (OMI) during 2005–2014. Following the conclusions of previous studies that the EMG method provides robust and accurate emission estimates under strong-wind conditions, we derive top-down NO x emissions from each urban area by applying the EMG method to OMI data with wind speeds greater than 3–5 m s -1 . Meanwhile, we find that OMI NO 2 observations under weak-wind conditions (i.e., < 3 m s -1 ) are qualitatively better correlated with the surface NO x source strength in comparison to all-wind OMI maps; and therefore we use them to calculate the satellite-observed NO 2 burdens of urban areas and compare with NO x emission estimates. The EMG results show that OMI-derived NO x emissions are highly correlated ( R > 0.93) with weak-wind OMI NO 2 burdens as well as bottom-up NO x emission estimates over 35 urban areas, implying a linear response of the OMI observations to surface emissions under weak-wind conditions. The simultaneous, EMG-obtained, effective NO 2 lifetimes (~3.5 ± 1.3 h), however, are biased low in comparison to the summertime NO 2 chemical lifetimes. In general, isolated urban areas with NO x emission intensities greater than ~ 2 Mg h -1 produce statistically significant weak-wind signals in three-year average OMI data. From 2005 to 2014, we estimate that total OMI-derived NO x emissions over all selected US urban areas decreased by 49%, consistent with reductions of 43, 47, 49, and 44% in the total bottom-up NO x emissions, the sum of weak-wind OMI NO 2 columns, the total weak-wind OMI NO 2 burdens, and the averaged NO 2 concentrations, respectively, reflecting the success of NO x control programs for both mobile sources and power plants. The decrease rates of these NO x -related quantities are found to be faster (i.e., -6.8 to -9.3% yr -1 ) before 2010 and slower (i.e., -3.4 to -4.9% yr -1 ) after 2010. For individual urban areas, we calculate the R values of pair-wise trends among the OMI-derived and bottom-up NO x emissions, the weak-wind OMI NO 2 burdens, and ground-based NO 2 measurements; and high correlations are found for all urban areas (median R = 0.8), particularly large ones ( R up to 0.97). The results of the current work indicate that using the EMG method and considering the wind effect, the OMI data allow for the estimation of NO x emissions from urban areas and the direct constraint of emission trends with reasonable accuracy.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Estimates of climate system properties incorporating recent climate change

Abstract. Historical time series of surface temperature and ocean heat content changes are commonly used metrics to diagnose climate change and estimate properties of the climate system. We show that recent trends, namely the slowing of surface temperature rise at the beginning of the 21st century and the acceleration of heat stored in the deep ocean, have a substantial impact on these estimates. Using the Massachusetts Institute of Technology Earth System Model (MESM), we vary three model parameters that influence the behavior of the climate system: effective climate sensitivity (ECS), the effective ocean diffusivity of heat anomalies by all mixing processes (Kv), and the net anthropogenic aerosol forcing scaling factor. Each model run is compared to observed changes in decadal mean surface temperature anomalies and the trend in global mean ocean heat content change to derive a joint probability distribution function for the model parameters. Marginal distributions for individual parameters are found by integrating over the other two parameters. To investigate how the inclusion of recent temperature changes affects our estimates, we systematically include additional data by choosing periods that end in 1990, 2000, and 2010. We find that estimates of ECS increase in response to rising global surface temperatures when data beyond 1990 are included, but due to the slowdown of surface temperature rise in the early 21st century, estimates when using data up to 2000 are greater than when data up to 2010 are used. We also show that estimates of Kv increase in response to the acceleration of heat stored in the ocean as data beyond 1990 are included. Further, we highlight how including spatial patterns of surface temperature change modifies the estimates. We show that including latitudinal structure in the climate change signal impacts properties with spatial dependence, namely the aerosol forcing pattern, more than properties defined for the global mean, climate sensitivity, and ocean diffusivity.

Libardoni, Alex G.↗

NASA NTRS1977

Optimal estimation for discrete time jump processes

Optimum estimates of nonobservable random variables or random processes which influence the rate functions of a discrete time jump process (DTJP) are obtained. The approach is based on the a posteriori probability of a nonobservable event expressed in terms of the a priori probability of that event and of the sample function probability of the DTJP. A general representation for optimum estimates and recursive equations for minimum mean squared error (MMSE) estimates are obtained. MMSE estimates are nonlinear functions of the observations. The problem of estimating the rate of a DTJP when the rate is a random variable with a probability density function of the form cx super K (l-x) super m and show that the MMSE estimates are linear in this case. This class of density functions explains why there are insignificant differences between optimum unconstrained and linear MMSE estimates in a variety of problems.

Vaca, M. V.↗

NASA NTRS1977

Wheat productivity estimates using LANDSAT data

The author has identified the following significant results. Large area LANDSAT yield estimates were generated. These results were compared with estimates computed using a meteorological yield model (CCEA). Both of these estimates were compared with Kansas Crop and Livestock Reporting Service (KCLRS) estimates of yield, in an attempt to assess the relative and absolute accuracy of the LANDSAT and CCEA estimates. Results were inconclusive. A large area direct wheat prediction procedure was implemented. Initial results have produced a wheat production estimate comparable with the KCLRS estimate.

Nalepka, R. F.↗

NASA NTRS1978

On attitude estimation schemes for fine-pointing control

This paper studies single-axis equations of motion that are applicable to a spacecraft or to a space experiment pointing assembly whose motion has been perfectly isolated from the carrier vehicle. It considers four state estimators for implementation in the control loop for a stellar observation experiment. The first three estimators are very general and do not make use of input torque in their prediction models, while the proposed fourth estimator utilizes this information. It is shown via closed-loop covariance analysis that the best achievable pointing performance with the best of the first three estimators is limited to about 0.125 arc-sec (rms) with the given rate-gyro and star-tracker inaccuracies. It is also shown that the fourth estimator has the capability of achieving a pointing performance far superior to the performance achievable using the first three estimators. The fourth estimator relies on the ability to accurately generate the desired control torque (i.e., low input noise).

Joshi, S. M.↗

NASA NTRS1978

Optimal estimation for discrete time jump processes

Optimum estimates of nonobservable random variables or random processes which influence the rate functions of a discrete time jump process (DTJP) are derived. The approach used is based on the a posteriori probability of a nonobservable event expressed in terms of the a priori probability of that event and of the sample function probability of the DTJP. Thus a general representation is obtained for optimum estimates, and recursive equations are derived for minimum mean-squared error (MMSE) estimates. In general, MMSE estimates are nonlinear functions of the observations. The problem is considered of estimating the rate of a DTJP when the rate is a random variable with a beta probability density function and the jump amplitudes are binomially distributed. It is shown that the MMSE estimates are linear. The class of beta density functions is rather rich and explains why there are insignificant differences between optimum unconstrained and linear MMSE estimates in a variety of problems.

Vaca, M. V.↗

NASA NTRS1979

Application of parametric weight and cost estimating relationships to future transport aircraft

A model comprised of system level weight and cost estimating relationships for transport aircraft is presented. In order to determine the production cost of future aircraft its weight is first estimated based on performance parameters, and then the cost is estimated as a function of weight. For initial evaluation CERs were applied to actual system weights of six aircraft (3 military and 3 commercial) with mean empty weights ranging from 30,000 to 300,000 lb. The resulting cost estimates were compared with actual costs. The average absolute error was only 4.3%. Then the model was applied to five aircraft still in the design phase (Boeing 757, 767 and 777, and BAC HS146-100 and HS146-200). While the estimates for the 757 and 767 are within 2 to 3 percent of their assumed break-even costs, it is recognized that these are very sensitive to the validity of the estimated weights, inflation factor, the amount assumed for nonrecurring costs, etc., and it is suggested that the model may be used in conjunction with other information such as RDT&E cost estimates and market forecasts. The model will help NASA evaluate new technologies and production costs of future aircraft.

Beltramo, M. N.↗

NASA NTRS1980

As-built design specification for proportion estimate software subsystem

The Proportion Estimate Processor evaluates four estimation techniques in order to get an improved estimate of the proportion of a scene that is planted in a selected crop. The four techniques to be evaluated were provided by the techniques development section and are: (1) random sampling; (2) proportional allocation, relative count estimate; (3) proportional allocation, Bayesian estimate; and (4) sequential Bayesian allocation. The user is given two options for computation of the estimated mean square error. These are referred to as the cluster calculation option and the segment calculation option. The software for the Proportion Estimate Processor is operational on the IBM 3031 computer.

Obrien, S.↗

NASA NTRS1980

Development of advanced techniques for rotorcraft state estimation and parameter identification

An integrated methodology for rotorcraft system identification consists of rotorcraft mathematical modeling, three distinct data processing steps, and a technique for designing inputs to improve the identifiability of the data. These elements are as follows: (1) a Kalman filter smoother algorithm which estimates states and sensor errors from error corrupted data. Gust time histories and statistics may also be estimated; (2) a model structure estimation algorithm for isolating a model which adequately explains the data; (3) a maximum likelihood algorithm for estimating the parameters and estimates for the variance of these estimates; and (4) an input design algorithm, based on a maximum likelihood approach, which provides inputs to improve the accuracy of parameter estimates. Each step is discussed with examples to both flight and simulated data cases.

Hall, W. E., Jr.↗

NASA NTRS1980

Model error estimation for large space systems

In-flight estimation of large structure model errors may have to be carried out in order to detect inevitable deficiencies in large structure controller/estimator models. These error estimates can most efficiently be obtained by the minimization of a quadratic functional of the model errors and on the subsequent analysis of the resulting optimal model error estimates. An integral operator approach to estimation leads to a geometrical interpretation of the model error estimation process. One of the significant insights gained with this interpretation is that the actual but unknown model errors can be decomposed as the sum of two distinct components that are orthogonal in some sense. One of these components is a so-called minimal error vector that retains many of the significant dynamics of the actual errors. The basic ideas in the model error estimation approach are first set forth with a two-dimensional analogy that has most of the essential features of the general estimation problem. The generalized results are then established and their application to a reference large structure model illustrated.

Rodriguez, G.↗

NASA NTRS1981

Landsat-based estimation of California's irrigated land

The procedure developed uses two-phase sampling, stratification and multidate Landsat imagery to produce the estimate. Maximizing the advantages of both spectral data and field patterns available from Landsat, three dates of Landsat imagery are interpreted to provide county-wide estimates of the proportion of land irrigated. Ground data, collected on a subset of the area interpreted on Landsat, are used to calibrate the satellite estimate. The satellite and ground measurements are reduced to proportion data and linked using a regression estimator to produce the estimate of irrigated land. It is estimated that 3.99 million hectares were irrigated with a relative standard error of + or - 1.18% at the 99% confidence level. Acreage tabulations provided by the Department of Water Resources show that the Landsat-based estimate differed by less than 0.4% at the state level.

Wall, S. L.↗

NASA NTRS1982

The use of baseline measurements and geophysical models for the estimation of crustal deformations and the terrestrial reference system

Four possible estimators are investigated for the monitoring of crustal deformations from a combination of repeated baseline length measurements and adopted geophysical models, particularly an absolute motion plate model. The first estimator is an extension of the familiar free adjustment. The next two are Bayesian type estimators, one weak and one strong. Finally, a weighted constraint estimator is presented. The properties of these four estimators are outlined and their physical interpretations discussed. A series of simulations are performed to test the four estimators and to determine whether or not to incorporate a plate model for the monitoring of deformations. The application of these estimations to the maintenance of a new conventional terrestrial reference system is discussed.

Bock, Y.↗

NASA NTRS1983

A function space approach to state and model error estimation for elliptic systems

An approach is advanced for the concurrent estimation of the state and of the model errors of a system described by elliptic equations. The estimates are obtained by a deterministic least-squares approach that seeks to minimize a quadratic functional of the model errors, or equivalently, to find the vector of smallest norm subject to linear constraints in a suitably defined function space. The minimum norm solution can be obtained by solving either a Fredholm integral equation of the second kind for the case with continuously distributed data or a related matrix equation for the problem with discretely located measurements. Solution of either one of these equations is obtained in a batch-processing mode in which all of the data is processed simultaneously or, in certain restricted geometries, in a spatially scanning mode in which the data is processed recursively. After the methods for computation of the optimal estimates are developed, an analysis of the second-order statistics of the estimates and of the corresponding estimation error is conducted. Based on this analysis, explicit expressions for the mean-square estimation error associated with both the state and model error estimates are then developed.

Rodriguez, G.↗

NASA NTRS1984

Sampling system for wheat (Triticum aestivum L) area estimation using digital LANDSAT MSS data and aerial photographs

A procedure to estimate wheat (Triticum aestivum L) area using sampling technique based on aerial photographs and digital LANDSAT MSS data is developed. Aerial photographs covering 720 square km are visually analyzed. To estimate wheat area, a regression approach is applied using different sample sizes and various sampling units. As the size of sampling unit decreased, the percentage of sampled area required to obtain similar estimation performance also decreased. The lowest percentage of the area sampled for wheat estimation with relatively high precision and accuracy through regression estimation is 13.90% using 10 square km as the sampling unit. Wheat area estimation using only aerial photographs is less precise and accurate than those obtained by regression estimation.

Parada, N. D. J.↗

NASA NTRS1983

Estimating crustal deformations from a combination of baseline measurements and geophysical models

Three possible estimation algorithms are presented for the monitoring of crustal deformations from a combination of repeated baseline measurements and prior deformation information. Attention is given to best linear minimum bias estimation, best linear estimation, and Bayesian estimation. The application of the deformation estimators to the maintenance of a conventional terrestrial reference system is considered. It is shown that if no prior deformation model is available, the free adjustment is the preferred algorithm. In the presence of prior information, however, it is better to use a somewhat incorrect model than to ignore it altogether. The best estimation model of the ones tested is the best linear estimation.

Bock, Y.↗

NASA NTRS1985

Testing an Energy Balance Model for Estimating Actual Evapotranspiration Using Remotely Sensed Data

An energy-balance model is used to estimate daily evapotranspiration for 3 days for a barley field and a wheat field near Hannover, Federal Republic of Germany. The model was calibrated using once-daily estimates of surface temperatures, which may be remotely sensed. The evaporation estimates were within the 95% error bounds of independent eddy correlation estimates for the daytime periods for all three days for both sites, but the energy-balance estimates are generally higher; it is unclear which estimate is biassed. Soil moisture in the top 2 cm of soil, which may be remotely sensed, may be used to improve these evaporation estimates under partial ground cover. Sensitivity studies indicate the amount of ground data required is not excessive.

Gurney, R. J.↗