Kalman filter data assimilation: Targeting observations and parameter estimation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bellsky, Thomas, E-mail: bellskyt@asu.edu; Kostelich, Eric J.; Mahalov, Alex

2014-06-15

This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly locatedmore » observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.« less

Vehicle States Observer Using Adaptive Tire-Road Friction Estimator

NASA Astrophysics Data System (ADS)

Kwak, Byunghak; Park, Youngjin

Vehicle stability control system is a new idea which can enhance the vehicle stability and handling in the emergency situation. This system requires the information of the yaw rate, sideslip angle and road friction in order to control the traction and braking forces at the individual wheels. This paper proposes an observer for the vehicle stability control system. This observer consisted of the state observer for vehicle motion estimation and the road condition estimator for the identification of the coefficient of the road friction. The state observer uses 2 degrees-of-freedom bicycle model and estimates the system variables based on the Kalman filter. The road condition estimator uses the same vehicle model and identifies the coefficient of the tire-road friction based on the recursive least square method. Both estimators make use of each other information. We show the effectiveness and feasibility of the proposed scheme under various road conditions through computer simulations of a fifteen degree-of-freedom non-linear vehicle model.

Triple collocation-based estimation of spatially correlated observation error covariance in remote sensing soil moisture data assimilation

NASA Astrophysics Data System (ADS)

Wu, Kai; Shu, Hong; Nie, Lei; Jiao, Zhenhang

2018-01-01

Spatially correlated errors are typically ignored in data assimilation, thus degenerating the observation error covariance R to a diagonal matrix. We argue that a nondiagonal R carries more observation information making assimilation results more accurate. A method, denoted TC_Cov, was proposed for soil moisture data assimilation to estimate spatially correlated observation error covariance based on triple collocation (TC). Assimilation experiments were carried out to test the performance of TC_Cov. AMSR-E soil moisture was assimilated with a diagonal R matrix computed using the TC and assimilated using a nondiagonal R matrix, as estimated by proposed TC_Cov. The ensemble Kalman filter was considered as the assimilation method. Our assimilation results were validated against climate change initiative data and ground-based soil moisture measurements using the Pearson correlation coefficient and unbiased root mean square difference metrics. These experiments confirmed that deterioration of diagonal R assimilation results occurred when model simulation is more accurate than observation data. Furthermore, nondiagonal R achieved higher correlation coefficient and lower ubRMSD values over diagonal R in experiments and demonstrated the effectiveness of TC_Cov to estimate richly structuralized R in data assimilation. In sum, compared with diagonal R, nondiagonal R may relieve the detrimental effects of assimilation when simulated model results outperform observation data.

Observer variability in estimating numbers: An experiment

USGS Publications Warehouse

Erwin, R.M.

1982-01-01

Census estimates of bird populations provide an essential framework for a host of research and management questions. However, with some exceptions, the reliability of numerical estimates and the factors influencing them have received insufficient attention. Independent of the problems associated with habitat type, weather conditions, cryptic coloration, ete., estimates may vary widely due only to intrinsic differences in observers? abilities to estimate numbers. Lessons learned in the field of perceptual psychology may be usefully applied to 'real world' problems in field ornithology. Based largely on dot discrimination tests in the laboratory, it was found that numerical abundance, density of objects, spatial configuration, color, background, and other variables influence individual accuracy in estimating numbers. The primary purpose of the present experiment was to assess the effects of observer, prior experience, and numerical range on accuracy in estimating numbers of waterfowl from black-and-white photographs. By using photographs of animals rather than black dots, I felt the results could be applied more meaningfully to field situations. Further, reinforcement was provided throughout some experiments to examine the influence of training on accuracy.

Estimation of soft sediment thickness in Kuala Lumpur based on microtremor observation data

NASA Astrophysics Data System (ADS)

Chiew, Chang Chyau; Cheah, Yi Ben; Tan, Chin Guan; Lau, Tze Liang

2017-10-01

Seismic site effect is one of the major concerns in earthquake engineering. Soft ground tends to amplify the seismic wave in surficial geological layers. The determination of soft ground thickness on the surface layers of the earth is an important input for seismic hazard assessment. This paper presents an easy and convenient approach to estimate the soft sediment thickness at the site using microtremor observation technique. A total number of 133 survey points were conducted in selected sites around Kuala Lumpur area using a microtremor measuring instrument, but only 103 survey points contributed to the seismic microzonation and sediment thickness plots. The bedrock of Kuala Lumpur area is formed by Kenny Hill Formation, limestone, granite, and the Hawthornden Schist; however, the thickness of surface soft ground formed by alluvial deposits, mine tailings, and residual soils remains unknown. Hence, the predominant frequency of the ground in each site was determined based on Nakamura method. A total number of 14 sites with known depth to bedrock from the supply of geotechnical reports in the study area were determined. An empirical correlation was developed to relate the ground predominant frequency and soft ground thickness. This correlation may contribute to local soil underlying the subsurface of Kuala Lumpur area. The finding provides an important relationship for engineers to estimate the soft ground thickness in Kuala Lumpur area based on the dynamic characteristics of the ground measured from microtremor observation.

Estimating thermal performance curves from repeated field observations

USGS Publications Warehouse

Childress, Evan; Letcher, Benjamin H.

2017-01-01

Estimating thermal performance of organisms is critical for understanding population distributions and dynamics and predicting responses to climate change. Typically, performance curves are estimated using laboratory studies to isolate temperature effects, but other abiotic and biotic factors influence temperature-performance relationships in nature reducing these models' predictive ability. We present a model for estimating thermal performance curves from repeated field observations that includes environmental and individual variation. We fit the model in a Bayesian framework using MCMC sampling, which allowed for estimation of unobserved latent growth while propagating uncertainty. Fitting the model to simulated data varying in sampling design and parameter values demonstrated that the parameter estimates were accurate, precise, and unbiased. Fitting the model to individual growth data from wild trout revealed high out-of-sample predictive ability relative to laboratory-derived models, which produced more biased predictions for field performance. The field-based estimates of thermal maxima were lower than those based on laboratory studies. Under warming temperature scenarios, field-derived performance models predicted stronger declines in body size than laboratory-derived models, suggesting that laboratory-based models may underestimate climate change effects. The presented model estimates true, realized field performance, avoiding assumptions required for applying laboratory-based models to field performance, which should improve estimates of performance under climate change and advance thermal ecology.

Observation-based Estimate of Climate Sensitivity with a Scaling Climate Response Function

NASA Astrophysics Data System (ADS)

Hébert, Raphael; Lovejoy, Shaun

2016-04-01

To properly adress the anthropogenic impacts upon the earth system, an estimate of the climate sensitivity to radiative forcing is essential. Observation-based estimates of climate sensitivity are often limited by their ability to take into account the slower response of the climate system imparted mainly by the large thermal inertia of oceans, they are nevertheless essential to provide an alternative to estimates from global circulation models and increase our confidence in estimates of climate sensitivity by the multiplicity of approaches. It is straightforward to calculate the Effective Climate Sensitivity(EffCS) as the ratio of temperature change to the change in radiative forcing; the result is almost identical to the Transient Climate Response(TCR), but it underestimates the Equilibrium Climate Sensitivity(ECS). A study of global mean temperature is thus presented assuming a Scaling Climate Response Function to deterministic radiative forcing. This general form is justified as there exists a scaling symmetry respected by the dynamics, and boundary conditions, over a wide range of scales and it allows for long-range dependencies while retaining only 3 parameter which are estimated empirically. The range of memory is modulated by the scaling exponent H. We can calculate, analytically, a one-to-one relation between the scaling exponent H and the ratio of EffCS to TCR and EffCS to ECS. The scaling exponent of the power law is estimated by a regression of temperature as a function of forcing. We consider for the analysis 4 different datasets of historical global mean temperature and 100 scenario runs of the Coupled Model Intercomparison Project Phase 5 distributed among the 4 Representative Concentration Pathways(RCP) scenarios. We find that the error function for the estimate on historical temperature is very wide and thus, many scaling exponent can be used without meaningful changes in the fit residuals of historical temperatures; their response in the year 2100

Tire Force Estimation using a Proportional Integral Observer

NASA Astrophysics Data System (ADS)

Farhat, Ahmad; Koenig, Damien; Hernandez-Alcantara, Diana; Morales-Menendez, Ruben

2017-01-01

This paper addresses a method for detecting critical stability situations in the lateral vehicle dynamics by estimating the non-linear part of the tire forces. These forces indicate the road holding performance of the vehicle. The estimation method is based on a robust fault detection and estimation approach which minimize the disturbance and uncertainties to residual sensitivity. It consists in the design of a Proportional Integral Observer (PIO), while minimizing the well known H ∞ norm for the worst case uncertainties and disturbance attenuation, and combining a transient response specification. This multi-objective problem is formulated as a Linear Matrix Inequalities (LMI) feasibility problem where a cost function subject to LMI constraints is minimized. This approach is employed to generate a set of switched robust observers for uncertain switched systems, where the convergence of the observer is ensured using a Multiple Lyapunov Function (MLF). Whilst the forces to be estimated can not be physically measured, a simulation scenario with CarSimTM is presented to illustrate the developed method.

View Estimation Based on Value System

NASA Astrophysics Data System (ADS)

Takahashi, Yasutake; Shimada, Kouki; Asada, Minoru

Estimation of a caregiver's view is one of the most important capabilities for a child to understand the behavior demonstrated by the caregiver, that is, to infer the intention of behavior and/or to learn the observed behavior efficiently. We hypothesize that the child develops this ability in the same way as behavior learning motivated by an intrinsic reward, that is, he/she updates the model of the estimated view of his/her own during the behavior imitated from the observation of the behavior demonstrated by the caregiver based on minimizing the estimation error of the reward during the behavior. From this view, this paper shows a method for acquiring such a capability based on a value system from which values can be obtained by reinforcement learning. The parameters of the view estimation are updated based on the temporal difference error (hereafter TD error: estimation error of the state value), analogous to the way such that the parameters of the state value of the behavior are updated based on the TD error. Experiments with simple humanoid robots show the validity of the method, and the developmental process parallel to young children's estimation of its own view during the imitation of the observed behavior of the caregiver is discussed.

Advances in Assimilation of Satellite-Based Passive Microwave Observations for Soil-Moisture Estimation

NASA Technical Reports Server (NTRS)

De Lannoy, Gabrielle J. M.; Pauwels, Valentijn; Reichle, Rolf H.; Draper, Clara; Koster, Randy; Liu, Qing

2012-01-01

Satellite-based microwave measurements have long shown potential to provide global information about soil moisture. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS, [1]) mission as well as the future National Aeronautics and Space Administration (NASA) Soil Moisture Active and Passive (SMAP, [2]) mission measure passive microwave emission at L-band frequencies, at a relatively coarse (40 km) spatial resolution. In addition, SMAP will measure active microwave signals at a higher spatial resolution (3 km). These new L-band missions have a greater sensing depth (of -5cm) compared with past and present C- and X-band microwave sensors. ESA currently also disseminates retrievals of SMOS surface soil moisture that are derived from SMOS brightness temperature observations and ancillary data. In this research, we address two major challenges with the assimilation of recent/future satellite-based microwave measurements: (i) assimilation of soil moisture retrievals versus brightness temperatures for surface and root-zone soil moisture estimation and (ii) scale-mismatches between satellite observations, models and in situ validation data.

A comparative study of kalman filtering based observer and sliding mode observer for state of charge estimation

NASA Astrophysics Data System (ADS)

Ben Sassi, Hicham; Errahimi, Fatima; Es-Sbai, Najia; Alaoui, Chakib

2018-05-01

Nowadays, electric mobility is starting to define society and is becoming more and more irreplaceable and essential to daily activities. Safe and durable battery is of a great significance for this type of mobility, hence the increasing interest of research activity oriented to battery studies, in order to assure safe operating mode and to control the battery in case of any abnormal functioning conditions that could damage the battery if not properly managed. Lithium-ion technology is considered the most suitable existing technology for electrical storage, because of their interesting features such as their relatively long cycle life, lighter weight, their high energy density, However, there is a lot of work that is still needed to be done in order to assure safe operating lithium-ion batteries, starting with their internal status monitoring, cell balancing within a battery pack, and thermal management. Tasks that are accomplished by the battery management system (BMS) which uses the state of charge (SOC) as an indicator of the internal charge level of the battery, in order to avoid unpredicted system interruption. Since the state of charge is an inner state of a the battery which cannot be directly measured, a powerful estimation technique is inevitable, in this paper we investigate the performances of tow estimation strategies; kalman filtering based observers and sliding mode observers, both strategies are compared in terms of accuracy, design requirement, and overall performances.

Observationally constrained estimates of carbonaceous aerosol radiative forcing.

PubMed

Chung, Chul E; Ramanathan, V; Decremer, Damien

2012-07-17

Carbonaceous aerosols (CA) emitted by fossil and biomass fuels consist of black carbon (BC), a strong absorber of solar radiation, and organic matter (OM). OM scatters as well as absorbs solar radiation. The absorbing component of OM, which is ignored in most climate models, is referred to as brown carbon (BrC). Model estimates of the global CA radiative forcing range from 0 to 0.7 Wm(-2), to be compared with the Intergovernmental Panel on Climate Change's estimate for the pre-Industrial to the present net radiative forcing of about 1.6 Wm(-2). This study provides a model-independent, observationally based estimate of the CA direct radiative forcing. Ground-based aerosol network data is integrated with field data and satellite-based aerosol observations to provide a decadal (2001 through 2009) global view of the CA optical properties and direct radiative forcing. The estimated global CA direct radiative effect is about 0.75 Wm(-2) (0.5 to 1.0). This study identifies the global importance of BrC, which is shown to contribute about 20% to 550-nm CA solar absorption globally. Because of the inclusion of BrC, the net effect of OM is close to zero and the CA forcing is nearly equal to that of BC. The CA direct radiative forcing is estimated to be about 0.65 (0.5 to about 0.8) Wm(-2), thus comparable to or exceeding that by methane. Caused in part by BrC absorption, CAs have a net warming effect even over open biomass-burning regions in Africa and the Amazon.

Observationally constrained estimates of carbonaceous aerosol radiative forcing

PubMed Central

Chung, Chul E.; Ramanathan, V.; Decremer, Damien

2012-01-01

Carbonaceous aerosols (CA) emitted by fossil and biomass fuels consist of black carbon (BC), a strong absorber of solar radiation, and organic matter (OM). OM scatters as well as absorbs solar radiation. The absorbing component of OM, which is ignored in most climate models, is referred to as brown carbon (BrC). Model estimates of the global CA radiative forcing range from 0 to 0.7 Wm-2, to be compared with the Intergovernmental Panel on Climate Change’s estimate for the pre-Industrial to the present net radiative forcing of about 1.6 Wm-2. This study provides a model-independent, observationally based estimate of the CA direct radiative forcing. Ground-based aerosol network data is integrated with field data and satellite-based aerosol observations to provide a decadal (2001 through 2009) global view of the CA optical properties and direct radiative forcing. The estimated global CA direct radiative effect is about 0.75 Wm-2 (0.5 to 1.0). This study identifies the global importance of BrC, which is shown to contribute about 20% to 550-nm CA solar absorption globally. Because of the inclusion of BrC, the net effect of OM is close to zero and the CA forcing is nearly equal to that of BC. The CA direct radiative forcing is estimated to be about 0.65 (0.5 to about 0.8) Wm-2, thus comparable to or exceeding that by methane. Caused in part by BrC absorption, CAs have a net warming effect even over open biomass-burning regions in Africa and the Amazon. PMID:22753522

Snow Water Equivalent estimation based on satellite observation

NASA Astrophysics Data System (ADS)

Macchiavello, G.; Pesce, F.; Boni, G.; Gabellani, S.

2009-09-01

The availability of remotely sensed images and them analysis is a powerful tool for monitoring the extension and typology of snow cover over territory where the in situ measurements are often difficult. Information on snow are fundamental for monitoring and forecasting the available water above all in regions at mid latitudes as Mediterranean where snowmelt may cause floods. The hydrological model requirements and the daily acquisitions of MODIS (Moderate Resolution Imaging Spectroradiometer), drove, in previous research activities, to the development of a method to automatically map the snow cover from multi-spectral images. But, the major hydrological parameter related to the snow pack is the Snow Water Equivalent (SWE). This represents a direct measure of stored water in the basin. Because of it, the work was focused to the daily estimation of SWE from MODIS images. But, the complexity of this aim, based only on optical data, doesn’t find any information in literature. Since, from the spectral range of MODIS data it is not possible to extract a direct relation between spectral information and the SWE. Then a new method, respectful of the physic of the snow, was defined and developed. Reminding that the snow water equivalent is the product of the three factors as snow density, snow depth and the snow covered areas, the proposed approach works separately on each of these physical behaviors. Referring to the physical characteristic of snow, the snow density is function of the snow age, then it was studied a new method to evaluate this. Where, a module for snow age simulation from albedo information was developed. It activates an age counter updated by new snow information set to estimate snow age from zero accumulation status to the end of melting season. The height of the snow pack, can be retrieved by adopting relation between vegetation and snow depth distributions. This computes snow height distribution by the relation between snow cover fraction and the

Estimating sensitivity and specificity for technology assessment based on observer studies.

PubMed

Nishikawa, Robert M; Pesce, Lorenzo L

2013-07-01

The goal of this study was to determine the accuracy and precision of using scores from a receiver operating characteristic rating scale to estimate sensitivity and specificity. We used data collected in a previous study that measured the improvements in radiologists' ability to classify mammographic microcalcification clusters as benign or malignant with and without the use of a computer-aided diagnosis scheme. Sensitivity and specificity were estimated from the rating data from a question that directly asked the radiologists their biopsy recommendations, which was used as the "truth," because it is the actual recall decision, thus it is their subjective truth. By thresholding the rating data, sensitivity and specificity were estimated for different threshold values. Because of interreader and intrareader variability, estimated sensitivity and specificity values for individual readers could be as much as 100% in error when using rating data compared to using the biopsy recommendation data. When pooled together, the estimates using thresholding the rating data were in good agreement with sensitivity and specificity estimated from the recommendation data. However, the statistical power of the rating data estimates was lower. By simply asking the observer his or her explicit recommendation (eg, biopsy or no biopsy), sensitivity and specificity can be measured directly, giving a more accurate description of empirical variability and the power of the study can be maximized. Copyright © 2013 AUR. Published by Elsevier Inc. All rights reserved.

A global trait-based approach to estimate leaf nitrogen functional allocation from observations

DOE PAGES

Ghimire, Bardan; Riley, William J.; Koven, Charles D.; ...

2017-03-28

Nitrogen is one of the most important nutrients for plant growth and a major constituent of proteins that regulate photosynthetic and respiratory processes. However, a comprehensive global analysis of nitrogen allocation in leaves for major processes with respect to different plant functional types is currently lacking. This study integrated observations from global databases with photosynthesis and respiration models to determine plant-functional-type-specific allocation patterns of leaf nitrogen for photosynthesis (Rubisco, electron transport, light absorption) and respiration (growth and maintenance), and by difference from observed total leaf nitrogen, an unexplained “residual” nitrogen pool. Based on our analysis, crops partition the largest fractionmore » of nitrogen to photosynthesis (57%) and respiration (5%) followed by herbaceous plants (44% and 4%). Tropical broadleaf evergreen trees partition the least to photosynthesis (25%) and respiration (2%) followed by needle-leaved evergreen trees (28% and 3%). In trees (especially needle-leaved evergreen and tropical broadleaf evergreen trees) a large fraction (70% and 73% respectively) of nitrogen was not explained by photosynthetic or respiratory functions. Compared to crops and herbaceous plants, this large residual pool is hypothesized to emerge from larger investments in cell wall proteins, lipids, amino acids, nucleic acid, CO2 fixation proteins (other than Rubisco), secondary compounds, and other proteins. Our estimates are different from previous studies due to differences in methodology and assumptions used in deriving nitrogen allocation estimates. Unlike previous studies, we integrate and infer nitrogen allocation estimates across multiple plant functional types, and report substantial differences in nitrogen allocation across different plant functional types. Furthermore, the resulting pattern of nitrogen allocation provides insights on mechanisms that operate at a cellular scale within leaves

A global trait-based approach to estimate leaf nitrogen functional allocation from observations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ghimire, Bardan; Riley, William J.; Koven, Charles D.

Nitrogen is one of the most important nutrients for plant growth and a major constituent of proteins that regulate photosynthetic and respiratory processes. However, a comprehensive global analysis of nitrogen allocation in leaves for major processes with respect to different plant functional types is currently lacking. This study integrated observations from global databases with photosynthesis and respiration models to determine plant-functional-type-specific allocation patterns of leaf nitrogen for photosynthesis (Rubisco, electron transport, light absorption) and respiration (growth and maintenance), and by difference from observed total leaf nitrogen, an unexplained “residual” nitrogen pool. Based on our analysis, crops partition the largest fractionmore » of nitrogen to photosynthesis (57%) and respiration (5%) followed by herbaceous plants (44% and 4%). Tropical broadleaf evergreen trees partition the least to photosynthesis (25%) and respiration (2%) followed by needle-leaved evergreen trees (28% and 3%). In trees (especially needle-leaved evergreen and tropical broadleaf evergreen trees) a large fraction (70% and 73% respectively) of nitrogen was not explained by photosynthetic or respiratory functions. Compared to crops and herbaceous plants, this large residual pool is hypothesized to emerge from larger investments in cell wall proteins, lipids, amino acids, nucleic acid, CO2 fixation proteins (other than Rubisco), secondary compounds, and other proteins. Our estimates are different from previous studies due to differences in methodology and assumptions used in deriving nitrogen allocation estimates. Unlike previous studies, we integrate and infer nitrogen allocation estimates across multiple plant functional types, and report substantial differences in nitrogen allocation across different plant functional types. Furthermore, the resulting pattern of nitrogen allocation provides insights on mechanisms that operate at a cellular scale within leaves

Noncommuting observables in quantum detection and estimation theory

NASA Technical Reports Server (NTRS)

Helstrom, C. W.

1972-01-01

Basing decisions and estimates on simultaneous approximate measurements of noncommuting observables in a quantum receiver is shown to be equivalent to measuring commuting projection operators on a larger Hilbert space than that of the receiver itself. The quantum-mechanical Cramer-Rao inequalities derived from right logarithmic derivatives and symmetrized logarithmic derivatives of the density operator are compared, and it is shown that the latter give superior lower bounds on the error variances of individual unbiased estimates of arrival time and carrier frequency of a coherent signal. For a suitably weighted sum of the error variances of simultaneous estimates of these, the former yield the superior lower bound under some conditions.

Basin Scale Estimates of Evapotranspiration Using GRACE and other Observations

NASA Technical Reports Server (NTRS)

Rodell, M.; Famiglietti, J. S.; Chen, J.; Seneviratne, S. I.; Viterbo, P.; Holl, S.; Wilson, C. R.

2004-01-01

Evapotranspiration is integral to studies of the Earth system, yet it is difficult to measure on regional scales. One estimation technique is a terrestrial water budget, i.e., total precipitation minus the sum of evapotranspiration and net runoff equals the change in water storage. Gravity Recovery and Climate Experiment (GRACE) satellite gravity observations are now enabling closure of this equation by providing the terrestrial water storage change. Equations are presented here for estimating evapotranspiration using observation based information, taking into account the unique nature of GRACE observations. GRACE water storage changes are first substantiated by comparing with results from a land surface model and a combined atmospheric-terrestrial water budget approach. Evapotranspiration is then estimated for 14 time periods over the Mississippi River basin and compared with output from three modeling systems. The GRACE estimates generally lay in the middle of the models and may provide skill in evaluating modeled evapotranspiration.

Nonlinear Estimation of Discrete-Time Signals Under Random Observation Delay

DOE Office of Scientific and Technical Information (OSTI.GOV)

Caballero-Aguila, R.; Jimenez-Lopez, J. D.; Hermoso-Carazo, A.

2008-11-06

This paper presents an approximation to the nonlinear least-squares estimation problem of discrete-time stochastic signals using nonlinear observations with additive white noise which can be randomly delayed by one sampling time. The observation delay is modelled by a sequence of independent Bernoulli random variables whose values, zero or one, indicate that the real observation arrives on time or it is delayed and, hence, the available measurement to estimate the signal is not up-to-date. Assuming that the state-space model generating the signal is unknown and only the covariance functions of the processes involved in the observation equation are ready for use,more » a filtering algorithm based on linear approximations of the real observations is proposed.« less

An observationally constrained estimate of global dust aerosol optical depth

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ridley, David A.; Heald, Colette L.; Kok, Jasper F.

Here, the role of mineral dust in climate and ecosystems has been largely quantified using global climate and chemistry model simulations of dust emission, transport, and deposition. However, differences between these model simulations are substantial, with estimates of global dust aerosol optical depth (AOD) that vary by over a factor of 5. Here we develop an observationally based estimate of the global dust AOD, using multiple satellite platforms, in situ AOD observations and four state-of-the-science global models over 2004–2008. We estimate that the global dust AOD at 550 nm is 0.030 ± 0.005 (1σ), higher than the AeroCom model medianmore » (0.023) and substantially narrowing the uncertainty. The methodology used provides regional, seasonal dust AOD and the associated statistical uncertainty for key dust regions around the globe with which model dust schemes can be evaluated. Exploring the regional and seasonal differences in dust AOD between our observationally based estimate and the four models in this study, we find that emissions in Africa are often overrepresented at the expense of Asian and Middle Eastern emissions and that dust removal appears to be too rapid in most models.« less

An observationally constrained estimate of global dust aerosol optical depth

DOE PAGES

Ridley, David A.; Heald, Colette L.; Kok, Jasper F.; ...

2016-12-06

Here, the role of mineral dust in climate and ecosystems has been largely quantified using global climate and chemistry model simulations of dust emission, transport, and deposition. However, differences between these model simulations are substantial, with estimates of global dust aerosol optical depth (AOD) that vary by over a factor of 5. Here we develop an observationally based estimate of the global dust AOD, using multiple satellite platforms, in situ AOD observations and four state-of-the-science global models over 2004–2008. We estimate that the global dust AOD at 550 nm is 0.030 ± 0.005 (1σ), higher than the AeroCom model medianmore » (0.023) and substantially narrowing the uncertainty. The methodology used provides regional, seasonal dust AOD and the associated statistical uncertainty for key dust regions around the globe with which model dust schemes can be evaluated. Exploring the regional and seasonal differences in dust AOD between our observationally based estimate and the four models in this study, we find that emissions in Africa are often overrepresented at the expense of Asian and Middle Eastern emissions and that dust removal appears to be too rapid in most models.« less

Fine-tuning satellite-based rainfall estimates

NASA Astrophysics Data System (ADS)

Harsa, Hastuadi; Buono, Agus; Hidayat, Rahmat; Achyar, Jaumil; Noviati, Sri; Kurniawan, Roni; Praja, Alfan S.

2018-05-01

Rainfall datasets are available from various sources, including satellite estimates and ground observation. The locations of ground observation scatter sparsely. Therefore, the use of satellite estimates is advantageous, because satellite estimates can provide data on places where the ground observations do not present. However, in general, the satellite estimates data contain bias, since they are product of algorithms that transform the sensors response into rainfall values. Another cause may come from the number of ground observations used by the algorithms as the reference in determining the rainfall values. This paper describe the application of bias correction method to modify the satellite-based dataset by adding a number of ground observation locations that have not been used before by the algorithm. The bias correction was performed by utilizing Quantile Mapping procedure between ground observation data and satellite estimates data. Since Quantile Mapping required mean and standard deviation of both the reference and the being-corrected data, thus the Inverse Distance Weighting scheme was applied beforehand to the mean and standard deviation of the observation data in order to provide a spatial composition of them, which were originally scattered. Therefore, it was possible to provide a reference data point at the same location with that of the satellite estimates. The results show that the new dataset have statistically better representation of the rainfall values recorded by the ground observation than the previous dataset.

Estimation of time averages from irregularly spaced observations - With application to coastal zone color scanner estimates of chlorophyll concentration

NASA Technical Reports Server (NTRS)

Chelton, Dudley B.; Schlax, Michael G.

1991-01-01

The sampling error of an arbitrary linear estimate of a time-averaged quantity constructed from a time series of irregularly spaced observations at a fixed located is quantified through a formalism. The method is applied to satellite observations of chlorophyll from the coastal zone color scanner. The two specific linear estimates under consideration are the composite average formed from the simple average of all observations within the averaging period and the optimal estimate formed by minimizing the mean squared error of the temporal average based on all the observations in the time series. The resulting suboptimal estimates are shown to be more accurate than composite averages. Suboptimal estimates are also found to be nearly as accurate as optimal estimates using the correct signal and measurement error variances and correlation functions for realistic ranges of these parameters, which makes it a viable practical alternative to the composite average method generally employed at present.

Combining Satellite Microwave Radiometer and Radar Observations to Estimate Atmospheric Latent Heating Profiles

NASA Technical Reports Server (NTRS)

Grecu, Mircea; Olson, William S.; Shie, Chung-Lin; L'Ecuyer, Tristan S.; Tao, Wei-Kuo

2009-01-01

In this study, satellite passive microwave sensor observations from the TRMM Microwave Imager (TMI) are utilized to make estimates of latent + eddy sensible heating rates (Q1-QR) in regions of precipitation. The TMI heating algorithm (TRAIN) is calibrated, or "trained" using relatively accurate estimates of heating based upon spaceborne Precipitation Radar (PR) observations collocated with the TMI observations over a one-month period. The heating estimation technique is based upon a previously described Bayesian methodology, but with improvements in supporting cloud-resolving model simulations, an adjustment of precipitation echo tops to compensate for model biases, and a separate scaling of convective and stratiform heating components that leads to an approximate balance between estimated vertically-integrated condensation and surface precipitation. Estimates of Q1-QR from TMI compare favorably with the PR training estimates and show only modest sensitivity to the cloud-resolving model simulations of heating used to construct the training data. Moreover, the net condensation in the corresponding annual mean satellite latent heating profile is within a few percent of the annual mean surface precipitation rate over the tropical and subtropical oceans where the algorithm is applied. Comparisons of Q1 produced by combining TMI Q1-QR with independently derived estimates of QR show reasonable agreement with rawinsonde-based analyses of Q1 from two field campaigns, although the satellite estimates exhibit heating profile structure with sharper and more intense heating peaks than the rawinsonde estimates. 2

Estimating Global Impervious Surface based on Social-economic Data and Satellite Observations

NASA Astrophysics Data System (ADS)

Zeng, Z.; Zhang, K.; Xue, X.; Hong, Y.

2016-12-01

Impervious surface areas around the globe are expanding and significantly altering the surface energy balance, hydrology cycle and ecosystem services. Many studies have underlined the importance of impervious surface, r from hydrological modeling to contaminant transport monitoring and urban development estimation. Therefore accurate estimation of the global impervious surface is important for both physical and social sciences. Given the limited coverage of high spatial resolution imagery and ground survey, using satellite remote sensing and geospatial data to estimate global impervious areas is a practical approach. Based on the previous work of area-weighted imperviousness for north branch of the Chicago River provided by HDR, this study developed a method to determine the percentage of impervious surface using latest global land cover categories from multi-source satellite observations, population density and gross domestic product (GDP) data. Percent impervious surface at 30-meter resolution were mapped. We found that 1.33% of the CONUS (105,814 km2) and 0.475% of the land surface (640,370km2) are impervious surfaces. To test the utility and practicality of the proposed method, National Land Cover Database (NLCD) 2011 percent developed imperviousness for the conterminous United States was used to evaluate our results. The average difference between the derived imperviousness from our method and the NLCD data across CONUS is 1.14%, while difference between our results and the NLCD data are within ±1% over 81.63% of the CONUS. The distribution of global impervious surface map indicates that impervious surfaces are primarily concentrated in China, India, Japan, USA and Europe where are highly populated and/or developed. This study proposes a straightforward way of mapping global imperviousness, which can provide useful information for hydrologic modeling and other applications.

Estimation of Untracked Geosynchronous Population from Short-Arc Angles-Only Observations

NASA Technical Reports Server (NTRS)

Healy, Liam; Matney, Mark

2017-01-01

Telescope observations of the geosynchronous regime will observe two basic types of objects --- objects related to geosynchronous earth orbit (GEO) satellites, and objects in highly elliptical geosynchronous transfer orbits (GTO). Because telescopes only measure angular rates, the GTO can occasionally mimic the motion of GEO objects over short arcs. A GEO census based solely on short arc telescope observations may be affected by these ``interlopers''. A census that includes multiple angular rates can get an accurate statistical estimate of the GTO population, and that then can be used to correct the estimate of the geosynchronous earth orbit population.

Estimating Soil Moisture from Satellite Microwave Observations

NASA Technical Reports Server (NTRS)

Owe, M.; VandeGriend, A. A.; deJeu, R.; deVries, J.; Seyhan, E.

1998-01-01

Cooperative research in microwave remote sensing between the Hydrological Sciences Branch of the NASA Goddard Space Flight Center and the Earth Sciences Faculty of the Vrije Universiteit Amsterdam began with the Botswana Water and Energy Balance Experiment and has continued through a series of highly successful International Research Programs. The collaboration between these two research institutions has resulted in significant scientific achievements, most notably in the area of satellite-based microwave remote sensing of soil moisture. The Botswana Program was the first joint research initiative between these two institutions, and provided a unique data base which included historical data sets of Scanning Multifrequency Microwave Radiometer (SN4NM) data, climate information, and extensive soil moisture measurements over several large experimental sites in southeast Botswana. These data were the basis for the development of new approaches in physically-based inverse modelling of soil moisture from satellite microwave observations. Among the results from this study were quantitative estimates of vegetation transmission properties at microwave frequencies. A single polarization modelling approach which used horizontally polarized microwave observations combined with monthly composites of Normalized Difference Vegetation Index was developed, and yielded good results. After more precise field experimentation with a ground-based radiometer system, a dual-polarization approach was subsequently developed. This new approach realized significant improvements in soil moisture estimation by satellite. Results from the Botswana study were subsequently applied to a desertification monitoring study for the country of Spain within the framework of the European Community science research programs EFEDA and RESMEDES. A dual frequency approach with only microwave data was used for this application. The Microwave Polarization Difference Index (MPDI) was calculated from 37 GHz data

Estimating fire severity and carbon emissions over Australian tropical savannas based on passive microwave satellite observations

NASA Astrophysics Data System (ADS)

Chen, X.; Liu, Y.; Evans, J. P.; Parinussa, R.

2017-12-01

Carbon emissions from large-scale fire activity over the Australian tropical savannas have strong inter-annual variability, due mainly to variations in fuel accumulation in response to rainfall. We investigated the use of a recently developed satellite-based vegetation optical depth (VOD) dataset to estimate fire severity and carbon emission. VOD is sensitive to the dynamics of all aboveground vegetation and available nearly every two days. For areas burned during 2003 - 2010, we calculated the VOD change (ΔVOD) pre- and post-fire and the associated loss in above ground biomass carbon. Both results compare well with widely-accepted approaches: ΔVOD agreed well with the Normalized Burn Ratio change (ΔNBR) and carbon loss with modelled emissions from the Global Fire Emissions Database (GFED). We found that the ΔVOD and ΔNBR are generally linearly related. The Pearson correlation coefficients (R) between VOD- and GFED-based fire carbon emissions for monthly and annual total estimates are very high, 0.92 and 0.96 respectively. A key feature of fire carbon emissions is the strong inter-annual variation, ranging from 21.1 million tonnes in 2010 to 84.3 million tonnes in 2004. This study demonstrates that a reasonable estimate of fire carbon emissions can be achieved in a timely manner based on multiple satellite observations over the regions where the emissions are primarily from aboveground vegetation loss, which can be complementary to the currently used approaches.

Observation- and Model-Based Estimates of Particulate Dry Nitrogen Deposition to the Oceans.

PubMed

Baker, Alex R; Kanakidou, Maria; Altieri, Katye E; Daskalakis, Nikos; Okin, Gregory S; Myriokefalitakis, Stelios; Dentener, Frank; Uematsu, Mitsuo; Sarin, Manmohan M; Duce, Robert A; Galloway, James N; Keene, William C; Singh, Arvind; Zamora, Lauren; Lamarque, Jean-Francois; Hsu, Shih-Chieh; Rohekar, Shital S; Prospero, Joseph M

2017-01-01

access to speciated parameters (NO 3 - and NH 4 + ) that are more relevant to the observed parameters and which are not available in ACCMIP. Dry deposition fluxes (CalDep) were calculated from the observed concentrations using estimates of dry deposition velocities. Model - observation ratios, weighted by grid-cell area and numbers of observations, (R A,n ) were used to assess the performance of the models. Comparison in the three study regions suggests that TM4 over-estimates NO 3 - concentrations (R A,n = 1.4 - 2.9) and under-estimates NH 4 + concentrations (R A,n = 0.5 - 0.7), with spatial distributions in the tropical Atlantic and northern Indian Ocean not being reproduced by the model. In the case of NH 4 + in the Indian Ocean, this discrepancy was probably due to seasonal biases in the sampling. Similar patterns were observed in the various comparisons of CalDep to ModDep (R A,n = 0.6 - 2.6 for NO 3 - , 0.6 - 3.1 for NH 4 + ). Values of R A,n for NH x CalDep - ModDep comparisons were approximately double the corresponding values for NH 4 + CalDep - ModDep comparisons due to the significant fraction of gas-phase NH 3 deposition incorporated in the TM4 and ACCMIP NH x model products. All of the comparisons suffered due to the scarcity of observational data and the large uncertainty in dry deposition velocities used to derive deposition fluxes from concentrations. These uncertainties have been a major limitation on estimates of the flux of material to the oceans for several decades. Recommendations are made for improvements in N deposition estimation through changes in observations, modelling and model - observation comparison procedures. Validation of modelled dry deposition requires effective comparisons to observable aerosol-phase species concentrations and this cannot be achieved if model products only report dry deposition flux over the ocean.

An adaptive observer for on-line tool wear estimation in turning, Part I: Theory

NASA Astrophysics Data System (ADS)

Danai, Kourosh; Ulsoy, A. Galip

1987-04-01

On-line sensing of tool wear has been a long-standing goal of the manufacturing engineering community. In the absence of any reliable on-line tool wear sensors, a new model-based approach for tool wear estimation has been proposed. This approach is an adaptive observer, based on force measurement, which uses both parameter and state estimation techniques. The design of the adaptive observer is based upon a dynamic state model of tool wear in turning. This paper (Part I) presents the model, and explains its use as the basis for the adaptive observer design. This model uses flank wear and crater wear as state variables, feed as the input, and the cutting force as the output. The suitability of the model as the basis for adaptive observation is also verified. The implementation of the adaptive observer requires the design of a state observer and a parameter estimator. To obtain the model parameters for tuning the adaptive observer procedures for linearisation of the non-linear model are specified. The implementation of the adaptive observer in turning and experimental results are presented in a companion paper (Part II).

A robust observer based on H∞ filtering with parameter uncertainties combined with Neural Networks for estimation of vehicle roll angle

NASA Astrophysics Data System (ADS)

Boada, Beatriz L.; Boada, Maria Jesus L.; Vargas-Melendez, Leandro; Diaz, Vicente

2018-01-01

Nowadays, one of the main objectives in road transport is to decrease the number of accident victims. Rollover accidents caused nearly 33% of all deaths from passenger vehicle crashes. Roll Stability Control (RSC) systems prevent vehicles from untripped rollover accidents. The lateral load transfer is the main parameter which is taken into account in the RSC systems. This parameter is related to the roll angle, which can be directly measured from a dual-antenna GPS. Nevertheless, this is a costly technique. For this reason, roll angle has to be estimated. In this paper, a novel observer based on H∞ filtering in combination with a neural network (NN) for the vehicle roll angle estimation is proposed. The design of this observer is based on four main criteria: to use a simplified vehicle model, to use signals of sensors which are installed onboard in current vehicles, to consider the inaccuracy in the system model and to attenuate the effect of the external disturbances. Experimental results show the effectiveness of the proposed observer.

Estimation of High-Frequency Earth-Space Radio Wave Signals via Ground-Based Polarimetric Radar Observations

NASA Technical Reports Server (NTRS)

Bolen, Steve; Chandrasekar, V.

2002-01-01

Expanding human presence in space, and enabling the commercialization of this frontier, is part of the strategic goals for NASA's Human Exploration and Development of Space (HEDS) enterprise. Future near-Earth and planetary missions will support the use of high-frequency Earth-space communication systems. Additionally, increased commercial demand on low-frequency Earth-space links in the S- and C-band spectra have led to increased interest in the use of higher frequencies in regions like Ku and Ka-band. Attenuation of high-frequency signals, due to a precipitating medium, can be quite severe and can cause considerable disruptions in a communications link that traverses such a medium. Previously, ground radar measurements were made along the Earth-space path and compared to satellite beacon data that was transmitted to a ground station. In this paper, quantitative estimation of the attenuation along the propagation path is made via inter-comparisons of radar data taken from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and ground-based polarimetric radar observations. Theoretical relationships between the expected specific attenuation (k) of spaceborne measurements with ground-based measurements of reflectivity (Zh) and differential propagation phase shift (Kdp) are developed for various hydrometeors that could be present along the propagation path, which are used to estimate the two-way path-integrated attenuation (PIA) on the PR return echo. Resolution volume matching and alignment of the radar systems is performed, and a direct comparison of PR return echo with ground radar attenuation estimates is made directly on a beam-by-beam basis. The technique is validated using data collected from the TExas and Florida UNderflights (TEFLUN-B) experiment and the TRMM large Biosphere-Atmosphere experiment in Amazonia (LBA) campaign. Attenuation estimation derived from this method can be used for strategiC planning of communication systems for

A diagnostic model to estimate winds and small-scale drag from Mars Observer PMIRR data

NASA Technical Reports Server (NTRS)

Barnes, J. R.

1993-01-01

Theoretical and modeling studies indicate that small-scale drag due to breaking gravity waves is likely to be of considerable importance for the circulation in the middle atmospheric region (approximately 40-100 km altitude) on Mars. Recent earth-based spectroscopic observations have provided evidence for the existence of circulation features, in particular, a warm winter polar region, associated with gravity wave drag. Since the Mars Observer PMIRR experiment will obtain temperature profiles extending from the surface up to about 80 km altitude, it will be extensively sampling middle atmospheric regions in which gravity wave drag may play a dominant role. Estimating the drag then becomes crucial to the estimation of the atmospheric winds from the PMIRR-observed temperatures. An interative diagnostic model based upon one previously developed and tested with earth satellite temperature data will be applied to the PMIRR measurements to produce estimates of the small-scale zonal drag and three-dimensional wind fields in the Mars middle atmosphere. This model is based on the primitive equations, and can allow for time dependence (the time tendencies used may be based upon those computed in a Fast Fourier Mapping procedure). The small-scale zonal drag is estimated as the residual in the zonal momentum equation; the horizontal winds having first been estimated from the meridional momentum equation and the continuity equation. The scheme estimates the vertical motions from the thermodynamic equation, and thus needs estimates of the diabatic heating based upon the observed temperatures. The latter will be generated using a radiative model. It is hoped that the diagnostic scheme will be able to produce good estimates of the zonal gravity wave drag in the Mars middle atmosphere, estimates that can then be used in other diagnostic or assimilation efforts, as well as more theoretical studies.

Estimating Precipitation Input to a Watershed by Combining Gauge and Radar Derived Observations

NASA Astrophysics Data System (ADS)

Ercan, M. B.; Goodall, J. L.

2011-12-01

One challenge in creating an accurate watershed model is obtaining estimates of precipitation intensity over the watershed area. While precipitation measurements are generally available from gauging stations and radar instruments, both of these approaches for measuring precipitation have strengths and weakness. A typical way of addressing this challenge is to use gauged precipitation estimates to calibrate radar based estimates, however this study proposes a slightly different approach in which the optimal daily precipitation value is selected from either the gauged or the radar estimates based on the observed streamflow for that day. Our proposed approach is perhaps most relevant for cases of modeling watersheds that do not have a nearby precipitation gauge, or for regions that experience convective storms that are often highly spatially variable. Using the Eno River watershed located in Orange County, NC, three different precipitation datasets were created to predict streamflow at the watershed outlet for the time period 2005-2010 using the Soil and Water Assessment Tool (SWAT): (1) estimates based on only precipitation gauging stations, (2) estimates based only on gauged-corrected radar observations, and (3) the combination of precipitation estimates from the gauge and radar data determined using our proposed approach. The results show that the combined precipitation approach significantly improves streamflow predictions (Nash-Sutcliffe Coefficient, E = 0.66) when compared to the gauged estimates alone (E = 0.47) and the radar based estimates alone (E = 0.45). Our study was limited to one watershed, therefore additional studies are needed to control for factors such as climate, ecology, and hydrogeology that will likely influence the results of the analysis.

Lidar-Based Estimates of Above-Ground Biomass in the Continental US and Mexico Using Ground, Airborne, and Satellite Observations

NASA Technical Reports Server (NTRS)

Nelson, Ross; Margolis, Hank; Montesano, Paul; Sun, Guoqing; Cook, Bruce; Corp, Larry; Andersen, Hans-Erik; DeJong, Ben; Pellat, Fernando Paz; Fickel, Thaddeus;

Spacecraft Angular Rates Estimation with Gyrowheel Based on Extended High Gain Observer.

PubMed

Liu, Xiaokun; Yao, Yu; Ma, Kemao; Zhao, Hui; He, Fenghua

2016-04-14

A gyrowheel (GW) is a kind of electronic electric-mechanical servo system, which can be applied to a spacecraft attitude control system (ACS) as both an actuator and a sensor simultaneously. In order to solve the problem of two-dimensional spacecraft angular rate sensing as a GW outputting three-dimensional control torque, this paper proposed a method of an extended high gain observer (EHGO) with the derived GW mathematical model to implement the spacecraft angular rate estimation when the GW rotor is working at large angles. For this purpose, the GW dynamic equation is firstly derived with the second kind Lagrange method, and the relationship between the measurable and unmeasurable variables is built. Then, the EHGO is designed to estimate and calculate spacecraft angular rates with the GW, and the stability of the designed EHGO is proven by the Lyapunov function. Moreover, considering the engineering application, the effect of measurement noise in the tilt angle sensors on the estimation accuracy of the EHGO is analyzed. Finally, the numerical simulation is performed to illustrate the validity of the method proposed in this paper.

Replacing climatological potential evapotranspiration estimates with dynamic satellite-based observations in operational hydrologic prediction models

NASA Astrophysics Data System (ADS)

Franz, K. J.; Bowman, A. L.; Hogue, T. S.; Kim, J.; Spies, R.

2011-12-01

In the face of a changing climate, growing populations, and increased human habitation in hydrologically risky locations, both short- and long-range planners increasingly require robust and reliable streamflow forecast information. Current operational forecasting utilizes watershed-scale, conceptual models driven by ground-based (commonly point-scale) observations of precipitation and temperature and climatological potential evapotranspiration (PET) estimates. The PET values are derived from historic pan evaporation observations and remain static from year-to-year. The need for regional dynamic PET values is vital for improved operational forecasting. With the advent of satellite remote sensing and the adoption of a more flexible operational forecast system by the National Weather Service, incorporation of advanced data products is now more feasible than in years past. In this study, we will test a previously developed satellite-derived PET product (UCLA MODIS-PET) in the National Weather Service forecast models and compare the model results to current methods. The UCLA MODIS-PET method is based on the Priestley-Taylor formulation, is driven with MODIS satellite products, and produces a daily, 250m PET estimate. The focus area is eight headwater basins in the upper Midwest U.S. There is a need to develop improved forecasting methods for this region that are able to account for climatic and landscape changes more readily and effectively than current methods. This region is highly flood prone yet sensitive to prolonged dry periods in late summer and early fall, and is characterized by a highly managed landscape, which has drastically altered the natural hydrologic cycle. Our goal is to improve model simulations, and thereby, the initial conditions prior to the start of a forecast through the use of PET values that better reflect actual watershed conditions. The forecast models are being tested in both distributed and lumped mode.

Global precipitation estimates based on a technique for combining satellite-based estimates, rain gauge analysis, and NWP model precipitation information

NASA Technical Reports Server (NTRS)

Huffman, George J.; Adler, Robert F.; Rudolf, Bruno; Schneider, Udo; Keehn, Peter R.

1995-01-01

The 'satellite-gauge model' (SGM) technique is described for combining precipitation estimates from microwave satellite data, infrared satellite data, rain gauge analyses, and numerical weather prediction models into improved estimates of global precipitation. Throughout, monthly estimates on a 2.5 degrees x 2.5 degrees lat-long grid are employed. First, a multisatellite product is developed using a combination of low-orbit microwave and geosynchronous-orbit infrared data in the latitude range 40 degrees N - 40 degrees S (the adjusted geosynchronous precipitation index) and low-orbit microwave data alone at higher latitudes. Then the rain gauge analysis is brougth in, weighting each field by its inverse relative error variance to produce a nearly global, observationally based precipitation estimate. To produce a complete global estimate, the numerical model results are used to fill data voids in the combined satellite-gauge estimate. Our sequential approach to combining estimates allows a user to select the multisatellite estimate, the satellite-gauge estimate, or the full SGM estimate (observationally based estimates plus the model information). The primary limitation in the method is imperfections in the estimation of relative error for the individual fields. The SGM results for one year of data (July 1987 to June 1988) show important differences from the individual estimates, including model estimates as well as climatological estimates. In general, the SGM results are drier in the subtropics than the model and climatological results, reflecting the relatively dry microwave estimates that dominate the SGM in oceanic regions.

Estimating the Grain Size Distribution of Mars based on Fragmentation Theory and Observations

NASA Astrophysics Data System (ADS)

Charalambous, C.; Pike, W. T.; Golombek, M.

2017-12-01

We present here a fundamental extension to the fragmentation theory [1] which yields estimates of the distribution of particle sizes of a planetary surface. The model is valid within the size regimes of surfaces whose genesis is best reflected by the evolution of fragmentation phenomena governed by either the process of meteoritic impacts, or by a mixture with aeolian transportation at the smaller sizes. The key parameter of the model, the regolith maturity index, can be estimated as an average of that observed at a local site using cratering size-frequency measurements, orbital and surface image-detected rock counts and observations of sub-mm particles at landing sites. Through validation of ground truth from previous landed missions, the basis of this approach has been used at the InSight landing ellipse on Mars to extrapolate rock size distributions in HiRISE images down to 5 cm rock size, both to determine the landing safety risk and the subsequent probability of obstruction by a rock of the deployed heat flow mole down to 3-5 m depth [2]. Here we focus on a continuous extrapolation down to 600 µm coarse sand particles, the upper size limit that may be present through aeolian processes [3]. The parameters of the model are first derived for the fragmentation process that has produced the observable rocks via meteorite impacts over time, and therefore extrapolation into a size regime that is affected by aeolian processes has limited justification without further refinement. Incorporating thermal inertia estimates, size distributions observed by the Spirit and Opportunity Microscopic Imager [4] and Atomic Force and Optical Microscopy from the Phoenix Lander [5], the model's parameters in combination with synthesis methods are quantitatively refined further to allow transition within the aeolian transportation size regime. In addition, due to the nature of the model emerging in fractional mass abundance, the percentage of material by volume or mass that resides

Consistent estimate of ocean warming, land ice melt and sea level rise from Observations

NASA Astrophysics Data System (ADS)

Blazquez, Alejandro; Meyssignac, Benoît; Lemoine, Jean Michel

2016-04-01

Based on the sea level budget closure approach, this study investigates the consistency of observed Global Mean Sea Level (GMSL) estimates from satellite altimetry, observed Ocean Thermal Expansion (OTE) estimates from in-situ hydrographic data (based on Argo for depth above 2000m and oceanic cruises below) and GRACE observations of land water storage and land ice melt for the period January 2004 to December 2014. The consistency between these datasets is a key issue if we want to constrain missing contributions to sea level rise such as the deep ocean contribution. Numerous previous studies have addressed this question by summing up the different contributions to sea level rise and comparing it to satellite altimetry observations (see for example Llovel et al. 2015, Dieng et al. 2015). Here we propose a novel approach which consists in correcting GRACE solutions over the ocean (essentially corrections of stripes and leakage from ice caps) with mass observations deduced from the difference between satellite altimetry GMSL and in-situ hydrographic data OTE estimates. We check that the resulting GRACE corrected solutions are consistent with original GRACE estimates of the geoid spherical harmonic coefficients within error bars and we compare the resulting GRACE estimates of land water storage and land ice melt with independent results from the literature. This method provides a new mass redistribution from GRACE consistent with observations from Altimetry and OTE. We test the sensibility of this method to the deep ocean contribution and the GIA models and propose best estimates.

An Empirical Orthogonal Function-Based Algorithm for Estimating Terrestrial Latent Heat Flux from Eddy Covariance, Meteorological and Satellite Observations

PubMed Central

Feng, Fei; Li, Xianglan; Yao, Yunjun; Liang, Shunlin; Chen, Jiquan; Zhao, Xiang; Jia, Kun; Pintér, Krisztina; McCaughey, J. Harry

2016-01-01

Accurate estimation of latent heat flux (LE) based on remote sensing data is critical in characterizing terrestrial ecosystems and modeling land surface processes. Many LE products were released during the past few decades, but their quality might not meet the requirements in terms of data consistency and estimation accuracy. Merging multiple algorithms could be an effective way to improve the quality of existing LE products. In this paper, we present a data integration method based on modified empirical orthogonal function (EOF) analysis to integrate the Moderate Resolution Imaging Spectroradiometer (MODIS) LE product (MOD16) and the Priestley-Taylor LE algorithm of Jet Propulsion Laboratory (PT-JPL) estimate. Twenty-two eddy covariance (EC) sites with LE observation were chosen to evaluate our algorithm, showing that the proposed EOF fusion method was capable of integrating the two satellite data sets with improved consistency and reduced uncertainties. Further efforts were needed to evaluate and improve the proposed algorithm at larger spatial scales and time periods, and over different land cover types. PMID:27472383

An Empirical Orthogonal Function-Based Algorithm for Estimating Terrestrial Latent Heat Flux from Eddy Covariance, Meteorological and Satellite Observations.

PubMed

Feng, Fei; Li, Xianglan; Yao, Yunjun; Liang, Shunlin; Chen, Jiquan; Zhao, Xiang; Jia, Kun; Pintér, Krisztina; McCaughey, J Harry

2016-01-01

Accurate estimation of latent heat flux (LE) based on remote sensing data is critical in characterizing terrestrial ecosystems and modeling land surface processes. Many LE products were released during the past few decades, but their quality might not meet the requirements in terms of data consistency and estimation accuracy. Merging multiple algorithms could be an effective way to improve the quality of existing LE products. In this paper, we present a data integration method based on modified empirical orthogonal function (EOF) analysis to integrate the Moderate Resolution Imaging Spectroradiometer (MODIS) LE product (MOD16) and the Priestley-Taylor LE algorithm of Jet Propulsion Laboratory (PT-JPL) estimate. Twenty-two eddy covariance (EC) sites with LE observation were chosen to evaluate our algorithm, showing that the proposed EOF fusion method was capable of integrating the two satellite data sets with improved consistency and reduced uncertainties. Further efforts were needed to evaluate and improve the proposed algorithm at larger spatial scales and time periods, and over different land cover types.

Spacecraft Angular Rates Estimation with Gyrowheel Based on Extended High Gain Observer

PubMed Central

Liu, Xiaokun; Yao, Yu; Ma, Kemao; Zhao, Hui; He, Fenghua

2016-01-01

A gyrowheel (GW) is a kind of electronic electric-mechanical servo system, which can be applied to a spacecraft attitude control system (ACS) as both an actuator and a sensor simultaneously. In order to solve the problem of two-dimensional spacecraft angular rate sensing as a GW outputting three-dimensional control torque, this paper proposed a method of an extended high gain observer (EHGO) with the derived GW mathematical model to implement the spacecraft angular rate estimation when the GW rotor is working at large angles. For this purpose, the GW dynamic equation is firstly derived with the second kind Lagrange method, and the relationship between the measurable and unmeasurable variables is built. Then, the EHGO is designed to estimate and calculate spacecraft angular rates with the GW, and the stability of the designed EHGO is proven by the Lyapunov function. Moreover, considering the engineering application, the effect of measurement noise in the tilt angle sensors on the estimation accuracy of the EHGO is analyzed. Finally, the numerical simulation is performed to illustrate the validity of the method proposed in this paper. PMID:27089347

First Estimate of the Exoplanet Population from Kepler Observations

NASA Astrophysics Data System (ADS)

Borucki, William J.; Koch, D. G.; Batalha, N.; Caldwell, D.; Dunham, E. W.; Gautier, T. N., III; Howell, S. B.; Jenkins, J. M.; Marcy, G. W.; Rowe, J.; Charbonneau, D.; Ciardi, D.; Ford, E. B.; Christiansen, J. L.; Kolodziejczak, J.; Prsa, A.

2011-05-01

William J. Borucki, David G. Koch, Natalie Batalha, Derek Buzasi , Doug Caldwell, David Charbonneau, Jessie L. Christiansen, David R. Ciardi, Edward Dunham, Eric B. Ford, Steve Thomas N. Gautier III, Steve Howell, Jon M. Jenkins, Jeffery Kolodziejczak, Geoffrey W. Marcy, Jason Rowe, and Andrej Prsa A model was developed to provide a first estimate of the intrinsic frequency of planetary candidates based on the number of detected planetary candidates and the measured noise for each of the 156,000 observed stars. The estimated distributions for the exoplanet frequency are presented with respect to the semi-major axis and the stellar effective temperature and represent values appropriate only to short-period candidates. Improved estimates are expected after a Monte Carlo study of the sensitivity of the data analysis pipeline to transit signals injected at the pixel level is completed.

Estimation of snowpack matching ground-truth data and MODIS satellite-based observations by using regression kriging

NASA Astrophysics Data System (ADS)

Juan Collados-Lara, Antonio; Pardo-Iguzquiza, Eulogio; Pulido-Velazquez, David

2016-04-01

The estimation of Snow Water Equivalent (SWE) is essential for an appropriate assessment of the available water resources in Alpine catchment. The hydrologic regime in these areas is dominated by the storage of water in the snowpack, which is discharged to rivers throughout the melt season. An accurate estimation of the resources will be necessary for an appropriate analysis of the system operation alternatives using basin scale management models. In order to obtain an appropriate estimation of the SWE we need to know the spatial distribution snowpack and snow density within the Snow Cover Area (SCA). Data for these snow variables can be extracted from in-situ point measurements and air-borne/space-borne remote sensing observations. Different interpolation and simulation techniques have been employed for the estimation of the cited variables. In this paper we propose to estimate snowpack from a reduced number of ground-truth data (1 or 2 campaigns per year with 23 observation point from 2000-2014) and MODIS satellite-based observations in the Sierra Nevada Mountain (Southern Spain). Regression based methodologies has been used to study snowpack distribution using different kind of explicative variables: geographic, topographic, climatic. 40 explicative variables were considered: the longitude, latitude, altitude, slope, eastness, northness, radiation, maximum upwind slope and some mathematical transformation of each of them [Ln(v), (v)^-1; (v)^2; (v)^0.5). Eight different structure of regression models have been tested (combining 1, 2, 3 or 4 explicative variables). Y=B0+B1Xi (1); Y=B0+B1XiXj (2); Y=B0+B1Xi+B2Xj (3); Y=B0+B1Xi+B2XjXl (4); Y=B0+B1XiXk+B2XjXl (5); Y=B0+B1Xi+B2Xj+B3Xl (6); Y=B0+B1Xi+B2Xj+B3XlXk (7); Y=B0+B1Xi+B2Xj+B3Xl+B4Xk (8). Where: Y is the snow depth; (Xi, Xj, Xl, Xk) are the prediction variables (any of the 40 variables); (B0, B1, B2, B3) are the coefficients to be estimated. The ground data are employed to calibrate the multiple regressions. In

Location- and lesion-dependent estimation of background tissue complexity for anthropomorphic model observer

NASA Astrophysics Data System (ADS)

Avanaki, Ali R. N.; Espig, Kathryn; Knippel, Eddie; Kimpe, Tom R. L.; Xthona, Albert; Maidment, Andrew D. A.

2016-03-01

In this paper, we specify a notion of background tissue complexity (BTC) as perceived by a human observer that is suited for use with model observers. This notion of BTC is a function of image location and lesion shape and size. We propose four unsupervised BTC estimators based on: (i) perceived pre- and post-lesion similarity of images, (ii) lesion border analysis (LBA; conspicuous lesion should be brighter than its surround), (iii) tissue anomaly detection, and (iv) mammogram density measurement. The latter two are existing methods we adapt for location- and lesion-dependent BTC estimation. To validate the BTC estimators, we ask human observers to measure BTC as the visibility threshold amplitude of an inserted lesion at specified locations in a mammogram. Both human-measured and computationally estimated BTC varied with lesion shape (from circular to oval), size (from small circular to larger circular), and location (different points across a mammogram). BTCs measured by different human observers are correlated (ρ=0.67). BTC estimators are highly correlated to each other (0.84observers (ρ<=0.81). With change in lesion shape or size, estimated BTC by LBA changes in the same direction as human-measured BTC. A generalization of proposed methods for viewing breast tomosynthesis sequences in cine mode is outlined. The proposed estimators, as-is or customized to a specific human observer, may be used to construct a BTC-aware model observer, with applications such as optimization of contrast-enhanced medical imaging systems, and creation of a diversified image dataset with characteristics of a desired population.

Using a thermal-based two source energy balance model with time-differencing to estimate surface energy fluxes with day-night MODIS observations

NASA Astrophysics Data System (ADS)

Guzinski, R.; Anderson, M. C.; Kustas, W. P.; Nieto, H.; Sandholt, I.

2013-02-01

The Dual Temperature Difference (DTD) model, introduced by Norman et al. (2000), uses a two source energy balance modelling scheme driven by remotely sensed observations of diurnal changes in land surface temperature (LST) to estimate surface energy fluxes. By using a time differential temperature measurement as input, the approach reduces model sensitivity to errors in absolute temperature retrieval. The original formulation of the DTD required an early morning LST observation (approximately 1 h after sunrise) when surface fluxes are minimal, limiting application to data provided by geostationary satellites at sub-hourly temporal resolution. The DTD model has been applied primarily during the active growth phase of agricultural crops and rangeland vegetation grasses, and has not been rigorously evaluated during senescence or in forested ecosystems. In this paper we present modifications to the DTD model that enable applications using thermal observation from polar orbiting satellites, such as Terra and Aqua, with day and night overpass times over the area of interest. This allows the application of the DTD model in high latitude regions where large viewing angles preclude the use of geostationary satellites, and also exploits the higher spatial resolution provided by polar orbiting satellites. A method for estimating nocturnal surface fluxes and a scheme for estimating the fraction of green vegetation are developed and evaluated. Modification for green vegetation fraction leads to significantly improved estimation of the heat fluxes from the vegetation canopy during senescence and in forests. Land-cover based modifications to the Priestley-Taylor scheme, used to estimate transpiration fluxes, are explored based on prior findings for conifer forests. When the modified DTD model is run with LST measurements acquired with the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra and Aqua satellites, generally satisfactory agreement with field

Estimating the number of double-strand breaks formed during meiosis from partial observation.

PubMed

Toyoizumi, Hiroshi; Tsubouchi, Hideo

2012-12-01

Analyzing the basic mechanism of DNA double-strand breaks (DSB) formation during meiosis is important for understanding sexual reproduction and genetic diversity. The location and amount of meiotic DSBs can be examined by using a common molecular biological technique called Southern blotting, but only a subset of the total DSBs can be observed; only DSB fragments still carrying the region recognized by a Southern blot probe are detected. With the assumption that DSB formation follows a nonhomogeneous Poisson process, we propose two estimators of the total number of DSBs on a chromosome: (1) an estimator based on the Nelson-Aalen estimator, and (2) an estimator based on a record value process. Further, we compared their asymptotic accuracy.

Variable input observer for state estimation of high-rate dynamics

NASA Astrophysics Data System (ADS)

Hong, Jonathan; Cao, Liang; Laflamme, Simon; Dodson, Jacob

2017-04-01

High-rate systems operating in the 10 μs to 10 ms timescale are likely to experience damaging effects due to rapid environmental changes (e.g., turbulence, ballistic impact). Some of these systems could benefit from real-time state estimation to enable their full potential. Examples of such systems include blast mitigation strategies, automotive airbag technologies, and hypersonic vehicles. Particular challenges in high-rate state estimation include: 1) complex time varying nonlinearities of system (e.g. noise, uncertainty, and disturbance); 2) rapid environmental changes; 3) requirement of high convergence rate. Here, we propose using a Variable Input Observer (VIO) concept to vary the input space as the event unfolds. When systems experience high-rate dynamics, rapid changes in the system occur. To investigate the VIO's potential, a VIO-based neuro-observer is constructed and studied using experimental data collected from a laboratory impact test. Results demonstrate that the input space is unique to different impact conditions, and that adjusting the input space throughout the dynamic event produces better estimations than using a traditional fixed input space strategy.

Effective wind speed estimation: Comparison between Kalman Filter and Takagi-Sugeno observer techniques.

PubMed

Gauterin, Eckhard; Kammerer, Philipp; Kühn, Martin; Schulte, Horst

2016-05-01

Advanced model-based control of wind turbines requires knowledge of the states and the wind speed. This paper benchmarks a nonlinear Takagi-Sugeno observer for wind speed estimation with enhanced Kalman Filter techniques: The performance and robustness towards model-structure uncertainties of the Takagi-Sugeno observer, a Linear, Extended and Unscented Kalman Filter are assessed. Hence the Takagi-Sugeno observer and enhanced Kalman Filter techniques are compared based on reduced-order models of a reference wind turbine with different modelling details. The objective is the systematic comparison with different design assumptions and requirements and the numerical evaluation of the reconstruction quality of the wind speed. Exemplified by a feedforward loop employing the reconstructed wind speed, the benefit of wind speed estimation within wind turbine control is illustrated. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

A Robust Nonlinear Observer for Real-Time Attitude Estimation Using Low-Cost MEMS Inertial Sensors

PubMed Central

Guerrero-Castellanos, José Fermi; Madrigal-Sastre, Heberto; Durand, Sylvain; Torres, Lizeth; Muñoz-Hernández, German Ardul

2013-01-01

This paper deals with the attitude estimation of a rigid body equipped with angular velocity sensors and reference vector sensors. A quaternion-based nonlinear observer is proposed in order to fuse all information sources and to obtain an accurate estimation of the attitude. It is shown that the observer error dynamics can be separated into two passive subsystems connected in “feedback”. Then, this property is used to show that the error dynamics is input-to-state stable when the measurement disturbance is seen as an input and the error as the state. These results allow one to affirm that the observer is “robustly stable”. The proposed observer is evaluated in real-time with the design and implementation of an Attitude and Heading Reference System (AHRS) based on low-cost MEMS (Micro-Electro-Mechanical Systems) Inertial Measure Unit (IMU) and magnetic sensors and a 16-bit microcontroller. The resulting estimates are compared with a high precision motion system to demonstrate its performance. PMID:24201316

Estimation of Global Subsurface Thermal Structure from Satellite Remote Sensing Observations Based on Machine Learning

NASA Astrophysics Data System (ADS)

Su, H.; Yan, X. H.

2017-12-01

Subsurface thermal structure of the global ocean is a key factor that reflects the impact of the global climate variability and change. Accurately determining and describing the global subsurface and deeper ocean thermal structure from satellite measurements is becoming even more important for understanding the ocean interior anomaly and dynamic processes during recent global warming and hiatus. It is essential but challenging to determine the extent to which such surface remote sensing observations can be used to develop information about the global ocean interior. This study proposed a Support Vector Regression (SVR) method to estimate Subsurface Temperature Anomaly (STA) in the global ocean. The SVR model can well estimate the global STA upper 1000 m through a suite of satellite remote sensing observations of sea surface parameters (including Sea Surface Height Anomaly (SSHA), Sea Surface Temperature Anomaly (SSTA), Sea Surface Salinity Anomaly (SSSA) and Sea Surface Wind Anomaly (SSWA)) with in situ Argo data for training and testing at different depth levels. Here, we employed the MSE and R2 to assess SVR performance on the STA estimation. The results from the SVR model were validated for the accuracy and reliability using the worldwide Argo STA data. The average MSE and R2 of the 15 levels are 0.0090 / 0.0086 / 0.0087 and 0.443 / 0.457 / 0.485 for 2-attributes (SSHA, SSTA) / 3-attributes (SSHA, SSTA, SSSA) / 4-attributes (SSHA, SSTA, SSSA, SSWA) SVR, respectively. The estimation accuracy was improved by including SSSA and SSWA for SVR input (MSE decreased by 0.4% / 0.3% and R2 increased by 1.4% / 4.2% on average). While, the estimation accuracy gradually decreased with the increase of the depth from 500 m. The results showed that SSSA and SSWA, in addition to SSTA and SSHA, are useful parameters that can help estimate the subsurface thermal structure, as well as improve the STA estimation accuracy. In future, we can figure out more potential and useful sea

Sieve estimation in semiparametric modeling of longitudinal data with informative observation times.

PubMed

Zhao, Xingqiu; Deng, Shirong; Liu, Li; Liu, Lei

2014-01-01

Analyzing irregularly spaced longitudinal data often involves modeling possibly correlated response and observation processes. In this article, we propose a new class of semiparametric mean models that allows for the interaction between the observation history and covariates, leaving patterns of the observation process to be arbitrary. For inference on the regression parameters and the baseline mean function, a spline-based least squares estimation approach is proposed. The consistency, rate of convergence, and asymptotic normality of the proposed estimators are established. Our new approach is different from the usual approaches relying on the model specification of the observation scheme, and it can be easily used for predicting the longitudinal response. Simulation studies demonstrate that the proposed inference procedure performs well and is more robust. The analyses of bladder tumor data and medical cost data are presented to illustrate the proposed method.

Observation- and model-based estimates of particulate dry nitrogen deposition to the oceans

NASA Astrophysics Data System (ADS)

Baker, Alex R.; Kanakidou, Maria; Altieri, Katye E.; Daskalakis, Nikos; Okin, Gregory S.; Myriokefalitakis, Stelios; Dentener, Frank; Uematsu, Mitsuo; Sarin, Manmohan M.; Duce, Robert A.; Galloway, James N.; Keene, William C.; Singh, Arvind; Zamora, Lauren; Lamarque, Jean-Francois; Hsu, Shih-Chieh; Rohekar, Shital S.; Prospero, Joseph M.

2017-07-01

TM4, while TM4 gives access to speciated parameters (NO3- and NH4+) that are more relevant to the observed parameters and which are not available in ACCMIP. Dry deposition fluxes (CalDep) were calculated from the observed concentrations using estimates of dry deposition velocities. Model-observation ratios (RA, n), weighted by grid-cell area and number of observations, were used to assess the performance of the models. Comparison in the three study regions suggests that TM4 overestimates NO3- concentrations (RA, n = 1.4-2.9) and underestimates NH4+ concentrations (RA, n = 0.5-0.7), with spatial distributions in the tropical Atlantic and northern Indian Ocean not being reproduced by the model. In the case of NH4+ in the Indian Ocean, this discrepancy was probably due to seasonal biases in the sampling. Similar patterns were observed in the various comparisons of CalDep to ModDep (RA, n = 0.6-2.6 for NO3-, 0.6-3.1 for NH4+). Values of RA, n for NHx CalDep-ModDep comparisons were approximately double the corresponding values for NH4+ CalDep-ModDep comparisons due to the significant fraction of gas-phase NH3 deposition incorporated in the TM4 and ACCMIP NHx model products. All of the comparisons suffered due to the scarcity of observational data and the large uncertainty in dry deposition velocities used to derive deposition fluxes from concentrations. These uncertainties have been a major limitation on estimates of the flux of material to the oceans for several decades. Recommendations are made for improvements in N deposition estimation through changes in observations, modelling and model-observation comparison procedures. Validation of modelled dry deposition requires effective comparisons to observable aerosol-phase species' concentrations, and this cannot be achieved if model products only report dry deposition flux over the ocean.

Estimating Coastal Turbidity using MODIS 250 m Band Observations

NASA Technical Reports Server (NTRS)

Davies, James E.; Moeller, Christopher C.; Gunshor, Mathew M.; Menzel, W. Paul; Walker, Nan D.

2004-01-01

Terra MODIS 250 m observations are being applied to a Suspended Sediment Concentration (SSC) algorithm that is under development for coastal case 2 waters where reflectance is dominated by sediment entrained in major fluvial outflows. An atmospheric correction based on MODIS observations in the 500 m resolution 1.6 and 2.1 micron bands is used to isolate the remote sensing reflectance in the MODIS 25Om resolution 650 and 865 nanometer bands. SSC estimates from remote sensing reflectance are based on accepted inherent optical properties of sediment types known to be prevalent in the U.S. Gulf of Mexico coastal zone. We present our findings for the Atchafalaya Bay region of the Louisiana Coast, in the form of processed imagery over the annual cycle. We also apply our algorithm to selected sites worldwide with a goal of extending the utility of our approach to the global direct broadcast community.

Estimation of regional surface CO2 fluxes with GOSAT observations using two inverse modeling approaches

NASA Astrophysics Data System (ADS)

Maksyutov, Shamil; Takagi, Hiroshi; Belikov, Dmitry A.; Saeki, Tazu; Zhuravlev, Ruslan; Ganshin, Alexander; Lukyanov, Alexander; Yoshida, Yukio; Oshchepkov, Sergey; Bril, Andrey; Saito, Makoto; Oda, Tomohiro; Valsala, Vinu K.; Saito, Ryu; Andres, Robert J.; Conway, Thomas; Tans, Pieter; Yokota, Tatsuya

2012-11-01

Inverse estimation of surface C02 fluxes is performed with atmospheric transport model using ground-based and GOSAT observations. The NIES-retrieved C02 column mixing (Xc02) and column averaging kernel are provided by GOSAT Level 2 product v. 2.0 and PPDF-DOAS method. Monthly mean C02 fluxes for 64 regions are estimated together with a global mean offset between GOSAT data and ground-based data. We used the fixed-lag Kalman filter to infer monthly fluxes for 42 sub-continental terrestrial regions and 22 oceanic basins. We estimate fluxes and compare results obtained by two inverse modeling approaches. In basic approach adopted in GOSAT Level4 product v. 2.01, we use aggregation of the GOSAT observations into monthly mean over 5x5 degree grids, fluxes are estimated independently for each region, and NIES atmospheric transport model is used for forward simulation. In the alternative method, the model-observation misfit is estimated for each observation separately and fluxes are spatially correlated using EOF analysis of the simulated flux variability similar to geostatistical approach, while transport simulation is enhanced by coupling with a Lagrangian transport model Flexpart. Both methods use using the same set of prior fluxes and region maps. Daily net ecosystem exchange (NEE) is predicted by the Vegetation Integrative Simulator for Trace gases (VISIT) optimized to match seasonal cycle of the atmospheric C02 . Monthly ocean-atmosphere C02 fluxes are produced with an ocean pC02 data assimilation system. Biomass burning fluxes were provided by the Global Fire Emissions Database (GFED); and monthly fossil fuel C02 emissions are estimated with ODIAC inventory. The results of analyzing one year of the GOSAT data suggest that when both GOSAT and ground-based data are used together, fluxes in tropical and other remote regions with lower associated uncertainties are obtained than in the analysis using only ground-based data. With version 2.0 of L2 Xc02 the fluxes appear

Inter- and intra-observer agreement of BI-RADS-based subjective visual estimation of amount of fibroglandular breast tissue with magnetic resonance imaging: comparison to automated quantitative assessment.

PubMed

Wengert, G J; Helbich, T H; Woitek, R; Kapetas, P; Clauser, P; Baltzer, P A; Vogl, W-D; Weber, M; Meyer-Baese, A; Pinker, Katja

2016-11-01

To evaluate the inter-/intra-observer agreement of BI-RADS-based subjective visual estimation of the amount of fibroglandular tissue (FGT) with magnetic resonance imaging (MRI), and to investigate whether FGT assessment benefits from an automated, observer-independent, quantitative MRI measurement by comparing both approaches. Eighty women with no imaging abnormalities (BI-RADS 1 and 2) were included in this institutional review board (IRB)-approved prospective study. All women underwent un-enhanced breast MRI. Four radiologists independently assessed FGT with MRI by subjective visual estimation according to BI-RADS. Automated observer-independent quantitative measurement of FGT with MRI was performed using a previously described measurement system. Inter-/intra-observer agreements of qualitative and quantitative FGT measurements were assessed using Cohen's kappa (k). Inexperienced readers achieved moderate inter-/intra-observer agreement and experienced readers a substantial inter- and perfect intra-observer agreement for subjective visual estimation of FGT. Practice and experience reduced observer-dependency. Automated observer-independent quantitative measurement of FGT was successfully performed and revealed only fair to moderate agreement (k = 0.209-0.497) with subjective visual estimations of FGT. Subjective visual estimation of FGT with MRI shows moderate intra-/inter-observer agreement, which can be improved by practice and experience. Automated observer-independent quantitative measurements of FGT are necessary to allow a standardized risk evaluation. • Subjective FGT estimation with MRI shows moderate intra-/inter-observer agreement in inexperienced readers. • Inter-observer agreement can be improved by practice and experience. • Automated observer-independent quantitative measurements can provide reliable and standardized assessment of FGT with MRI.

Evaluation of Observation-Fused Regional Air Quality Model Results for Population Air Pollution Exposure Estimation

PubMed Central

Chen, Gang; Li, Jingyi; Ying, Qi; Sherman, Seth; Perkins, Neil; Rajeshwari, Sundaram; Mendola, Pauline

2014-01-01

In this study, Community Multiscale Air Quality (CMAQ) model was applied to predict ambient gaseous and particulate concentrations during 2001 to 2010 in 15 hospital referral regions (HRRs) using a 36-km horizontal resolution domain. An inverse distance weighting based method was applied to produce exposure estimates based on observation-fused regional pollutant concentration fields using the differences between observations and predictions at grid cells where air quality monitors were located. Although the raw CMAQ model is capable of producing satisfying results for O3 and PM2.5 based on EPA guidelines, using the observation data fusing technique to correct CMAQ predictions leads to significant improvement of model performance for all gaseous and particulate pollutants. Regional average concentrations were calculated using five different methods: 1) inverse distance weighting of observation data alone, 2) raw CMAQ results, 3) observation-fused CMAQ results, 4) population-averaged raw CMAQ results and 5) population-averaged fused CMAQ results. It shows that while O3 (as well as NOx) monitoring networks in the HRR regions are dense enough to provide consistent regional average exposure estimation based on monitoring data alone, PM2.5 observation sites (as well as monitors for CO, SO2, PM10 and PM2.5 components) are usually sparse and the difference between the average concentrations estimated by the inverse distance interpolated observations, raw CMAQ and fused CMAQ results can be significantly different. Population-weighted average should be used to account spatial variation in pollutant concentration and population density. Using raw CMAQ results or observations alone might lead to significant biases in health outcome analyses. PMID:24747248

Status of High Latitude Precipitation Estimates from Observations and Reanalyses

NASA Technical Reports Server (NTRS)

Behrangi, Ali; Christensen, Matthew; Richardson, Mark; Lebsock, Matthew; Stephens, Graeme; Huffman, George J.; Bolvin, David T.; Adler, Robert F.; Gardner, Alex; Lambrigtsen, Bjorn H.;

Estimation of Multiple Parameters over Vegetated Surfaces by Integrating Optical-Thermal Remote Sensing Observations

NASA Astrophysics Data System (ADS)

Ma, H.

2016-12-01

Land surface parameters from remote sensing observations are critical in monitoring and modeling of global climate change and biogeochemical cycles. Current methods for estimating land surface parameters are generally parameter-specific algorithms and are based on instantaneous physical models, which result in spatial, temporal and physical inconsistencies in current global products. Besides, optical and Thermal Infrared (TIR) remote sensing observations are usually separated to use based on different models , and the Middle InfraRed (MIR) observations have received little attention due to the complexity of the radiometric signal that mixes both reflected and emitted fluxes. In this paper, we proposed a unified algorithm for simultaneously retrieving a total of seven land surface parameters, including Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), land surface albedo, Land Surface Temperature (LST), surface emissivity, downward and upward longwave radiation, by exploiting remote sensing observations from visible to TIR domain based on a common physical Radiative Transfer (RT) model and a data assimilation framework. The coupled PROSPECT-VISIR and 4SAIL RT model were used for canopy reflectance modeling. At first, LAI was estimated using a data assimilation method that combines MODIS daily reflectance observation and a phenology model. The estimated LAI values were then input into the RT model to simulate surface spectral emissivity and surface albedo. Besides, the background albedo and the transmittance of solar radiation, and the canopy albedo were also calculated to produce FAPAR. Once the spectral emissivity of seven MODIS MIR to TIR bands were retrieved, LST can be estimated from the atmospheric corrected surface radiance by exploiting an optimization method. At last, the upward longwave radiation were estimated using the retrieved LST, broadband emissivity (converted from spectral emissivity) and the downward longwave

Comparison of GPCP Monthly and Daily Precipitation Estimates with High-Latitude Gauge Observations

NASA Technical Reports Server (NTRS)

Bolvin, David T.; Adler, Robert G.; Nelkin, Eric J.; Poutiainen, Jani

2008-01-01

It is very important to know how much rain and snow falls around the world for uses that range from crop forecasting to disaster response, drought monitoring to flood forecasting, and weather analysis to climate research. Precipitation is usually measured with rain gauges, but rain gauges don t exist in areas that are sparsely populated, which tends to be a good portion of the globe. To overcome this, meteorologists use satellite data to estimate global precipitation. However, it is difficult to estimate rain and especially snow in cold climates using most current satellites. The satellite sensors are often "confused" by a snowy or frozen surface and therefore cannot distinguish precipitation. One commonly used satellite-based precipitation data set, the Global Precipitation Climatology Project (GPCP) data, overcomes this frozen-surface problem through the innovative use of two sources of satellite data, the Television Infrared Observation Satellite Operational Vertical Sounder (TOVS) and the Atmospheric Infrared Sounder (AIRS). Though the GPCP estimates are generally considered a very reliable source of precipitation, it has been difficult to assess the quality of these estimates in cold climates due to the lack of gauges. Recently, the Finnish Meteorological Institute (FMI) has provided a 12-year span of high-quality daily rain gauge observations, covering all of Finland, that can be used to compare with the GPCP data to determine how well the satellites estimate cold-climate precipitation. Comparison of the monthly GPCP satellite-based estimates and the FMI gauge observations shows remarkably good agreement, with the GPCP estimates being 6% lower in the amount of precipitation than the FMI observations. Furthermore, the month-to-month correlation between the GPCP and FMI is very high at 0.95 (1.0 is perfect). The daily GPCP estimates replicate the FMI daily occurrences of precipitation with a correlation of 0.55 in the summer and 0.45 in the winter. The winter

Model-based estimation for dynamic cardiac studies using ECT.

PubMed

Chiao, P C; Rogers, W L; Clinthorne, N H; Fessler, J A; Hero, A O

1994-01-01

The authors develop a strategy for joint estimation of physiological parameters and myocardial boundaries using ECT (emission computed tomography). They construct an observation model to relate parameters of interest to the projection data and to account for limited ECT system resolution and measurement noise. The authors then use a maximum likelihood (ML) estimator to jointly estimate all the parameters directly from the projection data without reconstruction of intermediate images. They also simulate myocardial perfusion studies based on a simplified heart model to evaluate the performance of the model-based joint ML estimator and compare this performance to the Cramer-Rao lower bound. Finally, the authors discuss model assumptions and potential uses of the joint estimation strategy.

Limitation of Ground-based Estimates of Solar Irradiance Due to Atmospheric Variations

NASA Technical Reports Server (NTRS)

Wen, Guoyong; Cahalan, Robert F.; Holben, Brent N.

2003-01-01

The uncertainty in ground-based estimates of solar irradiance is quantitatively related to the temporal variability of the atmosphere's optical thickness. The upper and lower bounds of the accuracy of estimates using the Langley Plot technique are proportional to the standard deviation of aerosol optical thickness (approx. +/- 13 sigma(delta tau)). The estimates of spectral solar irradiance (SSI) in two Cimel sun photometer channels from the Mauna Loa site of AERONET are compared with satellite observations from SOLSTICE (Solar Stellar Irradiance Comparison Experiment) on UARS (Upper Atmospheric Research Satellite) for almost two years of data. The true solar variations related to the 27-day solar rotation cycle observed from SOLSTICE are about 0.15% at the two sun photometer channels. The variability in ground-based estimates is statistically one order of magnitude larger. Even though about 30% of these estimates from all Level 2.0 Cimel data fall within the 0.4 to approx. 0.5% variation level, ground-based estimates are not able to capture the 27-day solar variation observed from SOLSTICE.

Estimating moisture transport over oceans using space-based observations

NASA Technical Reports Server (NTRS)

Liu, W. Timothy; Wenqing, Tang

2005-01-01

The moisture transport integrated over the depth of the atmosphere (0) is estimated over oceans using satellite data. The transport is the product of the precipitable water and an equivalent velocity (ue), which, by definition, is the depth-averaged wind velocity weighted by humidity. An artificial neural network is employed to construct a relation between the surface wind velocity measured by the spaceborne scatterometer and coincident ue derived using humidity and wind profiles measured by rawinsondes and produced by reanalysis of operational numerical weather prediction (NWP). On the basis of this relation, 0 fields are produced over global tropical and subtropical oceans (40_N- 40_S) at 0.25_ latitude-longitude and twice daily resolutions from August 1999 to December 2003 using surface wind vector from QuikSCAT and precipitable water from the Tropical Rain Measuring Mission. The derived ue were found to capture the major temporal variability when compared with radiosonde measurements. The average error over global oceans, when compared with NWP data, was comparable with the instrument accuracy specification of space-based scatterometers. The global distribution exhibits the known characteristics of, and reveals more detailed variability than in, previous data.

Nonparametric EROC analysis for observer performance evaluation on joint detection and estimation tasks

NASA Astrophysics Data System (ADS)

Wunderlich, Adam; Goossens, Bart

2014-03-01

The majority of the literature on task-based image quality assessment has focused on lesion detection tasks, using the receiver operating characteristic (ROC) curve, or related variants, to measure performance. However, since many clinical image evaluation tasks involve both detection and estimation (e.g., estimation of kidney stone composition, estimation of tumor size), there is a growing interest in performance evaluation for joint detection and estimation tasks. To evaluate observer performance on such tasks, Clarkson introduced the estimation ROC (EROC) curve, and the area under the EROC curve as a summary figure of merit. In the present work, we propose nonparametric estimators for practical EROC analysis from experimental data, including estimators for the area under the EROC curve and its variance. The estimators are illustrated with a practical example comparing MRI images reconstructed from different k-space sampling trajectories.

Model-based estimation for dynamic cardiac studies using ECT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chiao, P.C.; Rogers, W.L.; Clinthorne, N.H.

1994-06-01

In this paper, the authors develop a strategy for joint estimation of physiological parameters and myocardial boundaries using ECT (Emission Computed Tomography). The authors construct an observation model to relate parameters of interest to the projection data and to account for limited ECT system resolution and measurement noise. The authors then use a maximum likelihood (ML) estimator to jointly estimate all the parameters directly from the projection data without reconstruction of intermediate images. The authors also simulate myocardial perfusion studies based on a simplified heart model to evaluate the performance of the model-based joint ML estimator and compare this performancemore » to the Cramer-Rao lower bound. Finally, model assumptions and potential uses of the joint estimation strategy are discussed.« less

Small-mammal density estimation: A field comparison of grid-based vs. web-based density estimators

USGS Publications Warehouse

Parmenter, R.R.; Yates, Terry L.; Anderson, D.R.; Burnham, K.P.; Dunnum, J.L.; Franklin, A.B.; Friggens, M.T.; Lubow, B.C.; Miller, M.; Olson, G.S.; Parmenter, Cheryl A.; Pollard, J.; Rexstad, E.; Shenk, T.M.; Stanley, T.R.; White, Gary C.

2003-01-01

��blind” test allowed us to evaluate the influence of expertise and experience in calculating density estimates in comparison to simply using default values in programs CAPTURE and DISTANCE. While the rodent sample sizes were considerably smaller than the recommended minimum for good model results, we found that several models performed well empirically, including the web-based uniform and half-normal models in program DISTANCE, and the grid-based models Mb and Mbh in program CAPTURE (with AÌ‚ adjusted by species-specific full mean maximum distance moved (MMDM) values). These models produced accurate DÌ‚ values (with 95% confidence intervals that included the true D values) and exhibited acceptable bias but poor precision. However, in linear regression analyses comparing each model's DÌ‚ values to the true D values over the range of observed test densities, only the web-based uniform model exhibited a regression slope near 1.0; all other models showed substantial slope deviations, indicating biased estimates at higher or lower density values. In addition, the grid-based DÌ‚ analyses using full MMDM values for WÌ‚ area adjustments required a number of theoretical assumptions of uncertain validity, and we therefore viewed their empirical successes with caution. Finally, density estimates from the independent analysts were highly variable, but estimates from web-based approaches had smaller mean square errors and better achieved confidence-interval coverage of D than did grid-based approaches. Our results support the contention that web-based approaches for density estimation of small-mammal populations are both theoretically and empirically superior to grid-based approaches, even when sample size is far less than often recommended. In view of the increasing need for standardized environmental measures for comparisons among ecosystems and through time, analytical models based on distance sampling appear to offer accurate density estimation approaches for research

Estimation and correction of different flavors of surface observation biases in ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Lorente-Plazas, Raquel; Hacker, Josua P.; Collins, Nancy; Lee, Jared A.

2017-04-01

The impact of assimilating surface observations has been shown in several publications, for improving weather prediction inside of the boundary layer as well as the flow aloft. However, the assimilation of surface observations is often far from optimal due to the presence of both model and observation biases. The sources of these biases can be diverse: an instrumental offset, errors associated to the comparison of point-based observations and grid-cell average, etc. To overcome this challenge, a method was developed using the ensemble Kalman filter. The approach consists on representing each observation bias as a parameter. These bias parameters are added to the forward operator and they extend the state vector. As opposed to the observation bias estimation approaches most common in operational systems (e.g. for satellite radiances), the state vector and parameters are simultaneously updated by applying the Kalman filter equations to the augmented state. The method to estimate and correct the observation bias is evaluated using observing system simulation experiments (OSSEs) with the Weather Research and Forecasting (WRF) model. OSSEs are constructed for the conventional observation network including radiosondes, aircraft observations, atmospheric motion vectors, and surface observations. Three different kinds of biases are added to 2-meter temperature for synthetic METARs. From the simplest to more sophisticated, imposed biases are: (1) a spatially invariant bias, (2) a spatially varying bias proportional to topographic height differences between the model and the observations, and (3) bias that is proportional to the temperature. The target region characterized by complex terrain is the western U.S. on a domain with 30-km grid spacing. Observations are assimilated every 3 hours using an 80-member ensemble during September 2012. Results demonstrate that the approach is able to estimate and correct the bias when it is spatially invariant (experiment 1). More

Mechanisms behind the estimation of photosynthesis traits from leaf reflectance observations

NASA Astrophysics Data System (ADS)

Dechant, Benjamin; Cuntz, Matthias; Doktor, Daniel; Vohland, Michael

2016-04-01

was fully based on the correlation to Narea, the PLS regression model for Jmax,25 was not entirely based on it. Analyses of the contributions of different parts of the reflectance spectrum revealed that the information contributing to the Jmax,25 PLS regression model in addition to the main source of information, Narea, was mainly located in the visible part of the spectrum (500-900 nm). Estimated chlorophyll content could be excluded as potential source of this extra information. The PLS regression coefficients of the Jmax,25 model indicated possible contributions from chlorophyll fluorescence and cytochrome f content. In summary, we found that the main mechanism behind the estimation of V cmax,25 and Jmax,25 from leaf reflectance observations is the correlation to Narea but that there is additional information related to Jmax,25 mainly in the visible part of the spectrum.

Space-based observations of megacity carbon dioxide

NASA Astrophysics Data System (ADS)

Kort, Eric A.; Frankenberg, Christian; Miller, Charles E.; Oda, Tom

2012-09-01

Urban areas now house more than half the world's population, and are estimated to contribute over 70% of global energy-related CO2 emissions. Many cities have emission reduction policies in place, but lack objective, observation-based methods for verifying their outcomes. Here we demonstrate the potential of satellite-borne instruments to provide accurate global monitoring of megacity CO2 emissions using GOSAT observations of column averaged CO2 dry air mole fraction (XCO2) collected over Los Angeles and Mumbai. By differencing observations over the megacity with those in nearby background, we observe robust, statistically significant XCO2 enhancements of 3.2 ± 1.5 ppm for Los Angeles and 2.4 ± 1.2 ppm for Mumbai, and find these enhancements can be exploited to track anthropogenic emission trends over time. We estimate that XCO2 changes as small as 0.7 ppm in Los Angeles, corresponding to a 22% change in emissions, could be detected with GOSAT at the 95% confidence level.

Comparing GOSAT Observations of Localized CO2 Enhancements by Large Emitters with Inventory-Based Estimates

NASA Technical Reports Server (NTRS)

Janardanan, Rajesh; Maksyutov, Shamil; Oda, Tomohiro; Saito, Makoto; Kaiser, Johannes W.; Ganshin, Alexander; Stohl, Andreas; Matsunaga, Tsuneo; Yoshida, Yukio; Yokota, Tatsuya

2016-01-01

We employed an atmospheric transport model to attribute column-averaged CO2 mixing ratios (XCO2) observed by Greenhouse gases Observing SATellite (GOSAT) to emissions due to large sources such as megacities and power plants. XCO2 enhancements estimated from observations were compared to model simulations implemented at the spatial resolution of the satellite observation footprint (0.1deg × 0.1deg). We found that the simulated XCO2 enhancements agree with the observed over several continental regions across the globe, for example, for North America with an observation to simulation ratio of 1.05 +/- 0.38 (p<0.1), but with a larger ratio over East Asia (1.22 +/- 0.32; p<0.05). The obtained observation-model discrepancy (22%) for East Asia is comparable to the uncertainties in Chinese emission inventories (approx.15%) suggested by recent reports. Our results suggest that by increasing the number of observations around emission sources, satellite instruments like GOSAT can provide a tool for detecting biases in reported emission inventories.

A stochastic estimation procedure for intermittently-observed semi-Markov multistate models with back transitions.

PubMed

Aralis, Hilary; Brookmeyer, Ron

2017-01-01

Multistate models provide an important method for analyzing a wide range of life history processes including disease progression and patient recovery following medical intervention. Panel data consisting of the states occupied by an individual at a series of discrete time points are often used to estimate transition intensities of the underlying continuous-time process. When transition intensities depend on the time elapsed in the current state and back transitions between states are possible, this intermittent observation process presents difficulties in estimation due to intractability of the likelihood function. In this manuscript, we present an iterative stochastic expectation-maximization algorithm that relies on a simulation-based approximation to the likelihood function and implement this algorithm using rejection sampling. In a simulation study, we demonstrate the feasibility and performance of the proposed procedure. We then demonstrate application of the algorithm to a study of dementia, the Nun Study, consisting of intermittently-observed elderly subjects in one of four possible states corresponding to intact cognition, impaired cognition, dementia, and death. We show that the proposed stochastic expectation-maximization algorithm substantially reduces bias in model parameter estimates compared to an alternative approach used in the literature, minimal path estimation. We conclude that in estimating intermittently observed semi-Markov models, the proposed approach is a computationally feasible and accurate estimation procedure that leads to substantial improvements in back transition estimates.

Applying an Inverse Model to Estimate Ammonia Emissions at Cattle Feedlots Using Three Different Observation-Based Approaches

NASA Astrophysics Data System (ADS)

Shonkwiler, K. B.; Ham, J. M.; Nash, C.

2014-12-01

Accurately quantifying emissions of ammonia (NH3) from confined animal feeding operations (CAFOs) is vital not only to the livestock industry, but essential to understanding nitrogen cycling along the Front Range of Colorado, USA, where intensive agriculture, urban sprawl, and pristine ecosystems (e.g., Rocky Mtn Nat'l Park) lie within 100-km of each other. Most observation-based techniques for estimating NH3 emissions can be expensive and highly technical. Many methods rely on concentration observations on location, which implicitly depends on weather conditions. A system for sampling NH3 using on-site weather data was developed to allow remote measurement of NH3 in a simple, cost-effective way. These systems use passive diffusive cartridges (Radiello, Sigma-Aldrich) that provide time-averaged concentrations representative of a typical two-week deployment. Cartridge exposure is robotically managed so they are only visible when winds are 1.4 m/s or greater from the direction of the CAFO. These concentration data can be coupled with stability parameters (measured on-site) in a simple inverse model to estimate emissions (FIDES, UMR Environnement et Grandes Cultures). Few studies have directly compared emissions estimates of NH3 using concentration data obtained from multiple measurement systems at different temporal and spatial scales. Therefore, in the summer and autumn of 2014, several conditional sampler systems were deployed at a 25,000-head cattle feedlot concomitant with an open-path infrared laser (GasFinder2, Boreal Laser Inc.) and a Cavity Ring Down Spectrometer (CRDS) (G1103, Picarro Inc.) which each measured instantaneous NH3 concentrations. This study will test the sampler technology by first comparing concentration data from the three different methods. In livestock research, it is common to estimate NH3 emissions by using such instantaneous data in a backward Lagrangian stochastic (bLs) model (WindTrax, Thunder Beach Sci.) Considering this, NH3 fluxes

Reducing the number of reconstructions needed for estimating channelized observer performance

NASA Astrophysics Data System (ADS)

Pineda, Angel R.; Miedema, Hope; Brenner, Melissa; Altaf, Sana

2018-03-01

A challenge for task-based optimization is the time required for each reconstructed image in applications where reconstructions are time consuming. Our goal is to reduce the number of reconstructions needed to estimate the area under the receiver operating characteristic curve (AUC) of the infinitely-trained optimal channelized linear observer. We explore the use of classifiers which either do not invert the channel covariance matrix or do feature selection. We also study the assumption that multiple low contrast signals in the same image of a non-linear reconstruction do not significantly change the estimate of the AUC. We compared the AUC of several classifiers (Hotelling, logistic regression, logistic regression using Firth bias reduction and the least absolute shrinkage and selection operator (LASSO)) with a small number of observations both for normal simulated data and images from a total variation reconstruction in magnetic resonance imaging (MRI). We used 10 Laguerre-Gauss channels and the Mann-Whitney estimator for AUC. For this data, our results show that at small sample sizes feature selection using the LASSO technique can decrease bias of the AUC estimation with increased variance and that for large sample sizes the difference between these classifiers is small. We also compared the use of multiple signals in a single reconstructed image to reduce the number of reconstructions in a total variation reconstruction for accelerated imaging in MRI. We found that AUC estimation using multiple low contrast signals in the same image resulted in similar AUC estimates as doing a single reconstruction per signal leading to a 13x reduction in the number of reconstructions needed.

Blending Model Output with satellite-based and in-situ observations to produce high-resolution estimates of population exposure to wildfire smoke

NASA Astrophysics Data System (ADS)

Lassman, William

In the western US, emissions from wildfires and prescribed fire have been associated with degradation of regional air quality. Whereas atmospheric aerosol particles with aerodynamic diameters less than 2.5 mum (PM2.5) have known impacts on human health, there is uncertainty in how particle composition, concentrations, and exposure duration impact the associated health response. Due to changes in climate and land-management, wildfires have increased in frequency and severity, and this trend is expected to continue. Consequently, wildfires are expected to become an increasingly important source of PM2.5 in the western US. While composition and source of the aerosol is thought to be an important factor in the resulting human health-effects, this is currently not well-understood; therefore, there is a need to develop a quantitative understanding of wildfire-smoke-specific health effects. A necessary step in this process is to determine who was exposed to wildfire smoke, the concentration of the smoke during exposure, and the duration of the exposure. Three different tools are commonly used to assess exposure to wildfire smoke: in-situ measurements, satellite-based observations, and chemical-transport model (CTM) simulations, and each of these exposure-estimation tools have associated strengths and weakness. In this thesis, we investigate the utility of blending these tools together to produce highly accurate estimates of smoke exposure during the 2012 fire season in Washington for use in an epidemiological case study. For blending, we use a ridge regression model, as well as a geographically weighted ridge regression model. We evaluate the performance of the three individual exposure-estimate techniques and the two blended techniques using Leave-One-Out Cross-Validation. Due to the number of in-situ monitors present during this time period, we find that predictions based on in-situ monitors were more accurate for this particular fire season than the CTM simulations and

A switched systems approach to image-based estimation

NASA Astrophysics Data System (ADS)

Parikh, Anup

With the advent of technological improvements in imaging systems and computational resources, as well as the development of image-based reconstruction techniques, it is necessary to understand algorithm performance when subject to real world conditions. Specifically, this dissertation focuses on the stability and performance of a class of image-based observers in the presence of intermittent measurements, caused by e.g., occlusions, limited FOV, feature tracking losses, communication losses, or finite frame rates. Observers or filters that are exponentially stable under persistent observability may have unbounded error growth during intermittent sensing, even while providing seemingly accurate state estimates. In Chapter 3, dwell time conditions are developed to guarantee state estimation error convergence to an ultimate bound for a class of observers while undergoing measurement loss. Bounds are developed on the unstable growth of the estimation errors during the periods when the object being tracked is not visible. A Lyapunov-based analysis for the switched system is performed to develop an inequality in terms of the duration of time the observer can view the moving object and the duration of time the object is out of the field of view. In Chapter 4, a motion model is used to predict the evolution of the states of the system while the object is not visible. This reduces the growth rate of the bounding function to an exponential and enables the use of traditional switched systems Lyapunov analysis techniques. The stability analysis results in an average dwell time condition to guarantee state error convergence with a known decay rate. In comparison with the results in Chapter 3, the estimation errors converge to zero rather than a ball, with relaxed switching conditions, at the cost of requiring additional information about the motion of the feature. In some applications, a motion model of the object may not be available. Numerous adaptive techniques have been

Application of super-twisting observers to the estimation of state and unknown inputs in an anaerobic digestion system.

PubMed

Sbarciog, M; Moreno, J A; Vande Wouwer, A

2014-01-01

This paper presents the estimation of the unknown states and inputs of an anaerobic digestion system characterized by a two-step reaction model. The estimation is based on the measurement of the two substrate concentrations and of the outflow rate of biogas and relies on the use of an observer, consisting of three parts. The first is a generalized super-twisting observer, which estimates a linear combination of the two input concentrations. The second is an asymptotic observer, which provides one of the two biomass concentrations, whereas the third is a super-twisting observer for one of the input concentrations and the second biomass concentration.

Estimating daily climatologies for climate indices derived from climate model data and observations

PubMed Central

Mahlstein, Irina; Spirig, Christoph; Liniger, Mark A; Appenzeller, Christof

2015-01-01

Climate indices help to describe the past, present, and the future climate. They are usually closer related to possible impacts and are therefore more illustrative to users than simple climate means. Indices are often based on daily data series and thresholds. It is shown that the percentile-based thresholds are sensitive to the method of computation, and so are the climatological daily mean and the daily standard deviation, which are used for bias corrections of daily climate model data. Sample size issues of either the observed reference period or the model data lead to uncertainties in these estimations. A large number of past ensemble seasonal forecasts, called hindcasts, is used to explore these sampling uncertainties and to compare two different approaches. Based on a perfect model approach it is shown that a fitting approach can improve substantially the estimates of daily climatologies of percentile-based thresholds over land areas, as well as the mean and the variability. These improvements are relevant for bias removal in long-range forecasts or predictions of climate indices based on percentile thresholds. But also for climate change studies, the method shows potential for use. Key Points More robust estimates of daily climate characteristics Statistical fitting approach Based on a perfect model approach PMID:26042192

Estimating daily time series of streamflow using hydrological model calibrated based on satellite observations of river water surface width: Toward real world applications.

PubMed

Sun, Wenchao; Ishidaira, Hiroshi; Bastola, Satish; Yu, Jingshan

2015-05-01

Lacking observation data for calibration constrains applications of hydrological models to estimate daily time series of streamflow. Recent improvements in remote sensing enable detection of river water-surface width from satellite observations, making possible the tracking of streamflow from space. In this study, a method calibrating hydrological models using river width derived from remote sensing is demonstrated through application to the ungauged Irrawaddy Basin in Myanmar. Generalized likelihood uncertainty estimation (GLUE) is selected as a tool for automatic calibration and uncertainty analysis. Of 50,000 randomly generated parameter sets, 997 are identified as behavioral, based on comparing model simulation with satellite observations. The uncertainty band of streamflow simulation can span most of 10-year average monthly observed streamflow for moderate and high flow conditions. Nash-Sutcliffe efficiency is 95.7% for the simulated streamflow at the 50% quantile. These results indicate that application to the target basin is generally successful. Beyond evaluating the method in a basin lacking streamflow data, difficulties and possible solutions for applications in the real world are addressed to promote future use of the proposed method in more ungauged basins. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Observability and Estimation of Distributed Space Systems via Local Information-Exchange Networks

NASA Technical Reports Server (NTRS)

Rahmani, Amirreza; Mesbahi, Mehran; Fathpour, Nanaz; Hadaegh, Fred Y.

2008-01-01

In this work, we develop an approach to formation estimation by explicitly characterizing formation's system-theoretic attributes in terms of the underlying inter-spacecraft information-exchange network. In particular, we approach the formation observer/estimator design by relaxing the accessibility to the global state information by a centralized observer/estimator- and in turn- providing an analysis and synthesis framework for formation observers/estimators that rely on local measurements. The noveltyof our approach hinges upon the explicit examination of the underlying distributed spacecraft network in the realm of guidance, navigation, and control algorithmic analysis and design. The overarching goal of our general research program, some of whose results are reported in this paper, is the development of distributed spacecraft estimation algorithms that are scalable, modular, and robust to variations inthe topology and link characteristics of the formation information exchange network. In this work, we consider the observability of a spacecraft formation from a single observation node and utilize the agreement protocol as a mechanism for observing formation states from local measurements. Specifically, we show how the symmetry structure of the network, characterized in terms of its automorphism group, directly relates to the observability of the corresponding multi-agent system The ramification of this notion of observability over networks is then explored in the context of distributed formation estimation.

Using satellite-based rainfall estimates for streamflow modelling: Bagmati Basin

USGS Publications Warehouse

Shrestha, M.S.; Artan, Guleid A.; Bajracharya, S.R.; Sharma, R. R.

2008-01-01

In this study, we have described a hydrologic modelling system that uses satellite-based rainfall estimates and weather forecast data for the Bagmati River Basin of Nepal. The hydrologic model described is the US Geological Survey (USGS) Geospatial Stream Flow Model (GeoSFM). The GeoSFM is a spatially semidistributed, physically based hydrologic model. We have used the GeoSFM to estimate the streamflow of the Bagmati Basin at Pandhera Dovan hydrometric station. To determine the hydrologic connectivity, we have used the USGS Hydro1k DEM dataset. The model was forced by daily estimates of rainfall and evapotranspiration derived from weather model data. The rainfall estimates used for the modelling are those produced by the National Oceanic and Atmospheric Administration Climate Prediction Centre and observed at ground rain gauge stations. The model parameters were estimated from globally available soil and land cover datasets – the Digital Soil Map of the World by FAO and the USGS Global Land Cover dataset. The model predicted the daily streamflow at Pandhera Dovan gauging station. The comparison of the simulated and observed flows at Pandhera Dovan showed that the GeoSFM model performed well in simulating the flows of the Bagmati Basin.

A-Train Aerosol Observations Preliminary Comparisons with AeroCom Models and Pathways to Observationally Based All-Sky Estimates

NASA Technical Reports Server (NTRS)

Redemann, J.; Livingston, J.; Shinozuka, Y.; Kacenelenbogen, M.; Russell, P.; LeBlanc, S.; Vaughan, M.; Ferrare, R.; Hostetler, C.; Rogers, R.;

Smooth empirical Bayes estimation of observation error variances in linear systems

NASA Technical Reports Server (NTRS)

Martz, H. F., Jr.; Lian, M. W.

1972-01-01

A smooth empirical Bayes estimator was developed for estimating the unknown random scale component of each of a set of observation error variances. It is shown that the estimator possesses a smaller average squared error loss than other estimators for a discrete time linear system.

Water Budget Estimation by Assimilating Multiple Observations and Hydrological Modeling Using Constrained Ensemble Kalman Filtering

NASA Astrophysics Data System (ADS)

Pan, M.; Wood, E. F.

2004-05-01

This study explores a method to estimate various components of the water cycle (ET, runoff, land storage, etc.) based on a number of different info sources, including both observations and observation-enhanced model simulations. Different from existing data assimilations, this constrained Kalman filtering approach keeps the water budget perfectly closed while updating the states of the underlying model (VIC model) optimally using observations. Assimilating different data sources in this way has several advantages: (1) physical model is included to make estimation time series smooth, missing-free, and more physically consistent; (2) uncertainties in the model and observations are properly addressed; (3) model is constrained by observation thus to reduce model biases; (4) balance of water is always preserved along the assimilation. Experiments are carried out in Southern Great Plain region where necessary observations have been collected. This method may also be implemented in other applications with physical constraints (e.g. energy cycles) and at different scales.

Using a thermal-based two source energy balance model with time-differencing to estimate surface energy fluxes with day-night MODIS observations

NASA Astrophysics Data System (ADS)

Guzinski, R.; Anderson, M. C.; Kustas, W. P.; Nieto, H.; Sandholt, I.

2013-07-01

The Dual Temperature Difference (DTD) model, introduced by Norman et al. (2000), uses a two source energy balance modelling scheme driven by remotely sensed observations of diurnal changes in land surface temperature (LST) to estimate surface energy fluxes. By using a time-differential temperature measurement as input, the approach reduces model sensitivity to errors in absolute temperature retrieval. The original formulation of the DTD required an early morning LST observation (approximately 1 h after sunrise) when surface fluxes are minimal, limiting application to data provided by geostationary satellites at sub-hourly temporal resolution. The DTD model has been applied primarily during the active growth phase of agricultural crops and rangeland vegetation grasses, and has not been rigorously evaluated during senescence or in forested ecosystems. In this paper we present modifications to the DTD model that enable applications using thermal observations from polar orbiting satellites, such as Terra and Aqua, with day and night overpass times over the area of interest. This allows the application of the DTD model in high latitude regions where large viewing angles preclude the use of geostationary satellites, and also exploits the higher spatial resolution provided by polar orbiting satellites. A method for estimating nocturnal surface fluxes and a scheme for estimating the fraction of green vegetation are developed and evaluated. Modification for green vegetation fraction leads to significantly improved estimation of the heat fluxes from the vegetation canopy during senescence and in forests. When the modified DTD model is run with LST measurements acquired with the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra and Aqua satellites, generally satisfactory agreement with field measurements is obtained for a number of ecosystems in Denmark and the United States. Finally, regional maps of energy fluxes are produced for the Danish

Estimation of single plane unbalance parameters of a rotor-bearing system using Kalman filtering based force estimation technique

NASA Astrophysics Data System (ADS)

Shrivastava, Akash; Mohanty, A. R.

2018-03-01

This paper proposes a model-based method to estimate single plane unbalance parameters (amplitude and phase angle) in a rotor using Kalman filter and recursive least square based input force estimation technique. Kalman filter based input force estimation technique requires state-space model and response measurements. A modified system equivalent reduction expansion process (SEREP) technique is employed to obtain a reduced-order model of the rotor system so that limited response measurements can be used. The method is demonstrated using numerical simulations on a rotor-disk-bearing system. Results are presented for different measurement sets including displacement, velocity, and rotational response. Effects of measurement noise level, filter parameters (process noise covariance and forgetting factor), and modeling error are also presented and it is observed that the unbalance parameter estimation is robust with respect to measurement noise.

MP estimation applied to platykurtic sets of geodetic observations

NASA Astrophysics Data System (ADS)

Wiśniewski, Zbigniew

2017-06-01

MP estimation is a method which concerns estimating of the location parameters when the probabilistic models of observations differ from the normal distributions in the kurtosis or asymmetry. The system of Pearson's distributions is the probabilistic basis for the method. So far, such a method was applied and analyzed mostly for leptokurtic or mesokurtic distributions (Pearson's distributions of types IV or VII), which predominate practical cases. The analyses of geodetic or astronomical observations show that we may also deal with sets which have moderate asymmetry or small negative excess kurtosis. Asymmetry might result from the influence of many small systematic errors, which were not eliminated during preprocessing of data. The excess kurtosis can be related with bigger or smaller (in relations to the Hagen hypothesis) frequency of occurrence of the elementary errors which are close to zero. Considering that fact, this paper focuses on the estimation with application of the Pearson platykurtic distributions of types I or II. The paper presents the solution of the corresponding optimization problem and its basic properties. Although platykurtic distributions are rare in practice, it was an interesting issue to find out what results can be provided by MP estimation in the case of such observation distributions. The numerical tests which are presented in the paper are rather limited; however, they allow us to draw some general conclusions.

Constraining CO emission estimates using atmospheric observations

NASA Astrophysics Data System (ADS)

Hooghiemstra, P. B.

2012-06-01

We apply a four-dimensional variational (4D-Var) data assimilation system to optimize carbon monoxide (CO) emissions and to reduce the uncertainty of emission estimates from individual sources using the chemistry transport model TM5. In the first study only a limited amount of surface network observations from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA/ESRL) Global Monitoring Division (GMD) is used to test the 4D-Var system. Uncertainty reduction up to 60% in yearly emissions is observed over well-constrained regions and the inferred emissions compare well with recent studies for 2004. However, since the observations only constrain total CO emissions, the 4D-Var system has difficulties separating anthropogenic and biogenic sources in particular. The inferred emissions are validated with NOAA aircraft data over North America and the agreement is significantly improved from the prior to posterior simulation. Validation with the Measurements Of Pollution In The Troposphere (MOPITT) instrument shows a slight improved agreement over the well-constrained Northern Hemisphere and in the tropics (except for the African continent). However, the model simulation with posterior emissions underestimates MOPITT CO total columns on the remote Southern Hemisphere (SH) by about 10%. This is caused by a reduction in SH CO sources mainly due to surface stations on the high southern latitudes. In the second study, we compare two global inversions to estimate carbon monoxide (CO) emissions for 2004. Either surface flask observations from NOAA or CO total columns from the MOPITT instrument are assimilated in a 4D-Var framework. In the Southern Hemisphere (SH) three important findings are reported. First, due to their different vertical sensitivity, the stations-only inversion increases SH biomass burning emissions by 108 Tg CO/yr more than the MOPITT-only inversion. Conversely, the MOPITT-only inversion results in SH natural emissions

Fine particulate matter emissions inventories: comparisons of emissions estimates with observations from recent field programs.

PubMed

Simon, Heather; Allen, David T; Wittig, Ann E

2008-02-01

Emissions inventories of fine particulate matter (PM2.5) were compared with estimates of emissions based on data emerging from U.S. Environment Protection Agency Particulate Matter Supersites and other field programs. Six source categories for PM2.5 emissions were reviewed: on-road mobile sources, nonroad mobile sources, cooking, biomass combustion, fugitive dust, and stationary sources. Ammonia emissions from all of the source categories were also examined. Regional emissions inventories of PM in the exhaust from on-road and nonroad sources were generally consistent with ambient observations, though uncertainties in some emission factors were twice as large as the emission factors. In contrast, emissions inventories of road dust were up to an order of magnitude larger than ambient observations, and estimated brake wear and tire dust emissions were half as large as ambient observations in urban areas. Although comprehensive nationwide emissions inventories of PM2.5 from cooking sources and biomass burning are not yet available, observational data in urban areas suggest that cooking sources account for approximately 5-20% of total primary emissions (excluding dust), and biomass burning sources are highly dependent on region. Finally, relatively few observational data were available to assess the accuracy of emission estimates for stationary sources. Overall, the uncertainties in primary emissions for PM2.s are substantial. Similar uncertainties exist for ammonia emissions. Because of these uncertainties, the design of PM2.5 control strategies should be based on inventories that have been refined by a combination of bottom-up and top-down methods.

The implementation of contour-based object orientation estimation algorithm in FPGA-based on-board vision system

NASA Astrophysics Data System (ADS)

Alpatov, Boris; Babayan, Pavel; Ershov, Maksim; Strotov, Valery

2016-10-01

This paper describes the implementation of the orientation estimation algorithm in FPGA-based vision system. An approach to estimate an orientation of objects lacking axial symmetry is proposed. Suggested algorithm is intended to estimate orientation of a specific known 3D object based on object 3D model. The proposed orientation estimation algorithm consists of two stages: learning and estimation. Learning stage is devoted to the exploring of studied object. Using 3D model we can gather set of training images by capturing 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the estimation stage of the algorithm. The estimation stage is focusing on matching process between an observed image descriptor and the training image descriptors. The experimental research was performed using a set of images of Airbus A380. The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.

Observation-based estimate of the Fukushima radionuclide in the North Pacific

NASA Astrophysics Data System (ADS)

Yoshida, Sachiko; Jayne, Steven; Macdonald, Alison; Buesseler, Ken; Rypina, Irina

2014-05-01

Contaminated waters from Fukushima nuclear power plant (FNPP) were discharged directly into the North Pacific Ocean in March 2011. Coastal current system in this region and time scale of the water exchange with the open ocean is not well understood, however both observational evidence and numerical model simulation results indicate relatively rapid advection of contaminants eastward into the highly energetic mixed water region in the confluence of the Kuroshio and Oyashio. Surface drifters deployed near the FNPP in early summer 2011 show trajectories crossing the North Pacific generally following the large scale ocean circulation after one year. Previously obtained cesium (Cs) samples from multiple cruises near FNPP and off shore region between 2011 and 2013 are collected and evaluated to diagnose the propagating Cs signal crossing North Pacific Ocean. In this presentation, we use radionuclides of Fukushima origin as a tracer to understand the North Pacific circulation and mixing process after two years of release. Large numbers of the observation are repeatedly took place near shore where Cs shows still relatively higher about 10-30 Bq/m3 in 2013. Temperature-salinity (T-S) properties for the available hydrographic data indicate that the majority of the samples were obtained in the region where the water is highly influenced by the warm-salty Kuroshio origin water. Depth profiles of 35N section in March-May 2013 cruise of the U.S. Climate Variability and Predictability and Carbon (CLIVAR) repeat Hydrography sections are examined to track the radionuclide penetration into the subsurface ocean and the subduction pathways along isopycnal surfaces. Available large drifter datasets that accumulated over decades of field work can guide us in estimating the spread of these radionuclides. By applying an innovative statistical analysis to the drifter data, we investigate the spreading of radionuclides in the Pacific Ocean over 5-year time scales.

Evaluating Satellite-based Rainfall Estimates for Basin-scale Hydrologic Modeling

NASA Astrophysics Data System (ADS)

Yilmaz, K. K.; Hogue, T. S.; Hsu, K.; Gupta, H. V.; Mahani, S. E.; Sorooshian, S.

2003-12-01

The reliability of any hydrologic simulation and basin outflow prediction effort depends primarily on the rainfall estimates. The problem of estimating rainfall becomes more obvious in basins with scarce or no rain gauges. We present an evaluation of satellite-based rainfall estimates for basin-scale hydrologic modeling with particular interest in ungauged basins. The initial phase of this study focuses on comparison of mean areal rainfall estimates from ground-based rain gauge network, NEXRAD radar Stage-III, and satellite-based PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and their influence on hydrologic model simulations over several basins in the U.S. Six-hourly accumulations of the above competing mean areal rainfall estimates are used as input to the Sacramento Soil Moisture Accounting Model. Preliminary experiments for the Leaf River Basin in Mississippi, for the period of March 2000 - June 2002, reveals that seasonality plays an important role in the comparison. There is an overestimation during the summer and underestimation during the winter in satellite-based rainfall with respect to the competing rainfall estimates. The consequence of this result on the hydrologic model is that simulated discharge underestimates the major observed peak discharges during early spring for the basin under study. Future research will entail developing correction procedures, which depend on different factors such as seasonality, geographic location and basin size, for satellite-based rainfall estimates over basins with dense rain gauge network and/or radar coverage. Extension of these correction procedures to satellite-based rainfall estimates over ungauged basins with similar characteristics has the potential for reducing the input uncertainty in ungauged basin modeling efforts.

Model-Based Estimation of Knee Stiffness

PubMed Central

Pfeifer, Serge; Vallery, Heike; Hardegger, Michael; Riener, Robert; Perreault, Eric J.

2013-01-01

During natural locomotion, the stiffness of the human knee is modulated continuously and subconsciously according to the demands of activity and terrain. Given modern actuator technology, powered transfemoral prostheses could theoretically provide a similar degree of sophistication and function. However, experimentally quantifying knee stiffness modulation during natural gait is challenging. Alternatively, joint stiffness could be estimated in a less disruptive manner using electromyography (EMG) combined with kinetic and kinematic measurements to estimate muscle force, together with models that relate muscle force to stiffness. Here we present the first step in that process, where we develop such an approach and evaluate it in isometric conditions, where experimental measurements are more feasible. Our EMG-guided modeling approach allows us to consider conditions with antagonistic muscle activation, a phenomenon commonly observed in physiological gait. Our validation shows that model-based estimates of knee joint stiffness coincide well with experimental data obtained using conventional perturbation techniques. We conclude that knee stiffness can be accurately estimated in isometric conditions without applying perturbations, which presents an important step towards our ultimate goal of quantifying knee stiffness during gait. PMID:22801482

SNR-based queue observations at CFHT

NASA Astrophysics Data System (ADS)

Devost, Daniel; Moutou, Claire; Manset, Nadine; Mahoney, Billy; Burdullis, Todd; Cuillandre, Jean-Charles; Racine, René

2016-07-01

In an effort to optimize the night time utilizing the exquisite weather on Maunakea, CFHT has equipped its dome with vents and is now moving its Queued Scheduled Observing (QSO)1 based operations toward Signal to Noise Ratio (SNR) observing. In this new mode, individual exposure times for a science program are estimated using a model that uses measurements of the weather conditions as input and the science program is considered completed when the depth required by the scientific requirements are reached. These changes allow CFHT to make better use of the excellent seeing conditions provided by Maunakea, allowing us to complete programs in a shorter time than allocated to the science programs.

Improved Estimate of Phobos Secular Acceleration from MOLA Observations

NASA Technical Reports Server (NTRS)

Bills, Bruce; Neumann, Gregory; Smith, David; Zuber, Maria

2004-01-01

We report on new observations of the orbital position of Phobos, and use them to obtain a new and improved estimate of the rate of secular acceleration in longitude due to tidal dissipation within Mars. Phobos is the inner-most natural satellite of Mars, and one of the few natural satellites in the solar system with orbital period shorter than the rotation period of its primary. As a result, any departure from a perfect elastic response by Mars in the tides raised on it by Phobos will cause a transfer of angular momentum from the orbit of Phobos to the spin of Mars. Since its discovery in 1877, Phobos has completed over 145,500 orbits, and has one of the best studied orbits in the solar system, with over 6000 earth-based astrometric observations, and over 300 spacecraft observations. As early as 1945, Sharpless noted that there is a secular acceleration in mean longitude, with rate (1.88 + 0.25) 10(exp -3) degrees per square year. In preparation for the 1989 Russian spacecraft mission to Phobos, considerable work was done compiling past observations, and refining the orbital model. All of the published estimates from that era are in good agreement. A typical solution (Jacobson et al., 1989) yields (1.249 + 0.018) 10(exp -3) degrees per square year. The MOLA instrument on MGS is a laser altimeter, and was designed to measure the topography of Mars. However, it has also been used to make observations of the position of Phobos. In 1998, a direct range measurement was made, which indicated that Phobos was slightly ahead of the predicted position. The MOLA detector views the surface of Mars in a narrow field of view, at 1064 nanometer wavelength, and can detect shadows cast by Phobos on the surface of Mars. We have found 15 such serendipitous shadow transit events over the interval from xx to xx, and all of them show Phobos to be ahead of schedule, and getting progressively farther ahead of the predicted position. In contrast, the cross-track positions are quite close

Ensemble-Based Parameter Estimation in a Coupled General Circulation Model

DOE PAGES

Liu, Y.; Liu, Z.; Zhang, S.; ...

2014-09-10

Parameter estimation provides a potentially powerful approach to reduce model bias for complex climate models. Here, in a twin experiment framework, the authors perform the first parameter estimation in a fully coupled ocean–atmosphere general circulation model using an ensemble coupled data assimilation system facilitated with parameter estimation. The authors first perform single-parameter estimation and then multiple-parameter estimation. In the case of the single-parameter estimation, the error of the parameter [solar penetration depth (SPD)] is reduced by over 90% after ~40 years of assimilation of the conventional observations of monthly sea surface temperature (SST) and salinity (SSS). The results of multiple-parametermore » estimation are less reliable than those of single-parameter estimation when only the monthly SST and SSS are assimilated. Assimilating additional observations of atmospheric data of temperature and wind improves the reliability of multiple-parameter estimation. The errors of the parameters are reduced by 90% in ~8 years of assimilation. Finally, the improved parameters also improve the model climatology. With the optimized parameters, the bias of the climatology of SST is reduced by ~90%. Altogether, this study suggests the feasibility of ensemble-based parameter estimation in a fully coupled general circulation model.« less

An optimal pole-matching observer design for estimating tyre-road friction force

NASA Astrophysics Data System (ADS)

Faraji, Mohammad; Johari Majd, Vahid; Saghafi, Behrooz; Sojoodi, Mahdi

2010-10-01

In this paper, considering the dynamical model of tyre-road contacts, we design a nonlinear observer for the on-line estimation of tyre-road friction force using the average lumped LuGre model without any simplification. The design is the extension of a previously offered observer to allow a muchmore realistic estimation by considering the effect of the rolling resistance and a term related to the relative velocity in the observer. Our aim is not to introduce a new friction model, but to present a more accurate nonlinear observer for the assumed model. We derive linear matrix equality conditions to obtain an observer gain with minimum pole mismatch for the desired observer error dynamic system. We prove the convergence of the observer for the non-simplified model. Finally, we compare the performance of the proposed observer with that of the previously mentioned nonlinear observer, which shows significant improvement in the accuracy of estimation.

Genetic algorithm-based improved DOA estimation using fourth-order cumulants

NASA Astrophysics Data System (ADS)

Ahmed, Ammar; Tufail, Muhammad

2017-05-01

Genetic algorithm (GA)-based direction of arrival (DOA) estimation is proposed using fourth-order cumulants (FOC) and ESPRIT principle which results in Multiple Invariance Cumulant ESPRIT algorithm. In the existing FOC ESPRIT formulations, only one invariance is utilised to estimate DOAs. The unused multiple invariances (MIs) must be exploited simultaneously in order to improve the estimation accuracy. In this paper, a fitness function based on a carefully designed cumulant matrix is developed which incorporates MIs present in the sensor array. Better DOA estimation can be achieved by minimising this fitness function. Moreover, the effectiveness of Newton's method as well as GA for this optimisation problem has been illustrated. Simulation results show that the proposed algorithm provides improved estimation accuracy compared to existing algorithms, especially in the case of low SNR, less number of snapshots, closely spaced sources and high signal and noise correlation. Moreover, it is observed that the optimisation using Newton's method is more likely to converge to false local optima resulting in erroneous results. However, GA-based optimisation has been found attractive due to its global optimisation capability.

Yield Estimation for Semipalatinsk Underground Nuclear Explosions Using Seismic Surface-wave Observations at Near-regional Distances

NASA Astrophysics Data System (ADS)

Adushkin, V. V.

- A statistical procedure is described for estimating the yields of underground nuclear tests at the former Soviet Semipalatinsk test site using the peak amplitudes of short-period surface waves observed at near-regional distances (Δ < 150 km) from these explosions. This methodology is then applied to data recorded from a large sample of the Semipalatinsk explosions, including the Soviet JVE explosion of September 14, 1988, and it is demonstrated that it provides seismic estimates of explosion yield which are typically within 20% of the yields determined for these same explosions using more accurate, non-seismic techniques based on near-source observations.

Estimation of Longitudinal Force and Sideslip Angle for Intelligent Four-Wheel Independent Drive Electric Vehicles by Observer Iteration and Information Fusion.

PubMed

Chen, Te; Chen, Long; Xu, Xing; Cai, Yingfeng; Jiang, Haobin; Sun, Xiaoqiang

2018-04-20

Exact estimation of longitudinal force and sideslip angle is important for lateral stability and path-following control of four-wheel independent driven electric vehicle. This paper presents an effective method for longitudinal force and sideslip angle estimation by observer iteration and information fusion for four-wheel independent drive electric vehicles. The electric driving wheel model is introduced into the vehicle modeling process and used for longitudinal force estimation, the longitudinal force reconstruction equation is obtained via model decoupling, the a Luenberger observer and high-order sliding mode observer are united for longitudinal force observer design, and the Kalman filter is applied to restrain the influence of noise. Via the estimated longitudinal force, an estimation strategy is then proposed based on observer iteration and information fusion, in which the Luenberger observer is applied to achieve the transcendental estimation utilizing less sensor measurements, the extended Kalman filter is used for a posteriori estimation with higher accuracy, and a fuzzy weight controller is used to enhance the adaptive ability of observer system. Simulations and experiments are carried out, and the effectiveness of proposed estimation method is verified.

Estimation of Longitudinal Force and Sideslip Angle for Intelligent Four-Wheel Independent Drive Electric Vehicles by Observer Iteration and Information Fusion

PubMed Central

Chen, Long; Xu, Xing; Cai, Yingfeng; Jiang, Haobin; Sun, Xiaoqiang

2018-01-01

Exact estimation of longitudinal force and sideslip angle is important for lateral stability and path-following control of four-wheel independent driven electric vehicle. This paper presents an effective method for longitudinal force and sideslip angle estimation by observer iteration and information fusion for four-wheel independent drive electric vehicles. The electric driving wheel model is introduced into the vehicle modeling process and used for longitudinal force estimation, the longitudinal force reconstruction equation is obtained via model decoupling, the a Luenberger observer and high-order sliding mode observer are united for longitudinal force observer design, and the Kalman filter is applied to restrain the influence of noise. Via the estimated longitudinal force, an estimation strategy is then proposed based on observer iteration and information fusion, in which the Luenberger observer is applied to achieve the transcendental estimation utilizing less sensor measurements, the extended Kalman filter is used for a posteriori estimation with higher accuracy, and a fuzzy weight controller is used to enhance the adaptive ability of observer system. Simulations and experiments are carried out, and the effectiveness of proposed estimation method is verified. PMID:29677124

A double-observer approach for estimating detection probability and abundance from point counts

USGS Publications Warehouse

Nichols, J.D.; Hines, J.E.; Sauer, J.R.; Fallon, F.W.; Fallon, J.E.; Heglund, P.J.

2000-01-01

Although point counts are frequently used in ornithological studies, basic assumptions about detection probabilities often are untested. We apply a double-observer approach developed to estimate detection probabilities for aerial surveys (Cook and Jacobson 1979) to avian point counts. At each point count, a designated 'primary' observer indicates to another ('secondary') observer all birds detected. The secondary observer records all detections of the primary observer as well as any birds not detected by the primary observer. Observers alternate primary and secondary roles during the course of the survey. The approach permits estimation of observer-specific detection probabilities and bird abundance. We developed a set of models that incorporate different assumptions about sources of variation (e.g. observer, bird species) in detection probability. Seventeen field trials were conducted, and models were fit to the resulting data using program SURVIV. Single-observer point counts generally miss varying proportions of the birds actually present, and observer and bird species were found to be relevant sources of variation in detection probabilities. Overall detection probabilities (probability of being detected by at least one of the two observers) estimated using the double-observer approach were very high (>0.95), yielding precise estimates of avian abundance. We consider problems with the approach and recommend possible solutions, including restriction of the approach to fixed-radius counts to reduce the effect of variation in the effective radius of detection among various observers and to provide a basis for using spatial sampling to estimate bird abundance on large areas of interest. We believe that most questions meriting the effort required to carry out point counts also merit serious attempts to estimate detection probabilities associated with the counts. The double-observer approach is a method that can be used for this purpose.

A Deep Neural Network Model for Rainfall Estimation UsingPolarimetric WSR-88DP Radar Observations

NASA Astrophysics Data System (ADS)

Tan, H.; Chandra, C. V.; Chen, H.

2016-12-01

Rainfall estimation based on radar measurements has been an important topic for a few decades. Generally, radar rainfall estimation is conducted through parametric algorisms such as reflectivity-rainfall relation (i.e., Z-R relation). On the other hand, neural networks are developed for ground rainfall estimation based on radar measurements. This nonparametric method, which takes into account of both radar observations and rainfall measurements from ground rain gauges, has been demonstrated successfully for rainfall rate estimation. However, the neural network-based rainfall estimation is limited in practice due to the model complexity and structure, data quality, as well as different rainfall microphysics. Recently, the deep learning approach has been introduced in pattern recognition and machine learning areas. Compared to traditional neural networks, the deep learning based methodologies have larger number of hidden layers and more complex structure for data representation. Through a hierarchical learning process, the high level structured information and knowledge can be extracted automatically from low level features of the data. In this paper, we introduce a novel deep neural network model for rainfall estimation based on ground polarimetric radar measurements .The model is designed to capture the complex abstractions of radar measurements at different levels using multiple layers feature identification and extraction. The abstractions at different levels can be used independently or fused with other data resource such as satellite-based rainfall products and/or topographic data to represent the rain characteristics at certain location. In particular, the WSR-88DP radar and rain gauge data collected in Dallas - Fort Worth Metroplex and Florida are used extensively to train the model, and for demonstration purposes. Quantitative evaluation of the deep neural network based rainfall products will also be presented, which is based on an independent rain gauge

A non-stationary cost-benefit based bivariate extreme flood estimation approach

NASA Astrophysics Data System (ADS)

Qi, Wei; Liu, Junguo

2018-02-01

Cost-benefit analysis and flood frequency analysis have been integrated into a comprehensive framework to estimate cost effective design values. However, previous cost-benefit based extreme flood estimation is based on stationary assumptions and analyze dependent flood variables separately. A Non-Stationary Cost-Benefit based bivariate design flood estimation (NSCOBE) approach is developed in this study to investigate influence of non-stationarities in both the dependence of flood variables and the marginal distributions on extreme flood estimation. The dependence is modeled utilizing copula functions. Previous design flood selection criteria are not suitable for NSCOBE since they ignore time changing dependence of flood variables. Therefore, a risk calculation approach is proposed based on non-stationarities in both marginal probability distributions and copula functions. A case study with 54-year observed data is utilized to illustrate the application of NSCOBE. Results show NSCOBE can effectively integrate non-stationarities in both copula functions and marginal distributions into cost-benefit based design flood estimation. It is also found that there is a trade-off between maximum probability of exceedance calculated from copula functions and marginal distributions. This study for the first time provides a new approach towards a better understanding of influence of non-stationarities in both copula functions and marginal distributions on extreme flood estimation, and could be beneficial to cost-benefit based non-stationary bivariate design flood estimation across the world.

A global validation of ERA-Interim integrated water vapor estimates using ground-based GNSS observations

NASA Astrophysics Data System (ADS)

Ahmed, F.; Dousa, J.; Hunegnaw, A.; Teferle, F. N.; Bingley, R.

2017-12-01

Integrated water vapor (IWV) derived from climate reanalysis models, such as the European Centre for Medium-range Weather Forecasts (ECMWF) ReAnalysis-Interim (ERA-Interim), is widely used in many atmospheric applications. Therefore, it is of interest to assess the quality of this reanalysis product using available observations. Observations from Global Navigation Satellite Systems (GNSS) are, as of now, available for a period of over 2 decades and their global availability makes it possible to validate the IWV obtained from climate reanalysis models in different geographical and climatic regions. In this study, primarily, three 5-year long homogeneously reprocessed GNSS-derived IWV datasets containing over 400 globally distributed ground-based GNSS stations have been used to validate the IWV estimates obtained from the ERA-Interim climate reanalysis model in 25 different climate zones. The IWV from ERA-Interim has been obtained by vertically integrating the specific humidity at all model levels above the locations of GNSS stations. It has been studied how the difference between the ERA-Interim IWV and the GNSS-derived IWV varies with respect to the different climate zones as well as with respect to the difference in the model orography and latitude. The results show a dependence of the ability of ERA-Interim to model the IWV on difference in climate types and latitude. This dependence, however, is dictated by the concentration of water vapor in different climate zones and at different latitudes. Furthermore, as a secondary focus of this study, the weighted mean atmospheric temperature (Tm) obtained from ERA-Interim has been compared to its equivalent obtained using two widely used approximations globally.

Development of a technique for estimating noise covariances using multiple observers

NASA Technical Reports Server (NTRS)

Bundick, W. Thomas

1988-01-01

Friedland's technique for estimating the unknown noise variances of a linear system using multiple observers has been extended by developing a general solution for the estimates of the variances, developing the statistics (mean and standard deviation) of these estimates, and demonstrating the solution on two examples.

Model-based estimation of individual fitness

USGS Publications Warehouse

Link, W.A.; Cooch, E.G.; Cam, E.

2002-01-01

Fitness is the currency of natural selection, a measure of the propagation rate of genotypes into future generations. Its various definitions have the common feature that they are functions of survival and fertility rates. At the individual level, the operative level for natural selection, these rates must be understood as latent features, genetically determined propensities existing at birth. This conception of rates requires that individual fitness be defined and estimated by consideration of the individual in a modelled relation to a group of similar individuals; the only alternative is to consider a sample of size one, unless a clone of identical individuals is available. We present hierarchical models describing individual heterogeneity in survival and fertility rates and allowing for associations between these rates at the individual level. We apply these models to an analysis of life histories of Kittiwakes (Rissa tridactyla ) observed at several colonies on the Brittany coast of France. We compare Bayesian estimation of the population distribution of individual fitness with estimation based on treating individual life histories in isolation, as samples of size one (e.g. McGraw & Caswell, 1996).

Statistical properties of Fourier-based time-lag estimates

NASA Astrophysics Data System (ADS)

Epitropakis, A.; Papadakis, I. E.

2016-06-01

Context. The study of X-ray time-lag spectra in active galactic nuclei (AGN) is currently an active research area, since it has the potential to illuminate the physics and geometry of the innermost region (I.e. close to the putative super-massive black hole) in these objects. To obtain reliable information from these studies, the statistical properties of time-lags estimated from data must be known as accurately as possible. Aims: We investigated the statistical properties of Fourier-based time-lag estimates (I.e. based on the cross-periodogram), using evenly sampled time series with no missing points. Our aim is to provide practical "guidelines" on estimating time-lags that are minimally biased (I.e. whose mean is close to their intrinsic value) and have known errors. Methods: Our investigation is based on both analytical work and extensive numerical simulations. The latter consisted of generating artificial time series with various signal-to-noise ratios and sampling patterns/durations similar to those offered by AGN observations with present and past X-ray satellites. We also considered a range of different model time-lag spectra commonly assumed in X-ray analyses of compact accreting systems. Results: Discrete sampling, binning and finite light curve duration cause the mean of the time-lag estimates to have a smaller magnitude than their intrinsic values. Smoothing (I.e. binning over consecutive frequencies) of the cross-periodogram can add extra bias at low frequencies. The use of light curves with low signal-to-noise ratio reduces the intrinsic coherence, and can introduce a bias to the sample coherence, time-lag estimates, and their predicted error. Conclusions: Our results have direct implications for X-ray time-lag studies in AGN, but can also be applied to similar studies in other research fields. We find that: a) time-lags should be estimated at frequencies lower than ≈ 1/2 the Nyquist frequency to minimise the effects of discrete binning of the

A multi-year estimate of methane fluxes in Alaska from CARVE atmospheric observations

PubMed Central

Miller, Scot M.; Miller, Charles E.; Commane, Roisin; Chang, Rachel Y.-W.; Dinardo, Steven J.; Henderson, John M.; Karion, Anna; Lindaas, Jakob; Melton, Joe R.; Miller, John B.; Sweeney, Colm; Wofsy, Steven C.; Michalak, Anna M.

2016-01-01

Methane (CH4) fluxes from Alaska and other arctic regions may be sensitive to thawing permafrost and future climate change, but estimates of both current and future fluxes from the region are uncertain. This study estimates CH4 fluxes across Alaska for 2012–2014 using aircraft observations from the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) and a geostatistical inverse model (GIM). We find that a simple flux model based on a daily soil temperature map and a static map of wetland extent reproduces the atmospheric CH4 observations at the state-wide, multi-year scale more effectively than global-scale, state-of-the-art process-based models. This result points to a simple and effective way of representing CH4 flux patterns across Alaska. It further suggests that contemporary process-based models can improve their representation of key processes that control fluxes at regional scales, and that more complex processes included in these models cannot be evaluated given the information content of available atmospheric CH4 observations. In addition, we find that CH4 emissions from the North Slope of Alaska account for 24% of the total statewide flux of 1.74 ± 0.44 Tg CH4 (for May–Oct.). Contemporary global-scale process models only attribute an average of 3% of the total flux to this region. This mismatch occurs for two reasons: process models likely underestimate wetland area in regions without visible surface water, and these models prematurely shut down CH4 fluxes at soil temperatures near 0°C. As a consequence, wetlands covered by vegetation and wetlands with persistently cold soils could be larger contributors to natural CH4 fluxes than in process estimates. Lastly, we find that the seasonality of CH4 fluxes varied during 2012–2014, but that total emissions did not differ significantly among years, despite substantial differences in soil temperature and precipitation; year-to-year variability in these environmental conditions did not affect

A multi-year estimate of methane fluxes in Alaska from CARVE atmospheric observations.

PubMed

Miller, Scot M; Miller, Charles E; Commane, Roisin; Chang, Rachel Y-W; Dinardo, Steven J; Henderson, John M; Karion, Anna; Lindaas, Jakob; Melton, Joe R; Miller, John B; Sweeney, Colm; Wofsy, Steven C; Michalak, Anna M

2016-10-01

Methane (CH 4 ) fluxes from Alaska and other arctic regions may be sensitive to thawing permafrost and future climate change, but estimates of both current and future fluxes from the region are uncertain. This study estimates CH 4 fluxes across Alaska for 2012-2014 using aircraft observations from the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) and a geostatistical inverse model (GIM). We find that a simple flux model based on a daily soil temperature map and a static map of wetland extent reproduces the atmospheric CH 4 observations at the state-wide, multi-year scale more effectively than global-scale, state-of-the-art process-based models. This result points to a simple and effective way of representing CH 4 flux patterns across Alaska. It further suggests that contemporary process-based models can improve their representation of key processes that control fluxes at regional scales, and that more complex processes included in these models cannot be evaluated given the information content of available atmospheric CH 4 observations. In addition, we find that CH 4 emissions from the North Slope of Alaska account for 24% of the total statewide flux of 1.74 ± 0.44 Tg CH 4 ( for May-Oct.). Contemporary global-scale process models only attribute an average of 3% of the total flux to this region. This mismatch occurs for two reasons: process models likely underestimate wetland area in regions without visible surface water, and these models prematurely shut down CH 4 fluxes at soil temperatures near 0°C. As a consequence, wetlands covered by vegetation and wetlands with persistently cold soils could be larger contributors to natural CH 4 fluxes than in process estimates. Lastly, we find that the seasonality of CH 4 fluxes varied during 2012-2014, but that total emissions did not differ significantly among years, despite substantial differences in soil temperature and precipitation; year-to-year variability in these environmental conditions did not

Estimation of interplate coupling along Nankai trough considering the block motion model based on onland GNSS and seafloor GPS/A observation data using MCMC method

NASA Astrophysics Data System (ADS)

Kimura, H.; Ito, T.; Tadokoro, K.

2017-12-01

Introduction In southwest Japan, Philippine sea plate is subducting under the overriding plate such as Amurian plate, and mega interplate earthquakes has occurred at about 100 years interval. There is no occurrence of mega interplate earthquakes in southwest Japan, although it has passed about 70 years since the last mega interplate earthquakes: 1944 and 1946 along Nankai trough, meaning that the strain has been accumulated at plate interface. Therefore, it is essential to reveal the interplate coupling more precisely for predicting or understanding the mechanism of next occurring mega interplate earthquake. Recently, seafloor geodetic observation revealed the detailed interplate coupling distribution in expected source region of Nankai trough earthquake (e.g., Yokota et al. [2016]). In this study, we estimated interplate coupling in southwest Japan, considering block motion model and using seafloor geodetic observation data as well as onland GNSS observation data, based on Markov Chain Monte Carlo (MCMC) method. Method Observed crustal deformation is assumed that sum of rigid block motion and elastic deformation due to coupling at block boundaries. We modeled this relationship as a non-linear inverse problem that the unknown parameters are Euler pole of each block and coupling at each subfault, and solved them simultaneously based on MCMC method. Input data we used in this study are 863 onland GNSS observation data and 24 seafloor GPS/A observation data. We made some block division models based on the map of active fault tracing and selected the best model based on Akaike's Information Criterion (AIC): that is consist of 12 blocks. Result We find that the interplate coupling along Nankai trough has heterogeneous spatial distribution, strong at the depth of 0 to 20km at off Tokai region, and 0 to 30km at off Shikoku region. Moreover, we find that observed crustal deformation at off Tokai region is well explained by elastic deformation due to subducting Izu Micro

A novel method for state of charge estimation of lithium-ion batteries using a nonlinear observer

NASA Astrophysics Data System (ADS)

Xia, Bizhong; Chen, Chaoren; Tian, Yong; Sun, Wei; Xu, Zhihui; Zheng, Weiwei

2014-12-01

The state of charge (SOC) is important for the safety and reliability of battery operation since it indicates the remaining capacity of a battery. However, as the internal state of each cell cannot be directly measured, the value of the SOC has to be estimated. In this paper, a novel method for SOC estimation in electric vehicles (EVs) using a nonlinear observer (NLO) is presented. One advantage of this method is that it does not need complicated matrix operations, so the computation cost can be reduced. As a key step in design of the nonlinear observer, the state-space equations based on the equivalent circuit model are derived. The Lyapunov stability theory is employed to prove the convergence of the nonlinear observer. Four experiments are carried out to evaluate the performance of the presented method. The results show that the SOC estimation error converges to 3% within 130 s while the initial SOC error reaches 20%, and does not exceed 4.5% while the measurement suffers both 2.5% voltage noise and 5% current noise. Besides, the presented method has advantages over the extended Kalman filter (EKF) and sliding mode observer (SMO) algorithms in terms of computation cost, estimation accuracy and convergence rate.

On-line estimation of suspended solids in biological reactors of WWTPs using a Kalman observer.

PubMed

Beltrán, S; Irizar, I; Monclús, H; Rodríguez-Roda, I; Ayesa, E

2009-01-01

The total amount of solids in Wastewater Treatment Plants (WWTPs) and their distribution among the different elements and lines play a crucial role in the stability, performance and operational costs of the process. However, an accurate prediction of the evolution of solids concentration in the different elements of a WWTP is not a straightforward task. This paper presents the design, development and validation of a generic Kalman observer for the on-line estimation of solids concentration in the tank reactors of WWTPs. The proposed observer is based on the fact that the information about the evolution of the total amount of solids in the plant can be supplied by the available on-line Suspended Solids (SS) analysers, while their distribution can be simultaneously estimated from the hydraulic pattern of the plant. The proposed observer has been applied to the on-line estimation of SS in the reactors of a pilot-scale Membrane Bio-Reactor (MBR). The results obtained have shown that the experimental information supplied by a sole on-line SS analyser located in the first reactor of the pilot plant, in combination with updated information about internal flow rates data, has been able to give a reasonable estimation of the evolution of the SS concentration in all the tanks.

Estimating fluid-induced stress change from observed deformation

DOE PAGES

Vasco, D. W.; Harness, Paul; Pride, Steve; ...

2016-12-19

Observed deformation is sensitive to a changing stress field within the Earth. There are, however, several impediments to a direct inversion of geodetic measurements for changes in stress. Estimating six independent components of stress change from a smaller number of displacement or strain components is inherently non-unique. The reliance upon surface measurements leads to a loss of resolution, due to the attenuation of higher spatial frequencies in the displacement field with distance from a source. Here, we adopt a technique suited to the estimation of stress changes due to the injection and/or withdrawal of fluids at depth. In this approachmore » the surface displacement data provides an estimate of the volume change responsible for the deformation, rather than stress changes themselves. The inversion for volume change is constrained by the fluid fluxes into and out of the reservoir. The distribution of volume change is used to calculate the displacements in the region above the reservoir. Estimates of stress change follow from differentiating the displacement field in conjunction with a geomechanical model of the o verburden. We also apply the technique to Interferometric Synthetic Aperture Radar (InSAR) observations gathered over a petroleum reservoir in the San Joaquin Valley of California. An analysis of the InSAR range changes reveals that the stress field in the overburden varies rapidly both in space and in time. The inferred stress variations are found to be compatible with the documented failure of a well in the field.« less

Estimating fluid-induced stress change from observed deformation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vasco, D. W.; Harness, Paul; Pride, Steve

Observed deformation is sensitive to a changing stress field within the Earth. There are, however, several impediments to a direct inversion of geodetic measurements for changes in stress. Estimating six independent components of stress change from a smaller number of displacement or strain components is inherently non-unique. The reliance upon surface measurements leads to a loss of resolution, due to the attenuation of higher spatial frequencies in the displacement field with distance from a source. Here, we adopt a technique suited to the estimation of stress changes due to the injection and/or withdrawal of fluids at depth. In this approachmore » the surface displacement data provides an estimate of the volume change responsible for the deformation, rather than stress changes themselves. The inversion for volume change is constrained by the fluid fluxes into and out of the reservoir. The distribution of volume change is used to calculate the displacements in the region above the reservoir. Estimates of stress change follow from differentiating the displacement field in conjunction with a geomechanical model of the o verburden. We also apply the technique to Interferometric Synthetic Aperture Radar (InSAR) observations gathered over a petroleum reservoir in the San Joaquin Valley of California. An analysis of the InSAR range changes reveals that the stress field in the overburden varies rapidly both in space and in time. The inferred stress variations are found to be compatible with the documented failure of a well in the field.« less

Adaptive compressed sensing of multi-view videos based on the sparsity estimation

NASA Astrophysics Data System (ADS)

Yang, Senlin; Li, Xilong; Chong, Xin

2017-11-01

The conventional compressive sensing for videos based on the non-adaptive linear projections, and the measurement times is usually set empirically. As a result, the quality of videos reconstruction is always affected. Firstly, the block-based compressed sensing (BCS) with conventional selection for compressive measurements was described. Then an estimation method for the sparsity of multi-view videos was proposed based on the two dimensional discrete wavelet transform (2D DWT). With an energy threshold given beforehand, the DWT coefficients were processed with both energy normalization and sorting by descending order, and the sparsity of the multi-view video can be achieved by the proportion of dominant coefficients. And finally, the simulation result shows that, the method can estimate the sparsity of video frame effectively, and provides an active basis for the selection of compressive observation times. The result also shows that, since the selection of observation times is based on the sparsity estimated with the energy threshold provided, the proposed method can ensure the reconstruction quality of multi-view videos.

Estimation of the radial force using a disturbance force observer for a magnetically levitated centrifugal blood pump.

PubMed

Pai, C N; Shinshi, T; Shimokohbe, A

2010-01-01

Evaluation of the hydraulic forces in a magnetically levitated (maglev) centrifugal blood pump is important from the point of view of the magnetic bearing design. Direct measurement is difficult due to the absence of a rotor shaft, and computational fluid dynamic analysis demands considerable computational resource and time. To solve this problem, disturbance force observers were developed, using the radial controlled magnetic bearing of a centrifugal blood pump, to estimate the radial forces on the maglev impeller. In order to design the disturbance observer, the radial dynamic characteristics of a maglev impeller were evaluated under different working conditions. It was observed that the working fluid affects the additional mass and damping, while the rotational speed affects the damping and stiffness of the maglev system. Based on these results, disturbance force observers were designed and implemented. The designed disturbance force observers present a bandwidth of 45 Hz. In non-pulsatile conditions, the magnitude of the estimated radial thrust increases in proportion to the flowrate, and the rotational speed has little effect on the force direction. At 5 l/min against 100 mmHg, the estimated radial thrust is 0.95 N. In pulsatile conditions, this method was capable of estimating the pulsatile radial thrust with good response.

Studies on Training Ground Observers to Estimate Range to Aerial Targets.

ERIC Educational Resources Information Center

McCluskey, Michael R.; And Others

Six pilot studies were conducted to determine the effects of training on range estimation performance for aerial targets, and to identify some of the relevant variables. Observers were trained to estimate ranges of 350, 400, 800, 1,500, or 2,500 meters. Several variations of range estimation training methods were used, including immediate…

A variational technique to estimate snowfall rate from coincident radar, snowflake, and fall-speed observations

NASA Astrophysics Data System (ADS)

Cooper, Steven J.; Wood, Norman B.; L'Ecuyer, Tristan S.

2017-07-01

Estimates of snowfall rate as derived from radar reflectivities alone are non-unique. Different combinations of snowflake microphysical properties and particle fall speeds can conspire to produce nearly identical snowfall rates for given radar reflectivity signatures. Such ambiguities can result in retrieval uncertainties on the order of 100-200 % for individual events. Here, we use observations of particle size distribution (PSD), fall speed, and snowflake habit from the Multi-Angle Snowflake Camera (MASC) to constrain estimates of snowfall derived from Ka-band ARM zenith radar (KAZR) measurements at the Atmospheric Radiation Measurement (ARM) North Slope Alaska (NSA) Climate Research Facility site at Barrow. MASC measurements of microphysical properties with uncertainties are introduced into a modified form of the optimal-estimation CloudSat snowfall algorithm (2C-SNOW-PROFILE) via the a priori guess and variance terms. Use of the MASC fall speed, MASC PSD, and CloudSat snow particle model as base assumptions resulted in retrieved total accumulations with a -18 % difference relative to nearby National Weather Service (NWS) observations over five snow events. The average error was 36 % for the individual events. Use of different but reasonable combinations of retrieval assumptions resulted in estimated snowfall accumulations with differences ranging from -64 to +122 % for the same storm events. Retrieved snowfall rates were particularly sensitive to assumed fall speed and habit, suggesting that in situ measurements can help to constrain key snowfall retrieval uncertainties. More accurate knowledge of these properties dependent upon location and meteorological conditions should help refine and improve ground- and space-based radar estimates of snowfall.

NASA Software Cost Estimation Model: An Analogy Based Estimation Model

NASA Technical Reports Server (NTRS)

Hihn, Jairus; Juster, Leora; Menzies, Tim; Mathew, George; Johnson, James

2015-01-01

The cost estimation of software development activities is increasingly critical for large scale integrated projects such as those at DOD and NASA especially as the software systems become larger and more complex. As an example MSL (Mars Scientific Laboratory) developed at the Jet Propulsion Laboratory launched with over 2 million lines of code making it the largest robotic spacecraft ever flown (Based on the size of the software). Software development activities are also notorious for their cost growth, with NASA flight software averaging over 50% cost growth. All across the agency, estimators and analysts are increasingly being tasked to develop reliable cost estimates in support of program planning and execution. While there has been extensive work on improving parametric methods there is very little focus on the use of models based on analogy and clustering algorithms. In this paper we summarize our findings on effort/cost model estimation and model development based on ten years of software effort estimation research using data mining and machine learning methods to develop estimation models based on analogy and clustering. The NASA Software Cost Model performance is evaluated by comparing it to COCOMO II, linear regression, and K-­ nearest neighbor prediction model performance on the same data set.

Neural Network-Based Retrieval of Surface and Root Zone Soil Moisture using Multi-Frequency Remotely-Sensed Observations

NASA Astrophysics Data System (ADS)

Hamed Alemohammad, Seyed; Kolassa, Jana; Prigent, Catherine; Aires, Filipe; Gentine, Pierre

2017-04-01

Knowledge of root zone soil moisture is essential in studying plant's response to different stress conditions since plant photosynthetic activity and transpiration rate are constrained by the water available through their roots. Current global root zone soil moisture estimates are based on either outputs from physical models constrained by observations, or assimilation of remotely-sensed microwave-based surface soil moisture estimates with physical model outputs. However, quality of these estimates are limited by the accuracy of the model representations of physical processes (such as radiative transfer, infiltration, percolation, and evapotranspiration) as well as errors in the estimates of the surface parameters. Additionally, statistical approaches provide an alternative efficient platform to develop root zone soil moisture retrieval algorithms from remotely-sensed observations. In this study, we present a new neural network based retrieval algorithm to estimate surface and root zone soil moisture from passive microwave observations of SMAP satellite (L-band) and AMSR2 instrument (X-band). SMAP early morning observations are ideal for surface soil moisture retrieval. AMSR2 mid-night observations are used here as an indicator of plant hydraulic properties that are related to root zone soil moisture. The combined observations from SMAP and AMSR2 together with other ancillary observations including the Solar-Induced Fluorescence (SIF) estimates from GOME-2 instrument provide necessary information to estimate surface and root zone soil moisture. The algorithm is applied to observations from the first 18 months of SMAP mission and retrievals are validated against in-situ observations and other global datasets.

Saturn Ring Rain: New Observations and Estimates of Water Influx

NASA Astrophysics Data System (ADS)

Moore, L.; O'Donoghue, J.; Mueller-Wodarg, I.; Galand, M.; Mendillo, M.

2014-04-01

We estimate the maximum rates of water influx from Saturn's rings based on ionospheric model reproductions of derived H3+ column densities. On 17 April 2011 over two hours of near-infrared spectral data were obtained of Saturn using the Near InfraRed Spectrograph (NIRSPEC) instrument on the 10-m Keck II telescope. Two bright H3+ rotationalvibrational emission lines were visible nearly from pole to pole, allowing low-latitude ionospheric emissions to be studied for the first time, and revealing significant latitudinal structure, with local extrema in one hemisphere being mirrored at magnetically conjugate latitudes in the opposite hemisphere. In addition, those minima and maxima mapped to latitudes of increased or decreased density, respectively, in Saturn's rings, implying a direct ringatmosphere connection in which charged water group particles from the rings are guided by magnetic field lines as they "rain" down upon the atmosphere. Water products act to quench the local ionosphere, and therefore modify the H3+ densities and their observed emissions. Using the Saturn Thermosphere Ionosphere Model (STIM), a 3-D model of Saturn's upper atmosphere, we derive the maximum rates of water influx required from the rings in order to reproduce the H3+ column densities observed on 17 April 2011. We estimate the globally averaged maximum ringderived water influx to be (1.6-12)x105 cm-2 sec-1, which represents a maximum total global influx of water from Saturn's rings to its atmosphere of (1.0-6.8)x1026 sec-1. We will also present the initial findings of Keck ring rain observing campaigns from April 2013 and May 2014.

Sampling effort and estimates of species richness based on prepositioned area electrofisher samples

USGS Publications Warehouse

Bowen, Z.H.; Freeman, Mary C.

1998-01-01

Estimates of species richness based on electrofishing data are commonly used to describe the structure of fish communities. One electrofishing method for sampling riverine fishes that has become popular in the last decade is the prepositioned area electrofisher (PAE). We investigated the relationship between sampling effort and fish species richness at seven sites in the Tallapoosa River system, USA based on 1,400 PAE samples collected during 1994 and 1995. First, we estimated species richness at each site using the first-order jackknife and compared observed values for species richness and jackknife estimates of species richness to estimates based on historical collection data. Second, we used a permutation procedure and nonlinear regression to examine rates of species accumulation. Third, we used regression to predict the number of PAE samples required to collect the jackknife estimate of species richness at each site during 1994 and 1995. We found that jackknife estimates of species richness generally were less than or equal to estimates based on historical collection data. The relationship between PAE electrofishing effort and species richness in the Tallapoosa River was described by a positive asymptotic curve as found in other studies using different electrofishing gears in wadable streams. Results from nonlinear regression analyses indicted that rates of species accumulation were variable among sites and between years. Across sites and years, predictions of sampling effort required to collect jackknife estimates of species richness suggested that doubling sampling effort (to 200 PAEs) would typically increase observed species richness by not more than six species. However, sampling effort beyond about 60 PAE samples typically increased observed species richness by < 10%. We recommend using historical collection data in conjunction with a preliminary sample size of at least 70 PAE samples to evaluate estimates of species richness in medium-sized rivers

Observing Tsunamis in the Ionosphere Using Ground Based GPS Measurements

NASA Technical Reports Server (NTRS)

Galvan, D. A.; Komjathy, A.; Song, Y. Tony; Stephens, P.; Hickey, M. P.; Foster, J.

2011-01-01

Ground-based Global Positioning System (GPS) measurements of ionospheric Total Electron Content (TEC) show variations consistent with atmospheric internal gravity waves caused by ocean tsunamis following recent seismic events, including the Tohoku tsunami of March 11, 2011. We observe fluctuations correlated in time, space, and wave properties with this tsunami in TEC estimates processed using JPL's Global Ionospheric Mapping Software. These TEC estimates were band-pass filtered to remove ionospheric TEC variations with periods outside the typical range of internal gravity waves caused by tsunamis. Observable variations in TEC appear correlated with the Tohoku tsunami near the epicenter, at Hawaii, and near the west coast of North America. Disturbance magnitudes are 1-10% of the background TEC value. Observations near the epicenter are compared to estimates of expected tsunami-driven TEC variations produced by Embry Riddle Aeronautical University's Spectral Full Wave Model, an atmosphere-ionosphere coupling model, and found to be in good agreement. The potential exists to apply these detection techniques to real-time GPS TEC data, providing estimates of tsunami speed and amplitude that may be useful for future early warning systems.

Performance comparison of attitude determination, attitude estimation, and nonlinear observers algorithms

NASA Astrophysics Data System (ADS)

MOHAMMED, M. A. SI; BOUSSADIA, H.; BELLAR, A.; ADNANE, A.

2017-01-01

This paper presents a brief synthesis and useful performance analysis of different attitude filtering algorithms (attitude determination algorithms, attitude estimation algorithms, and nonlinear observers) applied to Low Earth Orbit Satellite in terms of accuracy, convergence time, amount of memory, and computation time. This latter is calculated in two ways, using a personal computer and also using On-board computer 750 (OBC 750) that is being used in many SSTL Earth observation missions. The use of this comparative study could be an aided design tool to the designer to choose from an attitude determination or attitude estimation or attitude observer algorithms. The simulation results clearly indicate that the nonlinear Observer is the more logical choice.

Attitude determination and parameter estimation using vector observations - Theory

NASA Technical Reports Server (NTRS)

Markley, F. Landis

1989-01-01

Procedures for attitude determination based on Wahba's loss function are generalized to include the estimation of parameters other than the attitude, such as sensor biases. Optimization with respect to the attitude is carried out using the q-method, which does not require an a priori estimate of the attitude. Optimization with respect to the other parameters employs an iterative approach, which does require an a priori estimate of these parameters. Conventional state estimation methods require a priori estimates of both the parameters and the attitude, while the algorithm presented in this paper always computes the exact optimal attitude for given values of the parameters. Expressions for the covariance of the attitude and parameter estimates are derived.

Direct Aerosol Radiative Forcing from Combined A-Train Observations - Preliminary Comparisons with AeroCom Models and Pathways to Observationally Based All-sky Estimates

NASA Astrophysics Data System (ADS)

Redemann, J.; Livingston, J. M.; Shinozuka, Y.; Kacenelenbogen, M. S.; Russell, P. B.; LeBlanc, S. E.; Vaughan, M.; Ferrare, R. A.; Hostetler, C. A.; Rogers, R. R.; Burton, S. P.; Torres, O.; Remer, L. A.; Stier, P.; Schutgens, N.

2014-12-01

We describe a technique for combining CALIOP aerosol backscatter, MODIS spectral AOD (aerosol optical depth), and OMI AAOD (absorption aerosol optical depth) retrievals for the purpose of estimating full spectral sets of aerosol radiative properties, and ultimately for calculating the 3-D distribution of direct aerosol radiative forcing. We present results using one year of data collected in 2007 and show comparisons of the aerosol radiative property estimates to collocated AERONET retrievals. Use of the recently released MODIS Collection 6 data for aerosol optical depths derived with the dark target and deep blue algorithms has extended the coverage of the multi-sensor estimates towards higher latitudes. Initial calculations of seasonal clear-sky aerosol radiative forcing based on our multi-sensor aerosol retrievals compare well with over-ocean and top of the atmosphere IPCC-2007 model-based results, and with more recent assessments in the "Climate Change Science Program Report: Atmospheric Aerosol Properties and Climate Impacts" (2009). For the first time, we present comparisons of our multi-sensor aerosol direct radiative forcing estimates to values derived from a subset of models that participated in the latest AeroCom initiative. We discuss the major challenges that exist in extending our clear-sky results to all-sky conditions. On the basis of comparisons to suborbital measurements, we present some of the limitations of the MODIS and CALIOP retrievals in the presence of adjacent or underlying clouds. Strategies for meeting these challenges are discussed.

Google Haul Out: Earth Observation Imagery and Digital Aerial Surveys in Coastal Wildlife Management and Abundance Estimation

PubMed Central

Moxley, Jerry H.; Bogomolni, Andrea; Hammill, Mike O.; Moore, Kathleen M. T.; Polito, Michael J.; Sette, Lisa; Sharp, W. Brian; Waring, Gordon T.; Gilbert, James R.; Halpin, Patrick N.; Johnston, David W.

2017-01-01

Abstract As the sampling frequency and resolution of Earth observation imagery increase, there are growing opportunities for novel applications in population monitoring. New methods are required to apply established analytical approaches to data collected from new observation platforms (e.g., satellites and unmanned aerial vehicles). Here, we present a method that estimates regional seasonal abundances for an understudied and growing population of gray seals (Halichoerus grypus) in southeastern Massachusetts, using opportunistic observations in Google Earth imagery. Abundance estimates are derived from digital aerial survey counts by adapting established correction-based analyses with telemetry behavioral observation to quantify survey biases. The result is a first regional understanding of gray seal abundance in the northeast US through opportunistic Earth observation imagery and repurposed animal telemetry data. As species observation data from Earth observation imagery become more ubiquitous, such methods provide a robust, adaptable, and cost-effective solution to monitoring animal colonies and understanding species abundances. PMID:29599542

Google Haul Out: Earth Observation Imagery and Digital Aerial Surveys in Coastal Wildlife Management and Abundance Estimation.

PubMed

Moxley, Jerry H; Bogomolni, Andrea; Hammill, Mike O; Moore, Kathleen M T; Polito, Michael J; Sette, Lisa; Sharp, W Brian; Waring, Gordon T; Gilbert, James R; Halpin, Patrick N; Johnston, David W

2017-08-01

As the sampling frequency and resolution of Earth observation imagery increase, there are growing opportunities for novel applications in population monitoring. New methods are required to apply established analytical approaches to data collected from new observation platforms (e.g., satellites and unmanned aerial vehicles). Here, we present a method that estimates regional seasonal abundances for an understudied and growing population of gray seals (Halichoerus grypus) in southeastern Massachusetts, using opportunistic observations in Google Earth imagery. Abundance estimates are derived from digital aerial survey counts by adapting established correction-based analyses with telemetry behavioral observation to quantify survey biases. The result is a first regional understanding of gray seal abundance in the northeast US through opportunistic Earth observation imagery and repurposed animal telemetry data. As species observation data from Earth observation imagery become more ubiquitous, such methods provide a robust, adaptable, and cost-effective solution to monitoring animal colonies and understanding species abundances.

Self-testing of binary observables based on commutation

NASA Astrophysics Data System (ADS)

Kaniewski, Jedrzej

2017-06-01

We consider the problem of certifying binary observables based on a Bell inequality violation alone, a task known as self-testing of measurements. We introduce a family of commutation-based measures, which encode all the distinct arrangements of two projective observables on a qubit. These quantities by construction take into account the usual limitations of self-testing and since they are "weighted" by the (reduced) state, they automatically deal with rank-deficient reduced density matrices. We show that these measures can be estimated from the observed Bell violation in several scenarios and the proofs rely only on standard linear algebra. The trade-offs turn out to be tight, and in particular, they give nontrivial statements for arbitrarily small violations. On the other extreme, observing the maximal violation allows us to deduce precisely the form of the observables, which immediately leads to a complete rigidity statement. In particular, we show that for all n ≥3 the n -partite Mermin-Ardehali-Belinskii-Klyshko inequality self-tests the n -partite Greenberger-Horne-Zeilinger state and maximally incompatible qubit measurements on every party. Our results imply that any pair of projective observables on a qubit can be certified in a truly robust manner. Finally, we show that commutation-based measures give a convenient way of expressing relations among more than two observables.

Estimating surface soil moisture from SMAP observations using a Neural Network technique.

PubMed

Kolassa, J; Reichle, R H; Liu, Q; Alemohammad, S H; Gentine, P; Aida, K; Asanuma, J; Bircher, S; Caldwell, T; Colliander, A; Cosh, M; Collins, C Holifield; Jackson, T J; Martínez-Fernández, J; McNairn, H; Pacheco, A; Thibeault, M; Walker, J P

2018-01-01

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m 3 m -3 , 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m 3 m -3 , 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones.

Estimation in Linear Systems Featuring Correlated Uncertain Observations Coming from Multiple Sensors

NASA Astrophysics Data System (ADS)

Caballero-Águila, R.; Hermoso-Carazo, A.; Linares-Pérez, J.

2009-08-01

In this paper, the state least-squares linear estimation problem from correlated uncertain observations coming from multiple sensors is addressed. It is assumed that, at each sensor, the state is measured in the presence of additive white noise and that the uncertainty in the observations is characterized by a set of Bernoulli random variables which are only correlated at consecutive time instants. Assuming that the statistical properties of such variables are not necessarily the same for all the sensors, a recursive filtering algorithm is proposed, and the performance of the estimators is illustrated by a numerical simulation example wherein a signal is estimated from correlated uncertain observations coming from two sensors with different uncertainty characteristics.

Standardizing the double-observer survey method for estimating mountain ungulate prey of the endangered snow leopard.

PubMed

Suryawanshi, Kulbhushansingh R; Bhatnagar, Yash Veer; Mishra, Charudutt

2012-07-01

Mountain ungulates around the world have been threatened by illegal hunting, habitat modification, increased livestock grazing, disease and development. Mountain ungulates play an important functional role in grasslands as primary consumers and as prey for wild carnivores, and monitoring of their populations is important for conservation purposes. However, most of the several currently available methods of estimating wild ungulate abundance are either difficult to implement or too expensive for mountainous terrain. A rigorous method of sampling ungulate abundance in mountainous areas that can allow for some measure of sampling error is therefore much needed. To this end, we used a combination of field data and computer simulations to test the critical assumptions associated with double-observer technique based on capture-recapture theory. The technique was modified and adapted to estimate the populations of bharal (Pseudois nayaur) and ibex (Capra sibirica) at five different sites. Conducting the two double-observer surveys simultaneously led to underestimation of the population by 15%. We therefore recommend separating the surveys in space or time. The overall detection probability for the two observers was 0.74 and 0.79. Our surveys estimated mountain ungulate populations (± 95% confidence interval) of 735 (± 44), 580 (± 46), 509 (± 53), 184 (± 40) and 30 (± 14) individuals at the five sites, respectively. A detection probability of 0.75 was found to be sufficient to detect a change of 20% in populations of >420 individuals. Based on these results, we believe that this method is sufficiently precise for scientific and conservation purposes and therefore recommend the use of the double-observer approach (with the two surveys separated in time or space) for the estimation and monitoring of mountain ungulate populations.

Model-based estimation of individual fitness

USGS Publications Warehouse

Link, W.A.; Cooch, E.G.; Cam, E.

2002-01-01

Fitness is the currency of natural selection, a measure of the propagation rate of genotypes into future generations. Its various definitions have the common feature that they are functions of survival and fertility rates. At the individual level, the operative level for natural selection, these rates must be understood as latent features, genetically determined propensities existing at birth. This conception of rates requires that individual fitness be defined and estimated by consideration of the individual in a modelled relation to a group of similar individuals; the only alternative is to consider a sample of size one, unless a clone of identical individuals is available. We present hierarchical models describing individual heterogeneity in survival and fertility rates and allowing for associations between these rates at the individual level. We apply these models to an analysis of life histories of Kittiwakes (Rissa tridactyla) observed at several colonies on the Brittany coast of France. We compare Bayesian estimation of the population distribution of individual fitness with estimation based on treating individual life histories in isolation, as samples of size one (e.g. McGraw and Caswell, 1996).

Fast Emission Estimates in China Constrained by Satellite Observations (Invited)

NASA Astrophysics Data System (ADS)

Mijling, B.; van der A, R.

2013-12-01

Emission inventories of air pollutants are crucial information for policy makers and form important input data for air quality models. Unfortunately, bottom-up emission inventories, compiled from large quantities of statistical data, are easily outdated for an emerging economy such as China, where rapid economic growth changes emissions accordingly. Alternatively, top-down emission estimates from satellite observations of air constituents have important advantages of being spatial consistent, having high temporal resolution, and enabling emission updates shortly after the satellite data become available. Constraining emissions from concentration measurements is, however, computationally challenging. Within the GlobEmission project of the European Space Agency (ESA) a new algorithm has been developed, specifically designed for fast daily emission estimates of short-lived atmospheric species on a mesoscopic scale (0.25 × 0.25 degree) from satellite observations of column concentrations. The algorithm needs only one forward model run from a chemical transport model to calculate the sensitivity of concentration to emission, using trajectory analysis to account for transport away from the source. By using a Kalman filter in the inverse step, optimal use of the a priori knowledge and the newly observed data is made. We apply the algorithm for NOx emission estimates in East China, using the CHIMERE model together with tropospheric NO2 column retrievals of the OMI and GOME-2 satellite instruments. The observations are used to construct a monthly emission time series, which reveal important emission trends such as the emission reduction measures during the Beijing Olympic Games, and the impact and recovery from the global economic crisis. The algorithm is also able to detect emerging sources (e.g. new power plants) and improve emission information for areas where proxy data are not or badly known (e.g. shipping emissions). The new emission estimates result in a better

Bias-adjusted satellite-based rainfall estimates for predicting floods: Narayani Basin

USGS Publications Warehouse

Artan, Guleid A.; Tokar, S.A.; Gautam, D.K.; Bajracharya, S.R.; Shrestha, M.S.

2011-01-01

In Nepal, as the spatial distribution of rain gauges is not sufficient to provide detailed perspective on the highly varied spatial nature of rainfall, satellite-based rainfall estimates provides the opportunity for timely estimation. This paper presents the flood prediction of Narayani Basin at the Devghat hydrometric station (32 000 km2) using bias-adjusted satellite rainfall estimates and the Geospatial Stream Flow Model (GeoSFM), a spatially distributed, physically based hydrologic model. The GeoSFM with gridded gauge observed rainfall inputs using kriging interpolation from 2003 was used for calibration and 2004 for validation to simulate stream flow with both having a Nash Sutcliff Efficiency of above 0.7. With the National Oceanic and Atmospheric Administration Climate Prediction Centre's rainfall estimates (CPC_RFE2.0), using the same calibrated parameters, for 2003 the model performance deteriorated but improved after recalibration with CPC_RFE2.0 indicating the need to recalibrate the model with satellite-based rainfall estimates. Adjusting the CPC_RFE2.0 by a seasonal, monthly and 7-day moving average ratio, improvement in model performance was achieved. Furthermore, a new gauge-satellite merged rainfall estimates obtained from ingestion of local rain gauge data resulted in significant improvement in flood predictability. The results indicate the applicability of satellite-based rainfall estimates in flood prediction with appropriate bias correction.

Assimilation of active and passive microwave observations for improved estimates of soil moisture and crop growth

USDA-ARS?s Scientific Manuscript database

An Ensemble Kalman Filter-based data assimilation framework that links a crop growth model with active and passive (AP) microwave models was developed to improve estimates of soil moisture (SM) and vegetation biomass over a growing season of soybean. Complementarities in AP observations were incorpo...

Sigmoid function based integral-derivative observer and application to autopilot design

NASA Astrophysics Data System (ADS)

Shao, Xingling; Wang, Honglun; Liu, Jun; Tang, Jun; Li, Jie; Zhang, Xiaoming; Shen, Chong

2017-02-01

To handle problems of accurate signal reconstruction and controller implementation with integral and derivative components in the presence of noisy measurement, motivated by the design principle of sigmoid function based tracking differentiator and nonlinear continuous integral-derivative observer, a novel integral-derivative observer (SIDO) using sigmoid function is developed. The key merit of the proposed SIDO is that it can simultaneously provide continuous integral and differential estimates with almost no drift phenomena and chattering effect, as well as acceptable noise-tolerance performance from output measurement, and the stability is established based on exponential stability and singular perturbation theory. In addition, the effectiveness of SIDO in suppressing drift phenomena and high frequency noises is firstly revealed using describing function and confirmed through simulation comparisons. Finally, the theoretical results on SIDO are demonstrated with application to autopilot design: 1) the integral and tracking estimates are extracted from the sensed pitch angular rate contaminated by nonwhite noises in feedback loop, 2) the PID(proportional-integral-derivative) based attitude controller is realized by adopting the error estimates offered by SIDO instead of using the ideal integral and derivative operator to achieve satisfactory tracking performance under control constraint.

Surface Soil Moisture Memory Estimated from Models and SMAP Observations

NASA Astrophysics Data System (ADS)

He, Q.; Mccoll, K. A.; Li, C.; Lu, H.; Akbar, R.; Pan, M.; Entekhabi, D.

2017-12-01

Soil moisture memory(SMM), which is loosely defined as the time taken by soil to forget an anomaly, has been proved to be important in land-atmosphere interaction. There are many metrics to calculate the SMM timescale, for example, the timescale based on the time-series autocorrelation, the timescale ignoring the soil moisture time series and the timescale which only considers soil moisture increment. Recently, a new timescale based on `Water Cycle Fraction' (Kaighin et al., 2017), in which the impact of precipitation on soil moisture memory is considered, has been put up but not been fully evaluated in global. In this study, we compared the surface SMM derived from SMAP observations with that from land surface model simulations (i.e., the SMAP Nature Run (NR) provided by the Goddard Earth Observing System, version 5) (Rolf et al., 2014). Three timescale metrics were used to quantify the surface SMM as: T0 based on the soil moisture time series autocorrelation, deT0 based on the detrending soil moisture time series autocorrelation, and tHalf based on the Water Cycle Fraction. The comparisons indicate that: (1) there are big gaps between the T0 derived from SMAP and that from NR (2) the gaps get small for deT0 case, in which the seasonality of surface soil moisture was removed with a moving average filter; (3) the tHalf estimated from SMAP is much closer to that from NR. The results demonstrate that surface SMM can vary dramatically among different metrics, while the memory derived from land surface model differs from the one from SMAP observation. tHalf, with considering the impact of precipitation, may be a good choice to quantify surface SMM and have high potential in studies related to land atmosphere interactions. References McColl. K.A., S.H. Alemohammad, R. Akbar, A.G. Konings, S. Yueh, D. Entekhabi. The Global Distribution and Dynamics of Surface Soil Moisture, Nature Geoscience, 2017 Reichle. R., L. Qing, D.L. Gabrielle, A. Joe. The "SMAP_Nature_v03" Data

Estimation of the Length of Day (LOD) from DORIS observations

NASA Astrophysics Data System (ADS)

Štěpánek, Petr; Hugentobler, Urs; Buday, Michal; Filler, Vratislav

2018-07-01

The paper is devoted to the estimation of the Length of the Day (LOD) from DORIS observations and summarizes the first successful experiment with LOD estimation at the level of geodetic precision. This result is confirmed by 9 years of DORIS data (2006.0-2015.0). The mean difference of the non smoothed LOD series with respect to the IERS C04 model reaches a value of tens μs and a standard deviation around 120 μs for the last years of the campaign 2012.0-2015.0. However, the mean difference with respect to the reference model varies over time, reaching negative values from -60 to -20 μs for the years 2006-2011 and positive values from 80 to 120 μs for the years 2012-2014. The time variable mean difference with respect to the changes in DORIS satellite constellation is discussed, as well as the possibility of the bias reduction applying the long-term averages of the cross-track harmonics or adjustment of the geopotential coefficient C20. Moreover, the possibility of LOD adjustment in the standard DORIS solution is discussed with focus on the station coordinates estimation. In addition, the power spectrum of the difference between estimated LOD and the reference model was performed, showing the domination of the annual signal. Also LOD estimated from single-satellite DORIS solutions was analyzed to identify satellite-specific issues. The paper includes a correlation analysis of the orbit parameters, Earth rotation parameters and the geopotential coefficient C20, based on covariance matrices from weekly solutions. High correlation around 0.96 was found for LOD and the sine amplitude of the cross-track harmonic empirical acceleration, which was also confirmed analytically.

Analytic Perturbation Method for Estimating Ground Flash Fraction from Satellite Lightning Observations

NASA Technical Reports Server (NTRS)

Koshak, William; Solakiewicz, Richard

2013-01-01

An analytic perturbation method is introduced for estimating the lightning ground flash fraction in a set of N lightning flashes observed by a satellite lightning mapper. The value of N is large, typically in the thousands, and the observations consist of the maximum optical group area produced by each flash. The method is tested using simulated observations that are based on Optical Transient Detector (OTD) and Lightning Imaging Sensor (LIS) data. National Lightning Detection NetworkTM (NLDN) data is used to determine the flash-type (ground or cloud) of the satellite-observed flashes, and provides the ground flash fraction truth for the simulation runs. It is found that the mean ground flash fraction retrieval errors are below 0.04 across the full range 0-1 under certain simulation conditions. In general, it is demonstrated that the retrieval errors depend on many factors (i.e., the number, N, of satellite observations, the magnitude of random and systematic measurement errors, and the number of samples used to form certain climate distributions employed in the model).

Observations and estimates of wave-driven water level extremes at the Marshall Islands

NASA Astrophysics Data System (ADS)

Merrifield, M. A.; Becker, J. M.; Ford, M.; Yao, Y.

2014-10-01

Wave-driven extreme water levels are examined for coastlines protected by fringing reefs using field observations obtained in the Republic of the Marshall Islands. The 2% exceedence water level near the shoreline due to waves is estimated empirically for the study sites from breaking wave height at the outer reef and by combining separate contributions from setup, sea and swell, and infragravity waves, which are estimated based on breaking wave height and water level over the reef flat. Although each component exhibits a tidal dependence, they sum to yield a 2% exceedence level that does not. A hindcast based on the breaking wave height parameterization is used to assess factors leading to flooding at Roi-Namur caused by an energetic swell event during December 2008. Extreme water levels similar to December 2008 are projected to increase significantly with rising sea level as more wave and tide events combine to exceed inundation threshold levels.

Estimating Field Scale Crop Evapotranspiration using Landsat and MODIS Satellite Observations

NASA Astrophysics Data System (ADS)

Wong, A.; Jin, Y.; Snyder, R. L.; Daniele, Z.; Gao, F.

2016-12-01

Irrigation accounts for 80% of human freshwater consumption, and most of it return to the atmosphere through Evapotranspiration (ET). Given the challenges of already-stressed water resources and ground water regulation in California, a cost-effective, timely, and consistent spatial estimate of crop ET, from the farm to watershed level, is becoming increasingly important. The Priestley-Taylor (PT) approach, calibrated with field data and driven by satellite observations, shows great promise for accurate ET estimates across diverse ecosystems. We here aim to improve the robustness of the PT approach in agricultural lands, to enable growers and farm managers to tailor irrigation management based on in-field spatial variability and in-season variation. We optimized the PT coefficients for each crop type with available ET measurements from eddy covariance towers and/or surface renewal stations at six crop fields (Alfalfa, Almond, Citrus, Corn, Pistachio and Rice) in California. Good agreement was found between satellite-based estimates and field measurements of net radiation, with a RMSE of less than 36 W m-2. The crop type specific optimization performed well, with a RMSE of 30 W m-2 and a correlation of 0.81 for predicted daily latent heat flux. The calibrated algorithm was used to estimate ET at 30 m resolution over the Sacramento-San Joaquin Delta region for 2015 water year. It captures well the seasonal dynamics and spatial distribution of ET in Sacramento-San Joaquin Delta. A continuous monitoring of the dynamics and spatial heterogeneity of canopy and consumptive water use at a field scale, will help the growers to be well prepared and informed to adaptively manage water, canopy, and grove density to maximize the yield with the least amount of water.

A variational technique to estimate snowfall rate from coincident radar, snowflake, and fall-speed observations

DOE PAGES

Cooper, Steven J.; Wood, Norman B.; L'Ecuyer, Tristan S.

2017-07-20

Estimates of snowfall rate as derived from radar reflectivities alone are non-unique. Different combinations of snowflake microphysical properties and particle fall speeds can conspire to produce nearly identical snowfall rates for given radar reflectivity signatures. Such ambiguities can result in retrieval uncertainties on the order of 100–200% for individual events. Here, we use observations of particle size distribution (PSD), fall speed, and snowflake habit from the Multi-Angle Snowflake Camera (MASC) to constrain estimates of snowfall derived from Ka-band ARM zenith radar (KAZR) measurements at the Atmospheric Radiation Measurement (ARM) North Slope Alaska (NSA) Climate Research Facility site at Barrow. MASCmore » measurements of microphysical properties with uncertainties are introduced into a modified form of the optimal-estimation CloudSat snowfall algorithm (2C-SNOW-PROFILE) via the a priori guess and variance terms. Use of the MASC fall speed, MASC PSD, and CloudSat snow particle model as base assumptions resulted in retrieved total accumulations with a -18% difference relative to nearby National Weather Service (NWS) observations over five snow events. The average error was 36% for the individual events. The use of different but reasonable combinations of retrieval assumptions resulted in estimated snowfall accumulations with differences ranging from -64 to +122% for the same storm events. Retrieved snowfall rates were particularly sensitive to assumed fall speed and habit, suggesting that in situ measurements can help to constrain key snowfall retrieval uncertainties. Furthermore, accurate knowledge of these properties dependent upon location and meteorological conditions should help refine and improve ground- and space-based radar estimates of snowfall.« less

A variational technique to estimate snowfall rate from coincident radar, snowflake, and fall-speed observations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cooper, Steven J.; Wood, Norman B.; L'Ecuyer, Tristan S.

Estimates of snowfall rate as derived from radar reflectivities alone are non-unique. Different combinations of snowflake microphysical properties and particle fall speeds can conspire to produce nearly identical snowfall rates for given radar reflectivity signatures. Such ambiguities can result in retrieval uncertainties on the order of 100–200% for individual events. Here, we use observations of particle size distribution (PSD), fall speed, and snowflake habit from the Multi-Angle Snowflake Camera (MASC) to constrain estimates of snowfall derived from Ka-band ARM zenith radar (KAZR) measurements at the Atmospheric Radiation Measurement (ARM) North Slope Alaska (NSA) Climate Research Facility site at Barrow. MASCmore » measurements of microphysical properties with uncertainties are introduced into a modified form of the optimal-estimation CloudSat snowfall algorithm (2C-SNOW-PROFILE) via the a priori guess and variance terms. Use of the MASC fall speed, MASC PSD, and CloudSat snow particle model as base assumptions resulted in retrieved total accumulations with a -18% difference relative to nearby National Weather Service (NWS) observations over five snow events. The average error was 36% for the individual events. The use of different but reasonable combinations of retrieval assumptions resulted in estimated snowfall accumulations with differences ranging from -64 to +122% for the same storm events. Retrieved snowfall rates were particularly sensitive to assumed fall speed and habit, suggesting that in situ measurements can help to constrain key snowfall retrieval uncertainties. Furthermore, accurate knowledge of these properties dependent upon location and meteorological conditions should help refine and improve ground- and space-based radar estimates of snowfall.« less

Validation of Ground-based Optical Estimates of Auroral Electron Precipitation Energy Deposition

NASA Astrophysics Data System (ADS)

Hampton, D. L.; Grubbs, G. A., II; Conde, M.; Lynch, K. A.; Michell, R.; Zettergren, M. D.; Samara, M.; Ahrns, M. J.

2017-12-01

One of the major energy inputs into the high latitude ionosphere and mesosphere is auroral electron precipitation. Not only does the kinetic energy get deposited, the ensuing ionization in the E and F-region ionosphere modulates parallel and horizontal currents that can dissipate in the form of Joule heating. Global models to simulate these interactions typically use electron precipitation models that produce a poor representation of the spatial and temporal complexity of auroral activity as observed from the ground. This is largely due to these precipitation models being based on averages of multiple satellite overpasses separated by periods much longer than typical auroral feature durations. With the development of regional and continental observing networks (e.g. THEMIS ASI), the possibility of ground-based optical observations producing quantitative estimates of energy deposition with temporal and spatial scales comparable to those known to be exhibited in auroral activity become a real possibility. Like empirical precipitation models based on satellite overpasses such optics-based estimates are subject to assumptions and uncertainties, and therefore require validation. Three recent sounding rocket missions offer such an opportunity. The MICA (2012), GREECE (2014) and Isinglass (2017) missions involved detailed ground based observations of auroral arcs simultaneously with extensive on-board instrumentation. These have afforded an opportunity to examine the results of three optical methods of determining auroral electron energy flux, namely 1) ratio of auroral emissions, 2) green line temperature vs. emission altitude, and 3) parametric estimates using white-light images. We present comparisons from all three methods for all three missions and summarize the temporal and spatial scales and coverage over which each is valid.

Kalman-Filter-Based Orientation Determination Using Inertial/Magnetic Sensors: Observability Analysis and Performance Evaluation

PubMed Central

Sabatini, Angelo Maria

2011-01-01

In this paper we present a quaternion-based Extended Kalman Filter (EKF) for estimating the three-dimensional orientation of a rigid body. The EKF exploits the measurements from an Inertial Measurement Unit (IMU) that is integrated with a tri-axial magnetic sensor. Magnetic disturbances and gyro bias errors are modeled and compensated by including them in the filter state vector. We employ the observability rank criterion based on Lie derivatives to verify the conditions under which the nonlinear system that describes the process of motion tracking by the IMU is observable, namely it may provide sufficient information for performing the estimation task with bounded estimation errors. The observability conditions are that the magnetic field, perturbed by first-order Gauss-Markov magnetic variations, and the gravity vector are not collinear and that the IMU is subject to some angular motions. Computer simulations and experimental testing are presented to evaluate the algorithm performance, including when the observability conditions are critical. PMID:22163689

Accuracy of self-reported drinking: observational verification of 'last occasion' drink estimates of young adults.

PubMed

Northcote, Jeremy; Livingston, Michael

2011-01-01

As a formative step towards determining the accuracy of self-reported drinking levels commonly used for estimating population alcohol use, the validity of a 'last occasion' self-reporting approach is tested with corresponding field observations of participants' drinking quantity. This study is the first known attempt to validate the accuracy of self-reported alcohol consumption using data from a natural setting. A total of 81 young adults (aged 18-25 years) were purposively selected in Perth, Western Australia. Participants were asked to report the number of alcoholic drinks consumed at nightlife venues 1-2 days after being observed by peer-based researchers on 239 occasions. Complete observation data and self-report estimates were available for 129 sessions, which were fitted with multi-level models assessing the relationship between observed and reported consumption. Participants accurately estimated their consumption when engaging in light to moderate drinking (eight or fewer drinks in a single session), with no significant difference between the mean reported consumption and the mean observed consumption. In contrast, participants underestimated their own consumption by increasing amounts when engaging in heavy drinking of more than eight drinks. It is suggested that recent recall methods in self-report surveys are potentially reasonably accurate measures of actual drinking levels for light to moderate drinkers, but that underestimating of alcohol consumption increases with heavy consumption. Some of the possible reasons for underestimation of heavy drinking are discussed, with both cognitive and socio-cultural factors considered.

On the Impact of Multi-GNSS Observations on Real-Time Precise Point Positioning Zenith Total Delay Estimates

NASA Astrophysics Data System (ADS)

Ding, Wenwu; Teferle, Norman; Kaźmierski, Kamil; Laurichesse, Denis; Yuan, Yunbin

2017-04-01

Observations from multiple Global Navigation Satellite System (GNSS) can improve the performance of real-time (RT) GNSS meteorology, in particular of the Zenith Total Delay (ZTD) estimates. RT ZTD estimates in combination with derived precipitable water vapour estimates can be used for weather now-casting and the tracking of severe weather events. While a number of published literature has already highlighted this positive development, in this study we describe an operational RT system for extracting ZTD using a modified version of the PPP-wizard (with PPP denoting Precise Point Positioning). Multi-GNSS, including GPS, GLONASS and Galileo, observation streams are processed using a RT PPP strategy based on RT satellite orbit and clock products from the Centre National d'Etudes Spatiales (CNES). A continuous experiment for 30 days was conducted, in which the RT observation streams of 20 globally distributed stations were processed. The initialization time and accuracy of the RT troposphere products using single and/or multi-system observations were evaluated. The effect of RT PPP ambiguity resolution was also evaluated. The results revealed that the RT troposphere products based on single system observations can fulfill the requirements of the meteorological application in now-casting systems. We noted that the GPS-only solution is better than the GLONASS-only solution in both initialization and accuracy. While the ZTD performance can be improved by applying RT PPP ambiguity resolution, the inclusion of observations from multiple GNSS has a more profound effect. Specifically, we saw that the ambiguity resolution is more effective in improving the accuracy, whereas the initialization process can be better accelerated by multi-GNSS observations. Combining all systems, RT troposphere products with an average accuracy of about 8 mm in ZTD were achieved after an initialization process of approximately 9 minutes, which supports the application of multi-GNSS observations

Estimating Soil Hydraulic Parameters using Gradient Based Approach

NASA Astrophysics Data System (ADS)

Rai, P. K.; Tripathi, S.

2017-12-01

The conventional way of estimating parameters of a differential equation is to minimize the error between the observations and their estimates. The estimates are produced from forward solution (numerical or analytical) of differential equation assuming a set of parameters. Parameter estimation using the conventional approach requires high computational cost, setting-up of initial and boundary conditions, and formation of difference equations in case the forward solution is obtained numerically. Gaussian process based approaches like Gaussian Process Ordinary Differential Equation (GPODE) and Adaptive Gradient Matching (AGM) have been developed to estimate the parameters of Ordinary Differential Equations without explicitly solving them. Claims have been made that these approaches can straightforwardly be extended to Partial Differential Equations; however, it has been never demonstrated. This study extends AGM approach to PDEs and applies it for estimating parameters of Richards equation. Unlike the conventional approach, the AGM approach does not require setting-up of initial and boundary conditions explicitly, which is often difficult in real world application of Richards equation. The developed methodology was applied to synthetic soil moisture data. It was seen that the proposed methodology can estimate the soil hydraulic parameters correctly and can be a potential alternative to the conventional method.

Estimate of carbonyl sulfide tropical oceanic surface fluxes using Aura Tropospheric Emission Spectrometer observations

NASA Astrophysics Data System (ADS)

Kuai, Le; Worden, John R.; Campbell, J. Elliott; Kulawik, Susan S.; Li, King-Fai; Lee, Meemong; Weidner, Richard J.; Montzka, Stephen A.; Moore, Fred L.; Berry, Joe A.; Baker, Ian; Denning, A. Scott; Bian, Huisheng; Bowman, Kevin W.; Liu, Junjie; Yung, Yuk L.

2015-10-01

Quantifying the carbonyl sulfide (OCS) land/ocean fluxes contributes to the understanding of both the sulfur and carbon cycles. The primary sources and sinks of OCS are very likely in a steady state because there is no significant observed trend or interannual variability in atmospheric OCS measurements. However, the magnitude and spatial distribution of the dominant ocean source are highly uncertain due to the lack of observations. In particular, estimates of the oceanic fluxes range from approximately 280 Gg S yr-1 to greater than 800 Gg S yr-1, with the larger flux needed to balance a similarly sized terrestrial sink that is inferred from NOAA continental sites. Here we estimate summer tropical oceanic fluxes of OCS in 2006 using a linear flux inversion algorithm and new OCS data acquired by the Aura Tropospheric Emissions Spectrometer (TES). Modeled OCS concentrations based on these updated fluxes are consistent with HIAPER Pole-to-Pole Observations during 4th airborne campaign and improve significantly over the a priori model concentrations. The TES tropical ocean estimate of 70 ± 16 Gg S in June, when extrapolated over the whole year (about 840 ± 192 Gg S yr-1 ), supports the hypothesis proposed by Berry et al. (2013) that the ocean flux is in the higher range of approximately 800 Gg S yr-1.

Mesospheric temperatures estimated from the meteor radar observations at Mohe, China

NASA Astrophysics Data System (ADS)

Liu, Libo; Liu, Huixin; Le, Huijun; Chen, Yiding; Sun, Yang-Yi; Ning, Baiqi; Hu, Lianhuan; Wan, Weixing; Li, Na; Xiong, Jiangang

2017-02-01

In this work, we report the estimation of mesospheric temperatures at 90 km height from the observations of the VHF all-sky meteor radar operated at Mohe (53.5°N, 122.3°E), China, since August 2011. The kinetic temperature profiles retrieved from the observations of Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) on board the Thermosphere, Ionosphere, Mesosphere, Energetics, and Dynamics satellite are processed to provide the temperature (TSABER) and temperature gradient (dT/dh) at 90 km height. Based on the SABER temperature profile data an empirical dT/dh model is developed for the Mohe latitude. First, we derive the temperatures from the meteor decay times (Tmeteor) and the Mohe dT/dh model gives prior information of temperature gradients. Second, the full width at half maximum (FWHM) of the meteor height profiles is calculated and further used to deduce the temperatures (TFWHM) based on the strong linear relationship between FWHM and TSABER. The temperatures at 90 km deduced from the decay times (Tmeteor) and from the meteor height distributions (TFWHM) at Mohe are validated/calibrated with TSABER. The temperatures present a considerable annual variation, being maximum in winter and minimum in summer. Harmonic analyses reveal that the temperatures have an annual variation consistent with TSABER. Our work suggests that FWHM has a good performance in routine estimation of the temperatures. It should be pointed out that the slope of FWHM as a function of TSABER is 10.1 at Mohe, which is different from that of 15.71 at King Sejong (62.2°S, 58.8°E) station.

Sliding Mode Observer-Based Current Sensor Fault Reconstruction and Unknown Load Disturbance Estimation for PMSM Driven System.

PubMed

Zhao, Kaihui; Li, Peng; Zhang, Changfan; Li, Xiangfei; He, Jing; Lin, Yuliang

2017-12-06

This paper proposes a new scheme of reconstructing current sensor faults and estimating unknown load disturbance for a permanent magnet synchronous motor (PMSM)-driven system. First, the original PMSM system is transformed into two subsystems; the first subsystem has unknown system load disturbances, which are unrelated to sensor faults, and the second subsystem has sensor faults, but is free from unknown load disturbances. Introducing a new state variable, the augmented subsystem that has sensor faults can be transformed into having actuator faults. Second, two sliding mode observers (SMOs) are designed: the unknown load disturbance is estimated by the first SMO in the subsystem, which has unknown load disturbance, and the sensor faults can be reconstructed using the second SMO in the augmented subsystem, which has sensor faults. The gains of the proposed SMOs and their stability analysis are developed via the solution of linear matrix inequality (LMI). Finally, the effectiveness of the proposed scheme was verified by simulations and experiments. The results demonstrate that the proposed scheme can reconstruct current sensor faults and estimate unknown load disturbance for the PMSM-driven system.

The Event Detection and the Apparent Velocity Estimation Based on Computer Vision

NASA Astrophysics Data System (ADS)

Shimojo, M.

2012-08-01

The high spatial and time resolution data obtained by the telescopes aboard Hinode revealed the new interesting dynamics in solar atmosphere. In order to detect such events and estimate the velocity of dynamics automatically, we examined the estimation methods of the optical flow based on the OpenCV that is the computer vision library. We applied the methods to the prominence eruption observed by NoRH, and the polar X-ray jet observed by XRT. As a result, it is clear that the methods work well for solar images if the images are optimized for the methods. It indicates that the optical flow estimation methods in the OpenCV library are very useful to analyze the solar phenomena.

Quantitative estimation of Tropical Rainfall Mapping Mission precipitation radar signals from ground-based polarimetric radar observations

NASA Astrophysics Data System (ADS)

Bolen, Steven M.; Chandrasekar, V.

2003-06-01

The Tropical Rainfall Mapping Mission (TRMM) is the first mission dedicated to measuring rainfall from space using radar. The precipitation radar (PR) is one of several instruments aboard the TRMM satellite that is operating in a nearly circular orbit with nominal altitude of 350 km, inclination of 35°, and period of 91.5 min. The PR is a single-frequency Ku-band instrument that is designed to yield information about the vertical storm structure so as to gain insight into the intensity and distribution of rainfall. Attenuation effects on PR measurements, however, can be significant and as high as 10-15 dB. This can seriously impair the accuracy of rain rate retrieval algorithms derived from PR signal returns. Quantitative estimation of PR attenuation is made along the PR beam via ground-based polarimetric observations to validate attenuation correction procedures used by the PR. The reflectivity (Zh) at horizontal polarization and specific differential phase (Kdp) are found along the beam from S-band ground radar measurements, and theoretical modeling is used to determine the expected specific attenuation (k) along the space-Earth path at Ku-band frequency from these measurements. A theoretical k-Kdp relationship is determined for rain when Kdp ≥ 0.5°/km, and a power law relationship, k = a Zhb, is determined for light rain and other types of hydrometers encountered along the path. After alignment and resolution volume matching is made between ground and PR measurements, the two-way path-integrated attenuation (PIA) is calculated along the PR propagation path by integrating the specific attenuation along the path. The PR reflectivity derived after removing the PIA is also compared against ground radar observations.

Uncertainty relation based on unbiased parameter estimations

NASA Astrophysics Data System (ADS)

Sun, Liang-Liang; Song, Yong-Shun; Qiao, Cong-Feng; Yu, Sixia; Chen, Zeng-Bing

2017-02-01

Heisenberg's uncertainty relation has been extensively studied in spirit of its well-known original form, in which the inaccuracy measures used exhibit some controversial properties and don't conform with quantum metrology, where the measurement precision is well defined in terms of estimation theory. In this paper, we treat the joint measurement of incompatible observables as a parameter estimation problem, i.e., estimating the parameters characterizing the statistics of the incompatible observables. Our crucial observation is that, in a sequential measurement scenario, the bias induced by the first unbiased measurement in the subsequent measurement can be eradicated by the information acquired, allowing one to extract unbiased information of the second measurement of an incompatible observable. In terms of Fisher information we propose a kind of information comparison measure and explore various types of trade-offs between the information gains and measurement precisions, which interpret the uncertainty relation as surplus variance trade-off over individual perfect measurements instead of a constraint on extracting complete information of incompatible observables.

Estimation of Road Friction Coefficient in Different Road Conditions Based on Vehicle Braking Dynamics

NASA Astrophysics Data System (ADS)

Zhao, You-Qun; Li, Hai-Qing; Lin, Fen; Wang, Jian; Ji, Xue-Wu

2017-07-01

The accurate estimation of road friction coefficient in the active safety control system has become increasingly prominent. Most previous studies on road friction estimation have only used vehicle longitudinal or lateral dynamics and often ignored the load transfer, which tends to cause inaccurate of the actual road friction coefficient. A novel method considering load transfer of front and rear axles is proposed to estimate road friction coefficient based on braking dynamic model of two-wheeled vehicle. Sliding mode control technique is used to build the ideal braking torque controller, which control target is to control the actual wheel slip ratio of front and rear wheels tracking the ideal wheel slip ratio. In order to eliminate the chattering problem of the sliding mode controller, integral switching surface is used to design the sliding mode surface. A second order linear extended state observer is designed to observe road friction coefficient based on wheel speed and braking torque of front and rear wheels. The proposed road friction coefficient estimation schemes are evaluated by simulation in ADAMS/Car. The results show that the estimated values can well agree with the actual values in different road conditions. The observer can estimate road friction coefficient exactly in real-time and resist external disturbance. The proposed research provides a novel method to estimate road friction coefficient with strong robustness and more accurate.

Precise Orbital and Geodetic Parameter Estimation using SLR Observations for ILRS AAC

NASA Astrophysics Data System (ADS)

Kim, Young-Rok; Park, Eunseo; Oh, Hyungjik Jay; Park, Sang-Young; Lim, Hyung-Chul; Park, Chandeok

2013-12-01

In this study, we present results of precise orbital geodetic parameter estimation using satellite laser ranging (SLR) observations for the International Laser Ranging Service (ILRS) associate analysis center (AAC). Using normal point observations of LAGEOS-1, LAGEOS-2, ETALON-1, and ETALON-2 in SLR consolidated laser ranging data format, the NASA/ GSFC GEODYN II and SOLVE software programs were utilized for precise orbit determination (POD) and finding solutions of a terrestrial reference frame (TRF) and Earth orientation parameters (EOPs). For POD, a weekly-based orbit determination strategy was employed to process SLR observations taken from 20 weeks in 2013. For solutions of TRF and EOPs, loosely constrained scheme was used to integrate POD results of four geodetic SLR satellites. The coordinates of 11 ILRS core sites were determined and daily polar motion and polar motion rates were estimated. The root mean square (RMS) value of post-fit residuals was used for orbit quality assessment, and both the stability of TRF and the precision of EOPs by external comparison were analyzed for verification of our solutions. Results of post-fit residuals show that the RMS of the orbits of LAGEOS-1 and LAGEOS-2 are 1.20 and 1.12 cm, and those of ETALON-1 and ETALON-2 are 1.02 and 1.11 cm, respectively. The stability analysis of TRF shows that the mean value of 3D stability of the coordinates of 11 ILRS core sites is 7.0 mm. An external comparison, with respect to International Earth rotation and Reference systems Service (IERS) 08 C04 results, shows that standard deviations of polar motion XP and YP are 0.754 milliarcseconds (mas) and 0.576 mas, respectively. Our results of precise orbital and geodetic parameter estimation are reasonable and help advance research at ILRS AAC.

Bias-adjusted satellite-based rainfall estimates for predicting floods: Narayani Basin

USGS Publications Warehouse

Shrestha, M.S.; Artan, G.A.; Bajracharya, S.R.; Gautam, D.K.; Tokar, S.A.

2011-01-01

In Nepal, as the spatial distribution of rain gauges is not sufficient to provide detailed perspective on the highly varied spatial nature of rainfall, satellite-based rainfall estimates provides the opportunity for timely estimation. This paper presents the flood prediction of Narayani Basin at the Devghat hydrometric station (32000km2) using bias-adjusted satellite rainfall estimates and the Geospatial Stream Flow Model (GeoSFM), a spatially distributed, physically based hydrologic model. The GeoSFM with gridded gauge observed rainfall inputs using kriging interpolation from 2003 was used for calibration and 2004 for validation to simulate stream flow with both having a Nash Sutcliff Efficiency of above 0.7. With the National Oceanic and Atmospheric Administration Climate Prediction Centre's rainfall estimates (CPC-RFE2.0), using the same calibrated parameters, for 2003 the model performance deteriorated but improved after recalibration with CPC-RFE2.0 indicating the need to recalibrate the model with satellite-based rainfall estimates. Adjusting the CPC-RFE2.0 by a seasonal, monthly and 7-day moving average ratio, improvement in model performance was achieved. Furthermore, a new gauge-satellite merged rainfall estimates obtained from ingestion of local rain gauge data resulted in significant improvement in flood predictability. The results indicate the applicability of satellite-based rainfall estimates in flood prediction with appropriate bias correction. ?? 2011 The Authors. Journal of Flood Risk Management ?? 2011 The Chartered Institution of Water and Environmental Management.

LOD estimation from DORIS observations

NASA Astrophysics Data System (ADS)

Stepanek, Petr; Filler, Vratislav; Buday, Michal; Hugentobler, Urs

2016-04-01

The difference between astronomically determined duration of the day and 86400 seconds is called length of day (LOD). The LOD could be also understood as the daily rate of the difference between the Universal Time UT1, based on the Earth rotation, and the International Atomic Time TAI. The LOD is estimated using various Satellite Geodesy techniques as GNSS and SLR, while absolute UT1-TAI difference is precisely determined by VLBI. Contrary to other IERS techniques, the LOD estimation using DORIS (Doppler Orbitography and Radiopositioning Integrated by satellite) measurement did not achieve a geodetic accuracy in the past, reaching the precision at the level of several ms per day. However, recent experiments performed by IDS (International DORIS Service) analysis centre at Geodetic Observatory Pecny show a possibility to reach accuracy around 0.1 ms per day, when not adjusting the cross-track harmonics in the Satellite orbit model. The paper presents the long term LOD series determined from the DORIS solutions. The series are compared with C04 as the reference. Results are discussed in the context of accuracy achieved with GNSS and SLR. Besides the multi-satellite DORIS solutions, also the LOD series from the individual DORIS satellite solutions are analysed.

A Sequential Multiplicative Extended Kalman Filter for Attitude Estimation Using Vector Observations.

PubMed

Qin, Fangjun; Chang, Lubin; Jiang, Sai; Zha, Feng

2018-05-03

In this paper, a sequential multiplicative extended Kalman filter (SMEKF) is proposed for attitude estimation using vector observations. In the proposed SMEKF, each of the vector observations is processed sequentially to update the attitude, which can make the measurement model linearization more accurate for the next vector observation. This is the main difference to Murrell’s variation of the MEKF, which does not update the attitude estimate during the sequential procedure. Meanwhile, the covariance is updated after all the vector observations have been processed, which is used to account for the special characteristics of the reset operation necessary for the attitude update. This is the main difference to the traditional sequential EKF, which updates the state covariance at each step of the sequential procedure. The numerical simulation study demonstrates that the proposed SMEKF has more consistent and accurate performance in a wide range of initial estimate errors compared to the MEKF and its traditional sequential forms.

A Sequential Multiplicative Extended Kalman Filter for Attitude Estimation Using Vector Observations

PubMed Central

Qin, Fangjun; Jiang, Sai; Zha, Feng

2018-01-01

In this paper, a sequential multiplicative extended Kalman filter (SMEKF) is proposed for attitude estimation using vector observations. In the proposed SMEKF, each of the vector observations is processed sequentially to update the attitude, which can make the measurement model linearization more accurate for the next vector observation. This is the main difference to Murrell’s variation of the MEKF, which does not update the attitude estimate during the sequential procedure. Meanwhile, the covariance is updated after all the vector observations have been processed, which is used to account for the special characteristics of the reset operation necessary for the attitude update. This is the main difference to the traditional sequential EKF, which updates the state covariance at each step of the sequential procedure. The numerical simulation study demonstrates that the proposed SMEKF has more consistent and accurate performance in a wide range of initial estimate errors compared to the MEKF and its traditional sequential forms. PMID:29751538

Evaluation of Global Observations-Based Evapotranspiration Datasets and IPCC AR4 Simulations

NASA Technical Reports Server (NTRS)

Mueller, B.; Seneviratne, S. I.; Jimenez, C.; Corti, T.; Hirschi, M.; Balsamo, G.; Ciais, P.; Dirmeyer, P.; Fisher, J. B.; Guo, Z.;

A tool to estimate the Fermi Large Area Telescope background for short-duration observations

DOE PAGES

Vasileiou, Vlasios

2013-07-25

Here, the proper estimation of the background is a crucial component of data analyses in astrophysics, such as source detection, temporal studies, spectroscopy, and localization. For the case of the Large Area Telescope (LAT) on board the Fermi spacecraft, approaches to estimate the background for short (≲1000 s duration) observations fail if they ignore the strong dependence of the LAT background on the continuously changing observational conditions. We present a (to be) publicly available background-estimation tool created and used by the LAT Collaboration in several analyses of Gamma Ray Bursts. This tool can accurately estimate the expected LAT background formore » any observational conditions, including, for example, observations with rapid variations of the Fermi spacecraft’s orientation occurring during automatic repointings.« less

Estimation of Sprite Streamers Altitude Based on the Spectrophotometric Observations

NASA Astrophysics Data System (ADS)

Ihaddadene, K. M. A.; Celestin, S. J.

2016-12-01

Sprites are transient luminous events (TLEs) that result from the electrostatic coupling between thunderstorm charges following a positive cloud-to-ground lightning (+CG) and the lower ionosphere. These fine-structured objects are composed of filamentary streamer discharges propagating in the D-region of the ionosphere and the mesosphere. Optical emissions from sprite streamers are used to estimate peak electric fields and electron energies [e.g., Kuo et al., GRL, 32, L19103, 2005 ; Adachi et al., GRL, 33, L17803, 2006]. It has been shown that significant correction factors need to be used to account for the spatial shift between distributions of optical emissions in streamers and peak electric fields in their heads [Celestin and Pasko, GRL, 37, L07804, 2010]. The latter study involved the excited species N2(C3Πu) and N2+(B2Σ u+), whose populations are considered to be in steady state. The species N2(C3Πu) and N2+(B2Σ u+) are responsible for the second positive (2PN2) and first negative (1NN2+) bands systems of N2 and N2+, respectively. In this work, we show how this technique can be extended to non-steady state optical emissions, such as those produced by N2(a1Π g) and N2(B3Π g) in the form of Lyman-Birge-Hopefield (LBH) and first positive (1PN2) bands systems, respectively. Additionally, we simulate numerically downward propagating sprite streamers and their optical emissions for the following bands systems: 1PN2, 2PN2, LBH, and 1NN2+, and expose the developed spectrophotometric technique to infer physical properties such as the altitude and the velocity of sprite streamers [Ihaddadene and Celestin, submitted for publication to JGR, 2016]. This study particularly aims at improving analysis of observational results in nadir-viewing geometry of the space missions GLIMS (JAXA), ASIM (ESA), and TARANIS (CNES).

Time Series Analysis of Remote Sensing Observations for Citrus Crop Growth Stage and Evapotranspiration Estimation

NASA Astrophysics Data System (ADS)

Sawant, S. A.; Chakraborty, M.; Suradhaniwar, S.; Adinarayana, J.; Durbha, S. S.

2016-06-01

Satellite based earth observation (EO) platforms have proved capability to spatio-temporally monitor changes on the earth's surface. Long term satellite missions have provided huge repository of optical remote sensing datasets, and United States Geological Survey (USGS) Landsat program is one of the oldest sources of optical EO datasets. This historical and near real time EO archive is a rich source of information to understand the seasonal changes in the horticultural crops. Citrus (Mandarin / Nagpur Orange) is one of the major horticultural crops cultivated in central India. Erratic behaviour of rainfall and dependency on groundwater for irrigation has wide impact on the citrus crop yield. Also, wide variations are reported in temperature and relative humidity causing early fruit onset and increase in crop water requirement. Therefore, there is need to study the crop growth stages and crop evapotranspiration at spatio-temporal scale for managing the scarce resources. In this study, an attempt has been made to understand the citrus crop growth stages using Normalized Difference Time Series (NDVI) time series data obtained from Landsat archives (http://earthexplorer.usgs.gov/). Total 388 Landsat 4, 5, 7 and 8 scenes (from year 1990 to Aug. 2015) for Worldwide Reference System (WRS) 2, path 145 and row 45 were selected to understand seasonal variations in citrus crop growth. Considering Landsat 30 meter spatial resolution to obtain homogeneous pixels with crop cover orchards larger than 2 hectare area was selected. To consider change in wavelength bandwidth (radiometric resolution) with Landsat sensors (i.e. 4, 5, 7 and 8) NDVI has been selected to obtain continuous sensor independent time series. The obtained crop growth stage information has been used to estimate citrus basal crop coefficient information (Kcb). Satellite based Kcb estimates were used with proximal agrometeorological sensing system

Observational estimation of radiative feedback to surface air temperature over Northern High Latitudes

NASA Astrophysics Data System (ADS)

Hwang, Jiwon; Choi, Yong-Sang; Kim, WonMoo; Su, Hui; Jiang, Jonathan H.

2018-01-01

The high-latitude climate system contains complicated, but largely veiled physical feedback processes. Climate predictions remain uncertain, especially for the Northern High Latitudes (NHL; north of 60°N), and observational constraint on climate modeling is vital. This study estimates local radiative feedbacks for NHL based on the CERES/Terra satellite observations during March 2000-November 2014. The local shortwave (SW) and longwave (LW) radiative feedback parameters are calculated from linear regression of radiative fluxes at the top of the atmosphere on surface air temperatures. These parameters are estimated by the de-seasonalization and 12-month moving average of the radiative fluxes over NHL. The estimated magnitudes of the SW and the LW radiative feedbacks in NHL are 1.88 ± 0.73 and 2.38 ± 0.59 W m-2 K-1, respectively. The parameters are further decomposed into individual feedback components associated with surface albedo, water vapor, lapse rate, and clouds, as a product of the change in climate variables from ERA-Interim reanalysis estimates and their pre-calculated radiative kernels. The results reveal the significant role of clouds in reducing the surface albedo feedback (1.13 ± 0.44 W m-2 K-1 in the cloud-free condition, and 0.49 ± 0.30 W m-2 K-1 in the all-sky condition), while the lapse rate feedback is predominant in LW radiation (1.33 ± 0.18 W m-2 K-1). However, a large portion of the local SW and LW radiative feedbacks were not simply explained by the sum of these individual feedbacks.

Site-occupancy distribution modeling to correct population-trend estimates derived from opportunistic observations

USGS Publications Warehouse

Kery, M.; Royle, J. Andrew; Schmid, Hans; Schaub, M.; Volet, B.; Hafliger, G.; Zbinden, N.

2010-01-01

Species' assessments must frequently be derived from opportunistic observations made by volunteers (i.e., citizen scientists). Interpretation of the resulting data to estimate population trends is plagued with problems, including teasing apart genuine population trends from variations in observation effort. We devised a way to correct for annual variation in effort when estimating trends in occupancy (species distribution) from faunal or floral databases of opportunistic observations. First, for all surveyed sites, detection histories (i.e., strings of detection-nondetection records) are generated. Within-season replicate surveys provide information on the detectability of an occupied site. Detectability directly represents observation effort; hence, estimating detectablity means correcting for observation effort. Second, site-occupancy models are applied directly to the detection-history data set (i.e., without aggregation by site and year) to estimate detectability and species distribution (occupancy, i.e., the true proportion of sites where a species occurs). Site-occupancy models also provide unbiased estimators of components of distributional change (i.e., colonization and extinction rates). We illustrate our method with data from a large citizen-science project in Switzerland in which field ornithologists record opportunistic observations. We analyzed data collected on four species: the widespread Kingfisher (Alcedo atthis. ) and Sparrowhawk (Accipiter nisus. ) and the scarce Rock Thrush (Monticola saxatilis. ) and Wallcreeper (Tichodroma muraria. ). Our method requires that all observed species are recorded. Detectability was <1 and varied over the years. Simulations suggested some robustness, but we advocate recording complete species lists (checklists), rather than recording individual records of single species. The representation of observation effort with its effect on detectability provides a solution to the problem of differences in effort encountered

Surface topography estimated by inversion of satellite gravity gradiometry observations

NASA Astrophysics Data System (ADS)

Ramillien, Guillaume

2015-04-01

An integration of mass elements is presented for evaluating the six components of the 2-order gravity tensor (i.e., second derivatives of the Newtonian mass integral for the gravitational potential) created by an uneven sphere topography consisting of juxtaposed vertical prisms. The method is based on Legendre polynomial series with the originality of taking elastic compensation of the topography by the Earth's surface into account. The speed of computation of the polynomial series increases logically with the observing altitude from the source of anomaly. Such a forward modelling can be easily used for reduction of observed gravity gradient anomalies by the effects of any spherical interface of density. Moreover, an iterative least-square inversion of the observed gravity tensor values Γαβ is proposed to estimate a regional set of topographic heights. Several tests of recovery have been made by considering simulated gradiometry anomaly data, and for varying satellite altitudes and a priori levels of accuracy. In the case of GOCE-type gradiometry anomalies measured at an altitude of ~300 km, the search converges down to a stable and smooth topography after 20-30 iterations while the final r.m.s. error is ~100 m. The possibility of cumulating satellite information from different orbit geometries is also examined for improving the prediction.

Lidar-based estimates of aboveground biomass in the continental US and Mexico using ground, airborne, and satellite observations

Treesearch

Ross Nelson; Hank Margolis; Paul Montesano; Guoqing Sun; Bruce Cook; Larry Corp; Hans-Erik Andersen; Ben deJong; Fernando Paz Pellat; Thaddeus Fickel; Jobriath Kauffman; Stephen Prisley

2017-01-01

Existing national forest inventory plots, an airborne lidar scanning (ALS) system, and a space profiling lidar system (ICESat-GLAS) are used to generate circa 2005 estimates of total aboveground dry biomass (AGB) in forest strata, by state, in the continental United States (CONUS) and Mexico. The airborne lidar is used to link ground observations of AGB to space lidar...

Multi-Mode Estimation for Small Fixed Wing Unmanned Aerial Vehicle Localization Based on a Linear Matrix Inequality Approach

PubMed Central

Elzoghby, Mostafa; Li, Fu; Arafa, Ibrahim. I.; Arif, Usman

2017-01-01

Information fusion from multiple sensors ensures the accuracy and robustness of a navigation system, especially in the absence of global positioning system (GPS) data which gets degraded in many cases. A way to deal with multi-mode estimation for a small fixed wing unmanned aerial vehicle (UAV) localization framework is proposed, which depends on utilizing a Luenberger observer-based linear matrix inequality (LMI) approach. The proposed estimation technique relies on the interaction between multiple measurement modes and a continuous observer. The state estimation is performed in a switching environment between multiple active sensors to exploit the available information as much as possible, especially in GPS-denied environments. Luenberger observer-based projection is implemented as a continuous observer to optimize the estimation performance. The observer gain might be chosen by solving a Lyapunov equation by means of a LMI algorithm. Convergence is achieved by utilizing the linear matrix inequality (LMI), based on Lyapunov stability which keeps the dynamic estimation error bounded by selecting the observer gain matrix (L). Simulation results are presented for a small UAV fixed wing localization problem. The results obtained using the proposed approach are compared with a single mode Extended Kalman Filter (EKF). Simulation results are presented to demonstrate the viability of the proposed strategy. PMID:28420214

Estimating the average treatment effect on survival based on observational data and using partly conditional modeling.

PubMed

Gong, Qi; Schaubel, Douglas E

2017-03-01

Treatments are frequently evaluated in terms of their effect on patient survival. In settings where randomization of treatment is not feasible, observational data are employed, necessitating correction for covariate imbalances. Treatments are usually compared using a hazard ratio. Most existing methods which quantify the treatment effect through the survival function are applicable to treatments assigned at time 0. In the data structure of our interest, subjects typically begin follow-up untreated; time-until-treatment, and the pretreatment death hazard are both heavily influenced by longitudinal covariates; and subjects may experience periods of treatment ineligibility. We propose semiparametric methods for estimating the average difference in restricted mean survival time attributable to a time-dependent treatment, the average effect of treatment among the treated, under current treatment assignment patterns. The pre- and posttreatment models are partly conditional, in that they use the covariate history up to the time of treatment. The pre-treatment model is estimated through recently developed landmark analysis methods. For each treated patient, fitted pre- and posttreatment survival curves are projected out, then averaged in a manner which accounts for the censoring of treatment times. Asymptotic properties are derived and evaluated through simulation. The proposed methods are applied to liver transplant data in order to estimate the effect of liver transplantation on survival among transplant recipients under current practice patterns. © 2016, The International Biometric Society.

Contour-based object orientation estimation

NASA Astrophysics Data System (ADS)

Alpatov, Boris; Babayan, Pavel

2016-04-01

Real-time object orientation estimation is an actual problem of computer vision nowadays. In this paper we propose an approach to estimate an orientation of objects lacking axial symmetry. Proposed algorithm is intended to estimate orientation of a specific known 3D object, so 3D model is required for learning. The proposed orientation estimation algorithm consists of 2 stages: learning and estimation. Learning stage is devoted to the exploring of studied object. Using 3D model we can gather set of training images by capturing 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. It minimizes the training image set. Gathered training image set is used for calculating descriptors, which will be used in the estimation stage of the algorithm. The estimation stage is focusing on matching process between an observed image descriptor and the training image descriptors. The experimental research was performed using a set of images of Airbus A380. The proposed orientation estimation algorithm showed good accuracy (mean error value less than 6°) in all case studies. The real-time performance of the algorithm was also demonstrated.

Estimation of Regional Carbon Balance from Atmospheric Observations

NASA Astrophysics Data System (ADS)

Denning, S.; Uliasz, M.; Skidmore, J.

2002-12-01

Variations in the concentration of CO2 and other trace gases in time and space contain information about sources and sinks at regional scales. Several methods have been developed to quantitatively extract this information from atmospheric measurements. Mass-balance techniques depend on the ability to repeatedly sample the same mass of air, which involves careful attention to airmass trajectories. Inverse and adjoint techniques rely on decomposition of the source field into quasi-independent "basis functions" that are propagated through transport models and then used to synthesize optimal linear combinations that best match observations. A recently proposed method for regional flux estimation from continuous measurements at tall towers relies on time-mean vertical gradients, and requires careful trajectory analysis to map the estimates onto regional ecosystems. Each of these techniques is likely to be applied to measurements made during the North American Carbon Program. We have also explored the use of Bayesian synthesis inversion at regional scales, using a Lagrangian particle dispersion model driven by mesoscale transport fields. Influence functions were calculated for each hypothetical observation in a realistic diurnally-varying flow. These influence functions were then treated as basis functions for the purpose of separate inversions for daytime photosynthesis and 24-hour mean ecosystem respiration. Our results highlight the importance of estimating CO2 fluxes through the lateral boundaries of the model. Respiration fluxes were well constrained by one or two hypothetical towers, regardless of inflow fluxes. Time-varying assimilation fluxes were less well constrained, and much more dependent on knowledge of inflow fluxes. The small net difference between respiration and photosynthesis was the most difficult to determine, being extremely sensitive to knowledge of inflow fluxes. Finally, we explored the feasibility of directly incorporating mid-day concentration

Estimation of Return Values of Wave Height: Consequences of Missing Observations

ERIC Educational Resources Information Center

Ryden, Jesper

2008-01-01

Extreme-value statistics is often used to estimate so-called return values (actually related to quantiles) for environmental quantities like wind speed or wave height. A basic method for estimation is the method of block maxima which consists in partitioning observations in blocks, where maxima from each block could be considered independent.…

Sliding Mode Observer-Based Current Sensor Fault Reconstruction and Unknown Load Disturbance Estimation for PMSM Driven System

PubMed Central

Li, Xiangfei; Lin, Yuliang

2017-01-01

This paper proposes a new scheme of reconstructing current sensor faults and estimating unknown load disturbance for a permanent magnet synchronous motor (PMSM)-driven system. First, the original PMSM system is transformed into two subsystems; the first subsystem has unknown system load disturbances, which are unrelated to sensor faults, and the second subsystem has sensor faults, but is free from unknown load disturbances. Introducing a new state variable, the augmented subsystem that has sensor faults can be transformed into having actuator faults. Second, two sliding mode observers (SMOs) are designed: the unknown load disturbance is estimated by the first SMO in the subsystem, which has unknown load disturbance, and the sensor faults can be reconstructed using the second SMO in the augmented subsystem, which has sensor faults. The gains of the proposed SMOs and their stability analysis are developed via the solution of linear matrix inequality (LMI). Finally, the effectiveness of the proposed scheme was verified by simulations and experiments. The results demonstrate that the proposed scheme can reconstruct current sensor faults and estimate unknown load disturbance for the PMSM-driven system. PMID:29211017

Spectrum-based estimators of the bivariate Hurst exponent

NASA Astrophysics Data System (ADS)

Kristoufek, Ladislav

2014-12-01

We discuss two alternate spectrum-based estimators of the bivariate Hurst exponent in the power-law cross-correlations setting, the cross-periodogram and local X -Whittle estimators, as generalizations of their univariate counterparts. As the spectrum-based estimators are dependent on a part of the spectrum taken into consideration during estimation, a simulation study showing performance of the estimators under varying bandwidth parameter as well as correlation between processes and their specification is provided as well. These estimators are less biased than the already existent averaged periodogram estimator, which, however, has slightly lower variance. The spectrum-based estimators can serve as a good complement to the popular time domain estimators.

Kalman filter-based EM-optical sensor fusion for needle deflection estimation.

PubMed

Jiang, Baichuan; Gao, Wenpeng; Kacher, Daniel; Nevo, Erez; Fetics, Barry; Lee, Thomas C; Jayender, Jagadeesan

2018-04-01

In many clinical procedures such as cryoablation that involves needle insertion, accurate placement of the needle's tip at the desired target is the major issue for optimizing the treatment and minimizing damage to the neighboring anatomy. However, due to the interaction force between the needle and tissue, considerable error in intraoperative tracking of the needle tip can be observed as needle deflects. In this paper, measurements data from an optical sensor at the needle base and a magnetic resonance (MR) gradient field-driven electromagnetic (EM) sensor placed 10 cm from the needle tip are used within a model-integrated Kalman filter-based sensor fusion scheme. Bending model-based estimations and EM-based direct estimation are used as the measurement vectors in the Kalman filter, thus establishing an online estimation approach. Static tip bending experiments show that the fusion method can reduce the mean error of the tip position estimation from 29.23 mm of the optical sensor-based approach to 3.15 mm of the fusion-based approach and from 39.96 to 6.90 mm, at the MRI isocenter and the MRI entrance, respectively. This work established a novel sensor fusion scheme that incorporates model information, which enables real-time tracking of needle deflection with MRI compatibility, in a free-hand operating setup.

Multi-scale Drivers of Variations in Atmospheric Evaporative Demand Based on Observations and Physically-based Modeling

NASA Astrophysics Data System (ADS)

Peng, L.; Sheffield, J.; Li, D.

2015-12-01

Evapotranspiration (ET) is a key link between the availability of water resources and climate change and climate variability. Variability of ET has important environmental and socioeconomic implications for managing hydrological hazards, food and energy production. Although there have been many observational and modeling studies of ET, how ET has varied and the drivers of the variations at different temporal scales remain elusive. Much of the uncertainty comes from the atmospheric evaporative demand (AED), which is the combined effect of radiative and aerodynamic controls. The inconsistencies among modeled AED estimates and the limited observational data may originate from multiple sources including the limited time span and uncertainties in the data. To fully investigate and untangle the intertwined drivers of AED, we present a spectrum analysis to identify key controls of AED across multiple temporal scales. We use long-term records of observed pan evaporation for 1961-2006 from 317 weather stations across China and physically-based model estimates of potential evapotranspiration (PET). The model estimates are based on surface meteorology and radiation derived from reanalysis, satellite retrievals and station data. Our analyses show that temperature plays a dominant role in regulating variability of AED at the inter-annual scale. At the monthly and seasonal scales, the primary control of AED shifts from radiation in humid regions to humidity in dry regions. Unlike many studies focusing on the spatial pattern of ET drivers based on a traditional supply and demand framework, this study underlines the importance of temporal scales when discussing controls of ET variations.

Estimates of Ground Temperature and Atmospheric Moisture from CERES Observations

NASA Technical Reports Server (NTRS)

Wu, Man Li C.; Schubert, Siegfried; Einaudi, Franco (Technical Monitor)

2000-01-01

A method is developed to retrieve surface ground temperature (Tg) and atmospheric moisture using clear sky fluxes (CSF) from CERES-TRMM observations. In general, the clear sky outgoing long-wave radiation (CLR) is sensitive to upper level moisture (q(sub h)) over wet regions and Tg over dry regions The clear sky window flux from 800 to 1200 /cm (RadWn) is sensitive to low level moisture (q(sub j)) and Tg. Combining these two measurements (CLR and RadWn), Tg and q(sub h) can be estimated over land, while q(sub h) and q(sub t) can be estimated over the oceans. The approach capitalizes on the availability of satellite estimates of CLR and RadWn and other auxiliary satellite data. The basic methodology employs off-line forward radiative transfer calculations to generate synthetic CSF data from two different global 4-dimensional data assimilation products. Simple linear regression is used to relate discrepancies in CSF to discrepancies in Tg, q(sub h) and q(sub t). The slopes of the regression lines define sensitivity parameters that can be exploited to help interpret mismatches between satellite observations and model-based estimates of CSF. For illustration, we analyze the discrepancies in the CSF between an early implementation of the Goddard Earth Observing System Data Assimilation System (GEOS-DAS) and a recent operational version of the European Center for Medium-Range Weather Prediction data assimilation system. In particular, our analysis of synthetic total and window region SCF differences (computed from two different assimilated data sets) shows that simple linear regression employing (Delta)Tg and broad layer (Delta)q(sub l) from 500 hPa to surface and (Delta)q(sub h) from 200 to 500 hPa provides a good approximation to the full radiative transfer calculations, typically explaining more than 90% of the 6-hourly variance in the flux differences. These simple regression relations can be inverted to "retrieve" the errors in the geophysical parameters

Table look-up estimation of signal and noise parameters from quantized observables

NASA Technical Reports Server (NTRS)

Vilnrotter, V. A.; Rodemich, E. R.

1986-01-01

A table look-up algorithm for estimating underlying signal and noise parameters from quantized observables is examined. A general mathematical model is developed, and a look-up table designed specifically for estimating parameters from four-bit quantized data is described. Estimator performance is evaluated both analytically and by means of numerical simulation, and an example is provided to illustrate the use of the look-up table for estimating signal-to-noise ratios commonly encountered in Voyager-type data.

Uniform stable observer for the disturbance estimation in two renewable energy systems.

PubMed

Rubio, José de Jesús; Ochoa, Genaro; Balcazar, Ricardo; Pacheco, Jaime

2015-09-01

In this study, an observer for the states and disturbance estimation in two renewable energy systems is introduced. The restrictions of the gains in the proposed observer are found to guarantee its stability and the convergence of its error; furthermore, these results are utilized to obtain a good estimation. The introduced technique is applied for the states and disturbance estimation in a wind turbine and an electric vehicle. The wind turbine has a rotatory tower to catch the incoming air to be transformed in electricity and the electric vehicle has generators connected with its wheels to catch the vehicle movement to be transformed in electricity. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Vegetation chlorophyll estimates in the Amazon from multi-angle MODIS observations and canopy reflectance model

NASA Astrophysics Data System (ADS)

Hilker, Thomas; Galvão, Lênio Soares; Aragão, Luiz E. O. C.; de Moura, Yhasmin M.; do Amaral, Cibele H.; Lyapustin, Alexei I.; Wu, Jin; Albert, Loren P.; Ferreira, Marciel José; Anderson, Liana O.; dos Santos, Victor A. H. F.; Prohaska, Neill; Tribuzy, Edgard; Barbosa Ceron, João Vitor; Saleska, Scott R.; Wang, Yujie; de Carvalho Gonçalves, José Francisco; de Oliveira Junior, Raimundo Cosme; Cardoso Rodrigues, João Victor Figueiredo; Garcia, Maquelle Neves

2017-06-01

As a preparatory study for future hyperspectral missions that can measure canopy chemistry, we introduce a novel approach to investigate whether multi-angle Moderate Resolution Imaging Spectroradiometer (MODIS) data can be used to generate a preliminary database with long-term estimates of chlorophyll. MODIS monthly chlorophyll estimates between 2000 and 2015, derived from a fully coupled canopy reflectance model (ProSAIL), were inspected for consistency with eddy covariance fluxes, tower-based hyperspectral images and chlorophyll measurements. MODIS chlorophyll estimates from the inverse model showed strong seasonal variations across two flux-tower sites in central and eastern Amazon. Marked increases in chlorophyll concentrations were observed during the early dry season. Remotely sensed chlorophyll concentrations were correlated to field measurements (r2 = 0.73 and r2 = 0.98) but the data deviated from the 1:1 line with root mean square errors (RMSE) ranging from 0.355 μg cm-2 (Tapajós tower) to 0.470 μg cm-2 (Manaus tower). The chlorophyll estimates were consistent with flux tower measurements of photosynthetically active radiation (PAR) and net ecosystem productivity (NEP). We also applied ProSAIL to mono-angle hyperspectral observations from a camera installed on a tower to scale modeled chlorophyll pigments to MODIS observations (r2 = 0.73). Chlorophyll pigment concentrations (ChlA+B) were correlated to changes in the amount of young and mature leaf area per month (0.59 ≤ r2 ≤ 0.64). Increases in MODIS observed ChlA+B were preceded by increased PAR during the dry season (0.61 ≤ r2 ≤ 0.62) and followed by changes in net carbon uptake. We conclude that, at these two sites, changes in LAI, coupled with changes in leaf chlorophyll, are comparable with seasonality of plant productivity. Our results allowed the preliminary development of a 15-year time series of chlorophyll estimates over the Amazon to support canopy chemistry studies using future

Using nonlinear programming to correct leakage and estimate mass change from GRACE observation and its application to Antarctica

NASA Astrophysics Data System (ADS)

Tang, Jingshi; Cheng, Haowen; Liu, Lin

2012-11-01

The Gravity Recovery And Climate Experiment (GRACE) mission has been providing high quality observations since its launch in 2002. Over the years, fruitful achievements have been obtained and the temporal gravity field has revealed the ongoing geophysical, hydrological and other processes. These discoveries help the scientists better understand various aspects of the Earth. However, errors exist in high degree and order spherical harmonics, which need to be processed before use. Filtering is one of the most commonly used techniques to smooth errors, yet it attenuates signals and also causes leakage of gravity signal into surrounding areas. This paper reports a new method to estimate the true mass change on the grid (expressed in equivalent water height or surface density). The mass change over the grid can be integrated to estimate regional or global mass change. This method assumes the GRACE-observed apparent mass change is only caused by the mass change on land. By comparing the computed and observed apparent mass change, the true mass change can be iteratively adjusted and estimated. The problem is solved with nonlinear programming (NLP) and yields solutions which are in good agreement with other GRACE-based estimates.

The design of nonlinear observers for wind turbine dynamic state and parameter estimation

NASA Astrophysics Data System (ADS)

Ritter, B.; Schild, A.; Feldt, M.; Konigorski, U.

2016-09-01

This contribution addresses the dynamic state and parameter estimation problem which arises with more advanced wind turbine controllers. These control devices need precise information about the system's current state to outperform conventional industrial controllers effectively. First, the necessity of a profound scientific treatment on nonlinear observers for wind turbine application is highlighted. Secondly, the full estimation problem is introduced and the variety of nonlinear filters is discussed. Finally, a tailored observer architecture is proposed and estimation results of an illustrative application example from a complex simulation set-up are presented.

BoolFilter: an R package for estimation and identification of partially-observed Boolean dynamical systems.

PubMed

Mcclenny, Levi D; Imani, Mahdi; Braga-Neto, Ulisses M

2017-11-25

Gene regulatory networks govern the function of key cellular processes, such as control of the cell cycle, response to stress, DNA repair mechanisms, and more. Boolean networks have been used successfully in modeling gene regulatory networks. In the Boolean network model, the transcriptional state of each gene is represented by 0 (inactive) or 1 (active), and the relationship among genes is represented by logical gates updated at discrete time points. However, the Boolean gene states are never observed directly, but only indirectly and incompletely through noisy measurements based on expression technologies such as cDNA microarrays, RNA-Seq, and cell imaging-based assays. The Partially-Observed Boolean Dynamical System (POBDS) signal model is distinct from other deterministic and stochastic Boolean network models in removing the requirement of a directly observable Boolean state vector and allowing uncertainty in the measurement process, addressing the scenario encountered in practice in transcriptomic analysis. BoolFilter is an R package that implements the POBDS model and associated algorithms for state and parameter estimation. It allows the user to estimate the Boolean states, network topology, and measurement parameters from time series of transcriptomic data using exact and approximated (particle) filters, as well as simulate the transcriptomic data for a given Boolean network model. Some of its infrastructure, such as the network interface, is the same as in the previously published R package for Boolean Networks BoolNet, which enhances compatibility and user accessibility to the new package. We introduce the R package BoolFilter for Partially-Observed Boolean Dynamical Systems (POBDS). The BoolFilter package provides a useful toolbox for the bioinformatics community, with state-of-the-art algorithms for simulation of time series transcriptomic data as well as the inverse process of system identification from data obtained with various expression

Assimilating Remote Sensing Observations of Leaf Area Index and Soil Moisture for Wheat Yield Estimates: An Observing System Simulation Experiment

NASA Technical Reports Server (NTRS)

Nearing, Grey S.; Crow, Wade T.; Thorp, Kelly R.; Moran, Mary S.; Reichle, Rolf H.; Gupta, Hoshin V.

2012-01-01

Observing system simulation experiments were used to investigate ensemble Bayesian state updating data assimilation of observations of leaf area index (LAI) and soil moisture (theta) for the purpose of improving single-season wheat yield estimates with the Decision Support System for Agrotechnology Transfer (DSSAT) CropSim-Ceres model. Assimilation was conducted in an energy-limited environment and a water-limited environment. Modeling uncertainty was prescribed to weather inputs, soil parameters and initial conditions, and cultivar parameters and through perturbations to model state transition equations. The ensemble Kalman filter and the sequential importance resampling filter were tested for the ability to attenuate effects of these types of uncertainty on yield estimates. LAI and theta observations were synthesized according to characteristics of existing remote sensing data, and effects of observation error were tested. Results indicate that the potential for assimilation to improve end-of-season yield estimates is low. Limitations are due to a lack of root zone soil moisture information, error in LAI observations, and a lack of correlation between leaf and grain growth.

Assessment of Receiver Signal Strength Sensing for Location Estimation Based on Fisher Information

PubMed Central

Nielsen, John; Nielsen, Christopher

2016-01-01

Currently there is almost ubiquitous availability of wireless signaling for data communications within commercial building complexes resulting in receiver signal strength (RSS) observables that are typically sufficient for generating viable location estimates of mobile wireless devices. However, while RSS observables are generally plentiful, achieving an accurate estimation of location is difficult due to several factors affecting the electromagnetic coupling between the mobile antenna and the building access points that are not modeled and hence contribute to the overall estimation uncertainty. Such uncertainty is typically mitigated with a moderate redundancy of RSS sensor observations in combination with other constraints imposed on the mobile trajectory. In this paper, the Fisher Information (FI) of a set of RSS sensor observations in the context of variables related to the mobile location is developed. This provides a practical method of determining the potential location accuracy for the given set of wireless signals available. Furthermore, the information value of individual RSS measurements can be quantified and the RSS observables weighted accordingly in estimation combining algorithms. The practical utility of using FI in this context was demonstrated experimentally with an extensive set of RSS measurements recorded in an office complex. The resulting deviation of the mobile location estimation based on application of weighted likelihood processing to the experimental RSS data was shown to agree closely with the Cramer Rao bound determined from the FI analysis. PMID:27669262

Maximum profile likelihood estimation of differential equation parameters through model based smoothing state estimates.

PubMed

Campbell, D A; Chkrebtii, O

2013-12-01

Statistical inference for biochemical models often faces a variety of characteristic challenges. In this paper we examine state and parameter estimation for the JAK-STAT intracellular signalling mechanism, which exemplifies the implementation intricacies common in many biochemical inference problems. We introduce an extension to the Generalized Smoothing approach for estimating delay differential equation models, addressing selection of complexity parameters, choice of the basis system, and appropriate optimization strategies. Motivated by the JAK-STAT system, we further extend the generalized smoothing approach to consider a nonlinear observation process with additional unknown parameters, and highlight how the approach handles unobserved states and unevenly spaced observations. The methodology developed is generally applicable to problems of estimation for differential equation models with delays, unobserved states, nonlinear observation processes, and partially observed histories. Crown Copyright © 2013. Published by Elsevier Inc. All rights reserved.

A determinant-based criterion for working correlation structure selection in generalized estimating equations.

PubMed

Jaman, Ajmery; Latif, Mahbub A H M; Bari, Wasimul; Wahed, Abdus S

2016-05-20

In generalized estimating equations (GEE), the correlation between the repeated observations on a subject is specified with a working correlation matrix. Correct specification of the working correlation structure ensures efficient estimators of the regression coefficients. Among the criteria used, in practice, for selecting working correlation structure, Rotnitzky-Jewell, Quasi Information Criterion (QIC) and Correlation Information Criterion (CIC) are based on the fact that if the assumed working correlation structure is correct then the model-based (naive) and the sandwich (robust) covariance estimators of the regression coefficient estimators should be close to each other. The sandwich covariance estimator, used in defining the Rotnitzky-Jewell, QIC and CIC criteria, is biased downward and has a larger variability than the corresponding model-based covariance estimator. Motivated by this fact, a new criterion is proposed in this paper based on the bias-corrected sandwich covariance estimator for selecting an appropriate working correlation structure in GEE. A comparison of the proposed and the competing criteria is shown using simulation studies with correlated binary responses. The results revealed that the proposed criterion generally performs better than the competing criteria. An example of selecting the appropriate working correlation structure has also been shown using the data from Madras Schizophrenia Study. Copyright © 2015 John Wiley & Sons, Ltd.

Intuitive Terrain Reconstruction Using Height Observation-Based Ground Segmentation and 3D Object Boundary Estimation

PubMed Central

Song, Wei; Cho, Kyungeun; Um, Kyhyun; Won, Chee Sun; Sim, Sungdae

2012-01-01

Mobile robot operators must make rapid decisions based on information about the robot’s surrounding environment. This means that terrain modeling and photorealistic visualization are required for the remote operation of mobile robots. We have produced a voxel map and textured mesh from the 2D and 3D datasets collected by a robot’s array of sensors, but some upper parts of objects are beyond the sensors’ measurements and these parts are missing in the terrain reconstruction result. This result is an incomplete terrain model. To solve this problem, we present a new ground segmentation method to detect non-ground data in the reconstructed voxel map. Our method uses height histograms to estimate the ground height range, and a Gibbs-Markov random field model to refine the segmentation results. To reconstruct a complete terrain model of the 3D environment, we develop a 3D boundary estimation method for non-ground objects. We apply a boundary detection technique to the 2D image, before estimating and refining the actual height values of the non-ground vertices in the reconstructed textured mesh. Our proposed methods were tested in an outdoor environment in which trees and buildings were not completely sensed. Our results show that the time required for ground segmentation is faster than that for data sensing, which is necessary for a real-time approach. In addition, those parts of objects that were not sensed are accurately recovered to retrieve their real-world appearances. PMID:23235454

Intuitive terrain reconstruction using height observation-based ground segmentation and 3D object boundary estimation.

PubMed

Song, Wei; Cho, Kyungeun; Um, Kyhyun; Won, Chee Sun; Sim, Sungdae

2012-12-12

Mobile robot operators must make rapid decisions based on information about the robot's surrounding environment. This means that terrain modeling and photorealistic visualization are required for the remote operation of mobile robots. We have produced a voxel map and textured mesh from the 2D and 3D datasets collected by a robot's array of sensors, but some upper parts of objects are beyond the sensors' measurements and these parts are missing in the terrain reconstruction result. This result is an incomplete terrain model. To solve this problem, we present a new ground segmentation method to detect non-ground data in the reconstructed voxel map. Our method uses height histograms to estimate the ground height range, and a Gibbs-Markov random field model to refine the segmentation results. To reconstruct a complete terrain model of the 3D environment, we develop a 3D boundary estimation method for non-ground objects. We apply a boundary detection technique to the 2D image, before estimating and refining the actual height values of the non-ground vertices in the reconstructed textured mesh. Our proposed methods were tested in an outdoor environment in which trees and buildings were not completely sensed. Our results show that the time required for ground segmentation is faster than that for data sensing, which is necessary for a real-time approach. In addition, those parts of objects that were not sensed are accurately recovered to retrieve their real-world appearances.

Rainfall estimation over-land using SMOS soil moisture observations: SM2RAIN, LMAA and SMART algorithms

NASA Astrophysics Data System (ADS)

Massari, Christian; Brocca, Luca; Pellarin, Thierry; Kerr, Yann; Crow, Wade; Cascon, Carlos; Ciabatta, Luca

2016-04-01

Recent advancements in the measurement of precipitation from space have provided estimates at scales that are commensurate with the needs of the hydrological and land-surface model communities. However, as demonstrated in a number of studies (Ebert et al. 2007, Tian et al. 2007, Stampoulis et al. 2012) satellite rainfall estimates are characterized by low accuracy in certain conditions and still suffer from a number of issues (e.g., bias) that may limit their utility in over-land applications (Serrat-Capdevila et al. 2014). In recent years many studies have demonstrated that soil moisture observations from ground and satellite sensors can be used for correcting satellite precipitation estimates (e.g. Crow et al., 2011; Pellarin et al., 2013), or directly estimating rainfall (SM2RAIN, Brocca et al., 2014). In this study, we carried out a detailed scientific analysis in which these three different methods are used for: i) estimating rainfall through satellite soil moisture observations (SM2RAIN, Brocca et al., 2014); ii) correcting rainfall through a Land surface Model Assimilation Algorithm (LMAA) (an improvement of a previous work of Crow et al. 2011 and Pellarin et al. 2013) and through the Soil Moisture Analysis Rainfall Tool (SMART, Crow et al. 2011). The analysis is carried within the ESA project "SMOS plus Rainfall" and involves 9 sites in Europe, Australia, Africa and USA containing high-quality hydrometeorological and soil moisture observations. Satellite soil moisture data from Soil Moisture and Ocean Salinity (SMOS) mission are employed for testing their potential in deriving a cumulated rainfall product at different temporal resolutions. The applicability and accuracy of the three algorithms is investigated also as a function of climatic and soil/land use conditions. A particular attention is paid to assess the expected limitations soil moisture based rainfall estimates such as soil saturation, freezing/snow conditions, SMOS RFI, irrigated areas

Power system observability and dynamic state estimation for stability monitoring using synchrophasor measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sun, Kai; Qi, Junjian; Kang, Wei

2016-08-01

Growing penetration of intermittent resources such as renewable generations increases the risk of instability in a power grid. This paper introduces the concept of observability and its computational algorithms for a power grid monitored by the wide-area measurement system (WAMS) based on synchrophasors, e.g. phasor measurement units (PMUs). The goal is to estimate real-time states of generators, especially for potentially unstable trajectories, the information that is critical for the detection of rotor angle instability of the grid. The paper studies the number and siting of synchrophasors in a power grid so that the state of the system can be accuratelymore » estimated in the presence of instability. An unscented Kalman filter (UKF) is adopted as a tool to estimate the dynamic states that are not directly measured by synchrophasors. The theory and its computational algorithms are illustrated in detail by using a 9-bus 3-generator power system model and then tested on a 140-bus 48-generator Northeast Power Coordinating Council power grid model. Case studies on those two systems demonstrate the performance of the proposed approach using a limited number of synchrophasors for dynamic state estimation for stability assessment and its robustness against moderate inaccuracies in model parameters.« less

Effectiveness and Sensitivity of the Arctic Observing Network in a Coupled Ocean-Sea Ice State Estimation Framework

NASA Astrophysics Data System (ADS)

Nguyen, A. T.; Heimbach, P.; Garg, V.; Ocana, V.

2016-12-01

Over the last few decades, various agencies have invested heavily in the development and deployment of Arctic ocean and sea ice observing platforms, especially moorings, profilers, gliders, and satellite-based instruments. These observational assets are heterogeneous in terms of variables sampled and spatio-temporal coverage, which calls for a dynamical synthesis framework of the diverse data streams. Here we introduce an adjoint-based Arctic Subpolar gyre sTate estimate (ASTE), a medium resolution model-data synthesis that leverages all the possible observational assets. Through an established formal state and parameter estimation framework, the ASTE framework produces a 2002-present ocean-sea ice state that can be used to address Arctic System science questions. It is dynamically and kinematically consistent with known equations of motion and consistent with observations. Four key aspects of ASTE will be discussed: (1) How well is ASTE constrained by the existing observations; (2) which data most effectively constrain the system, and what impact on the solution does spatial and temporal coverage have; (3) how much information does one set of observation (e.g. Fram Strait heat transport) carry about a remote, but dynamically linked component (e.g. heat content in the Beaufort Gyre); and (4) how can the framework be used to assess the value of hypothetical observations in constraining poorly observed parts of the Arctic Ocean and the implied mechanisms responsible for the changes occurring in the Arctic. We will discuss the suggested geographic distribution of new observations to maximize the impact on improving our understanding of the general circulation, water mass distribution and hydrographic changes in the Arctic.

Overcoming uncertainty with carbonyl sulfide-based GPP estimates: observing and modeling soil COS fluxes in terrestrial ecosystems

NASA Astrophysics Data System (ADS)

Whelan, M.; Hilton, T. W.; Berry, J. A.; Berkelhammer, M. B.; Desai, A. R.; Rastogi, B.; Campbell, J. E.

2015-12-01

Significant carbonyl sulfide (COS) exchange by soils limits the applicability of net ecosystem COS flux observations as a proxy for stomatal trace gas exchange. High frequency measurements of COS over urban and natural ecosystems offer a potential window into processes regulating the carbon and water cycle: photosynthetic carbon uptake and stomatal conductance. COS diffuses through plant stomata and is irreversibly consumed by enzymes involved in photosynthesis. In certain environments, the magnitude of soil COS fluxes may constitute one-quarter of COS uptake by plants. Here we present a way of anticipating conditions when anomalously large soil COS fluxes are likely to occur and be taken into account. Previous studies have pointed to either a tendency for soil uptake of COS from the atmosphere with a soil moisture optimum, or exponential COS production coincident with temperature. Data from field and laboratory studies were used to deconvolve the two processes. CO2 and COS fluxes were observed from forest, desert, grassland, and agricultural soils under a range of temperature and soil moisture conditions. We demonstrate how to estimate temperature and soil moisture impacts on COS soil production based on our cross-site incubations. By building a model of soil COS exchange that combines production and consumption terms, we offer a framework for interpreting the two disparate conclusions about soil COS exchange in previous studies. Such a construction should be used in ecosystem and continental scale modeling of COS fluxes to anticipate where the influence of soil COS exchange needs to be accounted for, resulting in greater utility of carbonyl sulfide as a tracer of plant physiological processes.

Analysis of CO, CH4 and AOD distributions over Eurasia and estimates of their long-term tendencies based on spectroscopic ground-based and satellite observations

NASA Astrophysics Data System (ADS)

Rakitin, Vadim; Elansky, Nikolai; Shtabkin, Yury; Dzhola, Anatoly; Pankratova, Natalia; Shilkin, Arseny

2017-04-01

Analysis of the CO and CH4 total column (TC) measurements and AOD data in urban and background regions of Eurasia for period from 1998 to 2016 years is presented. The trends estimates based on spectroscopic ground-based datasets of OIAP, SPSU, IAP CAC, NPO "Typhoon" and NDACC were compared with similar ones obtained with use of orbital data (MOPITT v6J and AIRS v6). Total decrease of CO TC in both urban (Moscow and Beijing) and background regions (ZSS, Peterhof, Obninsk, European NDACC sites) in 1998-2016 years changed to increase of CO in summer and autumn months in almost all background regions of Northern Eurasia after 2007. Negative trends of AOD were obtained for Europe, West Siberia and China for different seasons (including summer and autumn months) for time periods 2000-2016 and 2007-2016 with using both AERONET and MODIS Terra/Aqua datasets; AOD trends over East Siberia were positive that dui to influence of strong wild fires in 2010-2016 years in Siberia. Rate of CO TC decrease obtained with orbital data using are less than the same for ground based data with factor 1.5-2.0 for both urban and background regions. Rate of CH4 TC increased after 2007 in North-West Eurasian regions and didn't change in most of North-East regions. The negative AOD trends over Europe and West Siberia indirectly point to non-increase of wild-fires emissions over this region in latest years. Therefore the positive CO TC trends cannot be explained only by increase of wild-fires impact and anthropogenic emissions; possible reasons of such CO tendencies could be the changes in all atmospheric photochemistry system. This work was supported by the Russian Scientific Foundation under grant №14-47-00049 (in part of NDACC, AERONET and satellite trends estimates), under grant №16-17-10275 (in part of analysis of ground-based observations over Moscow and Obninsk) and partially by the Russian Foundation for Basic Research (grant № 16-05-00287 in part of provide of ground-based

Minimum Number of Observation Points for LEO Satellite Orbit Estimation by OWL Network

NASA Astrophysics Data System (ADS)

Park, Maru; Jo, Jung Hyun; Cho, Sungki; Choi, Jin; Kim, Chun-Hwey; Park, Jang-Hyun; Yim, Hong-Suh; Choi, Young-Jun; Moon, Hong-Kyu; Bae, Young-Ho; Park, Sun-Youp; Kim, Ji-Hye; Roh, Dong-Goo; Jang, Hyun-Jung; Park, Young-Sik; Jeong, Min-Ji

2015-12-01

By using the Optical Wide-field Patrol (OWL) network developed by the Korea Astronomy and Space Science Institute (KASI) we generated the right ascension and declination angle data from optical observation of Low Earth Orbit (LEO) satellites. We performed an analysis to verify the optimum number of observations needed per arc for successful estimation of orbit. The currently functioning OWL observatories are located in Daejeon (South Korea), Songino (Mongolia), and Oukaïmeden (Morocco). The Daejeon Observatory is functioning as a test bed. In this study, the observed targets were Gravity Probe B, COSMOS 1455, COSMOS 1726, COSMOS 2428, SEASAT 1, ATV-5, and CryoSat-2 (all in LEO). These satellites were observed from the test bed and the Songino Observatory of the OWL network during 21 nights in 2014 and 2015. After we estimated the orbit from systematically selected sets of observation points (20, 50, 100, and 150) for each pass, we compared the difference between the orbit estimates for each case, and the Two Line Element set (TLE) from the Joint Space Operation Center (JSpOC). Then, we determined the average of the difference and selected the optimal observation points by comparing the average values.

Measuring Housework Participation: The Gap between "Stylised" Questionnaire Estimates and Diary-Based Estimates

ERIC Educational Resources Information Center

Kan, Man Yee

2008-01-01

This article compares stylised (questionnaire-based) estimates and diary-based estimates of housework time collected from the same respondents. Data come from the Home On-line Study (1999-2001), a British national household survey that contains both types of estimates (sample size = 632 men and 666 women). It shows that the gap between the two…

Sensorless control for permanent magnet synchronous motor using a neural network based adaptive estimator

NASA Astrophysics Data System (ADS)

Kwon, Chung-Jin; Kim, Sung-Joong; Han, Woo-Young; Min, Won-Kyoung

2005-12-01

The rotor position and speed estimation of permanent-magnet synchronous motor(PMSM) was dealt with. By measuring the phase voltages and currents of the PMSM drive, two diagonally recurrent neural network(DRNN) based observers, a neural current observer and a neural velocity observer were developed. DRNN which has self-feedback of the hidden neurons ensures that the outputs of DRNN contain the whole past information of the system even if the inputs of DRNN are only the present states and inputs of the system. Thus the structure of DRNN may be simpler than that of feedforward and fully recurrent neural networks. If the backpropagation method was used for the training of the DRNN the problem of slow convergence arise. In order to reduce this problem, recursive prediction error(RPE) based learning method for the DRNN was presented. The simulation results show that the proposed approach gives a good estimation of rotor speed and position, and RPE based training has requires a shorter computation time compared to backpropagation based training.

Improving regression-model-based streamwater constituent load estimates derived from serially correlated data

USGS Publications Warehouse

Aulenbach, Brent T.

2013-01-01

A regression-model based approach is a commonly used, efficient method for estimating streamwater constituent load when there is a relationship between streamwater constituent concentration and continuous variables such as streamwater discharge, season and time. A subsetting experiment using a 30-year dataset of daily suspended sediment observations from the Mississippi River at Thebes, Illinois, was performed to determine optimal sampling frequency, model calibration period length, and regression model methodology, as well as to determine the effect of serial correlation of model residuals on load estimate precision. Two regression-based methods were used to estimate streamwater loads, the Adjusted Maximum Likelihood Estimator (AMLE), and the composite method, a hybrid load estimation approach. While both methods accurately and precisely estimated loads at the model’s calibration period time scale, precisions were progressively worse at shorter reporting periods, from annually to monthly. Serial correlation in model residuals resulted in observed AMLE precision to be significantly worse than the model calculated standard errors of prediction. The composite method effectively improved upon AMLE loads for shorter reporting periods, but required a sampling interval of at least 15-days or shorter, when the serial correlations in the observed load residuals were greater than 0.15. AMLE precision was better at shorter sampling intervals and when using the shortest model calibration periods, such that the regression models better fit the temporal changes in the concentration–discharge relationship. The models with the largest errors typically had poor high flow sampling coverage resulting in unrepresentative models. Increasing sampling frequency and/or targeted high flow sampling are more efficient approaches to ensure sufficient sampling and to avoid poorly performing models, than increasing calibration period length.

Service Lifetime Estimation of EPDM Rubber Based on Accelerated Aging Tests

NASA Astrophysics Data System (ADS)

Liu, Jie; Li, Xiangbo; Xu, Likun; He, Tao

2017-04-01

Service lifetime of ethylene propylene diene monomer (EPDM) rubber at room temperature (25 °C) was estimated based on accelerated aging tests. The study followed sealing stress loss on compressed cylinder samples by compression stress relaxation methods. The results showed that the cylinder samples of EPDM can quickly reach the physical relaxation equilibrium by using the over-compression method. The non-Arrhenius behavior occurred at the lowest aging temperature. A significant linear relationship was observed between compression set values and normalized stress decay results, and the relationship was not related to the ambient temperature of aging. It was estimated that the sealing stress loss in view of practical application would occur after around 86.8 years at 25 °C. The estimations at 25 °C based on the non-Arrhenius behavior were in agreement with compression set data from storage aging tests in natural environment.

A Coarse Alignment Method Based on Digital Filters and Reconstructed Observation Vectors

PubMed Central

Xu, Xiang; Xu, Xiaosu; Zhang, Tao; Li, Yao; Wang, Zhicheng

2017-01-01

In this paper, a coarse alignment method based on apparent gravitational motion is proposed. Due to the interference of the complex situations, the true observation vectors, which are calculated by the apparent gravity, are contaminated. The sources of the interference are analyzed in detail, and then a low-pass digital filter is designed in this paper for eliminating the high-frequency noise of the measurement observation vectors. To extract the effective observation vectors from the inertial sensors’ outputs, a parameter recognition and vector reconstruction method are designed, where an adaptive Kalman filter is employed to estimate the unknown parameters. Furthermore, a robust filter, which is based on Huber’s M-estimation theory, is developed for addressing the outliers of the measurement observation vectors due to the maneuver of the vehicle. A comprehensive experiment, which contains a simulation test and physical test, is designed to verify the performance of the proposed method, and the results show that the proposed method is equivalent to the popular apparent velocity method in swaying mode, but it is superior to the current methods while in moving mode when the strapdown inertial navigation system (SINS) is under entirely self-contained conditions. PMID:28353682

Observation and estimation of photosynthetically active radiation in Lhasa (Tibetan Plateau)

NASA Astrophysics Data System (ADS)

Peng, Simao; Du, Qingyun; Lin, Aiwen; Hu, Bo; Xiao, Ke; Xi, Yuliang

2015-03-01

In this study, we measured photosynthetically active radiation (PAR) and global solar radiation (G) in Lhasa, located on the Tibetan Plateau, from 2006 to 2012 to examine the PAR and PAR/G (PAR fraction) seasonal characteristics. The maximum and minimum values of both PAR and the PAR fraction occurred in summer and winter, respectively. Moreover, the PAR and PAR fraction annual averages were 38.64 mol m-2 d-1 and 1.84 mol M J-1, respectively. An efficient all-weather model used for estimating PAR under various sky conditions was developed based on the relationships among PAR, the cosine of the solar zenith angle and the clearness index in Lhasa. The model also produced acceptable estimations of PAR with high accuracy at the Donghu and Sanjiang weather stations. A PAR dataset was reconstructed from G using the newly developed model for the period 1961-2012. The modelled annual mean daily PAR was approximately 37.62 mol m-2 d-1. A significant decreasing trend (-0.61 mol m-2 per decade) over the last 50 years was observed on the Tibetan Plateau; this decrease was largest in autumn (-1.024 mol m-2 per decade), and relatively small decreases were observed in summer. The results also revealed that PAR began increasing at 0.164 mol m-2 per year from 1991 to 2012, which was inconsistent with the variations of G. The proposed all-weather PAR model could be useful for ecological modelling and agricultural processes in the Tibetan Plateau region of China.

OMI Satellite and Ground-Based Pandora Observations and Their Application to Surface NO2 Estimations at Terrestrial and Marine Sites

NASA Astrophysics Data System (ADS)

Kollonige, Debra E.; Thompson, Anne M.; Josipovic, Miroslav; Tzortziou, Maria; Beukes, Johan P.; Burger, Roelof; Martins, Douglas K.; van Zyl, Pieter G.; Vakkari, Ville; Laakso, Lauri

2018-01-01

The Pandora spectrometer that uses direct-Sun measurements to derive total column amounts of gases provides an approach for (1) validation of satellite instruments and (2) monitoring of total column (TC) ozone (O3) and nitrogen dioxide (NO2). We use for the first time Pandora and Ozone Monitoring Instrument (OMI) observations to estimate surface NO2 over marine and terrestrial sites downwind of urban pollution and compared with in situ measurements during campaigns in contrasting regions: (1) the South African Highveld (at Welgegund, 26°34'10″S, 26°56'21″E, 1,480 m asl, 120 km southwest of the Johannesburg-Pretoria megacity) and (2) shipboard U.S. mid-Atlantic coast during the 2014 Deposition of Atmospheric Nitrogen to Coastal Ecosystems (DANCE) cruise. In both cases, there were no local NOx sources but intermittent regional pollution influences. For TC NO2, OMI and Pandora difference is 20%, with Pandora higher most times. Surface NO2 values estimated from OMI and Pandora columns are compared to in situ NO2 for both locations. For Welgegund, the planetary boundary layer (PBL) height, used in converting column to surface NO2 value, has been estimated by three methods: co-located Atmospheric Infrared Sounder (AIRS) observations; a model simulation; and radiosonde data from Irene, 150 km northeast of the site. AIRS PBL heights agree within 10% of radiosonde-derived values. Absolute differences between Pandora- and OMI-estimated surface NO2 and the in situ data are better at the terrestrial site ( 0.5 ppbv and 1 ppbv or greater, respectively) than under clean marine air conditions, with differences usually >3 ppbv. Cloud cover and PBL variability influence these estimations.

Multi-scale assimilation of remotely sensed snow observations for hydrologic estimation

NASA Astrophysics Data System (ADS)

Andreadis, K.; Lettenmaier, D.

2008-12-01

Data assimilation provides a framework for optimally merging model predictions and remote sensing observations of snow properties (snow cover extent, water equivalent, grain size, melt state), ideally overcoming limitations of both. A synthetic twin experiment is used to evaluate a data assimilation system that would ingest remotely sensed observations from passive microwave and visible wavelength sensors (brightness temperature and snow cover extent derived products, respectively) with the objective of estimating snow water equivalent. Two data assimilation techniques are used, the Ensemble Kalman filter and the Ensemble Multiscale Kalman filter (EnMKF). One of the challenges inherent in such a data assimilation system is the discrepancy in spatial scales between the different types of snow-related observations. The EnMKF represents the sample model error covariance with a tree that relates the system state variables at different locations and scales through a set of parent-child relationships. This provides an attractive framework to efficiently assimilate observations at different spatial scales. This study provides a first assessment of the feasibility of a system that would assimilate observations from multiple sensors (MODIS snow cover and AMSR-E brightness temperatures) and at different spatial scales for snow water equivalent estimation. The relative value of the different types of observations is examined. Additionally, the error characteristics of both model and observations are discussed.

Yield estimation of sugarcane based on agrometeorological-spectral models

NASA Technical Reports Server (NTRS)

Rudorff, Bernardo Friedrich Theodor; Batista, Getulio Teixeira

1990-01-01

This work has the objective to assess the performance of a yield estimation model for sugarcane (Succharum officinarum). The model uses orbital gathered spectral data along with yield estimated from an agrometeorological model. The test site includes the sugarcane plantations of the Barra Grande Plant located in Lencois Paulista municipality in Sao Paulo State. Production data of four crop years were analyzed. Yield data observed in the first crop year (1983/84) were regressed against spectral and agrometeorological data of that same year. This provided the model to predict the yield for the following crop year i.e., 1984/85. The model to predict the yield of subsequent years (up to 1987/88) were developed similarly, incorporating all previous years data. The yield estimations obtained from these models explained 69, 54, and 50 percent of the yield variation in the 1984/85, 1985/86, and 1986/87 crop years, respectively. The accuracy of yield estimations based on spectral data only (vegetation index model) and on agrometeorological data only (agrometeorological model) were also investigated.

Entropy-based adaptive attitude estimation

NASA Astrophysics Data System (ADS)

Kiani, Maryam; Barzegar, Aylin; Pourtakdoust, Seid H.

2018-03-01

Gaussian approximation filters have increasingly been developed to enhance the accuracy of attitude estimation in space missions. The effective employment of these algorithms demands accurate knowledge of system dynamics and measurement models, as well as their noise characteristics, which are usually unavailable or unreliable. An innovation-based adaptive filtering approach has been adopted as a solution to this problem; however, it exhibits two major challenges, namely appropriate window size selection and guaranteed assurance of positive definiteness for the estimated noise covariance matrices. The current work presents two novel techniques based on relative entropy and confidence level concepts in order to address the abovementioned drawbacks. The proposed adaptation techniques are applied to two nonlinear state estimation algorithms of the extended Kalman filter and cubature Kalman filter for attitude estimation of a low earth orbit satellite equipped with three-axis magnetometers and Sun sensors. The effectiveness of the proposed adaptation scheme is demonstrated by means of comprehensive sensitivity analysis on the system and environmental parameters by using extensive independent Monte Carlo simulations.

Estimating evaporative vapor generation from automobiles based on parking activities.

PubMed

Dong, Xinyi; Tschantz, Michael; Fu, Joshua S

2015-07-01

A new approach is proposed to quantify the evaporative vapor generation based on real parking activity data. As compared to the existing methods, two improvements are applied in this new approach to reduce the uncertainties: First, evaporative vapor generation from diurnal parking events is usually calculated based on estimated average parking duration for the whole fleet, while in this study, vapor generation rate is calculated based on parking activities distribution. Second, rather than using the daily temperature gradient, this study uses hourly temperature observations to derive the hourly incremental vapor generation rates. The parking distribution and hourly incremental vapor generation rates are then adopted with Wade-Reddy's equation to estimate the weighted average evaporative generation. We find that hourly incremental rates can better describe the temporal variations of vapor generation, and the weighted vapor generation rate is 5-8% less than calculation without considering parking activity. Copyright © 2015 Elsevier Ltd. All rights reserved.

Estimates of Ground Temperature and Atmospheric Moisture from CERES Observations

NASA Technical Reports Server (NTRS)

Wu, Man Li C.; Schubert, Siegfried; Einaudi, Franco (Technical Monitor)

2000-01-01

A method is developed to retrieve surface ground temperature (T(sub g)) and atmospheric moisture using clear sky fluxes (CSF) from CERES-TRMM observations. In general, the clear sky outgoing longwave radiation (CLR) is sensitive to upper level moisture (q(sub l)) over wet regions and (T(sub g)) over dry regions The clear sky window flux from 800 to 1200/cm (RadWn) is sensitive to low level moisture (q(sub t)) and T(sub g). Combining these two measurements (CLR and RadWn), Tg and q(sub h) can be estimated over land, while q(sub h) and q(sub l) can be estimated over the oceans. The approach capitalizes on the availability of satellite estimates of CLR and RadWn and other auxiliary satellite data. The basic methodology employs off-line forward radiative transfer calculations to generate synthetic CSF data from two different global 4-dimensional data assimilation products. Simple linear regression is used to relate discrepancies in CSF to discrepancies in T(sub g), q(sub h) and q(sub l). The slopes of the regression lines define sensitivity parameters that can be exploited to help interpret mismatches between satellite observations and model-based estimates of CSF. For illustration, we analyze the discrepancies in the CSF between an early implementation of the Goddard Earth Observing System Data Assimilation System (GEOS-DAS) and a recent operational version of the European Center for Medium-Range Weather Prediction data assimilation system. In particular, our analysis of synthetic total and window region SCF differences (computed from two different assimilated data sets) shows that simple linear regression employing Delta(T(sub g)) and broad layer Delta(q(sub l) from .500 hPa to surface and Delta(q(sub h)) from 200 to .300 hPa provides a good approximation to the full radiative transfer calculations. typically explaining more than 90% of the 6-hourly variance in the flux differences. These simple regression relations can be inverted to "retrieve" the errors in the

Regional estimation of base recharge to ground water using water balance and a base-flow index.

PubMed

Szilagyi, Jozsef; Harvey, F Edwin; Ayers, Jerry F

2003-01-01

Naturally occurring long-term mean annual base recharge to ground water in Nebraska was estimated with the help of a water-balance approach and an objective automated technique for base-flow separation involving minimal parameter-optimization requirements. Base recharge is equal to total recharge minus the amount of evapotranspiration coming directly from ground water. The estimation of evapotranspiration in the water-balance equation avoids the need to specify a contributing drainage area for ground water, which in certain cases may be considerably different from the drainage area for surface runoff. Evapotranspiration was calculated by the WREVAP model at the Solar and Meteorological Surface Observation Network (SAMSON) sites. Long-term mean annual base recharge was derived by determining the product of estimated long-term mean annual runoff (the difference between precipitation and evapotranspiration) and the base-flow index (BFI). The BFI was calculated from discharge data obtained from the U.S. Geological Survey's gauging stations in Nebraska. Mapping was achieved by using geographic information systems (GIS) and geostatistics. This approach is best suited for regional-scale applications. It does not require complex hydrogeologic modeling nor detailed knowledge of soil characteristics, vegetation cover, or land-use practices. Long-term mean annual base recharge rates in excess of 110 mm/year resulted in the extreme eastern part of Nebraska. The western portion of the state expressed rates of only 15 to 20 mm annually, while the Sandhills region of north-central Nebraska was estimated to receive twice as much base recharge (40 to 50 mm/year) as areas south of it.

Estimation of the Aral Sea state predictability based on the open data sources and the unique field observations

NASA Astrophysics Data System (ADS)

Izhitskiy, Alexander; Ayzel, Georgy; Zavialov, Peter; Kurbaniyazov, Abilgazi

2016-04-01

The Aral Sea, formerly one of the four largest lakes in the world, has lost over 90% of its volume during the dramatical dessication mainly caused by the severe alteration of water budget of the basin. Shrinkage of the Aral Sea resulted in profound changes of the lake's ecosystem, that became a subject for a number of publications based on a wide range of methods such as field observations, remote sensing data analysis and numerical modeling. However, by the early 21th century, the number of field studies decreased significantly due to almost complete cessation of navigation and displacement of the Aral's shoreline far away from roads and other infrastructure. Thus, only a small amount of field data (salinity, temperature, etc.) for different regions of the lake is available for the last two decades. On the other hand, a set of the open data sources (sea level variability, atmospheric reanalysis) were developed for the region. The main idea of the presented study is to estimate the possibility of prediction of the Aral Sea state using coupled system of basic geoanalysis tools, numerical modeling of hydrological cycle (both for sea and land-surface interactions with atmosphere) and state-of-art machine learning techniques. Firstly, available in situ data, obtained in the Aral Sea by Shirshov Institute and other researchers, are concerned as the "base points of state" for each year within the studied period. Secondly, consistent patterns in the interannual variability of all other available parameters, taken from the open data sources and numerical modeling predictions, are founded out. As a result, such an approach allows predicting the future state of sea basing on the possible climatic scenario.

Sliding mode control-based linear functional observers for discrete-time stochastic systems

NASA Astrophysics Data System (ADS)

Singh, Satnesh; Janardhanan, Sivaramakrishnan

2017-11-01

Sliding mode control (SMC) is one of the most popular techniques to stabilise linear discrete-time stochastic systems. However, application of SMC becomes difficult when the system states are not available for feedback. This paper presents a new approach to design a SMC-based functional observer for discrete-time stochastic systems. The functional observer is based on the Kronecker product approach. Existence conditions and stability analysis of the proposed observer are given. The control input is estimated by a novel linear functional observer. This approach leads to a non-switching type of control, thereby eliminating the fundamental cause of chatter. Furthermore, the functional observer is designed in such a way that the effect of process and measurement noise is minimised. Simulation example is given to illustrate and validate the proposed design method.

Global maps of streamflow characteristics based on observations from several thousand catchments

NASA Astrophysics Data System (ADS)

Beck, Hylke; de Roo, Ad; van Dijk, Albert

2016-04-01

Streamflow (Q) estimation in ungauged catchments is one of the greatest challenges facing hydrologists. Observed Q from three to four thousand small-to-medium sized catchments (10--10 000~km^2) around the globe were used to train neural network ensembles to estimate Q characteristics based on climate and physiographic characteristics of the catchments. In total 17 Q characteristics were selected, including mean annual Q, baseflow index, and a number of flow percentiles. Testing coefficients of determination for the estimation of the Q characteristics ranged from 0.55 for the baseflow recession constant to 0.93 for the Q timing. Overall, climate indices dominated among the predictors. Predictors related to soils and geology were relatively unimportant, perhaps due to their data quality. The trained neural network ensembles were subsequently applied spatially over the entire ice-free land surface, resulting in global maps of the Q characteristics (0.125° resolution). These maps possess several unique features: they represent observation-driven estimates; are based on an unprecedentedly large set of catchments; and have associated uncertainty estimates. The maps can be used for various hydrological applications, including the diagnosis of macro-scale hydrological models. To demonstrate this, the produced maps were compared to equivalent maps derived from the simulated daily Q of four macro-scale hydrological models, highlighting various opportunities for improvement in model Q behavior. The produced dataset is available via http://water.jrc.ec.europa.eu.

Global maps of streamflow characteristics based on observations from several thousand catchments

NASA Astrophysics Data System (ADS)

Beck, Hylke; van Dijk, Albert; de Roo, Ad

2015-04-01

Streamflow (Q) estimation in ungauged catchments is one of the greatest challenges facing hydrologists. Observed Q from three to four thousand small-to-medium sized catchments (10-10000 km2) around the globe were used to train neural network ensembles to estimate Q characteristics based on climate and physiographic characteristics of the catchments. In total 17 Q characteristics were selected, including mean annual Q, baseflow index, and a number of flow percentiles. Testing coefficients of determination for the estimation of the Q characteristics ranged from 0.55 for the baseflow recession constant to 0.93 for the Q timing. Overall, climate indices dominated among the predictors. Predictors related to soils and geology were relatively unimportant, perhaps due to their data quality. The trained neural network ensembles were subsequently applied spatially over the entire ice-free land surface, resulting in global maps of the Q characteristics (0.125° resolution). These maps possess several unique features: they represent observation-driven estimates; are based on an unprecedentedly large set of catchments; and have associated uncertainty estimates. The maps can be used for various hydrological applications, including the diagnosis of macro-scale hydrological models. To demonstrate this, the produced maps were compared to equivalent maps derived from the simulated daily Q of four macro-scale hydrological models, highlighting various opportunities for improvement in model Q behavior. The produced dataset is available via http://water.jrc.ec.europa.eu.

Estimating snow depth of alpine snowpack via airborne multifrequency passive microwave radiance observations: Colorado, USA

NASA Astrophysics Data System (ADS)

Kim, R. S.; Durand, M. T.; Li, D.; Baldo, E.; Margulis, S. A.; Dumont, M.; Morin, S.

2017-12-01

This paper presents a newly-proposed snow depth retrieval approach for mountainous deep snow using airborne multifrequency passive microwave (PM) radiance observation. In contrast to previous snow depth estimations using satellite PM radiance assimilation, the newly-proposed method utilized single flight observation and deployed the snow hydrologic models. This method is promising since the satellite-based retrieval methods have difficulties to estimate snow depth due to their coarse resolution and computational effort. Indeed, this approach consists of particle filter using combinations of multiple PM frequencies and multi-layer snow physical model (i.e., Crocus) to resolve melt-refreeze crusts. The method was performed over NASA Cold Land Processes Experiment (CLPX) area in Colorado during 2002 and 2003. Results showed that there was a significant improvement over the prior snow depth estimates and the capability to reduce the prior snow depth biases. When applying our snow depth retrieval algorithm using a combination of four PM frequencies (10.7,18.7, 37.0 and 89.0 GHz), the RMSE values were reduced by 48 % at the snow depth transects sites where forest density was less than 5% despite deep snow conditions. This method displayed a sensitivity to different combinations of frequencies, model stratigraphy (i.e. different number of layering scheme for snow physical model) and estimation methods (particle filter and Kalman filter). The prior RMSE values at the forest-covered areas were reduced by 37 - 42 % even in the presence of forest cover.

Direct Radiative Effect of Aerosols Based on PARASOL and OMI Satellite Observations

NASA Technical Reports Server (NTRS)

Lacagnina, Carlo; Hasekamp, Otto P.; Torres, Omar

2017-01-01

Accurate portrayal of the aerosol characteristics is crucial to determine aerosol contribution to the Earth's radiation budget. We employ novel satellite retrievals to make a new measurement-based estimate of the shortwave direct radiative effect of aerosols (DREA), both over land and ocean. Global satellite measurements of aerosol optical depth, single-scattering albedo (SSA), and phase function from PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) are used in synergy with OMI (Ozone Monitoring Instrument) SSA. Aerosol information is combined with land-surface bidirectional reflectance distribution function and cloud characteristics from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite products. Eventual gaps in observations are filled with the state-of-the-art global aerosol model ECHAM5-HAM2. It is found that our estimate of DREA is largely insensitive to model choice. Radiative transfer calculations show that DREA at top-of-atmosphere is -4.6 +/- 1.5 W/sq m for cloud-free and -2.1 +/- 0.7 W/sq m for all-sky conditions, during year 2006. These fluxes are consistent with, albeit generally less negative over ocean than, former assessments. Unlike previous studies, our estimate is constrained by retrievals of global coverage SSA, which may justify different DREA values. Remarkable consistency is found in comparison with DREA based on CERES (Clouds and the Earth's Radiant Energy System) and MODIS observations.

Space-based infrared scanning sensor LOS determination and calibration using star observation

NASA Astrophysics Data System (ADS)

Chen, Jun; Xu, Zhan; An, Wei; Deng, Xin-Pu; Yang, Jun-Gang

2015-10-01

This paper provides a novel methodology for removing sensor bias from a space based infrared (IR) system (SBIRS) through the use of stars detected in the background field of the sensor. Space based IR system uses the LOS (line of sight) of target for target location. LOS determination and calibration is the key precondition of accurate location and tracking of targets in Space based IR system and the LOS calibration of scanning sensor is one of the difficulties. The subsequent changes of sensor bias are not been taking into account in the conventional LOS determination and calibration process. Based on the analysis of the imaging process of scanning sensor, a theoretical model based on the estimation of bias angles using star observation is proposed. By establishing the process model of the bias angles and the observation model of stars, using an extended Kalman filter (EKF) to estimate the bias angles, and then calibrating the sensor LOS. Time domain simulations results indicate that the proposed method has a high precision and smooth performance for sensor LOS determination and calibration. The timeliness and precision of target tracking process in the space based infrared (IR) tracking system could be met with the proposed algorithm.

Online vegetation parameter estimation using passive microwave remote sensing observations

USDA-ARS?s Scientific Manuscript database

In adaptive system identification the Kalman filter can be used to identify the coefficient of the observation operator of a linear system. Here the ensemble Kalman filter is tested for adaptive online estimation of the vegetation opacity parameter of a radiative transfer model. A state augmentatio...

Dictionary-based fiber orientation estimation with improved spatial consistency.

PubMed

Ye, Chuyang; Prince, Jerry L

2018-02-01

Diffusion magnetic resonance imaging (dMRI) has enabled in vivo investigation of white matter tracts. Fiber orientation (FO) estimation is a key step in tract reconstruction and has been a popular research topic in dMRI analysis. In particular, the sparsity assumption has been used in conjunction with a dictionary-based framework to achieve reliable FO estimation with a reduced number of gradient directions. Because image noise can have a deleterious effect on the accuracy of FO estimation, previous works have incorporated spatial consistency of FOs in the dictionary-based framework to improve the estimation. However, because FOs are only indirectly determined from the mixture fractions of dictionary atoms and not modeled as variables in the objective function, these methods do not incorporate FO smoothness directly, and their ability to produce smooth FOs could be limited. In this work, we propose an improvement to Fiber Orientation Reconstruction using Neighborhood Information (FORNI), which we call FORNI+; this method estimates FOs in a dictionary-based framework where FO smoothness is better enforced than in FORNI alone. We describe an objective function that explicitly models the actual FOs and the mixture fractions of dictionary atoms. Specifically, it consists of data fidelity between the observed signals and the signals represented by the dictionary, pairwise FO dissimilarity that encourages FO smoothness, and weighted ℓ 1 -norm terms that ensure the consistency between the actual FOs and the FO configuration suggested by the dictionary representation. The FOs and mixture fractions are then jointly estimated by minimizing the objective function using an iterative alternating optimization strategy. FORNI+ was evaluated on a simulation phantom, a physical phantom, and real brain dMRI data. In particular, in the real brain dMRI experiment, we have qualitatively and quantitatively evaluated the reproducibility of the proposed method. Results demonstrate that

Observability and Estimation of Distributed Space Systems via Local Information-Exchange Networks

NASA Technical Reports Server (NTRS)

Fathpour, Nanaz; Hadaegh, Fred Y.; Mesbahi, Mehran; Rahmani, Amirreza

2011-01-01

Spacecraft formation flying involves the coordination of states among multiple spacecraft through relative sensing, inter-spacecraft communication, and control. Most existing formation-flying estimation algorithms can only be supported via highly centralized, all-to-all, static relative sensing. New algorithms are proposed that are scalable, modular, and robust to variations in the topology and link characteristics of the formation exchange network. These distributed algorithms rely on a local information exchange network, relaxing the assumptions on existing algorithms. Distributed space systems rely on a signal transmission network among multiple spacecraft for their operation. Control and coordination among multiple spacecraft in a formation is facilitated via a network of relative sensing and interspacecraft communications. Guidance, navigation, and control rely on the sensing network. This network becomes more complex the more spacecraft are added, or as mission requirements become more complex. The observability of a formation state was observed by a set of local observations from a particular node in the formation. Formation observability can be parameterized in terms of the matrices appearing in the formation dynamics and observation matrices. An agreement protocol was used as a mechanism for observing formation states from local measurements. An agreement protocol is essentially an unforced dynamic system whose trajectory is governed by the interconnection geometry and initial condition of each node, with a goal of reaching a common value of interest. The observability of the interconnected system depends on the geometry of the network, as well as the position of the observer relative to the topology. For the first time, critical GN&C (guidance, navigation, and control estimation) subsystems are synthesized by bringing the contribution of the spacecraft information-exchange network to the forefront of algorithmic analysis and design. The result is a

Shock Formation Height in the Solar Corona Estimated from SDO and Radio Observations

NASA Technical Reports Server (NTRS)

Gopalswamy, N.; Nitta, N.

2011-01-01

Wave transients at EUV wavelengths and type II radio bursts are good indicators of shock formation in the solar corona. We use recent EUV wave observations from SDO and combine them with metric type II radio data to estimate the height in the corona where the shocks form. We compare the results with those obtained from other methods. We also estimate the shock formation heights independently using white-light observations of coronal mass ejections that ultimately drive the shocks.

Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting

NASA Astrophysics Data System (ADS)

Wani, Omar; Beckers, Joost V. L.; Weerts, Albrecht H.; Solomatine, Dimitri P.

2017-08-01

A non-parametric method is applied to quantify residual uncertainty in hydrologic streamflow forecasting. This method acts as a post-processor on deterministic model forecasts and generates a residual uncertainty distribution. Based on instance-based learning, it uses a k nearest-neighbour search for similar historical hydrometeorological conditions to determine uncertainty intervals from a set of historical errors, i.e. discrepancies between past forecast and observation. The performance of this method is assessed using test cases of hydrologic forecasting in two UK rivers: the Severn and Brue. Forecasts in retrospect were made and their uncertainties were estimated using kNN resampling and two alternative uncertainty estimators: quantile regression (QR) and uncertainty estimation based on local errors and clustering (UNEEC). Results show that kNN uncertainty estimation produces accurate and narrow uncertainty intervals with good probability coverage. Analysis also shows that the performance of this technique depends on the choice of search space. Nevertheless, the accuracy and reliability of uncertainty intervals generated using kNN resampling are at least comparable to those produced by QR and UNEEC. It is concluded that kNN uncertainty estimation is an interesting alternative to other post-processors, like QR and UNEEC, for estimating forecast uncertainty. Apart from its concept being simple and well understood, an advantage of this method is that it is relatively easy to implement.

Efficient estimation of ideal-observer performance in classification tasks involving high-dimensional complex backgrounds

PubMed Central

Park, Subok; Clarkson, Eric

2010-01-01

The Bayesian ideal observer is optimal among all observers and sets an absolute upper bound for the performance of any observer in classification tasks [Van Trees, Detection, Estimation, and Modulation Theory, Part I (Academic, 1968).]. Therefore, the ideal observer should be used for objective image quality assessment whenever possible. However, computation of ideal-observer performance is difficult in practice because this observer requires the full description of unknown, statistical properties of high-dimensional, complex data arising in real life problems. Previously, Markov-chain Monte Carlo (MCMC) methods were developed by Kupinski et al. [J. Opt. Soc. Am. A 20, 430(2003) ] and by Park et al. [J. Opt. Soc. Am. A 24, B136 (2007) and IEEE Trans. Med. Imaging 28, 657 (2009) ] to estimate the performance of the ideal observer and the channelized ideal observer (CIO), respectively, in classification tasks involving non-Gaussian random backgrounds. However, both algorithms had the disadvantage of long computation times. We propose a fast MCMC for real-time estimation of the likelihood ratio for the CIO. Our simulation results show that our method has the potential to speed up ideal-observer performance in tasks involving complex data when efficient channels are used for the CIO. PMID:19884916

Optical Tracking Data Validation and Orbit Estimation for Sparse Observations of Satellites by the OWL-Net.

PubMed

Choi, Jin; Jo, Jung Hyun; Yim, Hong-Suh; Choi, Eun-Jung; Cho, Sungki; Park, Jang-Hyun

2018-06-07

An Optical Wide-field patroL-Network (OWL-Net) has been developed for maintaining Korean low Earth orbit (LEO) satellites' orbital ephemeris. The OWL-Net consists of five optical tracking stations. Brightness signals of reflected sunlight of the targets were detected by a charged coupled device (CCD). A chopper system was adopted for fast astrometric data sampling, maximum 50 Hz, within a short observation time. The astrometric accuracy of the optical observation data was validated with precise orbital ephemeris such as Consolidated Prediction File (CPF) data and precise orbit determination result with onboard Global Positioning System (GPS) data from the target satellite. In the optical observation simulation of the OWL-Net for 2017, an average observation span for a single arc of 11 LEO observation targets was about 5 min, while an average optical observation separation time was 5 h. We estimated the position and velocity with an atmospheric drag coefficient of LEO observation targets using a sequential-batch orbit estimation technique after multi-arc batch orbit estimation. Post-fit residuals for the multi-arc batch orbit estimation and sequential-batch orbit estimation were analyzed for the optical measurements and reference orbit (CPF and GPS data). The post-fit residuals with reference show few tens-of-meters errors for in-track direction for multi-arc batch and sequential-batch orbit estimation results.

CYGNSS Surface Wind Observations and Surface Flux Estimates within Low-Latitude Extratropical Cyclones

NASA Astrophysics Data System (ADS)

Crespo, J.; Posselt, D. J.

2017-12-01

The Cyclone Global Navigation Satellite System (CYGNSS), launched in December 2016, aims to improve estimates of surface wind speeds over the tropical oceans. While CYGNSS's core mission is to provide better estimates of surface winds within the core of tropical cyclones, previous research has shown that the constellation, with its orbital inclination of 35°, also has the ability to observe numerous extratropical cyclones that form in the lower latitudes. Along with its high spatial and temporal resolution, CYGNSS can provide new insights into how extratropical cyclones develop and evolve, especially in the presence of thick clouds and precipitation. We will demonstrate this by presenting case studies of multiple extratropical cyclones observed by CYGNSS early on in its mission in both Northern and Southern Hemispheres. By using the improved estimates of surface wind speeds from CYGNSS, we can obtain better estimates of surface latent and sensible heat fluxes within and around extratropical cyclones. Surface heat fluxes, driven by surface winds and strong vertical gradients of water vapor and temperature, play a key role in marine cyclogenesis as they increase instability within the boundary layer and may contribute to extreme marine cyclogenesis. In the past, it has been difficult to estimate surface heat fluxes from space borne instruments, as these fluxes cannot be observed directly from space, and deficiencies in spatial coverage and attenuation from clouds and precipitation lead to inaccurate estimates of surface flux components, such as surface wind speeds. While CYGNSS only contributes estimates of surface wind speeds, we can combine this data with other reanalysis and satellite data to provide improved estimates of surface sensible and latent heat fluxes within and around extratropical cyclones and throughout the entire CYGNSS mission.

State and force observers based on multibody models and the indirect Kalman filter

NASA Astrophysics Data System (ADS)

Sanjurjo, Emilio; Dopico, Daniel; Luaces, Alberto; Naya, Miguel Ángel

2018-06-01

The aim of this work is to present two new methods to provide state observers by combining multibody simulations with indirect extended Kalman filters. One of the methods presented provides also input force estimation. The observers have been applied to two mechanism with four different sensor configurations, and compared to other multibody-based observers found in the literature to evaluate their behavior, namely, the unscented Kalman filter (UKF), and the indirect extended Kalman filter with simplified Jacobians (errorEKF). The new methods have some more computational cost than the errorEKF, but still much less than the UKF. Regarding their accuracy, both are better than the errorEKF. The method with input force estimation outperforms also the UKF, while the method without force estimation achieves results almost identical to those of the UKF. All the methods have been implemented as a reusable MATLAB® toolkit which has been released as Open Source in https://github.com/MBDS/mbde-matlab.

Influence function based variance estimation and missing data issues in case-cohort studies.

PubMed

Mark, S D; Katki, H

2001-12-01

Recognizing that the efficiency in relative risk estimation for the Cox proportional hazards model is largely constrained by the total number of cases, Prentice (1986) proposed the case-cohort design in which covariates are measured on all cases and on a random sample of the cohort. Subsequent to Prentice, other methods of estimation and sampling have been proposed for these designs. We formalize an approach to variance estimation suggested by Barlow (1994), and derive a robust variance estimator based on the influence function. We consider the applicability of the variance estimator to all the proposed case-cohort estimators, and derive the influence function when known sampling probabilities in the estimators are replaced by observed sampling fractions. We discuss the modifications required when cases are missing covariate information. The missingness may occur by chance, and be completely at random; or may occur as part of the sampling design, and depend upon other observed covariates. We provide an adaptation of S-plus code that allows estimating influence function variances in the presence of such missing covariates. Using examples from our current case-cohort studies on esophageal and gastric cancer, we illustrate how our results our useful in solving design and analytic issues that arise in practice.

Adaptive super-twisting observer for estimation of random road excitation profile in automotive suspension systems.

PubMed

Rath, J J; Veluvolu, K C; Defoort, M

2014-01-01

The estimation of road excitation profile is important for evaluation of vehicle stability and vehicle suspension performance for autonomous vehicle control systems. In this work, the nonlinear dynamics of the active automotive system that is excited by the unknown road excitation profile are considered for modeling. To address the issue of estimation of road profile, we develop an adaptive supertwisting observer for state and unknown road profile estimation. Under Lipschitz conditions for the nonlinear functions, the convergence of the estimation error is proven. Simulation results with Ford Fiesta MK2 demonstrate the effectiveness of the proposed observer for state and unknown input estimation for nonlinear active suspension system.

Adaptive Super-Twisting Observer for Estimation of Random Road Excitation Profile in Automotive Suspension Systems

PubMed Central

Rath, J. J.; Veluvolu, K. C.; Defoort, M.

2014-01-01

The estimation of road excitation profile is important for evaluation of vehicle stability and vehicle suspension performance for autonomous vehicle control systems. In this work, the nonlinear dynamics of the active automotive system that is excited by the unknown road excitation profile are considered for modeling. To address the issue of estimation of road profile, we develop an adaptive supertwisting observer for state and unknown road profile estimation. Under Lipschitz conditions for the nonlinear functions, the convergence of the estimation error is proven. Simulation results with Ford Fiesta MK2 demonstrate the effectiveness of the proposed observer for state and unknown input estimation for nonlinear active suspension system. PMID:24683321

Estimating surface soil moisture from SMAP observations using a neural network technique

USDA-ARS?s Scientific Manuscript database

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to June 2016 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observ...

Moving Sound Source Localization Based on Sequential Subspace Estimation in Actual Room Environments

NASA Astrophysics Data System (ADS)

Tsuji, Daisuke; Suyama, Kenji

This paper presents a novel method for moving sound source localization and its performance evaluation in actual room environments. The method is based on the MUSIC (MUltiple SIgnal Classification) which is one of the most high resolution localization methods. When using the MUSIC, a computation of eigenvectors of correlation matrix is required for the estimation. It needs often a high computational costs. Especially, in the situation of moving source, it becomes a crucial drawback because the estimation must be conducted at every the observation time. Moreover, since the correlation matrix varies its characteristics due to the spatial-temporal non-stationarity, the matrix have to be estimated using only a few observed samples. It makes the estimation accuracy degraded. In this paper, the PAST (Projection Approximation Subspace Tracking) is applied for sequentially estimating the eigenvectors spanning the subspace. In the PAST, the eigen-decomposition is not required, and therefore it is possible to reduce the computational costs. Several experimental results in the actual room environments are shown to present the superior performance of the proposed method.

MODIS Based Estimation of Forest Aboveground Biomass in China.

PubMed

Yin, Guodong; Zhang, Yuan; Sun, Yan; Wang, Tao; Zeng, Zhenzhong; Piao, Shilong

2015-01-01

Accurate estimation of forest biomass C stock is essential to understand carbon cycles. However, current estimates of Chinese forest biomass are mostly based on inventory-based timber volumes and empirical conversion factors at the provincial scale, which could introduce large uncertainties in forest biomass estimation. Here we provide a data-driven estimate of Chinese forest aboveground biomass from 2001 to 2013 at a spatial resolution of 1 km by integrating a recently reviewed plot-level ground-measured forest aboveground biomass database with geospatial information from 1-km Moderate-Resolution Imaging Spectroradiometer (MODIS) dataset in a machine learning algorithm (the model tree ensemble, MTE). We show that Chinese forest aboveground biomass is 8.56 Pg C, which is mainly contributed by evergreen needle-leaf forests and deciduous broadleaf forests. The mean forest aboveground biomass density is 56.1 Mg C ha-1, with high values observed in temperate humid regions. The responses of forest aboveground biomass density to mean annual temperature are closely tied to water conditions; that is, negative responses dominate regions with mean annual precipitation less than 1300 mm y-1 and positive responses prevail in regions with mean annual precipitation higher than 2800 mm y-1. During the 2000s, the forests in China sequestered C by 61.9 Tg C y-1, and this C sink is mainly distributed in north China and may be attributed to warming climate, rising CO2 concentration, N deposition, and growth of young forests.

MODIS Based Estimation of Forest Aboveground Biomass in China

PubMed Central

Sun, Yan; Wang, Tao; Zeng, Zhenzhong; Piao, Shilong

2015-01-01

Accurate estimation of forest biomass C stock is essential to understand carbon cycles. However, current estimates of Chinese forest biomass are mostly based on inventory-based timber volumes and empirical conversion factors at the provincial scale, which could introduce large uncertainties in forest biomass estimation. Here we provide a data-driven estimate of Chinese forest aboveground biomass from 2001 to 2013 at a spatial resolution of 1 km by integrating a recently reviewed plot-level ground-measured forest aboveground biomass database with geospatial information from 1-km Moderate-Resolution Imaging Spectroradiometer (MODIS) dataset in a machine learning algorithm (the model tree ensemble, MTE). We show that Chinese forest aboveground biomass is 8.56 Pg C, which is mainly contributed by evergreen needle-leaf forests and deciduous broadleaf forests. The mean forest aboveground biomass density is 56.1 Mg C ha−1, with high values observed in temperate humid regions. The responses of forest aboveground biomass density to mean annual temperature are closely tied to water conditions; that is, negative responses dominate regions with mean annual precipitation less than 1300 mm y−1 and positive responses prevail in regions with mean annual precipitation higher than 2800 mm y−1. During the 2000s, the forests in China sequestered C by 61.9 Tg C y−1, and this C sink is mainly distributed in north China and may be attributed to warming climate, rising CO2 concentration, N deposition, and growth of young forests. PMID:26115195

Evaluation and Application of Satellite-Based Latent Heating Profile Estimation Methods

NASA Technical Reports Server (NTRS)

Olson, William S.; Grecu, Mircea; Yang, Song; Tao, Wei-Kuo

2004-01-01

In recent years, methods for estimating atmospheric latent heating vertical structure from both passive and active microwave remote sensing have matured to the point where quantitative evaluation of these methods is the next logical step. Two approaches for heating algorithm evaluation are proposed: First, application of heating algorithms to synthetic data, based upon cloud-resolving model simulations, can be used to test the internal consistency of heating estimates in the absence of systematic errors in physical assumptions. Second, comparisons of satellite-retrieved vertical heating structures to independent ground-based estimates, such as rawinsonde-derived analyses of heating, provide an additional test. The two approaches are complementary, since systematic errors in heating indicated by the second approach may be confirmed by the first. A passive microwave and combined passive/active microwave heating retrieval algorithm are evaluated using the described approaches. In general, the passive microwave algorithm heating profile estimates are subject to biases due to the limited vertical heating structure information contained in the passive microwave observations. These biases may be partly overcome by including more environment-specific a priori information into the algorithm s database of candidate solution profiles. The combined passive/active microwave algorithm utilizes the much higher-resolution vertical structure information provided by spaceborne radar data to produce less biased estimates; however, the global spatio-temporal sampling by spaceborne radar is limited. In the present study, the passive/active microwave algorithm is used to construct a more physically-consistent and environment-specific set of candidate solution profiles for the passive microwave algorithm and to help evaluate errors in the passive algorithm s heating estimates. Although satellite estimates of latent heating are based upon instantaneous, footprint- scale data, suppression

Estimation of snow in extratropical cyclones from multiple frequency airborne radar observations. An Expectation-Maximization approach

NASA Astrophysics Data System (ADS)

Grecu, M.; Tian, L.; Heymsfield, G. M.

2017-12-01

A major challenge in deriving accurate estimates of physical properties of falling snow particles from single frequency space- or airborne radar observations is that snow particles exhibit a large variety of shapes and their electromagnetic scattering characteristics are highly dependent on these shapes. Triple frequency (Ku-Ka-W) radar observations are expected to facilitate the derivation of more accurate snow estimates because specific snow particle shapes tend to have specific signatures in the associated two-dimensional dual-reflectivity-ratio (DFR) space. However, the derivation of accurate snow estimates from triple frequency radar observations is by no means a trivial task. This is because the radar observations can be subject to non-negligible attenuation (especially at W-band when super-cooled water is present), which may significantly impact the interpretation of the information in the DFR space. Moreover, the electromagnetic scattering properties of snow particles are computationally expensive to derive, which makes the derivation of reliable parameterizations usable in estimation methodologies challenging. In this study, we formulate an two-step Expectation Maximization (EM) methodology to derive accurate snow estimates in Extratropical Cyclones (ECTs) from triple frequency airborne radar observations. The Expectation (E) step consists of a least-squares triple frequency estimation procedure applied with given assumptions regarding the relationships between the density of snow particles and their sizes, while the Maximization (M) step consists of the optimization of the assumptions used in step E. The electromagnetic scattering properties of snow particles are derived using the Rayleigh-Gans approximation. The methodology is applied to triple frequency radar observations collected during the Olympic Mountains Experiment (OLYMPEX). Results show that snowfall estimates above the freezing level in ETCs consistent with the triple frequency radar

Using the Fire Weather Index (FWI) to improve the estimation of fire emissions from fire radiative power (FRP) observations

NASA Astrophysics Data System (ADS)

Di Giuseppe, Francesca; Rémy, Samuel; Pappenberger, Florian; Wetterhall, Fredrik

2018-04-01

The atmospheric composition analysis and forecast for the European Copernicus Atmosphere Monitoring Services (CAMS) relies on biomass-burning fire emission estimates from the Global Fire Assimilation System (GFAS). The GFAS is a global system and converts fire radiative power (FRP) observations from MODIS satellites into smoke constituents. Missing observations are filled in using persistence, whereby observed FRP values from the previous day are progressed in time until a new observation is recorded. One of the consequences of this assumption is an increase of fire duration, which in turn translates into an increase of emissions estimated from fires compared to what is available from observations. In this study persistence is replaced by modelled predictions using the Canadian Fire Weather Index (FWI), which describes how atmospheric conditions affect the vegetation moisture content and ultimately fire duration. The skill in predicting emissions from biomass burning is improved with the new technique, which indicates that using an FWI-based model to infer emissions from FRP is better than persistence when observations are not available.

Clutch pressure estimation for a power-split hybrid transmission using nonlinear robust observer

NASA Astrophysics Data System (ADS)

Zhou, Bin; Zhang, Jianwu; Gao, Ji; Yu, Haisheng; Liu, Dong

2018-06-01

For a power-split hybrid transmission, using the brake clutch to realize the transition from electric drive mode to hybrid drive mode is an available strategy. Since the pressure information of the brake clutch is essential for the mode transition control, this research designs a nonlinear robust reduced-order observer to estimate the brake clutch pressure. Model uncertainties or disturbances are considered as additional inputs, thus the observer is designed in order that the error dynamics is input-to-state stable. The nonlinear characteristics of the system are expressed as the lookup tables in the observer. Moreover, the gain matrix of the observer is solved by two optimization procedures under the constraints of the linear matrix inequalities. The proposed observer is validated by offline simulation and online test, the results have shown that the observer achieves significant performance during the mode transition, as the estimation error is within a reasonable range, more importantly, it is asymptotically stable.

Probability Distribution Extraction from TEC Estimates based on Kernel Density Estimation

NASA Astrophysics Data System (ADS)

Demir, Uygar; Toker, Cenk; Çenet, Duygu

2016-07-01

Statistical analysis of the ionosphere, specifically the Total Electron Content (TEC), may reveal important information about its temporal and spatial characteristics. One of the core metrics that express the statistical properties of a stochastic process is its Probability Density Function (pdf). Furthermore, statistical parameters such as mean, variance and kurtosis, which can be derived from the pdf, may provide information about the spatial uniformity or clustering of the electron content. For example, the variance differentiates between a quiet ionosphere and a disturbed one, whereas kurtosis differentiates between a geomagnetic storm and an earthquake. Therefore, valuable information about the state of the ionosphere (and the natural phenomena that cause the disturbance) can be obtained by looking at the statistical parameters. In the literature, there are publications which try to fit the histogram of TEC estimates to some well-known pdf.s such as Gaussian, Exponential, etc. However, constraining a histogram to fit to a function with a fixed shape will increase estimation error, and all the information extracted from such pdf will continue to contain this error. In such techniques, it is highly likely to observe some artificial characteristics in the estimated pdf which is not present in the original data. In the present study, we use the Kernel Density Estimation (KDE) technique to estimate the pdf of the TEC. KDE is a non-parametric approach which does not impose a specific form on the TEC. As a result, better pdf estimates that almost perfectly fit to the observed TEC values can be obtained as compared to the techniques mentioned above. KDE is particularly good at representing the tail probabilities, and outliers. We also calculate the mean, variance and kurtosis of the measured TEC values. The technique is applied to the ionosphere over Turkey where the TEC values are estimated from the GNSS measurement from the TNPGN-Active (Turkish National Permanent

Process-based Cost Estimation for Ramjet/Scramjet Engines

NASA Technical Reports Server (NTRS)

Singh, Brijendra; Torres, Felix; Nesman, Miles; Reynolds, John

2003-01-01

Process-based cost estimation plays a key role in effecting cultural change that integrates distributed science, technology and engineering teams to rapidly create innovative and affordable products. Working together, NASA Glenn Research Center and Boeing Canoga Park have developed a methodology of process-based cost estimation bridging the methodologies of high-level parametric models and detailed bottoms-up estimation. The NASA GRC/Boeing CP process-based cost model provides a probabilistic structure of layered cost drivers. High-level inputs characterize mission requirements, system performance, and relevant economic factors. Design alternatives are extracted from a standard, product-specific work breakdown structure to pre-load lower-level cost driver inputs and generate the cost-risk analysis. As product design progresses and matures the lower level more detailed cost drivers can be re-accessed and the projected variation of input values narrowed, thereby generating a progressively more accurate estimate of cost-risk. Incorporated into the process-based cost model are techniques for decision analysis, specifically, the analytic hierarchy process (AHP) and functional utility analysis. Design alternatives may then be evaluated not just on cost-risk, but also user defined performance and schedule criteria. This implementation of full-trade study support contributes significantly to the realization of the integrated development environment. The process-based cost estimation model generates development and manufacturing cost estimates. The development team plans to expand the manufacturing process base from approximately 80 manufacturing processes to over 250 processes. Operation and support cost modeling is also envisioned. Process-based estimation considers the materials, resources, and processes in establishing cost-risk and rather depending on weight as an input, actually estimates weight along with cost and schedule.

Forensic age estimation based on development of third molars: a staging technique for magnetic resonance imaging.

PubMed

De Tobel, J; Phlypo, I; Fieuws, S; Politis, C; Verstraete, K L; Thevissen, P W

2017-12-01

The development of third molars can be evaluated with medical imaging to estimate age in subadults. The appearance of third molars on magnetic resonance imaging (MRI) differs greatly from that on radiographs. Therefore a specific staging technique is necessary to classify third molar development on MRI and to apply it for age estimation. To develop a specific staging technique to register third molar development on MRI and to evaluate its performance for age estimation in subadults. Using 3T MRI in three planes, all third molars were evaluated in 309 healthy Caucasian participants from 14 to 26 years old. According to the appearance of the developing third molars on MRI, descriptive criteria and schematic representations were established to define a specific staging technique. Two observers, with different levels of experience, staged all third molars independently with the developed technique. Intra- and inter-observer agreement were calculated. The data were imported in a Bayesian model for age estimation as described by Fieuws et al. (2016). This approach adequately handles correlation between age indicators and missing age indicators. It was used to calculate a point estimate and a prediction interval of the estimated age. Observed age minus predicted age was calculated, reflecting the error of the estimate. One-hundred and sixty-six third molars were agenetic. Five percent (51/1096) of upper third molars and 7% (70/1044) of lower third molars were not assessable. Kappa for inter-observer agreement ranged from 0.76 to 0.80. For intra-observer agreement kappa ranged from 0.80 to 0.89. However, two stage differences between observers or between staging sessions occurred in up to 2.2% (20/899) of assessments, probably due to a learning effect. Using the Bayesian model for age estimation, a mean absolute error of 2.0 years in females and 1.7 years in males was obtained. Root mean squared error equalled 2.38 years and 2.06 years respectively. The performance to

A Kalman Filter for SINS Self-Alignment Based on Vector Observation.

PubMed

Xu, Xiang; Xu, Xiaosu; Zhang, Tao; Li, Yao; Tong, Jinwu

2017-01-29

In this paper, a self-alignment method for strapdown inertial navigation systems based on the q -method is studied. In addition, an improved method based on integrating gravitational apparent motion to form apparent velocity is designed, which can reduce the random noises of the observation vectors. For further analysis, a novel self-alignment method using a Kalman filter based on adaptive filter technology is proposed, which transforms the self-alignment procedure into an attitude estimation using the observation vectors. In the proposed method, a linear psuedo-measurement equation is adopted by employing the transfer method between the quaternion and the observation vectors. Analysis and simulation indicate that the accuracy of the self-alignment is improved. Meanwhile, to improve the convergence rate of the proposed method, a new method based on parameter recognition and a reconstruction algorithm for apparent gravitation is devised, which can reduce the influence of the random noises of the observation vectors. Simulations and turntable tests are carried out, and the results indicate that the proposed method can acquire sound alignment results with lower standard variances, and can obtain higher alignment accuracy and a faster convergence rate.

A Kalman Filter for SINS Self-Alignment Based on Vector Observation

PubMed Central

Xu, Xiang; Xu, Xiaosu; Zhang, Tao; Li, Yao; Tong, Jinwu

2017-01-01

In this paper, a self-alignment method for strapdown inertial navigation systems based on the q-method is studied. In addition, an improved method based on integrating gravitational apparent motion to form apparent velocity is designed, which can reduce the random noises of the observation vectors. For further analysis, a novel self-alignment method using a Kalman filter based on adaptive filter technology is proposed, which transforms the self-alignment procedure into an attitude estimation using the observation vectors. In the proposed method, a linear psuedo-measurement equation is adopted by employing the transfer method between the quaternion and the observation vectors. Analysis and simulation indicate that the accuracy of the self-alignment is improved. Meanwhile, to improve the convergence rate of the proposed method, a new method based on parameter recognition and a reconstruction algorithm for apparent gravitation is devised, which can reduce the influence of the random noises of the observation vectors. Simulations and turntable tests are carried out, and the results indicate that the proposed method can acquire sound alignment results with lower standard variances, and can obtain higher alignment accuracy and a faster convergence rate. PMID:28146059

Discrete-time state estimation for stochastic polynomial systems over polynomial observations

NASA Astrophysics Data System (ADS)

Hernandez-Gonzalez, M.; Basin, M.; Stepanov, O.

2018-07-01

This paper presents a solution to the mean-square state estimation problem for stochastic nonlinear polynomial systems over polynomial observations confused with additive white Gaussian noises. The solution is given in two steps: (a) computing the time-update equations and (b) computing the measurement-update equations for the state estimate and error covariance matrix. A closed form of this filter is obtained by expressing conditional expectations of polynomial terms as functions of the state estimate and error covariance. As a particular case, the mean-square filtering equations are derived for a third-degree polynomial system with second-degree polynomial measurements. Numerical simulations show effectiveness of the proposed filter compared to the extended Kalman filter.

High-resolution CO2 and CH4 flux inverse modeling combining GOSAT, OCO-2 and ground-based observations

NASA Astrophysics Data System (ADS)

Maksyutov, S. S.; Oda, T.; Saito, M.; Ito, A.; Janardanan Achari, R.; Sasakawa, M.; Machida, T.; Kaiser, J. W.; Belikov, D.; Valsala, V.; O'Dell, C.; Yoshida, Y.; Matsunaga, T.

2017-12-01

We develop a high-resolution CO2 and CH4 flux inversion system that is based on the Lagrangian-Eulerian coupled tracer transport model, and is designed to estimate surface fluxes from atmospheric CO2 and CH4 data observed by the GOSAT and OCO-2 satellites and by global in-situ networks, including observation in Siberia. We use the Lagrangian particle dispersion model (LPDM) FLEXPART to estimate the surface flux footprints for each observation at 0.1-degree spatial resolution for three days of transport. The LPDM is coupled to a global atmospheric tracer transport model (NIES-TM). The adjoint of the coupled transport model is used in an iterative optimization procedure based on either quasi-Newtonian algorithm or singular value decomposition. Combining surface and satellite data for use in inversion requires correcting for biases present in satellite observation data, that is done in a two-step procedure. As a first step, bi-weekly corrections to prior flux fields are estimated for the period of 2009 to 2015 from in-situ CO2 and CH4 data from global observation network, included in Obspack-GVP (for CO2), WDCGG (CH4) and JR-STATION datasets. High-resolution prior fluxes were prepared for anthropogenic emissions (ODIAC and EDGAR), biomass burning (GFAS), and the terrestrial biosphere. The terrestrial biosphere flux was constructed using a vegetation mosaic map and separate simulations of CO2 fluxes by the VISIT model for each vegetation type present in a grid. The prior flux uncertainty for land is scaled proportionally to monthly mean GPP by the MODIS product for CO2 and EDGAR emissions for CH4. Use of the high-resolution transport leads to improved representation of the anthropogenic plumes, often observed at continental continuous observation sites. OCO-2 observations are aggregated to 1 second averages, to match the 0.1 degree resolution of the transport model. Before including satellite observations in the inversion, the monthly varying latitude-dependent bias is

Estimation of the measurement uncertainty in magnetic resonance velocimetry based on statistical models

NASA Astrophysics Data System (ADS)

Bruschewski, Martin; Freudenhammer, Daniel; Buchenberg, Waltraud B.; Schiffer, Heinz-Peter; Grundmann, Sven

2016-05-01

Velocity measurements with magnetic resonance velocimetry offer outstanding possibilities for experimental fluid mechanics. The purpose of this study was to provide practical guidelines for the estimation of the measurement uncertainty in such experiments. Based on various test cases, it is shown that the uncertainty estimate can vary substantially depending on how the uncertainty is obtained. The conventional approach to estimate the uncertainty from the noise in the artifact-free background can lead to wrong results. A deviation of up to -75 % is observed with the presented experiments. In addition, a similarly high deviation is demonstrated with the data from other studies. As a more accurate approach, the uncertainty is estimated directly from the image region with the flow sample. Two possible estimation methods are presented.

Estimating Long Term Surface Soil Moisture in the GCIP Area From Satellite Microwave Observations

NASA Technical Reports Server (NTRS)

Owe, Manfred; deJeu, Vrije; VandeGriend, Adriaan A.

2000-01-01

Soil moisture is an important component of the water and energy balances of the Earth's surface. Furthermore, it has been identified as a parameter of significant potential for improving the accuracy of large-scale land surface-atmosphere interaction models. However, accurate estimates of surface soil moisture are often difficult to make, especially at large spatial scales. Soil moisture is a highly variable land surface parameter, and while point measurements are usually accurate, they are representative only of the immediate site which was sampled. Simple averaging of point values to obtain spatial means often leads to substantial errors. Since remotely sensed observations are already a spatially averaged or areally integrated value, they are ideally suited for measuring land surface parameters, and as such, are a logical input to regional or larger scale land process models. A nine-year database of surface soil moisture is being developed for the Central United States from satellite microwave observations. This region forms much of the GCIP study area, and contains most of the Mississippi, Rio Grande, and Red River drainages. Daytime and nighttime microwave brightness temperatures were observed at a frequency of 6.6 GHz, by the Scanning Multichannel Microwave Radiometer (SMMR), onboard the Nimbus 7 satellite. The life of the SMMR instrument spanned from Nov. 1978 to Aug. 1987. At 6.6 GHz, the instrument provided a spatial resolution of approximately 150 km, and an orbital frequency over any pixel-sized area of about 2 daytime and 2 nighttime passes per week. Ground measurements of surface soil moisture from various locations throughout the study area are used to calibrate the microwave observations. Because ground measurements are usually only single point values, and since the time of satellite coverage does not always coincide with the ground measurements, the soil moisture data were used to calibrate a regional water balance for the top 1, 5, and 10 cm

Estimating TCR using an integrated model-observation framework that accounts for spatio-temporal variability and pre-industrial radiative imbalances.

NASA Astrophysics Data System (ADS)

Haustein, K.; Schurer, A. P.; Venema, V.

2016-12-01

Apart from a few exceptions (e.g. Aldrin et al. 2012, Skeie et al. 2013) TCR estimates with EBMs are based on global data. Since these estimates don't represent the true spatial-temporal behaviour for observed temperature as well as external forcing (Marvel et al. 2015), we have developed a two-box EBM framework that accounts for these effects. In addition, external forcing from anthropogenic aerosol and GHGs tends to have different response times in comparison to volcanic stratospheric aerosols. Using PMIP3 and an extended ensemble of HadCM3 simulations (Euro500; Schurer et al. 2014) GCM simulations for the pre-industrial period, we obtain the fast and slow response time constants required in the EBM. With the most recent anthropogenic and natural forcing estimates, we test a range of TCR values against observations. The TCR/ECS ratio necessary to achieve that goal is taken from CMIP5 as observationally OHC-based estimates are notoriously unreliable. Given that observed and modelled OHC estimates are in agreement (Cheng et al. 2016), we argue that this should be the standard procedure the make inferences about ECS. Alternatively, it should be distinguished between equilibrium and effective climate sensitivity. The preliminary best estimate for TCR is 1.6K (1.1-2.2K) with an associated ECS value of 2.9K (2.0-4.0K). This is in good agreement with other D&A techniques that do use spatio-temporal patterns as well (e.g. Jones et al. 2016, Gillet et al. 2013). Correcting for natural ENSO variability and tas/tos-related inaccuracies (Richardson et al. 2016) further increases the robustness of the estimated sensitivity range. Our results also indicate that the small radiative imbalance caused by the period of very strong volcanic eruptions just before the CMIP5 historical period starts (1809-1840) has noteworthy implications for the response to later volcanic eruptions and the temperature evolution after 1850. Simply put, CMIP5-type simulations are slightly more sensitive

A Novel Continuous Blood Pressure Estimation Approach Based on Data Mining Techniques.

PubMed

Miao, Fen; Fu, Nan; Zhang, Yuan-Ting; Ding, Xiao-Rong; Hong, Xi; He, Qingyun; Li, Ye

2017-11-01

Continuous blood pressure (BP) estimation using pulse transit time (PTT) is a promising method for unobtrusive BP measurement. However, the accuracy of this approach must be improved for it to be viable for a wide range of applications. This study proposes a novel continuous BP estimation approach that combines data mining techniques with a traditional mechanism-driven model. First, 14 features derived from simultaneous electrocardiogram and photoplethysmogram signals were extracted for beat-to-beat BP estimation. A genetic algorithm-based feature selection method was then used to select BP indicators for each subject. Multivariate linear regression and support vector regression were employed to develop the BP model. The accuracy and robustness of the proposed approach were validated for static, dynamic, and follow-up performance. Experimental results based on 73 subjects showed that the proposed approach exhibited excellent accuracy in static BP estimation, with a correlation coefficient and mean error of 0.852 and -0.001 ± 3.102 mmHg for systolic BP, and 0.790 and -0.004 ± 2.199 mmHg for diastolic BP. Similar performance was observed for dynamic BP estimation. The robustness results indicated that the estimation accuracy was lower by a certain degree one day after model construction but was relatively stable from one day to six months after construction. The proposed approach is superior to the state-of-the-art PTT-based model for an approximately 2-mmHg reduction in the standard derivation at different time intervals, thus providing potentially novel insights for cuffless BP estimation.

Balancing Score Adjusted Targeted Minimum Loss-based Estimation

PubMed Central

Lendle, Samuel David; Fireman, Bruce; van der Laan, Mark J.

2015-01-01

Adjusting for a balancing score is sufficient for bias reduction when estimating causal effects including the average treatment effect and effect among the treated. Estimators that adjust for the propensity score in a nonparametric way, such as matching on an estimate of the propensity score, can be consistent when the estimated propensity score is not consistent for the true propensity score but converges to some other balancing score. We call this property the balancing score property, and discuss a class of estimators that have this property. We introduce a targeted minimum loss-based estimator (TMLE) for a treatment-specific mean with the balancing score property that is additionally locally efficient and doubly robust. We investigate the new estimator’s performance relative to other estimators, including another TMLE, a propensity score matching estimator, an inverse probability of treatment weighted estimator, and a regression-based estimator in simulation studies. PMID:26561539

A spline-based parameter estimation technique for static models of elastic structures

NASA Technical Reports Server (NTRS)

Dutt, P.; Taasan, S.

1986-01-01

The problem of identifying the spatially varying coefficient of elasticity using an observed solution to the forward problem is considered. Under appropriate conditions this problem can be treated as a first order hyperbolic equation in the unknown coefficient. Some continuous dependence results are developed for this problem and a spline-based technique is proposed for approximating the unknown coefficient, based on these results. The convergence of the numerical scheme is established and error estimates obtained.

Model methodology for estimating pesticide concentration extremes based on sparse monitoring data

USGS Publications Warehouse

Vecchia, Aldo V.

2018-03-22

This report describes a new methodology for using sparse (weekly or less frequent observations) and potentially highly censored pesticide monitoring data to simulate daily pesticide concentrations and associated quantities used for acute and chronic exposure assessments, such as the annual maximum daily concentration. The new methodology is based on a statistical model that expresses log-transformed daily pesticide concentration in terms of a seasonal wave, flow-related variability, long-term trend, and serially correlated errors. Methods are described for estimating the model parameters, generating conditional simulations of daily pesticide concentration given sparse (weekly or less frequent) and potentially highly censored observations, and estimating concentration extremes based on the conditional simulations. The model can be applied to datasets with as few as 3 years of record, as few as 30 total observations, and as few as 10 uncensored observations. The model was applied to atrazine, carbaryl, chlorpyrifos, and fipronil data for U.S. Geological Survey pesticide sampling sites with sufficient data for applying the model. A total of 112 sites were analyzed for atrazine, 38 for carbaryl, 34 for chlorpyrifos, and 33 for fipronil. The results are summarized in this report; and, R functions, described in this report and provided in an accompanying model archive, can be used to fit the model parameters and generate conditional simulations of daily concentrations for use in investigations involving pesticide exposure risk and uncertainty.

Rule-Based Flight Software Cost Estimation

NASA Technical Reports Server (NTRS)

Stukes, Sherry A.; Spagnuolo, John N. Jr.

2015-01-01

This paper discusses the fundamental process for the computation of Flight Software (FSW) cost estimates. This process has been incorporated in a rule-based expert system [1] that can be used for Independent Cost Estimates (ICEs), Proposals, and for the validation of Cost Analysis Data Requirements (CADRe) submissions. A high-level directed graph (referred to here as a decision graph) illustrates the steps taken in the production of these estimated costs and serves as a basis of design for the expert system described in this paper. Detailed discussions are subsequently given elaborating upon the methodology, tools, charts, and caveats related to the various nodes of the graph. We present general principles for the estimation of FSW using SEER-SEM as an illustration of these principles when appropriate. Since Source Lines of Code (SLOC) is a major cost driver, a discussion of various SLOC data sources for the preparation of the estimates is given together with an explanation of how contractor SLOC estimates compare with the SLOC estimates used by JPL. Obtaining consistency in code counting will be presented as well as factors used in reconciling SLOC estimates from different code counters. When sufficient data is obtained, a mapping into the JPL Work Breakdown Structure (WBS) from the SEER-SEM output is illustrated. For across the board FSW estimates, as was done for the NASA Discovery Mission proposal estimates performed at JPL, a comparative high-level summary sheet for all missions with the SLOC, data description, brief mission description and the most relevant SEER-SEM parameter values is given to illustrate an encapsulation of the used and calculated data involved in the estimates. The rule-based expert system described provides the user with inputs useful or sufficient to run generic cost estimation programs. This system's incarnation is achieved via the C Language Integrated Production System (CLIPS) and will be addressed at the end of this paper.

Estimating carnivoran diets using a combination of carcass observations and scats from GPS clusters

PubMed Central

Tambling, C.J.; Laurence, S.D.; Bellan, S.E.; Cameron, E.Z.; du Toit, J.T.; Getz, W.M.

2011-01-01

Scat analysis is one of the most frequently used methods to assess carnivoran diets and Global Positioning System (GPS) cluster methods are increasingly being used to locate feeding sites for large carnivorans. However, both methods have inherent biases that limit their use. GPS methods to locate kill sites are biased towards large carcasses, while scat analysis over-estimates the biomass consumed from smaller prey. We combined carcass observations and scats collected along known movement routes, assessed using GPS data from four African lion (Panthera leo) prides in the Kruger National Park, South Africa, to determine how a combination of these two datasets change diet estimates. As expected, using carcasses alone under-estimated the number of feeding events on small species, primarily impala (Aepyceros melampus) and warthog (Phacochoerus africanus), in our case by more than 50% and thus significantly under-estimated the biomass consumed per pride per day in comparison to when the diet was assessed using carcass observations alone. We show that an approach that supplements carcass observations with scats that enables the identification of potentially missed feeding events increases the estimates of food intake rates for large carnivorans, with possible ramifications for predator-prey interaction studies dealing with biomass intake rate. PMID:22408290

Cloud-based shaft torque estimation for electric vehicle equipped with integrated motor-transmission system

NASA Astrophysics Data System (ADS)

Zhu, Xiaoyuan; Zhang, Hui; Yang, Bo; Zhang, Guichen

2018-01-01

In order to improve oscillation damping control performance as well as gear shift quality of electric vehicle equipped with integrated motor-transmission system, a cloud-based shaft torque estimation scheme is proposed in this paper by using measurable motor and wheel speed signals transmitted by wireless network. It can help reduce computational burden of onboard controllers and also relief network bandwidth requirement of individual vehicle. Considering possible delays during signal wireless transmission, delay-dependent full-order observer design is proposed to estimate the shaft torque in cloud server. With these random delays modeled by using homogenous Markov chain, robust H∞ performance is adopted to minimize the effect of wireless network-induced delays, signal measurement noise as well as system modeling uncertainties on shaft torque estimation error. Observer parameters are derived by solving linear matrix inequalities, and simulation results using acceleration test and tip-in, tip-out test demonstrate the effectiveness of proposed shaft torque observer design.

Mesospheric temperatures estimated from the meteor radar observations at Mohe, China

NASA Astrophysics Data System (ADS)

Liu, Libo; Liu, Huixin; Chen, Yiding; Le, Huijun

2017-04-01

In this work, we report the estimation of mesospheric temperatures at 90 km height from the observations of the VHF all-sky meteor radar operated at Mohe (53.5 °N, 122.3° E), China, since August 2011. The kinetic temperature profiles retrieved from the observations of Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) onboard the Thermosphere, Ionosphere, Mesosphere, Energetics, and Dynamics (TIMED) satellite are processed to provide the temperature (TSABER) and temperature gradient (dT/dh) at 90 km height. Based on the SABER temperature profile data an empirical dT/dh model is developed for the Mohe latitude. First, we derive the temperatures from the meteor decay times (Tmeteor) and the Mohe dT/dh model gives prior information of temperature gradients. Secondly, the full-width of half maximum (FWHM) of the meteor height profiles is calculated and further used to deduce the temperatures (TFWHM) based on the strong linear relationship between FWHM and TSABER. The temperatures at 90 km deduced from the decay times (Tmeteor) and from the meteor height distributions (TFWHM) at Mohe are validated/calibrated with TSABER. The temperatures present a considerable annual variation, being maximum in winter and minimum in summer. Harmonic analyses reveal that the temperatures have an annual variation consistent with TSABER. Our work suggests that the FWHM has a good performance in routine estimation of the temperatures. It should be pointed out that the slope of FWHM and TSABER is 10.1 at Mohe, which is different from that of 15.71 at King Sejong (62.2° S, 58.8° E) station. Acknowledgments The TIMED/SABER kinetic temperature (version 2.0) data are provided by the SABER team through http://saber.gats-inc.com/. The temperatures from the NRLMSISE-00 model are calculated using Aerospace Blockset toolbox of MATLAB (2016a). This research was supported by National Natural Science Foundation of China (41231065, 41321003). We acknowledge the use of meteor radar

Comparing methods for estimation of heterogeneous treatment effects using observational data from health care databases.

PubMed

Wendling, T; Jung, K; Callahan, A; Schuler, A; Shah, N H; Gallego, B

2018-06-03

There is growing interest in using routinely collected data from health care databases to study the safety and effectiveness of therapies in "real-world" conditions, as it can provide complementary evidence to that of randomized controlled trials. Causal inference from health care databases is challenging because the data are typically noisy, high dimensional, and most importantly, observational. It requires methods that can estimate heterogeneous treatment effects while controlling for confounding in high dimensions. Bayesian additive regression trees, causal forests, causal boosting, and causal multivariate adaptive regression splines are off-the-shelf methods that have shown good performance for estimation of heterogeneous treatment effects in observational studies of continuous outcomes. However, it is not clear how these methods would perform in health care database studies where outcomes are often binary and rare and data structures are complex. In this study, we evaluate these methods in simulation studies that recapitulate key characteristics of comparative effectiveness studies. We focus on the conditional average effect of a binary treatment on a binary outcome using the conditional risk difference as an estimand. To emulate health care database studies, we propose a simulation design where real covariate and treatment assignment data are used and only outcomes are simulated based on nonparametric models of the real outcomes. We apply this design to 4 published observational studies that used records from 2 major health care databases in the United States. Our results suggest that Bayesian additive regression trees and causal boosting consistently provide low bias in conditional risk difference estimates in the context of health care database studies. Copyright © 2018 John Wiley & Sons, Ltd.

On the multiple imputation variance estimator for control-based and delta-adjusted pattern mixture models.

PubMed

Tang, Yongqiang

2017-12-01

Control-based pattern mixture models (PMM) and delta-adjusted PMMs are commonly used as sensitivity analyses in clinical trials with non-ignorable dropout. These PMMs assume that the statistical behavior of outcomes varies by pattern in the experimental arm in the imputation procedure, but the imputed data are typically analyzed by a standard method such as the primary analysis model. In the multiple imputation (MI) inference, Rubin's variance estimator is generally biased when the imputation and analysis models are uncongenial. One objective of the article is to quantify the bias of Rubin's variance estimator in the control-based and delta-adjusted PMMs for longitudinal continuous outcomes. These PMMs assume the same observed data distribution as the mixed effects model for repeated measures (MMRM). We derive analytic expressions for the MI treatment effect estimator and the associated Rubin's variance in these PMMs and MMRM as functions of the maximum likelihood estimator from the MMRM analysis and the observed proportion of subjects in each dropout pattern when the number of imputations is infinite. The asymptotic bias is generally small or negligible in the delta-adjusted PMM, but can be sizable in the control-based PMM. This indicates that the inference based on Rubin's rule is approximately valid in the delta-adjusted PMM. A simple variance estimator is proposed to ensure asymptotically valid MI inferences in these PMMs, and compared with the bootstrap variance. The proposed method is illustrated by the analysis of an antidepressant trial, and its performance is further evaluated via a simulation study. © 2017, The International Biometric Society.

River runoff estimates based on remotely sensed surface velocities

NASA Astrophysics Data System (ADS)

Grünler, Steffen; Stammer, Detlef; Romeiser, Roland

2010-05-01

One promising technique for river runoff estimates from space is the retrieval of surface currents on the basis of synthetic aperture radar along-track interferometry (ATI). The German satellite TerraSAR-X, which was launched in June 2007, will permit ATI measurements in an experimental mode. Based on numerical simulations, we present findings of a research project in which the potential of satellite measurements of various parameters with different temporal and spatial sampling characteristics is evaluated. A sampling strategy for river runoff estimates is developed. We address the achievable accuracy and limitations of such estimates for different local flow conditions at selected test site. High-resolution three-dimensional current fields in the Elbe river (Germany) from a numerical model are used as reference data set and input for simulations of a variety of possible measuring and data interpretation strategies to be evaluated. Addressing the problem of aliasing we removed tidal signals from the sampling data. Discharge estimates on the basis of measured surface current fields and river widths from TerraSAR-X are successfully simulated. The differences of the resulted net discharge estimate are between 30-55% for a required continuously observation period of one year. We discuss the applicability of the measuring strategies to a number of major rivers. Further we show results of runoff estimates by the retrieval of surface current fields by real TerraSAR-X ATI data (AS mode) for the Elbe river study area.

Atmospheric Nitrogen Deposition to the Oceans: Observation- and Model-Based Estimates

NASA Astrophysics Data System (ADS)

Baker, Alex; Altieri, Katye; Okin, Greg; Dentener, Frank; Uematsu, Mitsuo; Kanakidou, Maria; Sarin, Manmohan; Duce, Robert; Galloway, Jim; Keene, Bill; Singh, Arvind; Zamora, Lauren; Lamarque, Jean-Francois; Hsu, Shih-Chieh

2014-05-01

The reactive nitrogen (Nr) burden of the atmosphere has been increased by a factor of 3-4 by anthropogenic activity since the industrial revolution. This has led to large increases in the deposition of nitrate and ammonium to the surface waters of the open ocean, particularly downwind of major human population centres, such as those in North America, Europe and Southeast Asia. In oligotrophic waters, this deposition has the potential to significantly impact marine productivity and the global carbon cycle. Global-scale understanding of N deposition to the oceans is reliant on our ability to produce effective models of reactive nitrogen emission, atmospheric chemistry, transport and deposition (including deposition to the land surface). The Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) recently completed a multi-model analysis of global N deposition, including comparisons to wet deposition observations from three regional networks in North America, Europe and Southeast Asia (Lamarque et al., Atmos. Chem. Phys., 13, 7977-8018, 2013). No similar datasets exist which would allow observation - model comparisons of wet deposition for the open oceans, because long-term wet deposition records are available for only a handful of remote island sites and rain collection over the open ocean itself is very difficult. In this work we attempt instead to use ~2600 observations of aerosol nitrate and ammonium concentrations, acquired chiefly from sampling aboard ships in the period 1995 - 2012, to assess the ACCMIP N deposition fields over the remote ocean. This database is non-uniformly distributed in time and space. We selected four ocean regions (the eastern North Atlantic, the South Atlantic, the northern Indian Ocean and northwest Pacific) where we considered the density and distribution of observational data is sufficient to provide effective comparison to the model ensemble. Two of these regions are adjacent to the land networks used in the ACCMIP

Methane Emissions from Bangladesh: Bridging the Gap Between Ground-based and Space-borne Estimates

NASA Astrophysics Data System (ADS)

Peters, C.; Bennartz, R.; Hornberger, G. M.

2015-12-01

Gaining an understanding of methane (CH4) emission sources and atmospheric dispersion is an essential part of climate change research. Large-scale and global studies often rely on satellite observations of column CH4 mixing ratio whereas high-spatial resolution estimates rely on ground-based measurements. Extrapolation of ground-based measurements on, for example, rice paddies to broad region scales is highly uncertain because of spatio-temporal variability. We explore the use of ground-based river stage measurements and independent satellite observations of flooded area along with satellite measurements of CH4 mixing ratio to estimate the extent of methane emissions. Bangladesh, which comprises most of the Ganges Brahmaputra Meghna (GBM) delta, is a region of particular interest for studying spatio-temporal variation of methane emissions due to (1) broadscale rice cultivation and (2) seasonal flooding and atmospheric convection during the monsoon. Bangladesh and its deltaic landscape exhibit a broad range of environmental, economic, and social circumstances that are relevant to many nations in South and Southeast Asia. We explore the seasonal enhancement of CH4 in Bangladesh using passive remote sensing spectrometer CH4 products from the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) and the Atmospheric Infrared Sounder (AIRS). The seasonal variation of CH4 is compared to independent estimates of seasonal flooding from water gauge stations and space-based passive microwave water-to-land fractions from the Tropical Rainfall Measuring Mission Microwave Imager (TRMM-TMI). Annual cycles in inundation (natural and anthropogenic) and atmospheric CH4 concentrations show highly correlated seasonal signals. NOAA's HYSPLIT model is used to determine atmospheric residence time of ground CH4 fluxes. Using the satellite observations, we can narrow the large uncertainty in extrapolation of ground-based CH4 emission estimates from rice paddies

On the Error State Selection for Stationary SINS Alignment and Calibration Kalman Filters—Part II: Observability/Estimability Analysis

PubMed Central

Silva, Felipe O.; Hemerly, Elder M.; Leite Filho, Waldemar C.

2017-01-01

This paper presents the second part of a study aiming at the error state selection in Kalman filters applied to the stationary self-alignment and calibration (SSAC) problem of strapdown inertial navigation systems (SINS). The observability properties of the system are systematically investigated, and the number of unobservable modes is established. Through the analytical manipulation of the full SINS error model, the unobservable modes of the system are determined, and the SSAC error states (except the velocity errors) are proven to be individually unobservable. The estimability of the system is determined through the examination of the major diagonal terms of the covariance matrix and their eigenvalues/eigenvectors. Filter order reduction based on observability analysis is shown to be inadequate, and several misconceptions regarding SSAC observability and estimability deficiencies are removed. As the main contributions of this paper, we demonstrate that, except for the position errors, all error states can be minimally estimated in the SSAC problem and, hence, should not be removed from the filter. Corroborating the conclusions of the first part of this study, a 12-state Kalman filter is found to be the optimal error state selection for SSAC purposes. Results from simulated and experimental tests support the outlined conclusions. PMID:28241494

A lower and more constrained estimate of climate sensitivity using updated observations and detailed radiative forcing time series

NASA Astrophysics Data System (ADS)

Skeie, R. B.; Berntsen, T.; Aldrin, M.; Holden, M.; Myhre, G.

2012-04-01

A key question in climate science is to quantify the sensitivity of the climate system to perturbation in the radiative forcing (RF). This sensitivity is often represented by the equilibrium climate sensitivity, but this quantity is poorly constrained with significant probabilities for high values. In this work the equilibrium climate sensitivity (ECS) is estimated based on observed near-surface temperature change from the instrumental record, changes in ocean heat content and detailed RF time series. RF time series from pre-industrial times to 2010 for all main anthropogenic and natural forcing mechanisms are estimated and the cloud lifetime effect and the semi-direct effect, which are not RF mechanisms in a strict sense, are included in the analysis. The RF time series are linked to the observations of ocean heat content and temperature change through an energy balance model and a stochastic model, using a Bayesian approach to estimate the ECS from the data. The posterior mean of the ECS is 1.9˚C with 90% credible interval (C.I.) ranging from 1.2 to 2.9˚C, which is tighter than previously published estimates. Observational data up to and including year 2010 are used in this study. This is at least ten additional years compared to the majority of previously published studies that have used the instrumental record in attempts to constrain the ECS. We show that the additional 10 years of data, and especially 10 years of additional ocean heat content data, have significantly narrowed the probability density function of the ECS. If only data up to and including year 2000 are used in the analysis, the 90% C.I. is 1.4 to 10.6˚C with a pronounced heavy tail in line with previous estimates of ECS constrained by observations in the 20th century. Also the transient climate response (TCR) is estimated in this study. Using observational data up to and including year 2010 gives a 90% C.I. of 1.0 to 2.1˚C, while the 90% C.I. is significantly broader ranging from 1.1 to 3

Estimation and comparision of curve numbers based on dynamic land use land cover change, observed rainfall-runoff data and land slope

NASA Astrophysics Data System (ADS)

Deshmukh, Dhananjay Suresh; Chaube, Umesh Chandra; Ekube Hailu, Ambaye; Aberra Gudeta, Dida; Tegene Kassa, Melaku

2013-06-01

The CN represents runoff potential is estimated using three different methods for three watersheds namely Barureva, Sher and Umar watershed located in Narmada basin. Among three watersheds, Sher watershed has gauging site for the runoff measurements. The CN computed from the observed rainfall-runoff events is termed as CN(PQ), land use and land cover (LULC) is termed as CN(LU) and the CN based on land slope is termed as SACN2. The estimated annual CN(PQ) varies from 69 to 87 over the 26 years data period with median 74 and average 75. The range of CN(PQ) from 70 to 79 are most significant values and these truly represent the AMC II condition for the Sher watershed. The annual CN(LU) was computed for all three watersheds using GIS and the years are 1973, 1989 and 2000. Satellite imagery of MSS, TM and ETM+ sensors are available for these years and obtained from the Global Land Cover Facility Data Center of Maryland University USA. The computed CN(LU) values show rising trend with the time and this trend is attributed to expansion of agriculture area in all watersheds. The predicted values of CN(LU) with time (year) can be used to predict runoff potential under the effect of change in LULC. Comparison of CN(LU) and CN(PQ) values shows close agreement and it also validates the classification of LULC. The estimation of slope adjusted SA-CN2 shows the significant difference over conventional CN for the hilly forest lands. For the micro watershed planning, SCS-CN method should be modified to incorporate the effect of change in land use and land cover along with effect of land slope.

Assimilating remote sensing observations of leaf area index and soil moisture for wheat yield estimates: An observing system simulation experiment

USDA-ARS?s Scientific Manuscript database

We develop a robust understanding of the effects of assimilating remote sensing observations of leaf area index and soil moisture (in the top 5 cm) on DSSAT-CSM CropSim-Ceres wheat yield estimates. Synthetic observing system simulation experiments compare the abilities of the Ensemble Kalman Filter...

Estimating random errors due to shot noise in backscatter lidar observations.

PubMed

Liu, Zhaoyan; Hunt, William; Vaughan, Mark; Hostetler, Chris; McGill, Matthew; Powell, Kathleen; Winker, David; Hu, Yongxiang

2006-06-20

We discuss the estimation of random errors due to shot noise in backscatter lidar observations that use either photomultiplier tube (PMT) or avalanche photodiode (APD) detectors. The statistical characteristics of photodetection are reviewed, and photon count distributions of solar background signals and laser backscatter signals are examined using airborne lidar observations at 532 nm using a photon-counting mode APD. Both distributions appear to be Poisson, indicating that the arrival at the photodetector of photons for these signals is a Poisson stochastic process. For Poisson- distributed signals, a proportional, one-to-one relationship is known to exist between the mean of a distribution and its variance. Although the multiplied photocurrent no longer follows a strict Poisson distribution in analog-mode APD and PMT detectors, the proportionality still exists between the mean and the variance of the multiplied photocurrent. We make use of this relationship by introducing the noise scale factor (NSF), which quantifies the constant of proportionality that exists between the root mean square of the random noise in a measurement and the square root of the mean signal. Using the NSF to estimate random errors in lidar measurements due to shot noise provides a significant advantage over the conventional error estimation techniques, in that with the NSF, uncertainties can be reliably calculated from or for a single data sample. Methods for evaluating the NSF are presented. Algorithms to compute the NSF are developed for the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations lidar and tested using data from the Lidar In-space Technology Experiment.

Estimating Random Errors Due to Shot Noise in Backscatter Lidar Observations

NASA Technical Reports Server (NTRS)

Liu, Zhaoyan; Hunt, William; Vaughan, Mark A.; Hostetler, Chris A.; McGill, Matthew J.; Powell, Kathy; Winker, David M.; Hu, Yongxiang

2006-01-01

In this paper, we discuss the estimation of random errors due to shot noise in backscatter lidar observations that use either photomultiplier tube (PMT) or avalanche photodiode (APD) detectors. The statistical characteristics of photodetection are reviewed, and photon count distributions of solar background signals and laser backscatter signals are examined using airborne lidar observations at 532 nm using a photon-counting mode APD. Both distributions appear to be Poisson, indicating that the arrival at the photodetector of photons for these signals is a Poisson stochastic process. For Poisson-distributed signals, a proportional, one-to-one relationship is known to exist between the mean of a distribution and its variance. Although the multiplied photocurrent no longer follows a strict Poisson distribution in analog-mode APD and PMT detectors, the proportionality still exists between the mean and the variance of the multiplied photocurrent. We make use of this relationship by introducing the noise scale factor (NSF), which quantifies the constant of proportionality that exists between the root-mean-square of the random noise in a measurement and the square root of the mean signal. Using the NSF to estimate random errors in lidar measurements due to shot noise provides a significant advantage over the conventional error estimation techniques, in that with the NSF uncertainties can be reliably calculated from/for a single data sample. Methods for evaluating the NSF are presented. Algorithms to compute the NSF are developed for the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar and tested using data from the Lidar In-space Technology Experiment (LITE). OCIS Codes:

State and actuator fault estimation observer design integrated in a riderless bicycle stabilization system.

PubMed

Brizuela Mendoza, Jorge Aurelio; Astorga Zaragoza, Carlos Manuel; Zavala Río, Arturo; Pattalochi, Leo; Canales Abarca, Francisco

2016-03-01

This paper deals with an observer design for Linear Parameter Varying (LPV) systems with high-order time-varying parameter dependency. The proposed design, considered as the main contribution of this paper, corresponds to an observer for the estimation of the actuator fault and the system state, considering measurement noise at the system outputs. The observer gains are computed by considering the extension of linear systems theory to polynomial LPV systems, in such a way that the observer reaches the characteristics of LPV systems. As a result, the actuator fault estimation is ready to be used in a Fault Tolerant Control scheme, where the estimated state with reduced noise should be used to generate the control law. The effectiveness of the proposed methodology has been tested using a riderless bicycle model with dependency on the translational velocity v, where the control objective corresponds to the system stabilization towards the upright position despite the variation of v along the closed-loop system trajectories. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Number of discernible colors for color-deficient observers estimated from the MacAdam limits.

PubMed

Perales, Esther; Martínez-Verdú, Francisco Miguel; Linhares, João Manuel Maciel; Nascimento, Sérgio Miguel Cardoso

2010-10-01

We estimated the number of colors perceived by color normal and color-deficient observers when looking at the theoretic limits of object-color stimuli. These limits, the optimal color stimuli, were computed for a color normal observer and CIE standard illuminant D65, and the resultant colors were expressed in the CIELAB and DIN99d color spaces. The corresponding color volumes for abnormal color vision were computed using models simulating for normal trichromatic observers the appearance for dichromats and anomalous trichomats. The number of colors perceived in each case was then computed from the color volumes enclosed by the optimal colors also known as MacAdam limits. It was estimated that dichromats perceive less than 1% of the colors perceived by normal trichromats and that anomalous trichromats perceive 50%-60% for anomalies in the medium-wavelength-sensitive and 60%-70% for anomalies in the long-wavelength-sensitive cones. Complementary estimates obtained similarly for the spectral locus of monochromatic stimuli suggest less impairment for color-deficient observers, a fact that is explained by the two-dimensional nature of the locus.

Estimating Pressure Reactivity Using Noninvasive Doppler-Based Systolic Flow Index.

PubMed

Zeiler, Frederick A; Smielewski, Peter; Donnelly, Joseph; Czosnyka, Marek; Menon, David K; Ercole, Ari

2018-04-05

The study objective was to derive models that estimate the pressure reactivity index (PRx) using the noninvasive transcranial Doppler (TCD) based systolic flow index (Sx_a) and mean flow index (Mx_a), both based on mean arterial pressure, in traumatic brain injury (TBI). Using a retrospective database of 347 patients with TBI with intracranial pressure and TCD time series recordings, we derived PRx, Sx_a, and Mx_a. We first derived the autocorrelative structure of PRx based on: (A) autoregressive integrative moving average (ARIMA) modeling in representative patients, and (B) within sequential linear mixed effects (LME) models with various embedded ARIMA error structures for PRx for the entire population. Finally, we performed sequential LME models with embedded PRx ARIMA modeling to find the best model for estimating PRx using Sx_a and Mx_a. Model adequacy was assessed via normally distributed residual density. Model superiority was assessed via Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), log likelihood (LL), and analysis of variance testing between models. The most appropriate ARIMA structure for PRx in this population was (2,0,2). This was applied in sequential LME modeling. Two models were superior (employing random effects in the independent variables and intercept): (A) PRx ∼ Sx_a, and (B) PRx ∼ Sx_a + Mx_a. Correlation between observed and estimated PRx with these two models was: (A) 0.794 (p < 0.0001, 95% confidence interval (CI) = 0.788-0.799), and (B) 0.814 (p < 0.0001, 95% CI = 0.809-0.819), with acceptable agreement on Bland-Altman analysis. Through using linear mixed effects modeling and accounting for the ARIMA structure of PRx, one can estimate PRx using noninvasive TCD-based indices. We have described our first attempts at such modeling and PRx estimation, establishing the strong link between two aspects of cerebral autoregulation: measures of cerebral blood flow and those of pulsatile cerebral blood

Geocenter Coordinates from a Combined Processing of LEO and Ground-based GPS Observations

NASA Astrophysics Data System (ADS)

Männel, Benjamin; Rothacher, Markus

2017-04-01

The GPS observations provided by the global IGS (International GNSS Service) tracking network play an important role for the realization of a unique terrestrial reference frame that is accurate enough to allow the monitoring of the Earth's system. Combining these ground-based data with GPS observations tracked by high-quality dual-frequency receivers on-board Low Earth Orbiters (LEO) might help to further improve the realization of the terrestrial reference frame and the estimation of the geocenter coordinates, GPS satellite orbits and Earth rotation parameters (ERP). To assess the scope of improvement, we processed a network of 50 globally distributed and stable IGS-stations together with four LEOs (GRACE-A, GRACE-B, OSTM/Jason-2 and GOCE) over a time interval of three years (2010-2012). To ensure fully consistent solutions the zero-difference phase observations of the ground stations and LEOs were processed in a common least-square adjustment, estimating GPS orbits, LEO orbits, station coordinates, ERPs, site-specific tropospheric delays, satellite and receiver clocks and ambiguities. We present the significant impact of the individual LEOs and a combination of all four LEOs on geocenter coordinates derived by using a translational approach (also called network shift approach). In addition, we present geocenter coordinates derived from the same set of GPS observations by using a unified approach. This approach combines the translational and the degree-one approach by estimating translations and surface deformations simultaneously. Based on comparisons against each other and against geocenter time series derived by other techniques the effect of the selected approach is assessed.

High-global warming potential F-gas emissions in California: comparison of ambient-based versus inventory-based emission estimates, and implications of refined estimates.

PubMed

Gallagher, Glenn; Zhan, Tao; Hsu, Ying-Kuang; Gupta, Pamela; Pederson, James; Croes, Bart; Blake, Donald R; Barletta, Barbara; Meinardi, Simone; Ashford, Paul; Vetter, Arnie; Saba, Sabine; Slim, Rayan; Palandre, Lionel; Clodic, Denis; Mathis, Pamela; Wagner, Mark; Forgie, Julia; Dwyer, Harry; Wolf, Katy

2014-01-21

To provide information for greenhouse gas reduction policies, the California Air Resources Board (CARB) inventories annual emissions of high-global-warming potential (GWP) fluorinated gases, the fastest growing sector of greenhouse gas (GHG) emissions globally. Baseline 2008 F-gas emissions estimates for selected chlorofluorocarbons (CFC-12), hydrochlorofluorocarbons (HCFC-22), and hydrofluorocarbons (HFC-134a) made with an inventory-based methodology were compared to emissions estimates made by ambient-based measurements. Significant discrepancies were found, with the inventory-based emissions methodology resulting in a systematic 42% under-estimation of CFC-12 emissions from older refrigeration equipment and older vehicles, and a systematic 114% overestimation of emissions for HFC-134a, a refrigerant substitute for phased-out CFCs. Initial, inventory-based estimates for all F-gas emissions had assumed that equipment is no longer in service once it reaches its average lifetime of use. Revised emission estimates using improved models for equipment age at end-of-life, inventories, and leak rates specific to California resulted in F-gas emissions estimates in closer agreement to ambient-based measurements. The discrepancies between inventory-based estimates and ambient-based measurements were reduced from -42% to -6% for CFC-12, and from +114% to +9% for HFC-134a.

Flatness-Based Tracking Control and Nonlinear Observer for a Micro Aerial Quadcopter

NASA Astrophysics Data System (ADS)

Rivera, G.; Sawodny, O.

2010-09-01

This paper deals with the design of a nonlinear observer and a differential flat based path tracking controller for a mini aerial quadcopter. Taking into account that only the inertial coordinates and the yaw angle are available for measurements, it is shown, that the system is differentially flat, allowing a systematic design of a nonlinear tracking control in open and closed loop. A nonlinear observer is carried out to estimate the roll and pitch angle as well as all the linear and angular velocities. Finally the performance of the feedback controller and observer are illustrated in a computer simulation.

A joint sparse representation-based method for double-trial evoked potentials estimation.

PubMed

Yu, Nannan; Liu, Haikuan; Wang, Xiaoyan; Lu, Hanbing

2013-12-01

In this paper, we present a novel approach to solving an evoked potentials estimating problem. Generally, the evoked potentials in two consecutive trials obtained by repeated identical stimuli of the nerves are extremely similar. In order to trace evoked potentials, we propose a joint sparse representation-based double-trial evoked potentials estimation method, taking full advantage of this similarity. The estimation process is performed in three stages: first, according to the similarity of evoked potentials and the randomness of a spontaneous electroencephalogram, the two consecutive observations of evoked potentials are considered as superpositions of the common component and the unique components; second, making use of their characteristics, the two sparse dictionaries are constructed; and finally, we apply the joint sparse representation method in order to extract the common component of double-trial observations, instead of the evoked potential in each trial. A series of experiments carried out on simulated and human test responses confirmed the superior performance of our method. © 2013 Elsevier Ltd. Published by Elsevier Ltd. All rights reserved.

Temporal regularization of ultrasound-based liver motion estimation for image-guided radiation therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

O’Shea, Tuathan P., E-mail: tuathan.oshea@icr.ac.uk; Bamber, Jeffrey C.; Harris, Emma J.

Purpose: Ultrasound-based motion estimation is an expanding subfield of image-guided radiation therapy. Although ultrasound can detect tissue motion that is a fraction of a millimeter, its accuracy is variable. For controlling linear accelerator tracking and gating, ultrasound motion estimates must remain highly accurate throughout the imaging sequence. This study presents a temporal regularization method for correlation-based template matching which aims to improve the accuracy of motion estimates. Methods: Liver ultrasound sequences (15–23 Hz imaging rate, 2.5–5.5 min length) from ten healthy volunteers under free breathing were used. Anatomical features (blood vessels) in each sequence were manually annotated for comparison withmore » normalized cross-correlation based template matching. Five sequences from a Siemens Acuson™ scanner were used for algorithm development (training set). Results from incremental tracking (IT) were compared with a temporal regularization method, which included a highly specific similarity metric and state observer, known as the α–β filter/similarity threshold (ABST). A further five sequences from an Elekta Clarity™ system were used for validation, without alteration of the tracking algorithm (validation set). Results: Overall, the ABST method produced marked improvements in vessel tracking accuracy. For the training set, the mean and 95th percentile (95%) errors (defined as the difference from manual annotations) were 1.6 and 1.4 mm, respectively (compared to 6.2 and 9.1 mm, respectively, for IT). For each sequence, the use of the state observer leads to improvement in the 95% error. For the validation set, the mean and 95% errors for the ABST method were 0.8 and 1.5 mm, respectively. Conclusions: Ultrasound-based motion estimation has potential to monitor liver translation over long time periods with high accuracy. Nonrigid motion (strain) and the quality of the ultrasound data are likely to have an impact on tracking

First observation-based estimates of cloud-free aerosol radiative forcing across China

Treesearch

Zhanqing Li; Kwon-Ho Lee; Yuesi Wang; Jinyuan Xin; Wei-Min Hao

2010-01-01

Heavy loading of aerosols in China is widely known, but little is known about their impact on regional radiation budgets, which is often expressed as aerosol radiative forcing (ARF). Cloud‐free direct ARF has either been estimated by models across the region or determined at a handful of locations with aerosol and/or radiation measurements. In this study, ARF...

Trend Estimates of AERONET-Observed and Model-Simulated AOTs Between 1993 and 2013

NASA Technical Reports Server (NTRS)

Yoon, J.; Pozzer, A.; Chang, D. Y.; Lelieveld, J.; Kim, J.; Kim, M.; Lee, Y. G.; Koo, J.-H.; Lee, J.; Moon, K. J.

2015-01-01

Recently, temporal changes in Aerosol Optical Thickness (AOT) have been investigated based on model simulations, satellite and ground-based observations. Most AOT trend studies used monthly or annual arithmetic means that discard details of the generally right-skewed AOT distributions. Potentially, such results can be biased by extreme values (including outliers). This study additionally uses percentiles (i.e., the lowest 5%, 25%, 50%, 75% and 95% of the monthly cumulative distributions fitted to Aerosol Robotic Network (AERONET)-observed and ECHAM/MESSy Atmospheric Chemistry (EMAC)-model simulated AOTs) that are less affected by outliers caused by measurement error, cloud contamination and occasional extreme aerosol events. Since the limited statistical representativeness of monthly percentiles and means can lead to bias, this study adopts the number of observations as a weighting factor, which improves the statistical robustness of trend estimates. By analyzing the aerosol composition of AERONET-observed and EMAC-simulated AOTs in selected regions of interest, we distinguish the dominant aerosol types and investigate the causes of regional AOT trends. The simulated and observed trends are generally consistent with a high correlation coefficient (R = 0.89) and small bias (slope+/-2(sigma) = 0.75 +/- 0.19). A significant decrease in EMAC-decomposed AOTs by water-soluble compounds and black carbon is found over the USA and the EU due to environmental regulation. In particular, a clear reversal in the AERONET AOT trend percentiles is found over the USA, probably related to the AOT diurnal cycle and the frequency of wildfires. In most of the selected regions of interest, EMAC-simulated trends are mainly attributed to the significant changes of the dominant aerosols; e.g., significant decrease in sea salt and water soluble compounds over Central America, increase in dust over Northern Africa and Middle East, and decrease in black carbon and organic carbon over

Empirically Driven Variable Selection for the Estimation of Causal Effects with Observational Data

ERIC Educational Resources Information Center

Keller, Bryan; Chen, Jianshen

2016-01-01

Observational studies are common in educational research, where subjects self-select or are otherwise non-randomly assigned to different interventions (e.g., educational programs, grade retention, special education). Unbiased estimation of a causal effect with observational data depends crucially on the assumption of ignorability, which specifies…

Evaluating detection and estimation capabilities of magnetometer-based vehicle sensors

NASA Astrophysics Data System (ADS)

Slater, David M.; Jacyna, Garry M.

2013-05-01

In an effort to secure the northern and southern United States borders, MITRE has been tasked with developing Modeling and Simulation (M&S) tools that accurately capture the mapping between algorithm-level Measures of Performance (MOP) and system-level Measures of Effectiveness (MOE) for current/future surveillance systems deployed by the the Customs and Border Protection Office of Technology Innovations and Acquisitions (OTIA). This analysis is part of a larger M&S undertaking. The focus is on two MOPs for magnetometer-based Unattended Ground Sensors (UGS). UGS are placed near roads to detect passing vehicles and estimate properties of the vehicle's trajectory such as bearing and speed. The first MOP considered is the probability of detection. We derive probabilities of detection for a network of sensors over an arbitrary number of observation periods and explore how the probability of detection changes when multiple sensors are employed. The performance of UGS is also evaluated based on the level of variance in the estimation of trajectory parameters. We derive the Cramer-Rao bounds for the variances of the estimated parameters in two cases: when no a priori information is known and when the parameters are assumed to be Gaussian with known variances. Sample results show that UGS perform significantly better in the latter case.

On the use of satellite-based estimates of rainfall temporal distribution to simulate the potential for malaria transmission in rural Africa

NASA Astrophysics Data System (ADS)

Yamana, Teresa K.; Eltahir, Elfatih A. B.

2011-02-01

This paper describes the use of satellite-based estimates of rainfall to force the Hydrology, Entomology and Malaria Transmission Simulator (HYDREMATS), a hydrology-based mechanistic model of malaria transmission. We first examined the temporal resolution of rainfall input required by HYDREMATS. Simulations conducted over Banizoumbou village in Niger showed that for reasonably accurate simulation of mosquito populations, the model requires rainfall data with at least 1 h resolution. We then investigated whether HYDREMATS could be effectively forced by satellite-based estimates of rainfall instead of ground-based observations. The Climate Prediction Center morphing technique (CMORPH) precipitation estimates distributed by the National Oceanic and Atmospheric Administration are available at a 30 min temporal resolution and 8 km spatial resolution. We compared mosquito populations simulated by HYDREMATS when the model is forced by adjusted CMORPH estimates and by ground observations. The results demonstrate that adjusted rainfall estimates from satellites can be used with a mechanistic model to accurately simulate the dynamics of mosquito populations.

An artificial network model for estimating the network structure underlying partially observed neuronal signals.

PubMed

Komatsu, Misako; Namikawa, Jun; Chao, Zenas C; Nagasaka, Yasuo; Fujii, Naotaka; Nakamura, Kiyohiko; Tani, Jun

2014-01-01

Many previous studies have proposed methods for quantifying neuronal interactions. However, these methods evaluated the interactions between recorded signals in an isolated network. In this study, we present a novel approach for estimating interactions between observed neuronal signals by theorizing that those signals are observed from only a part of the network that also includes unobserved structures. We propose a variant of the recurrent network model that consists of both observable and unobservable units. The observable units represent recorded neuronal activity, and the unobservable units are introduced to represent activity from unobserved structures in the network. The network structures are characterized by connective weights, i.e., the interaction intensities between individual units, which are estimated from recorded signals. We applied this model to multi-channel brain signals recorded from monkeys, and obtained robust network structures with physiological relevance. Furthermore, the network exhibited common features that portrayed cortical dynamics as inversely correlated interactions between excitatory and inhibitory populations of neurons, which are consistent with the previous view of cortical local circuits. Our results suggest that the novel concept of incorporating an unobserved structure into network estimations has theoretical advantages and could provide insights into brain dynamics beyond what can be directly observed. Copyright © 2014 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

Adaptive Window Zero-Crossing-Based Instantaneous Frequency Estimation

NASA Astrophysics Data System (ADS)

Sekhar, S. Chandra; Sreenivas, TV

2004-12-01

We address the problem of estimating instantaneous frequency (IF) of a real-valued constant amplitude time-varying sinusoid. Estimation of polynomial IF is formulated using the zero-crossings of the signal. We propose an algorithm to estimate nonpolynomial IF by local approximation using a low-order polynomial, over a short segment of the signal. This involves the choice of window length to minimize the mean square error (MSE). The optimal window length found by directly minimizing the MSE is a function of the higher-order derivatives of the IF which are not available a priori. However, an optimum solution is formulated using an adaptive window technique based on the concept of intersection of confidence intervals. The adaptive algorithm enables minimum MSE-IF (MMSE-IF) estimation without requiring a priori information about the IF. Simulation results show that the adaptive window zero-crossing-based IF estimation method is superior to fixed window methods and is also better than adaptive spectrogram and adaptive Wigner-Ville distribution (WVD)-based IF estimators for different signal-to-noise ratio (SNR).

ESTIMATING THE RADIUS OF THE CONVECTIVE CORE OF MAIN-SEQUENCE STARS FROM OBSERVED OSCILLATION FREQUENCIES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Wuming, E-mail: yangwuming@bnu.edu.cn, E-mail: yangwuming@ynao.ac.cn

The determination of the size of the convective core of main-sequence stars is usually dependent on the construction of models of stars. Here we introduce a method to estimate the radius of the convective core of main-sequence stars with masses between about 1.1 and 1.5 M {sub ⊙} from observed frequencies of low-degree p -modes. A formula is proposed to achieve the estimation. The values of the radius of the convective core of four known stars are successfully estimated by the formula. The radius of the convective core of KIC 9812850 estimated by the formula is 0.140 ± 0.028 Rmore » {sub ⊙}. In order to confirm this prediction, a grid of evolutionary models was computed. The value of the convective-core radius of the best-fit model of KIC 9812850 is 0.149 R {sub ⊙}, which is in good agreement with that estimated by the formula from observed frequencies. The formula aids in understanding the interior structure of stars directly from observed frequencies. The understanding is not dependent on the construction of models.« less

Natural Forest Biomass Estimation Based on Plantation Information Using PALSAR Data

PubMed Central

Avtar, Ram; Suzuki, Rikie; Sawada, Haruo

2014-01-01

Forests play a vital role in terrestrial carbon cycling; therefore, monitoring forest biomass at local to global scales has become a challenging issue in the context of climate change. In this study, we investigated the backscattering properties of Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data in cashew and rubber plantation areas of Cambodia. The PALSAR backscattering coefficient (σ0) had different responses in the two plantation types because of differences in biophysical parameters. The PALSAR σ0 showed a higher correlation with field-based measurements and lower saturation in cashew plants compared with rubber plants. Multiple linear regression (MLR) models based on field-based biomass of cashew (C-MLR) and rubber (R-MLR) plants with PALSAR σ0 were created. These MLR models were used to estimate natural forest biomass in Cambodia. The cashew plant-based MLR model (C-MLR) produced better results than the rubber plant-based MLR model (R-MLR). The C-MLR-estimated natural forest biomass was validated using forest inventory data for natural forests in Cambodia. The validation results showed a strong correlation (R2 = 0.64) between C-MLR-estimated natural forest biomass and field-based biomass, with RMSE  = 23.2 Mg/ha in deciduous forests. In high-biomass regions, such as dense evergreen forests, this model had a weaker correlation because of the high biomass and the multiple-story tree structure of evergreen forests, which caused saturation of the PALSAR signal. PMID:24465908

FuzzObserver

NASA Technical Reports Server (NTRS)

Howard, Ayanna; Bayard, David

2006-01-01

Fuzzy Feature Observation Planner for Small Body Proximity Observations (FuzzObserver) is a developmental computer program, to be used along with other software, for autonomous planning of maneuvers of a spacecraft near an asteroid, comet, or other small astronomical body. Selection of terrain features and estimation of the position of the spacecraft relative to these features is an essential part of such planning. FuzzObserver contributes to the selection and estimation by generating recommendations for spacecraft trajectory adjustments to maintain the spacecraft's ability to observe sufficient terrain features for estimating position. The input to FuzzObserver consists of data from terrain images, including sets of data on features acquired during descent toward, or traversal of, a body of interest. The name of this program reflects its use of fuzzy logic to reason about the terrain features represented by the data and extract corresponding trajectory-adjustment rules. Linguistic fuzzy sets and conditional statements enable fuzzy systems to make decisions based on heuristic rule-based knowledge derived by engineering experts. A major advantage of using fuzzy logic is that it involves simple arithmetic calculations that can be performed rapidly enough to be useful for planning within the short times typically available for spacecraft maneuvers.

Accelerometer-based wireless body area network to estimate intensity of therapy in post-acute rehabilitation

PubMed Central

Choquette, Stéphane; Hamel, Mathieu; Boissy, Patrick

2008-01-01

Background It has been suggested that there is a dose-response relationship between the amount of therapy and functional recovery in post-acute rehabilitation care. To this day, only the total time of therapy has been investigated as a potential determinant of this dose-response relationship because of methodological and measurement challenges. The primary objective of this study was to compare time and motion measures during real life physical therapy with estimates of active time (i.e. the time during which a patient is active physically) obtained with a wireless body area network (WBAN) of 3D accelerometer modules positioned at the hip, wrist and ankle. The secondary objective was to assess the differences in estimates of active time when using a single accelerometer module positioned at the hip. Methods Five patients (77.4 ± 5.2 y) with 4 different admission diagnoses (stroke, lower limb fracture, amputation and immobilization syndrome) were recruited in a post-acute rehabilitation center and observed during their physical therapy sessions throughout their stay. Active time was recorded by a trained observer using a continuous time and motion analysis program running on a Tablet-PC. Two WBAN configurations were used: 1) three accelerometer modules located at the hip, wrist and ankle (M3) and 2) one accelerometer located at the hip (M1). Acceleration signals from the WBANs were synchronized with the observations. Estimates of active time were computed based on the temporal density of the acceleration signals. Results A total of 62 physical therapy sessions were observed. Strong associations were found between WBANs estimates of active time and time and motion measures of active time. For the combined sessions, the intraclass correlation coefficient (ICC) was 0.93 (P ≤ 0.001) for M3 and 0.79 (P ≤ 0.001) for M1. The mean percentage of differences between observation measures and estimates from the WBAN of active time was -8.7% ± 2.0% using data from M3 and

Comparing NEXRAD Operational Precipitation Estimates and Raingage Observations of Intense Precipitation in the Missouri River Basin.

NASA Astrophysics Data System (ADS)

Young, C. B.

2002-05-01

Accurate observation of precipitation is critical to the study and modeling of land surface hydrologic processes. NEXRAD radar-based precipitation estimates are increasingly used in field experiments, hydrologic modeling, and water and energy budget studies due to their high spatial and temporal resolution, national coverage, and perceived accuracy. Extensive development and testing of NEXRAD precipitation algorithms have been carried out in the Southern Plains. Previous studies (Young et al. 2000, Young et al. 1999, Smith et al. 1996) indicate that NEXRAD operational products tend to underestimate precipitation at light rain rates. This study investigates the performance of NEXRAD precipitation estimates of high-intensity rainfall, focusing on flood-producing storms in the Missouri River Basin. NEXRAD estimates for these storms are compared with data from multiple raingage networks, including NWS recording and non-recording gages and ALERT raingage data for the Kansas City metropolitan area. Analyses include comparisons of gage and radar data at a wide range of temporal and spatial scales. Particular attention is paid to the October 4th, 1998, storm that produced severe flooding in Kansas City. NOTE: The phrase `NEXRAD operational products' in this abstract includes precipitation estimates generated using the Stage III and P1 algorithms. Both of these products estimate hourly accumulations on the (approximately) 4 km HRAP grid.

Fast emission estimates in China and South Africa constrained by satellite observations

NASA Astrophysics Data System (ADS)

Mijling, Bas; van der A, Ronald

2013-04-01

Emission inventories of air pollutants are crucial information for policy makers and form important input data for air quality models. Unfortunately, bottom-up emission inventories, compiled from large quantities of statistical data, are easily outdated for emerging economies such as China and South Africa, where rapid economic growth change emissions accordingly. Alternatively, top-down emission estimates from satellite observations of air constituents have important advantages of being spatial consistent, having high temporal resolution, and enabling emission updates shortly after the satellite data become available. However, constraining emissions from observations of concentrations is computationally challenging. Within the GlobEmission project (part of the Data User Element programme of ESA) a new algorithm has been developed, specifically designed for fast daily emission estimates of short-lived atmospheric species on a mesoscopic scale (0.25 × 0.25 degree) from satellite observations of column concentrations. The algorithm needs only one forward model run from a chemical transport model to calculate the sensitivity of concentration to emission, using trajectory analysis to account for transport away from the source. By using a Kalman filter in the inverse step, optimal use of the a priori knowledge and the newly observed data is made. We apply the algorithm for NOx emission estimates in East China and South Africa, using the CHIMERE chemical transport model together with tropospheric NO2 column retrievals of the OMI and GOME-2 satellite instruments. The observations are used to construct a monthly emission time series, which reveal important emission trends such as the emission reduction measures during the Beijing Olympic Games, and the impact and recovery from the global economic crisis. The algorithm is also able to detect emerging sources (e.g. new power plants) and improve emission information for areas where proxy data are not or badly known (e

Direct Aerosol Radiative Forcing Based on Combined A-Train Observations: Towards All-sky Estimates and Attribution to Aerosol Type

NASA Technical Reports Server (NTRS)

Redemann, Jens; Shinozuka, Y.; Kacenelenbogen, M.; Russell, P.; Vaughan, M.; Ferrare, R.; Hostetler, C.; Rogers, R.; Burton, S.; Livingston, J.;

Vibration-based angular speed estimation for multi-stage wind turbine gearboxes

NASA Astrophysics Data System (ADS)

Peeters, Cédric; Leclère, Quentin; Antoni, Jérôme; Guillaume, Patrick; Helsen, Jan

2017-05-01

Most processing tools based on frequency analysis of vibration signals are only applicable for stationary speed regimes. Speed variation causes the spectral content to smear, which encumbers most conventional fault detection techniques. To solve the problem of non-stationary speed conditions, the instantaneous angular speed (IAS) is estimated. Wind turbine gearboxes however are typically multi-stage gearboxes, consisting of multiple shafts, rotating at different speeds. Fitting a sensor (e.g. a tachometer) to every single stage is not always feasible. As such there is a need to estimate the IAS of every single shaft based on the vibration signals measured by the accelerometers. This paper investigates the performance of the multi-order probabilistic approach for IAS estimation on experimental case studies of wind turbines. This method takes into account the meshing orders of the gears present in the system and has the advantage that a priori it is not necessary to associate harmonics with a certain periodic mechanical event, which increases the robustness of the method. It is found that the MOPA has the potential to easily outperform standard band-pass filtering techniques for speed estimation. More knowledge of the gearbox kinematics is beneficial for the MOPA performance, but even with very little knowledge about the meshing orders, the MOPA still performs sufficiently well to compete with the standard speed estimation techniques. This observation is proven on two different data sets, both originating from vibration measurements on the gearbox housing of a wind turbine.

Model Based Optimal Control, Estimation, and Validation of Lithium-Ion Batteries

NASA Astrophysics Data System (ADS)

Perez, Hector Eduardo

This dissertation focuses on developing and experimentally validating model based control techniques to enhance the operation of lithium ion batteries, safely. An overview of the contributions to address the challenges that arise are provided below. Chapter 1: This chapter provides an introduction to battery fundamentals, models, and control and estimation techniques. Additionally, it provides motivation for the contributions of this dissertation. Chapter 2: This chapter examines reference governor (RG) methods for satisfying state constraints in Li-ion batteries. Mathematically, these constraints are formulated from a first principles electrochemical model. Consequently, the constraints explicitly model specific degradation mechanisms, such as lithium plating, lithium depletion, and overheating. This contrasts with the present paradigm of limiting measured voltage, current, and/or temperature. The critical challenges, however, are that (i) the electrochemical states evolve according to a system of nonlinear partial differential equations, and (ii) the states are not physically measurable. Assuming available state and parameter estimates, this chapter develops RGs for electrochemical battery models. The results demonstrate how electrochemical model state information can be utilized to ensure safe operation, while simultaneously enhancing energy capacity, power, and charge speeds in Li-ion batteries. Chapter 3: Complex multi-partial differential equation (PDE) electrochemical battery models are characterized by parameters that are often difficult to measure or identify. This parametric uncertainty influences the state estimates of electrochemical model-based observers for applications such as state-of-charge (SOC) estimation. This chapter develops two sensitivity-based interval observers that map bounded parameter uncertainty to state estimation intervals, within the context of electrochemical PDE models and SOC estimation. Theoretically, this chapter extends the

Robust linear discriminant analysis with distance based estimators

NASA Astrophysics Data System (ADS)

Lim, Yai-Fung; Yahaya, Sharipah Soaad Syed; Ali, Hazlina

2017-11-01

Linear discriminant analysis (LDA) is one of the supervised classification techniques concerning relationship between a categorical variable and a set of continuous variables. The main objective of LDA is to create a function to distinguish between populations and allocating future observations to previously defined populations. Under the assumptions of normality and homoscedasticity, the LDA yields optimal linear discriminant rule (LDR) between two or more groups. However, the optimality of LDA highly relies on the sample mean and pooled sample covariance matrix which are known to be sensitive to outliers. To alleviate these conflicts, a new robust LDA using distance based estimators known as minimum variance vector (MVV) has been proposed in this study. The MVV estimators were used to substitute the classical sample mean and classical sample covariance to form a robust linear discriminant rule (RLDR). Simulation and real data study were conducted to examine on the performance of the proposed RLDR measured in terms of misclassification error rates. The computational result showed that the proposed RLDR is better than the classical LDR and was comparable with the existing robust LDR.

SU-E-I-46: Sample-Size Dependence of Model Observers for Estimating Low-Contrast Detection Performance From CT Images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Reiser, I; Lu, Z

2014-06-01

Purpose: Recently, task-based assessment of diagnostic CT systems has attracted much attention. Detection task performance can be estimated using human observers, or mathematical observer models. While most models are well established, considerable bias can be introduced when performance is estimated from a limited number of image samples. Thus, the purpose of this work was to assess the effect of sample size on bias and uncertainty of two channelized Hotelling observers and a template-matching observer. Methods: The image data used for this study consisted of 100 signal-present and 100 signal-absent regions-of-interest, which were extracted from CT slices. The experimental conditions includedmore » two signal sizes and five different x-ray beam current settings (mAs). Human observer performance for these images was determined in 2-alternative forced choice experiments. These data were provided by the Mayo clinic in Rochester, MN. Detection performance was estimated from three observer models, including channelized Hotelling observers (CHO) with Gabor or Laguerre-Gauss (LG) channels, and a template-matching observer (TM). Different sample sizes were generated by randomly selecting a subset of image pairs, (N=20,40,60,80). Observer performance was quantified as proportion of correct responses (PC). Bias was quantified as the relative difference of PC for 20 and 80 image pairs. Results: For n=100, all observer models predicted human performance across mAs and signal sizes. Bias was 23% for CHO (Gabor), 7% for CHO (LG), and 3% for TM. The relative standard deviation, σ(PC)/PC at N=20 was highest for the TM observer (11%) and lowest for the CHO (Gabor) observer (5%). Conclusion: In order to make image quality assessment feasible in the clinical practice, a statistically efficient observer model, that can predict performance from few samples, is needed. Our results identified two observer models that may be suited for this task.« less

Estimating cetacean carrying capacity based on spacing behaviour.

PubMed

Braithwaite, Janelle E; Meeuwig, Jessica J; Jenner, K Curt S

2012-01-01

Conservation of large ocean wildlife requires an understanding of how they use space. In Western Australia, the humpback whale (Megaptera novaeangliae) population is growing at a minimum rate of 10% per year. An important consideration for conservation based management in space-limited environments, such as coastal resting areas, is the potential expansion in area use by humpback whales if the carrying capacity of existing areas is exceeded. Here we determined the theoretical carrying capacity of a known humpback resting area based on the spacing behaviour of pods, where a resting area is defined as a sheltered embayment along the coast. Two separate approaches were taken to estimate this distance. The first used the median nearest neighbour distance between pods in relatively dense areas, giving a spacing distance of 2.16 km (± 0.94). The second estimated the spacing distance as the radius at which 50% of the population included no other pods, and was calculated as 1.93 km (range: 1.62-2.50 km). Using these values, the maximum number of pods able to fit into the resting area was 698 and 872 pods, respectively. Given an average observed pod size of 1.7 whales, this equates to a carrying capacity estimate of between 1187 and 1482 whales at any given point in time. This study demonstrates that whale pods do maintain a distance from each other, which may determine the number of animals that can occupy aggregation areas where space is limited. This requirement for space has implications when considering boundaries for protected areas or competition for space with the fishing and resources sectors.

Comparison of MRI-based estimates of articular cartilage contact area in the tibiofemoral joint.

PubMed

Henderson, Christopher E; Higginson, Jill S; Barrance, Peter J

2011-01-01

Knee osteoarthritis (OA) detrimentally impacts the lives of millions of older Americans through pain and decreased functional ability. Unfortunately, the pathomechanics and associated deviations from joint homeostasis that OA patients experience are not well understood. Alterations in mechanical stress in the knee joint may play an essential role in OA; however, existing literature in this area is limited. The purpose of this study was to evaluate the ability of an existing magnetic resonance imaging (MRI)-based modeling method to estimate articular cartilage contact area in vivo. Imaging data of both knees were collected on a single subject with no history of knee pathology at three knee flexion angles. Intra-observer reliability and sensitivity studies were also performed to determine the role of operator-influenced elements of the data processing on the results. The method's articular cartilage contact area estimates were compared with existing contact area estimates in the literature. The method demonstrated an intra-observer reliability of 0.95 when assessed using Pearson's correlation coefficient and was found to be most sensitive to changes in the cartilage tracings on the peripheries of the compartment. The articular cartilage contact area estimates at full extension were similar to those reported in the literature. The relationships between tibiofemoral articular cartilage contact area and knee flexion were also qualitatively and quantitatively similar to those previously reported. The MRI-based knee modeling method was found to have high intra-observer reliability, sensitivity to peripheral articular cartilage tracings, and agreeability with previous investigations when using data from a single healthy adult. Future studies will implement this modeling method to investigate the role that mechanical stress may play in progression of knee OA through estimation of articular cartilage contact area.

The AMSR2 Satellite-based Microwave Snow Algorithm (SMSA) to estimate regional to global snow depth and snow water equivalent

NASA Astrophysics Data System (ADS)

Kelly, R. E. J.; Saberi, N.; Li, Q.

2017-12-01

With moderate to high spatial resolution (<1 km) regional to global snow water equivalent (SWE) observation approaches yet to be fully scoped and developed, the long-term satellite passive microwave record remains an important tool for cryosphere-climate diagnostics. A new satellite microwave remote sensing approach is described for estimating snow depth (SD) and snow water equivalent (SWE). The algorithm, called the Satellite-based Microwave Snow Algorithm (SMSA), uses Advanced Microwave Scanning Radiometer - 2 (AMSR2) observations aboard the Global Change Observation Mission - Water mission launched by the Japan Aerospace Exploration Agency in 2012. The approach is unique since it leverages observed brightness temperatures (Tb) with static ancillary data to parameterize a physically-based retrieval without requiring parameter constraints from in situ snow depth observations or historical snow depth climatology. After screening snow from non-snow surface targets (water bodies [including freeze/thaw state], rainfall, high altitude plateau regions [e.g. Tibetan plateau]), moderate and shallow snow depths are estimated by minimizing the difference between Dense Media Radiative Transfer model estimates (Tsang et al., 2000; Picard et al., 2011) and AMSR2 Tb observations to retrieve SWE and SD. Parameterization of the model combines a parsimonious snow grain size and density approach originally developed by Kelly et al. (2003). Evaluation of the SMSA performance is achieved using in situ snow depth data from a variety of standard and experiment data sources. Results presented from winter seasons 2012-13 to 2016-17 illustrate the improved performance of the new approach in comparison with the baseline AMSR2 algorithm estimates and approach the performance of the model assimilation-based approach of GlobSnow. Given the variation in estimation power of SWE by different land surface/climate models and selected satellite-derived passive microwave approaches, SMSA provides

Contributions of Precipitation and Soil Moisture Observations to the Skill of Soil Moisture Estimates in a Land Data Assimilation System

NASA Technical Reports Server (NTRS)

Reichle, Rolf H.; Liu, Qing; Bindlish, Rajat; Cosh, Michael H.; Crow, Wade T.; deJeu, Richard; DeLannoy, Gabrielle J. M.; Huffman, George J.; Jackson, Thomas J.

2011-01-01

The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates from a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates soil moisture skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). Soil moisture skill is measured against in situ observations in the continental United States at 44 single-profile sites within the Soil Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at four CalVal watersheds with high-quality distributed sensor networks that measure soil moisture at the scale of land model and satellite estimates. The average skill (in terms of the anomaly time series correlation coefficient R) of AMSR-E retrievals is R=0.39 versus SCAN and R=0.53 versus CalVal measurements. The skill of MERRA surface and root-zone soil moisture is R=0.42 and R=0.46, respectively, versus SCAN measurements, and MERRA surface moisture skill is R=0.56 versus CalVal measurements. Adding information from either precipitation observations or soil moisture retrievals increases surface soil moisture skill levels by IDDeltaR=0.06-0.08, and root zone soil moisture skill levels by DeltaR=0.05-0.07. Adding information from both sources increases surface soil moisture skill levels by DeltaR=0.13, and root zone soil moisture skill by DeltaR=0.11, demonstrating that precipitation corrections and assimilation of satellite soil moisture retrievals contribute similar and largely independent amounts of information.

Multichannel Singular Spectrum Analysis in the Estimates of Common Environmental Effects Affecting GPS Observations

NASA Astrophysics Data System (ADS)

Gruszczynska, Marta; Rosat, Severine; Klos, Anna; Gruszczynski, Maciej; Bogusz, Janusz

2018-03-01

We described a spatio-temporal analysis of environmental loading models: atmospheric, continental hydrology, and non-tidal ocean changes, based on multichannel singular spectrum analysis (MSSA). We extracted the common annual signal for 16 different sections related to climate zones: equatorial, arid, warm, snow, polar and continents. We used the loading models estimated for a set of 229 ITRF2014 (International Terrestrial Reference Frame) International GNSS Service (IGS) stations and discussed the amount of variance explained by individual modes, proving that the common annual signal accounts for 16, 24 and 68% of the total variance of non-tidal ocean, atmospheric and hydrological loading models, respectively. Having removed the common environmental MSSA seasonal curve from the corresponding GPS position time series, we found that the residual station-specific annual curve modelled with the least-squares estimation has the amplitude of maximum 2 mm. This means that the environmental loading models underestimate the seasonalities observed by the GPS system. The remaining signal present in the seasonal frequency band arises from the systematic errors which are not of common environmental or geophysical origin. Using common mode error (CME) estimates, we showed that the direct removal of environmental loading models from the GPS series causes an artificial loss in the CME power spectra between 10 and 80 cycles per year. When environmental effect is removed from GPS series with MSSA curves, no influence on the character of spectra of CME estimates was noticed.

Multichannel Singular Spectrum Analysis in the Estimates of Common Environmental Effects Affecting GPS Observations

NASA Astrophysics Data System (ADS)

Gruszczynska, Marta; Rosat, Severine; Klos, Anna; Gruszczynski, Maciej; Bogusz, Janusz

2018-05-01

We described a spatio-temporal analysis of environmental loading models: atmospheric, continental hydrology, and non-tidal ocean changes, based on multichannel singular spectrum analysis (MSSA). We extracted the common annual signal for 16 different sections related to climate zones: equatorial, arid, warm, snow, polar and continents. We used the loading models estimated for a set of 229 ITRF2014 (International Terrestrial Reference Frame) International GNSS Service (IGS) stations and discussed the amount of variance explained by individual modes, proving that the common annual signal accounts for 16, 24 and 68% of the total variance of non-tidal ocean, atmospheric and hydrological loading models, respectively. Having removed the common environmental MSSA seasonal curve from the corresponding GPS position time series, we found that the residual station-specific annual curve modelled with the least-squares estimation has the amplitude of maximum 2 mm. This means that the environmental loading models underestimate the seasonalities observed by the GPS system. The remaining signal present in the seasonal frequency band arises from the systematic errors which are not of common environmental or geophysical origin. Using common mode error (CME) estimates, we showed that the direct removal of environmental loading models from the GPS series causes an artificial loss in the CME power spectra between 10 and 80 cycles per year. When environmental effect is removed from GPS series with MSSA curves, no influence on the character of spectra of CME estimates was noticed.

Vector Observation-Aided/Attitude-Rate Estimation Using Global Positioning System Signals

NASA Technical Reports Server (NTRS)

Oshman, Yaakov; Markley, F. Landis

1997-01-01

A sequential filtering algorithm is presented for attitude and attitude-rate estimation from Global Positioning System (GPS) differential carrier phase measurements. A third-order, minimal-parameter method for solving the attitude matrix kinematic equation is used to parameterize the filter's state, which renders the resulting estimator computationally efficient. Borrowing from tracking theory concepts, the angular acceleration is modeled as an exponentially autocorrelated stochastic process, thus avoiding the use of the uncertain spacecraft dynamic model. The new formulation facilitates the use of aiding vector observations in a unified filtering algorithm, which can enhance the method's robustness and accuracy. Numerical examples are used to demonstrate the performance of the method.

Lateral eddy diffusivity estimates from simulated and observed drifter trajectories: a case study for the Agulhas Current system

NASA Astrophysics Data System (ADS)

Rühs, Siren; Zhurbas, Victor; Durgadoo, Jonathan V.; Biastoch, Arne

2017-04-01

The Lagrangian description of fluid motion by sets of individual particle trajectories is extensively used to characterize connectivity between distinct oceanic locations. One important factor influencing the connectivity is the average rate of particle dispersal, generally quantified as Lagrangian diffusivity. In addition to Lagrangian observing programs, Lagrangian analyses are performed by advecting particles with the simulated flow field of ocean general circulation models (OGCMs). However, depending on the spatio-temporal model resolution, not all scale-dependent processes are explicitly resolved in the simulated velocity fields. Consequently, the dispersal of advective Lagrangian trajectories has been assumed not to be sufficiently diffusive compared to observed particle spreading. In this study we present a detailed analysis of the spatially variable lateral eddy diffusivity characteristics of advective drifter trajectories simulated with realistically forced OGCMs and compare them with estimates based on observed drifter trajectories. The extended Agulhas Current system around South Africa, known for its intricate mesoscale dynamics, serves as a test case. We show that a state-of-the-art eddy-resolving OGCM indeed features theoretically derived dispersion characteristics for diffusive regimes and realistically represents Lagrangian eddy diffusivity characteristics obtained from observed surface drifter trajectories. The estimates for the maximum and asymptotic lateral single-particle eddy diffusivities obtained from the observed and simulated drifter trajectories show a good agreement in their spatial pattern and magnitude. We further assess the sensitivity of the simulated lateral eddy diffusivity estimates to the temporal and lateral OGCM output resolution and examine the impact of the different eddy diffusivity characteristics on the Lagrangian connectivity between the Indian Ocean and the South Atlantic.

A global logrank test for adaptive treatment strategies based on observational studies.

PubMed

Li, Zhiguo; Valenstein, Marcia; Pfeiffer, Paul; Ganoczy, Dara

2014-02-28

In studying adaptive treatment strategies, a natural question that is of paramount interest is whether there is any significant difference among all possible treatment strategies. When the outcome variable of interest is time-to-event, we propose an inverse probability weighted logrank test for testing the equivalence of a fixed set of pre-specified adaptive treatment strategies based on data from an observational study. The weights take into account both the possible selection bias in an observational study and the fact that the same subject may be consistent with more than one treatment strategy. The asymptotic distribution of the weighted logrank statistic under the null hypothesis is obtained. We show that, in an observational study where the treatment selection probabilities need to be estimated, the estimation of these probabilities does not have an effect on the asymptotic distribution of the weighted logrank statistic, as long as the estimation of the parameters in the models for these probabilities is n-consistent. Finite sample performance of the test is assessed via a simulation study. We also show in the simulation that the test can be pretty robust to misspecification of the models for the probabilities of treatment selection. The method is applied to analyze data on antidepressant adherence time from an observational database maintained at the Department of Veterans Affairs' Serious Mental Illness Treatment Research and Evaluation Center. Copyright © 2013 John Wiley & Sons, Ltd.

Transportation-cyber-physical-systems-oriented engine cylinder pressure estimation using high gain observer

NASA Astrophysics Data System (ADS)

Li, Yong-Fu; Xiao-Pei, Kou; Zheng, Tai-Xiong; Li, Yin-Guo

2015-05-01

In transportation cyber-physical-systems (T-CPS), vehicle-to-vehicle (V2V) communications play an important role in the coordination between individual vehicles as well as between vehicles and the roadside infrastructures, and engine cylinder pressure is significant for engine diagnosis on-line and torque control within the information exchange process under V2V communications. However, the parametric uncertainties caused from measurement noise in T-CPS lead to the dynamic performance deterioration of the engine cylinder pressure estimation. Considering the high accuracy requirement under V2V communications, a high gain observer based on the engine dynamic model is designed to improve the accuracy of pressure estimation. Then, the analyses about convergence, converge speed and stability of the corresponding error model are conducted using the Laplace and Lyapunov method. Finally, results from combination of Simulink with GT-Power based numerical experiments and comparisons demonstrate the effectiveness of the proposed approach with respect to robustness and accuracy. Project supported by the National Natural Science Foundation of China (Grant No. 61304197), the Scientific and Technological Talents of Chongqing, China (Grant No. cstc2014kjrc-qnrc30002), the Key Project of Application and Development of Chongqing, China (Grant No. cstc2014yykfB40001), the Natural Science Funds of Chongqing, China (Grant No. cstc2014jcyjA60003), and the Doctoral Start-up Funds of Chongqing University of Posts and Telecommunications, China (Grant No. A2012-26).

Family-oriented cardiac risk estimator: a Java web-based applet.

PubMed

Crouch, Michael A; Jadhav, Ashwin

2003-01-01

We developed a Java applet that calculates four different estimates of a person's 10-year risk for heart attack: (1) Estimate based on Framingham equation (2) Framingham equation estimate modified by C-reactive protein (CRP) level (3) Framingham estimate modified by family history of heart disease in parents or siblings (4) Framingham estimate modified by both CRP and family heart disease history. This web-based, family-oriented cardiac risk estimator uniquely considers family history and CRP while estimating risk.

Estimating two-way tables based on forest surveys

Treesearch

Charles T. Scott

2000-01-01

Forest survey analysts usually are interested in tables of values rather than single point estimates. A common error is to include only plots on which nonzero values of the attribute were observed when computing the variance of a mean. Similarly, analysts often exclude nonforest plots from the analysis. The development of the correct estimates of forest area, attribute...

Pilot-based parametric channel estimation algorithm for DCO-OFDM-based visual light communications

NASA Astrophysics Data System (ADS)

Qian, Xuewen; Deng, Honggui; He, Hailang

2017-10-01

Due to wide modulation bandwidth in optical communication, multipath channels may be non-sparse and deteriorate communication performance heavily. Traditional compressive sensing-based channel estimation algorithm cannot be employed in this kind of situation. In this paper, we propose a practical parametric channel estimation algorithm for orthogonal frequency division multiplexing (OFDM)-based visual light communication (VLC) systems based on modified zero correlation code (ZCC) pair that has the impulse-like correlation property. Simulation results show that the proposed algorithm achieves better performances than existing least squares (LS)-based algorithm in both bit error ratio (BER) and frequency response estimation.

Ionospheric Irregularities Characterization by Ground and Space-based GPS Observations

NASA Astrophysics Data System (ADS)

Zakharenkova, I.; Cherniak, I.; Krankowski, A.

2017-12-01

We present new results on detection and investigation of the topside ionospheric irregularities using GPS measurements from Precise Orbit Determination (POD) GPS antenna onboard Low Earth Orbit satellites. Our investigation is based on the recent ESA's Swarm mission launched on 22 November 2013 and consisted of three identical satellites, two of them fly in a tandem at an orbit altitude of 460 km while the third satellite - at an orbit altitude of 510 km. Each satellite is equipped with a zenith-looking antenna and 8-channel dual-frequency GPS receiver that delivered 1 Hz data for POD purposes, as well as Langmuir Probe instrument for in situ electron density. Additionally, we have analyzed GPS measurements onboard GRACE and TerraSAR-X satellite, which have rather similar to Swarm orbit altitude of 500 km. GPS measurements onboard MetOP-A and MetOP-B satellites (altitude of 840 km) can complement these observations in order to estimate an altitudinal extent of the ionospheric irregularities penetrating to higher altitudes. We demonstrate that space-based GPS observations can be effectively used for monitoring of the topside ionospheric irregularities occurrence in both high-latitude and equatorial regions and may essentially contribute to the multi-instrumental analysis of the ground-based and in situ data. Climatological characteristics of the equatorial ionospheric irregularities occurrence probability are derived from POD GPS measurements for all longitudinal sectors for the years 2013-2016. Several examples of strong geomagnetic storms, including the 2015 St. Patrick's Day storm, were analyzed to demonstrate differences between the climatlogical characteristics in space-based GPS data and storm-induced equatorial irregularities observations (postsunset suppression, night/morning-time occurrence). To support our observations and conclusions, we involve into our analysis in situ plasma density provided by Swarm constellation, GRACE KBR, DMSP satellites, as well

Direct estimation of tidally induced Earth rotation variations observed by VLBI

NASA Astrophysics Data System (ADS)

Englich, S.; Heinkelmann, R.; BOHM, J.; Schuh, H.

2009-09-01

The subject of our study is the investigation of periodical variations induced by solid Earth tides and ocean tides in Earth rotation parameters (ERP: polar motion, UT1)observed by VLBI. There are two strategies to determine the amplitudes and phases of Earth rotation variations from observations of space geodetic techniques. The common way is to derive time series of Earth rotation parameters first and to estimate amplitudes and phases in a second step. Results obtained by this means were shown in previous studies for zonal tidal variations (Englich et al.; 2008a) and variations caused by ocean tides (Englich et al.; 2008b). The alternative method is to estimate the tidal parameters directly within the VLBI data analysis procedure together with other parameters such as station coordinates, tropospheric delays, clocks etc. The purpose of this work was the application of this direct method to a combined VLBI data analysis using the software packages OCCAM (Version 6.1, Gauss-Markov-Model) and DOGSCS (Gerstl et al.; 2001). The theoretical basis and the preparatory steps for the implementation of this approach are presented here.

Observer-based monitoring of heat exchangers.

PubMed

Astorga-Zaragoza, Carlos-Manuel; Alvarado-Martínez, Víctor-Manuel; Zavala-Río, Arturo; Méndez-Ocaña, Rafael-Maxim; Guerrero-Ramírez, Gerardo-Vicente

2008-01-01

The goal of this work is to provide a method for monitoring performance degradation in counter-flow double-pipe heat exchangers. The overall heat transfer coefficient is estimated by an adaptive observer and monitored in order to infer when the heat exchanger needs preventive or corrective maintenance. A simplified mathematical model is used to synthesize the adaptive observer and a more complex model is used for simulation. The reliability of the proposed method was demonstrated via numerical simulations and laboratory experiments with a bench-scale pilot plant.

Use of NEXRAD radar-based observations for quality control of in-situ rain gauge measurements

NASA Astrophysics Data System (ADS)

Nelson, B. R.; Prat, O.; Leeper, R.

2017-12-01

Rain gauge quality control is an often over looked important step in the archive of historical precipitation estimates. We investigate the possibilities that exist for using ground based radar networks for quality control of rain gauge measurements. This process includes the point to pixel comparisons of the rain gauge measurements with NEXRAD observations. There are two NEXRAD based data sets used for reference; the NCEP stage IV and the NWS MRMS gridded data sets. The NCEP stage IV data set is available at 4km hourly for the period 2002-present and includes the radar-gauge bias adjusted precipitation estimate. The NWS MRMS data set includes several different variables such as reflectivity, radar-only estimates, precipitation flag, and radar-gauge bias adjusted precipitation estimates. The latter product provides for much more information to apply quality control such as identification of precipitation type, identification of storm type and Z-R relation. In addition, some of the variables are available at 5-minute scale. The rain gauge networks that are investigated are the Climate Reference Network (CRN), the Fischer-Porter Hourly Precipitation Database (HPD), and the Hydrometeorological Automated Data System (HADS). The CRN network is available at the 5-minute scale, the HPD network is available at the 15-minute and hourly scale, and HADS is available at the hourly scale. The varying scales present challenges for comparisons. However given the higher resolution radar-based products we identify an optimal combination of rain gauge observations that can be compared to the radar-based information. The quality control process focuses on identifying faulty gauges in direct comparison while a deeper investigation focuses on event-based differences from light rain to extreme storms.

Estimating Vegetation Rainfall Interception Using Remote Sensing Observations at Very High Resolution

NASA Astrophysics Data System (ADS)

Cui, Y.; Zhao, P.; Hong, Y.; Fan, W.; Yan, B.; Xie, H.

2017-12-01

Abstract: As an important compont of evapotranspiration, vegetation rainfall interception is the proportion of gross rainfall that is intercepted, stored and subsequently evaporated from all parts of vegetation during or following rainfall. Accurately quantifying the vegetation rainfall interception at a high resolution is critical for rainfall-runoff modeling and flood forecasting, and is also essential for understanding its further impact on local, regional, and even global water cycle dynamics. In this study, the Remote Sensing-based Gash model (RS-Gash model) is developed based on a modified Gash model for interception loss estimation using remote sensing observations at the regional scale, and has been applied and validated in the upper reach of the Heihe River Basin of China for different types of vegetation. To eliminate the scale error and the effect of mixed pixels, the RS-Gash model is applied at a fine scale of 30 m with the high resolution vegetation area index retrieved by using the unified model of bidirectional reflectance distribution function (BRDF-U) for the vegetation canopy. Field validation shows that the RMSE and R2 of the interception ratio are 3.7% and 0.9, respectively, indicating the model's strong stability and reliability at fine scale. The temporal variation of vegetation rainfall interception loss and its relationship with precipitation are further investigated. In summary, the RS-Gash model has demonstrated its effectiveness and reliability in estimating vegetation rainfall interception. When compared to the coarse resolution results, the application of this model at 30-m fine resolution is necessary to resolve the scaling issues as shown in this study. Keywords: rainfall interception; remote sensing; RS-Gash analytical model; high resolution

Determining the Best Method for Estimating the Observed Level of Maximum Detrainment Based on Radar Reflectivity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carletta, Nicholas D.; Mullendore, Gretchen L.; Starzec, Mariusz

Convective mass transport is the transport of mass from near the surface up to the upper troposphere and lower stratosphere (UTLS) by a deep convective updraft. This transport can alter the chemical makeup and water vapor balance of the UTLS, which affects cloud formation and the radiative properties of the atmosphere. It is therefore important to understand the exact altitudes at which mass is detrained from convection. The purpose of this study was to improve upon previously published methodologies for estimating the level of maximum detrainment (LMD) within convection using data from a single ground-based radar. Four methods were usedmore » to identify the LMD and validated against dual-Doppler derived vertical mass divergence fields for six cases with a variety of storm types. The best method for locating the LMD was determined to be the method that used a reflectivity texture technique to determine convective cores and a multi-layer echo identification to determine anvil locations. Although an improvement over previously published methods, the new methodology still produced unreliable results in certain regimes. The methodology worked best when applied to mature updrafts, as the anvil needs time to grow to a detectable size. Thus, radar reflectivity is found to be valuable in estimating the LMD, but storm maturity must also be considered for best results.« less

Estimation of vegetation photosynthetic capacity from space-based measurements of chlorophyll fluorescence for terrestrial biosphere models.

PubMed

Zhang, Yongguang; Guanter, Luis; Berry, Joseph A; Joiner, Joanna; van der Tol, Christiaan; Huete, Alfredo; Gitelson, Anatoly; Voigt, Maximilian; Köhler, Philipp

2014-12-01

Photosynthesis simulations by terrestrial biosphere models are usually based on the Farquhar's model, in which the maximum rate of carboxylation (Vcmax ) is a key control parameter of photosynthetic capacity. Even though Vcmax is known to vary substantially in space and time in response to environmental controls, it is typically parameterized in models with tabulated values associated to plant functional types. Remote sensing can be used to produce a spatially continuous and temporally resolved view on photosynthetic efficiency, but traditional vegetation observations based on spectral reflectance lack a direct link to plant photochemical processes. Alternatively, recent space-borne measurements of sun-induced chlorophyll fluorescence (SIF) can offer an observational constraint on photosynthesis simulations. Here, we show that top-of-canopy SIF measurements from space are sensitive to Vcmax at the ecosystem level, and present an approach to invert Vcmax from SIF data. We use the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) balance model to derive empirical relationships between seasonal Vcmax and SIF which are used to solve the inverse problem. We evaluate our Vcmax estimation method at six agricultural flux tower sites in the midwestern US using spaced-based SIF retrievals. Our Vcmax estimates agree well with literature values for corn and soybean plants (average values of 37 and 101 μmol m(-2)  s(-1) , respectively) and show plausible seasonal patterns. The effect of the updated seasonally varying Vcmax parameterization on simulated gross primary productivity (GPP) is tested by comparing to simulations with fixed Vcmax values. Validation against flux tower observations demonstrate that simulations of GPP and light use efficiency improve significantly when our time-resolved Vcmax estimates from SIF are used, with R(2) for GPP comparisons increasing from 0.85 to 0.93, and for light use efficiency from 0.44 to 0.83. Our results support the use of

Improving satellite-based post-fire evapotranspiration estimates in semi-arid regions

NASA Astrophysics Data System (ADS)

Poon, P.; Kinoshita, A. M.

2017-12-01

Climate change and anthropogenic factors contribute to the increased frequency, duration, and size of wildfires, which can alter ecosystem and hydrological processes. The loss of vegetation canopy and ground cover reduces interception and alters evapotranspiration (ET) dynamics in riparian areas, which can impact rainfall-runoff partitioning. Previous research evaluated the spatial and temporal trends of ET based on burn severity and observed an annual decrease of 120 mm on average for three years after fire. Building upon these results, this research focuses on the Coyote Fire in San Diego, California (USA), which burned a total of 76 km2 in 2003 to calibrate and improve satellite-based ET estimates in semi-arid regions affected by wildfire. The current work utilizes satellite-based products and techniques such as the Google Earth Engine Application programming interface (API). Various ET models (ie. Operational Simplified Surface Energy Balance Model (SSEBop)) are compared to the latent heat flux from two AmeriFlux eddy covariance towers, Sky Oaks Young (US-SO3), and Old Stand (US-SO2), from 2000 - 2015. The Old Stand tower has a low burn severity and the Young Stand tower has a moderate to high burn severity. Both towers are used to validate spatial ET estimates. Furthermore, variables and indices, such as Enhanced Vegetation Index (EVI), Normalized Difference Moisture Index (NDMI), and the Normalized Burn Ratio (NBR) are utilized to evaluate satellite-based ET through a multivariate statistical analysis at both sites. This point-scale study will able to improve ET estimates in spatially diverse regions. Results from this research will contribute to the development of a post-wildfire ET model for semi-arid regions. Accurate estimates of post-fire ET will provide a better representation of vegetation and hydrologic recovery, which can be used to improve hydrologic models and predictions.

Validation of Satellite-based Rainfall Estimates for Severe Storms (Hurricanes & Tornados)

NASA Astrophysics Data System (ADS)

Nourozi, N.; Mahani, S.; Khanbilvardi, R.

2005-12-01

Severe storms such as hurricanes and tornadoes cause devastating damages, almost every year, over a large section of the United States. More accurate forecasting intensity and track of a heavy storm can help to reduce if not to prevent its damages to lives, infrastructure, and economy. Estimating accurate high resolution quantitative precipitation (QPE) from a hurricane, required to improve the forecasting and warning capabilities, is still a challenging problem because of physical characteristics of the hurricane even when it is still over the ocean. Satellite imagery seems to be a valuable source of information for estimating and forecasting heavy precipitation and also flash floods, particularly for over the oceans where the traditional ground-based gauge and radar sources cannot provide any information. To improve the capability of a rainfall retrieval algorithm for estimating QPE of severe storms, its product is evaluated in this study. High (hourly 4km x 4km) resolutions satellite infrared-based rainfall products, from the NESDIS Hydro-Estimator (HE) and also PERSIANN (Precipitation Estimation from Remotely Sensed Information using an Artificial Neural Networks) algorithms, have been tested against NEXRAD stage-IV and rain gauge observations in this project. Three strong hurricanes: Charley (category 4), Jeanne (category 3), and Ivan (category 3) that caused devastating damages over Florida in the summer 2004, have been considered to be investigated. Preliminary results demonstrate that HE tends to underestimate rain rates when NEXRAD shows heavy storm (rain rates greater than 25 mm/hr) and to overestimate when NEXRAD gives low rainfall amounts, but PERSIANN tends to underestimate rain rates, in general.

Estimating snow water equivalent from GPS vertical site-position observations in the western United States

PubMed Central

Ouellette, Karli J; de Linage, Caroline; Famiglietti, James S

2013-01-01

[1] Accurate estimation of the characteristics of the winter snowpack is crucial for prediction of available water supply, flooding, and climate feedbacks. Remote sensing of snow has been most successful for quantifying the spatial extent of the snowpack, although satellite estimation of snow water equivalent (SWE), fractional snow covered area, and snow depth is improving. Here we show that GPS observations of vertical land surface loading reveal seasonal responses of the land surface to the total weight of snow, providing information about the stored SWE. We demonstrate that the seasonal signal in Scripps Orbit and Permanent Array Center (SOPAC) GPS vertical land surface position time series at six locations in the western United States is driven by elastic loading of the crust by the snowpack. GPS observations of land surface deformation are then used to predict the water load as a function of time at each location of interest and compared for validation to nearby Snowpack Telemetry observations of SWE. Estimates of soil moisture are included in the analysis and result in considerable improvement in the prediction of SWE. Citation: Ouellette, K. J., C. de Linage, and J. S. Famiglietti (2013), Estimating snow water equivalent from GPS vertical site-position observations in the western United States, Water Resour. Res., 49, 2508–2518, doi:10.1002/wrcr.20173. PMID:24223442

Neural network-based estimates of Southern Ocean net community production from in-situ O2 / Ar and satellite observation: a methodological study

NASA Astrophysics Data System (ADS)

Chang, C.-H.; Johnson, N. C.; Cassar, N.

2013-10-01

Southern Ocean organic carbon export plays an important role in the global carbon cycle, yet its basin-scale climatology and variability are uncertain due to limited coverage of in situ observations. In this study, a neural network approach based on the self-organizing map (SOM) is adopted to construct weekly gridded (1° × 1°) maps of organic carbon export for the Southern Ocean from 1998 to 2009. The SOM is trained with in situ measurements of O2 / Ar-derived net community production (NCP) that are tightly linked to the carbon export in the mixed layer on timescales of 1-2 weeks, and six potential NCP predictors: photosynthetically available radiation (PAR), particulate organic carbon (POC), chlorophyll (Chl), sea surface temperature (SST), sea surface height (SSH), and mixed layer depth (MLD). This non-parametric approach is based entirely on the observed statistical relationships between NCP and the predictors, and therefore is strongly constrained by observations. A thorough cross-validation yields three retained NCP predictors, Chl, PAR, and MLD. Our constructed NCP is further validated by good agreement with previously published independent in situ derived NCP of weekly or longer temporal resolution through real-time and climatological comparisons at various sampling sites. The resulting November-March NCP climatology reveals a pronounced zonal band of high NCP roughly following the subtropical front in the Atlantic, Indian and western Pacific sectors, and turns southeastward shortly after the dateline. Other regions of elevated NCP include the upwelling zones off Chile and Namibia, Patagonian Shelf, Antarctic coast, and areas surrounding the Islands of Kerguelen, South Georgia, and Crozet. This basin-scale NCP climatology closely resembles that of the satellite POC field and observed air-sea CO2 flux. The long-term mean area-integrated NCP south of 50° S from our dataset, 14 mmol C m-2 d-1, falls within the range of 8.3-24 mmol C m-2 d-1 from other model

Surface Runoff Estimation Using SMOS Observations, Rain-gauge Measurements and Satellite Precipitation Estimations. Comparison with Model Predictions

NASA Astrophysics Data System (ADS)

Garcia Leal, Julio A.; Lopez-Baeza, Ernesto; Khodayar, Samiro; Estrela, Teodoro; Fidalgo, Arancha; Gabaldo, Onofre; Kuligowski, Robert; Herrera, Eddy

Surface runoff is defined as the amount of water that originates from precipitation, does not infiltrates due to soil saturation and therefore circulates over the surface. A good estimation of runoff is useful for the design of draining systems, structures for flood control and soil utilisation. For runoff estimation there exist different methods such as (i) rational method, (ii) isochrone method, (iii) triangular hydrograph, (iv) non-dimensional SCS hydrograph, (v) Temez hydrograph, (vi) kinematic wave model, represented by the dynamics and kinematics equations for a uniforme precipitation regime, and (vii) SCS-CN (Soil Conservation Service Curve Number) model. This work presents a way of estimating precipitation runoff through the SCS-CN model, using SMOS (Soil Moisture and Ocean Salinity) mission soil moisture observations and rain-gauge measurements, as well as satellite precipitation estimations. The area of application is the Jucar River Basin Authority area where one of the objectives is to develop the SCS-CN model in a spatial way. The results were compared to simulations performed with the 7-km COSMO-CLM (COnsortium for Small-scale MOdelling, COSMO model in CLimate Mode) model. The use of SMOS soil moisture as input to the COSMO-CLM model will certainly improve model simulations.

Estimation of fire emissions from satellite-based measurements

NASA Astrophysics Data System (ADS)

Ichoku, C. M.; Kaufman, Y. J.

2004-12-01

Biomass burning is a worldwide phenomenon affecting many vegetated parts of the globe regularly. Fires emit large quantities of aerosol and trace gases into the atmosphere, thus influencing the atmospheric chemistry and climate. Traditional methods of fire emissions estimation achieved only limited success, because they were based on peripheral information such as rainfall patterns, vegetation types and changes, agricultural practices, and surface ozone concentrations. During the last several years, rapid developments in satellite remote sensing has allowed more direct estimation of smoke emissions using remotely-sensed fire data. However, current methods use fire pixel counts or burned areas, thereby depending on the accuracy of independent estimations of the biomass fuel loadings, combustion efficiency, and emission factors. With the enhanced radiometric range of its 4-micron fire channel, the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, which flies aboard both of the Earth Observing System (EOS) Terra and Aqua Satellites, is able to measure the rate of release of fire radiative energy (FRE) in MJ/s (something that older sensors could not do). MODIS also measures aerosol distribution. Taking advantage of these new resources, we have developed a procedure combining MODIS fire and aerosol products to derive FRE-based smoke emission coefficients (Ce in kg/MJ) for different regions of the globe. These coefficients are simply used to multiply FRE from MODIS to derive the emitted smoke aerosol mass. Results from this novel methodology are very encouraging. For instance, it was found that the smoke total particulate mass emission coefficient for the Brazilian Cerrado ecosystem (approximately 0.022 kg/MJ) is about twice the value for North America or Australia, but about 50 percent lower than the value for Zambia in southern Africa.

Estimation of Fire Emissions from Satellite-Based Measurements

NASA Technical Reports Server (NTRS)

Ichoku, Charles; Kaufman, Yoram J.

2004-01-01

Biomass burning is a worldwide phenomenon affecting many vegetated parts of the globe regularly. Fires emit large quantities of aerosol and trace gases into the atmosphere, thus influencing the atmospheric chemistry and climate. Traditional methods of fire emissions estimation achieved only limited success, because they were based on peripheral information such as rainfall patterns, vegetation types and changes, agricultural practices, and surface ozone concentrations. During the last several years, rapid developments in satellite remote sensing has allowed more direct estimation of smoke emissions using remotely-sensed fire data. However, current methods use fire pixel counts or burned areas, thereby depending on the accuracy of independent estimations of the biomass fuel loadings, combustion efficiency, and emission factors. With the enhanced radiometric range of its 4-micron fire channel, the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, which flies aboard both of the Earth Observing System EOS) Terra and Aqua Satellites, is able to measure the rate of release of fire radiative energy (FRE) in MJ/s (something that older sensors could not do). MODIS also measures aerosol distribution. Taking advantage of these new resources, we have developed a procedure combining MODIS fire and aerosol products to derive FRE-based smoke emission coefficients (C(e), in kg/MJ) for different regions of the globe. These coefficients are simply used to multiply FRE from MODIS to derive the emitted smoke aerosol mass. Results from this novel methodology are very encouraging. For instance, it was found that the smoke total particulate mass emission coefficient for the Brazilian Cerrado ecosystem (approximately 0.022 kg/MJ) is about twice the value for North America, Western Europe, or Australia, but about 50% lower than the value for southern Africa.

Facial Asymmetry-Based Age Group Estimation: Role in Recognizing Age-Separated Face Images.

PubMed

Sajid, Muhammad; Taj, Imtiaz Ahmad; Bajwa, Usama Ijaz; Ratyal, Naeem Iqbal

2018-04-23

Face recognition aims to establish the identity of a person based on facial characteristics. On the other hand, age group estimation is the automatic calculation of an individual's age range based on facial features. Recognizing age-separated face images is still a challenging research problem due to complex aging processes involving different types of facial tissues, skin, fat, muscles, and bones. Certain holistic and local facial features are used to recognize age-separated face images. However, most of the existing methods recognize face images without incorporating the knowledge learned from age group estimation. In this paper, we propose an age-assisted face recognition approach to handle aging variations. Inspired by the observation that facial asymmetry is an age-dependent intrinsic facial feature, we first use asymmetric facial dimensions to estimate the age group of a given face image. Deeply learned asymmetric facial features are then extracted for face recognition using a deep convolutional neural network (dCNN). Finally, we integrate the knowledge learned from the age group estimation into the face recognition algorithm using the same dCNN. This integration results in a significant improvement in the overall performance compared to using the face recognition algorithm alone. The experimental results on two large facial aging datasets, the MORPH and FERET sets, show that the proposed age group estimation based on the face recognition approach yields superior performance compared to some existing state-of-the-art methods. © 2018 American Academy of Forensic Sciences.

Towards Real-Time Maneuver Detection: Automatic State and Dynamics Estimation with the Adaptive Optimal Control Based Estimator

NASA Astrophysics Data System (ADS)

Lubey, D.; Scheeres, D.

Tracking objects in Earth orbit is fraught with complications. This is due to the large population of orbiting spacecraft and debris that continues to grow, passive (i.e. no direct communication) and data-sparse observations, and the presence of maneuvers and dynamics mismodeling. Accurate orbit determination in this environment requires an algorithm to capture both a system's state and its state dynamics in order to account for mismodelings. Previous studies by the authors yielded an algorithm called the Optimal Control Based Estimator (OCBE) - an algorithm that simultaneously estimates a system's state and optimal control policies that represent dynamic mismodeling in the system for an arbitrary orbit-observer setup. The stochastic properties of these estimated controls are then used to determine the presence of mismodelings (maneuver detection), as well as characterize and reconstruct the mismodelings. The purpose of this paper is to develop the OCBE into an accurate real-time orbit tracking and maneuver detection algorithm by automating the algorithm and removing its linear assumptions. This results in a nonlinear adaptive estimator. In its original form the OCBE had a parameter called the assumed dynamic uncertainty, which is selected by the user with each new measurement to reflect the level of dynamic mismodeling in the system. This human-in-the-loop approach precludes real-time application to orbit tracking problems due to their complexity. This paper focuses on the Adaptive OCBE, a version of the estimator where the assumed dynamic uncertainty is chosen automatically with each new measurement using maneuver detection results to ensure that state uncertainties are properly adjusted to account for all dynamic mismodelings. The paper also focuses on a nonlinear implementation of the estimator. Originally, the OCBE was derived from a nonlinear cost function then linearized about a nominal trajectory, which is assumed to be ballistic (i.e. the nominal optimal

Localized Dictionaries Based Orientation Field Estimation for Latent Fingerprints.

PubMed

Xiao Yang; Jianjiang Feng; Jie Zhou

2014-05-01

Dictionary based orientation field estimation approach has shown promising performance for latent fingerprints. In this paper, we seek to exploit stronger prior knowledge of fingerprints in order to further improve the performance. Realizing that ridge orientations at different locations of fingerprints have different characteristics, we propose a localized dictionaries-based orientation field estimation algorithm, in which noisy orientation patch at a location output by a local estimation approach is replaced by real orientation patch in the local dictionary at the same location. The precondition of applying localized dictionaries is that the pose of the latent fingerprint needs to be estimated. We propose a Hough transform-based fingerprint pose estimation algorithm, in which the predictions about fingerprint pose made by all orientation patches in the latent fingerprint are accumulated. Experimental results on challenging latent fingerprint datasets show the proposed method outperforms previous ones markedly.

Differences in estimating terrestrial water flux from three satellite-based Priestley-Taylor algorithms

NASA Astrophysics Data System (ADS)

Yao, Yunjun; Liang, Shunlin; Yu, Jian; Zhao, Shaohua; Lin, Yi; Jia, Kun; Zhang, Xiaotong; Cheng, Jie; Xie, Xianhong; Sun, Liang; Wang, Xuanyu; Zhang, Lilin

2017-04-01

Accurate estimates of terrestrial latent heat of evaporation (LE) for different biomes are essential to assess energy, water and carbon cycles. Different satellite- based Priestley-Taylor (PT) algorithms have been developed to estimate LE in different biomes. However, there are still large uncertainties in LE estimates for different PT algorithms. In this study, we evaluated differences in estimating terrestrial water flux in different biomes from three satellite-based PT algorithms using ground-observed data from eight eddy covariance (EC) flux towers of China. The results reveal that large differences in daily LE estimates exist based on EC measurements using three PT algorithms among eight ecosystem types. At the forest (CBS) site, all algorithms demonstrate high performance with low root mean square error (RMSE) (less than 16 W/m2) and high squared correlation coefficient (R2) (more than 0.9). At the village (HHV) site, the ATI-PT algorithm has the lowest RMSE (13.9 W/m2), with bias of 2.7 W/m2 and R2 of 0.66. At the irrigated crop (HHM) site, almost all models algorithms underestimate LE, indicating these algorithms may not capture wet soil evaporation by parameterization of the soil moisture. In contrast, the SM-PT algorithm shows high values of R2 (comparable to those of ATI-PT and VPD-PT) at most other (grass, wetland, desert and Gobi) biomes. There are no obvious differences in seasonal LE estimation using MODIS NDVI and LAI at most sites. However, all meteorological or satellite-based water-related parameters used in the PT algorithm have uncertainties for optimizing water constraints. This analysis highlights the need to improve PT algorithms with regard to water constraints.

A Genetic Algorithm Method for Direct estimation of paleostress states from heterogeneous fault-slip observations

NASA Astrophysics Data System (ADS)

Srivastava, D. C.

2016-12-01

A Genetic Algorithm Method for Direct estimation of paleostress states from heterogeneous fault-slip observationsDeepak C. Srivastava, Prithvi Thakur and Pravin K. GuptaDepartment of Earth Sciences, Indian Institute of Technology Roorkee, Roorkee 247667, India. Abstract Paleostress estimation from a group of heterogeneous fault-slip observations entails first the classification of the observations into homogeneous fault sets and then a separate inversion of each homogeneous set. This study combines these two issues into a nonlinear inverse problem and proposes a heuristic search method that inverts the heterogeneous fault-slip observations. The method estimates different paleostress states in a group of heterogeneous fault-slip observations and classifies it into homogeneous sets as a byproduct. It uses the genetic algorithm operators, elitism, selection, encoding, crossover and mutation. These processes translate into a guided search that finds successively fitter solutions and operate iteratively until the termination criteria is met and the globally fittest stress tensors are obtained. We explain the basic steps of the algorithm on a working example and demonstrate validity of the method on several synthetic and a natural group of heterogeneous fault-slip observations. The method is independent of any user-defined bias or any entrapment of solution in a local optimum. It succeeds even in the difficult situations where other classification methods are found to fail.

Estimating time-based instantaneous total mortality rate based on the age-structured abundance index

NASA Astrophysics Data System (ADS)

Wang, Yingbin; Jiao, Yan

2015-05-01

The instantaneous total mortality rate ( Z) of a fish population is one of the important parameters in fisheries stock assessment. The estimation of Z is crucial to fish population dynamics analysis, abundance and catch forecast, and fisheries management. A catch curve-based method for estimating time-based Z and its change trend from catch per unit effort (CPUE) data of multiple cohorts is developed. Unlike the traditional catch-curve method, the method developed here does not need the assumption of constant Z throughout the time, but the Z values in n continuous years are assumed constant, and then the Z values in different n continuous years are estimated using the age-based CPUE data within these years. The results of the simulation analyses show that the trends of the estimated time-based Z are consistent with the trends of the true Z, and the estimated rates of change from this approach are close to the true change rates (the relative differences between the change rates of the estimated Z and the true Z are smaller than 10%). Variations of both Z and recruitment can affect the estimates of Z value and the trend of Z. The most appropriate value of n can be different given the effects of different factors. Therefore, the appropriate value of n for different fisheries should be determined through a simulation analysis as we demonstrated in this study. Further analyses suggested that selectivity and age estimation are also two factors that can affect the estimated Z values if there is error in either of them, but the estimated change rates of Z are still close to the true change rates. We also applied this approach to the Atlantic cod ( Gadus morhua) fishery of eastern Newfoundland and Labrador from 1983 to 1997, and obtained reasonable estimates of time-based Z.

Estimating Soil and Vegetation Parameters using Synergies between Optical and Microwave Observations

NASA Astrophysics Data System (ADS)

Timmermans, J.; Gomez-Dans, J. L.; Lewis, P.; Loew, A.; Schlenz, F.; Mathieu, P. P.; Pounder, N. L.; Styles, J.

2017-12-01

The large amount of remote sensing data available provides a huge potential for various applications, such as crop monitoring. This potential has not been realized yet because inversion-algorithms mostly use a single sensor approach. Consequently, products that combine different low-level observations from different sensors are hard to find. The difficulty in a multi-sensor approach is that 1) different sensor types (microwave/ optical) require different radiative transfer (RT) models and 2) it require consistency between the models. The goal of this research was to investigate the synergistic potential of integrating optical (Opt) and passive microwave (PM) RT models within the Earth Observation Land Data Assimilation System (EOLDAS). EOLDAS uses a Bayesian data assimilation approach together with observation operators such as PROSAIL to estimate state variables. In order to use PM observations, the Community Microwave Emission Model was integrated into the system. Results show a high potential when both Opt and PM observations are used independently. Using only RapidEye only with SAIL RT model, LAI was estimated with R=0.68, with leaf water content and dry matter having lower correlations |R|<0.4. Results for retrieving soil temperature and leaf area index retrievals using only Elbarra observations were good with respectively R=[0.85, 0.79], and for soil moisture also very good with R=0.73 (focusing on dry-spells of at least 9 days only), and with R=0.89 and R=0.77 for respectively the trend and anomalies. Synergistically using Opt and MW observations also shows good potential. Results show that absolute errors decreased (with RMSE=1.22 and S=0.89), but with lower R=0.59; sparse optical observations only improved part of the temporal domain. This shows that PM observations provide good information for the overall trend of the retrieved LAI due to the regular acquisitions, while Opt observations provides better information of the absolute values of the LAI.

An uncertainty analysis for satellite-based estimates of cloud condensation nuclei number concentrations

NASA Astrophysics Data System (ADS)

Shinozuka, Y.; Clarke, A. D.; Nenes, A.; Jefferson, A.; Wood, R.; McNaughton, C. S.; Ström, J.; Tunved, P.; Redemann, J.; Thornhill, K. L., II; Moore, R.; Lathem, T. L.; Lin, J.; Yoon, Y. J.

2017-12-01

Aerosol-cloud interactions (ACI) are the largest source of uncertainty in estimates of anthropogenic radiative forcing responsible for the on-going climate change. ACI for warm clouds depend on the number concentration of cloud condensation nuclei (CCN), not on aerosol optical properties. Yet, aerosol optical depth (AOD) and its variants weighted by the spectral dependence over visible and near infrared wavelengths are commonly substituted for CCN in ACI studies. The substitution is motivated by the wide availability in space and time of satellite retrievals, an advantage over the sparse CCN measurements. If satellite-based CCN estimates are to continue to complement purely model-based ones, what CCN-AOD relationship should we assume and how large is the associated uncertainty? Our 2015 paper examines airborne and ground-based observations of aerosols to address these questions, focusing on the relationship between CCN and light extinction, σ, of dried particles averaged over one-kilometer horizontal distance. That paper discusses the way the CCN-AOD relationship is influenced not only by the CCN-σ relationship but also by the humidity response of light extinction, the vertical profile, the horizontal-temporal variability and the AOD measurement error. In this presentation, we apply these findings to passive satellite data to analyze the uncertainty in satellite-based CCN estimates.

Conceptual Research of Lunar-based Earth Observation for Polar Glacier Motion

NASA Astrophysics Data System (ADS)

Ruan, Zhixing; Liu, Guang; Ding, Yixing

2016-07-01

The ice flow velocity of glaciers is important for estimating the polar ice sheet mass balance, and it is of great significance for studies into rising sea level under the background of global warming. However so far the long-term and global measurements of these macro-scale motion processes of the polar glaciers have hardly been achieved by Earth Observation (EO) technique from the ground, aircraft or satellites in space. This paper, facing the demand for space technology for large-scale global environmental change observation,especially the changes of polar glaciers, and proposes a new concept involving setting up sensors on the lunar surface and using the Moon as a platform for Earth observation, transmitting the data back to Earth. Lunar-based Earth observation, which enables the Earth's large-scale, continuous, long-term dynamic motions to be measured, is expected to provide a new solution to the problems mentioned above. According to the pattern and characteristics of polar glaciers motion, we will propose a comprehensive investigation of Lunar-based Earth observation with synthetic aperture radar (SAR). Via theoretical modeling and experimental simulation inversion, intensive studies of Lunar-based Earth observation for the glacier motions in the polar regions will be implemented, including the InSAR basics theory, observation modes of InSAR and optimization methods of their key parameters. It will be of a great help to creatively expand the EO technique system from space. In addition, they will contribute to establishing the theoretical foundation for the realization of the global, long-term and continuous observation for the glacier motion phenomena in the Antarctic and the Arctic.

Estimability of geodetic parameters from space VLBI observables

NASA Technical Reports Server (NTRS)

Adam, Jozsef

1990-01-01

The feasibility of space very long base interferometry (VLBI) observables for geodesy and geodynamics is investigated. A brief review of space VLBI systems from the point of view of potential geodetic application is given. A selected notational convention is used to jointly treat the VLBI observables of different types of baselines within a combined ground/space VLBI network. The basic equations of the space VLBI observables appropriate for convariance analysis are derived and included. The corresponding equations for the ground-to-ground baseline VLBI observables are also given for a comparison. The simplified expression of the mathematical models for both space VLBI observables (time delay and delay rate) include the ground station coordinates, the satellite orbital elements, the earth rotation parameters, the radio source coordinates, and clock parameters. The observation equations with these parameters were examined in order to determine which of them are separable or nonseparable. Singularity problems arising from coordinate system definition and critical configuration are studied. Linear dependencies between partials are analytically derived. The mathematical models for ground-space baseline VLBI observables were tested with simulation data in the frame of some numerical experiments. Singularity due to datum defect is confirmed.

Ground-based radar monitoring of volcanic ash: a novel approach for the estimation of the bulk microphysical parameters

NASA Astrophysics Data System (ADS)

Vulpiani, Gianfranco; Ripepe, Maurizio

2017-04-01

The detection and quantitative retrieval of ash plumes is of significant interest due to the environmental, climatic, and socioeconomic effects of ash fallout which might cause hardship and damages in areas surrounding volcanoes, representing a serious hazard to aircrafts. Real-time monitoring of such phenomena is crucial for initializing ash dispersion models. Ground-based and space-borne remote sensing observations provide essential information for scientific and operational applications. Satellite visible-infrared radiometric observations from geostationary platforms are usually exploited for long-range trajectory tracking and for measuring low-level eruptions. Their imagery is available every 10-30 min and suffers from a relatively poor spatial resolution. Moreover, the field of view of geostationary radiometric measurements may be blocked by water and ice clouds at higher levels and the observations' overall utility is reduced at night. Ground-based microwave weather radars may represent an important tool for detecting and, to a certain extent, mitigating the hazards presented by ash clouds. The possibility of monitoring in all weather conditions at a fairly high spatial resolution (less than a few hundred meters) and every few minutes after the eruption is the major advantage of using ground-based microwave radar systems. Ground-based weather radar systems can also provide data for estimating the ash volume, total mass, and height of eruption clouds. Previous methodological studies have investigated the possibility of using ground-based single- and dual-polarization radar system for the remote sensing of volcanic ash cloud. In the present work, methodology was revised to overcome some limitations related to the assumed microphysics. New scattering simulations based on the T-matrix solution technique were used to set up the parametric algorithms adopted to estimate the mass concentration and ash mean diameter. Furthermore, because quantitative estimation of

An improved parameter estimation scheme for image modification detection based on DCT coefficient analysis.

PubMed

Yu, Liyang; Han, Qi; Niu, Xiamu; Yiu, S M; Fang, Junbin; Zhang, Ye

2016-02-01

Most of the existing image modification detection methods which are based on DCT coefficient analysis model the distribution of DCT coefficients as a mixture of a modified and an unchanged component. To separate the two components, two parameters, which are the primary quantization step, Q1, and the portion of the modified region, α, have to be estimated, and more accurate estimations of α and Q1 lead to better detection and localization results. Existing methods estimate α and Q1 in a completely blind manner, without considering the characteristics of the mixture model and the constraints to which α should conform. In this paper, we propose a more effective scheme for estimating α and Q1, based on the observations that, the curves on the surface of the likelihood function corresponding to the mixture model is largely smooth, and α can take values only in a discrete set. We conduct extensive experiments to evaluate the proposed method, and the experimental results confirm the efficacy of our method. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Using Forecast and Observed Weather Data to Assess Performance of Forecast Products in Identifying Heat Waves and Estimating Heat Wave Effects on Mortality

PubMed Central

Chen, Yeh-Hsin; Schwartz, Joel D.; Rood, Richard B.; O’Neill, Marie S.

2014-01-01

Background: Heat wave and health warning systems are activated based on forecasts of health-threatening hot weather. Objective: We estimated heat–mortality associations based on forecast and observed weather data in Detroit, Michigan, and compared the accuracy of forecast products for predicting heat waves. Methods: We derived and compared apparent temperature (AT) and heat wave days (with heat waves defined as ≥ 2 days of daily mean AT ≥ 95th percentile of warm-season average) from weather observations and six different forecast products. We used Poisson regression with and without adjustment for ozone and/or PM10 (particulate matter with aerodynamic diameter ≤ 10 μm) to estimate and compare associations of daily all-cause mortality with observed and predicted AT and heat wave days. Results: The 1-day-ahead forecast of a local operational product, Revised Digital Forecast, had about half the number of false positives compared with all other forecasts. On average, controlling for heat waves, days with observed AT = 25.3°C were associated with 3.5% higher mortality (95% CI: –1.6, 8.8%) than days with AT = 8.5°C. Observed heat wave days were associated with 6.2% higher mortality (95% CI: –0.4, 13.2%) than non–heat wave days. The accuracy of predictions varied, but associations between mortality and forecast heat generally tended to overestimate heat effects, whereas associations with forecast heat waves tended to underestimate heat wave effects, relative to associations based on observed weather metrics. Conclusions: Our findings suggest that incorporating knowledge of local conditions may improve the accuracy of predictions used to activate heat wave and health warning systems. Citation: Zhang K, Chen YH, Schwartz JD, Rood RB, O’Neill MS. 2014. Using forecast and observed weather data to assess performance of forecast products in identifying heat waves and estimating heat wave effects on mortality. Environ Health Perspect 122:912–918;�

An adjoint-based simultaneous estimation method of the asthenosphere's viscosity and afterslip using a fast and scalable finite-element adjoint solver

NASA Astrophysics Data System (ADS)

Agata, Ryoichiro; Ichimura, Tsuyoshi; Hori, Takane; Hirahara, Kazuro; Hashimoto, Chihiro; Hori, Muneo

2018-04-01

The simultaneous estimation of the asthenosphere's viscosity and coseismic slip/afterslip is expected to improve largely the consistency of the estimation results to observation data of crustal deformation collected in widely spread observation points, compared to estimations of slips only. Such an estimate can be formulated as a non-linear inverse problem of material properties of viscosity and input force that is equivalent to fault slips based on large-scale finite-element (FE) modeling of crustal deformation, in which the degree of freedom is in the order of 109. We formulated and developed a computationally efficient adjoint-based estimation method for this inverse problem, together with a fast and scalable FE solver for the associated forward and adjoint problems. In a numerical experiment that imitates the 2011 Tohoku-Oki earthquake, the advantage of the proposed method is confirmed by comparing the estimated results with those obtained using simplified estimation methods. The computational cost required for the optimization shows that the proposed method enabled the targeted estimation to be completed with moderate amount of computational resources.

A sampling design and model for estimating abundance of Nile crocodiles while accounting for heterogeneity of detectability of multiple observers

USGS Publications Warehouse

Shirley, Matthew H.; Dorazio, Robert M.; Abassery, Ekramy; Elhady, Amr A.; Mekki, Mohammed S.; Asran, Hosni H.

2012-01-01

As part of the development of a management program for Nile crocodiles in Lake Nasser, Egypt, we used a dependent double-observer sampling protocol with multiple observers to compute estimates of population size. To analyze the data, we developed a hierarchical model that allowed us to assess variation in detection probabilities among observers and survey dates, as well as account for variation in crocodile abundance among sites and habitats. We conducted surveys from July 2008-June 2009 in 15 areas of Lake Nasser that were representative of 3 main habitat categories. During these surveys, we sampled 1,086 km of lake shore wherein we detected 386 crocodiles. Analysis of the data revealed significant variability in both inter- and intra-observer detection probabilities. Our raw encounter rate was 0.355 crocodiles/km. When we accounted for observer effects and habitat, we estimated a surface population abundance of 2,581 (2,239-2,987, 95% credible intervals) crocodiles in Lake Nasser. Our results underscore the importance of well-trained, experienced monitoring personnel in order to decrease heterogeneity in intra-observer detection probability and to better detect changes in the population based on survey indices. This study will assist the Egyptian government establish a monitoring program as an integral part of future crocodile harvest activities in Lake Nasser

Distributed Damage Estimation for Prognostics based on Structural Model Decomposition

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Bregon, Anibal; Roychoudhury, Indranil

2011-01-01

Model-based prognostics approaches capture system knowledge in the form of physics-based models of components, and how they fail. These methods consist of a damage estimation phase, in which the health state of a component is estimated, and a prediction phase, in which the health state is projected forward in time to determine end of life. However, the damage estimation problem is often multi-dimensional and computationally intensive. We propose a model decomposition approach adapted from the diagnosis community, called possible conflicts, in order to both improve the computational efficiency of damage estimation, and formulate a damage estimation approach that is inherently distributed. Local state estimates are combined into a global state estimate from which prediction is performed. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the approach.

Maximum ikelihood estimation for the double-count method with independent observers

USGS Publications Warehouse

Manly, Bryan F.J.; McDonald, Lyman L.; Garner, Gerald W.

1996-01-01

Data collected under a double-count protocol during line transect surveys were analyzed using new maximum likelihood methods combined with Akaike's information criterion to provide estimates of the abundance of polar bear (Ursus maritimus Phipps) in a pilot study off the coast of Alaska. Visibility biases were corrected by modeling the detection probabilities using logistic regression functions. Independent variables that influenced the detection probabilities included perpendicular distance of bear groups from the flight line and the number of individuals in the groups. A series of models were considered which vary from (1) the simplest, where the probability of detection was the same for both observers and was not affected by either distance from the flight line or group size, to (2) models where probability of detection is different for the two observers and depends on both distance from the transect and group size. Estimation procedures are developed for the case when additional variables may affect detection probabilities. The methods are illustrated using data from the pilot polar bear survey and some recommendations are given for design of a survey over the larger Chukchi Sea between Russia and the United States.

Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

NASA Astrophysics Data System (ADS)

Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai

2013-09-01

In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

Tower-scale performance of four observation-based evapotranspiration algorithms within the WACMOS-ET project

NASA Astrophysics Data System (ADS)

Michel, Dominik; Miralles, Diego; Jimenez, Carlos; Ershadi, Ali; McCabe, Matthew F.; Hirschi, Martin; Seneviratne, Sonia I.; Jung, Martin; Wood, Eric F.; (Bob) Su, Z.; Timmermans, Joris; Chen, Xuelong; Fisher, Joshua B.; Mu, Quiaozen; Fernandez, Diego

2015-04-01

Research on climate variations and the development of predictive capabilities largely rely on globally available reference data series of the different components of the energy and water cycles. Several efforts have recently aimed at producing large-scale and long-term reference data sets of these components, e.g. based on in situ observations and remote sensing, in order to allow for diagnostic analyses of the drivers of temporal variations in the climate system. Evapotranspiration (ET) is an essential component of the energy and water cycle, which cannot be monitored directly on a global scale by remote sensing techniques. In recent years, several global multi-year ET data sets have been derived from remote sensing-based estimates, observation-driven land surface model simulations or atmospheric reanalyses. The LandFlux-EVAL initiative presented an ensemble-evaluation of these data sets over the time periods 1989-1995 and 1989-2005 (Mueller et al. 2013). The WACMOS-ET project (http://wacmoset.estellus.eu) started in the year 2012 and constitutes an ESA contribution to the GEWEX initiative LandFlux. It focuses on advancing the development of ET estimates at global, regional and tower scales. WACMOS-ET aims at developing a Reference Input Data Set exploiting European Earth Observations assets and deriving ET estimates produced by a set of four ET algorithms covering the period 2005-2007. The algorithms used are the SEBS (Su et al., 2002), Penman-Monteith from MODIS (Mu et al., 2011), the Priestley and Taylor JPL model (Fisher et al., 2008) and GLEAM (Miralles et al., 2011). The algorithms are run with Fluxnet tower observations, reanalysis data (ERA-Interim), and satellite forcings. They are cross-compared and validated against in-situ data. In this presentation the performance of the different ET algorithms with respect to different temporal resolutions, hydrological regimes, land cover types (including grassland, cropland, shrubland, vegetation mosaic, savanna

A novel SURE-based criterion for parametric PSF estimation.

PubMed

Xue, Feng; Blu, Thierry

2015-02-01

We propose an unbiased estimate of a filtered version of the mean squared error--the blur-SURE (Stein's unbiased risk estimate)--as a novel criterion for estimating an unknown point spread function (PSF) from the degraded image only. The PSF is obtained by minimizing this new objective functional over a family of Wiener processings. Based on this estimated blur kernel, we then perform nonblind deconvolution using our recently developed algorithm. The SURE-based framework is exemplified with a number of parametric PSF, involving a scaling factor that controls the blur size. A typical example of such parametrization is the Gaussian kernel. The experimental results demonstrate that minimizing the blur-SURE yields highly accurate estimates of the PSF parameters, which also result in a restoration quality that is very similar to the one obtained with the exact PSF, when plugged into our recent multi-Wiener SURE-LET deconvolution algorithm. The highly competitive results obtained outline the great potential of developing more powerful blind deconvolution algorithms based on SURE-like estimates.

A Systematic Approach for Model-Based Aircraft Engine Performance Estimation

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Garg, Sanjay

2010-01-01

A requirement for effective aircraft engine performance estimation is the ability to account for engine degradation, generally described in terms of unmeasurable health parameters such as efficiencies and flow capacities related to each major engine module. This paper presents a linear point design methodology for minimizing the degradation-induced error in model-based aircraft engine performance estimation applications. The technique specifically focuses on the underdetermined estimation problem, where there are more unknown health parameters than available sensor measurements. A condition for Kalman filter-based estimation is that the number of health parameters estimated cannot exceed the number of sensed measurements. In this paper, the estimated health parameter vector will be replaced by a reduced order tuner vector whose dimension is equivalent to the sensed measurement vector. The reduced order tuner vector is systematically selected to minimize the theoretical mean squared estimation error of a maximum a posteriori estimator formulation. This paper derives theoretical estimation errors at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared to the estimation accuracy achieved through conventional maximum a posteriori and Kalman filter estimation approaches. Maximum a posteriori estimation results demonstrate that reduced order tuning parameter vectors can be found that approximate the accuracy of estimating all health parameters directly. Kalman filter estimation results based on the same reduced order tuning parameter vectors demonstrate that significantly improved estimation accuracy can be achieved over the conventional approach of selecting a subset of health parameters to serve as the tuner vector. However, additional development is necessary to fully extend the methodology to Kalman filter-based

Comparing three approaches of evapotranspiration estimation in mixed urban vegetation; field-based, remote sensing-based and observational-based methods

USGS Publications Warehouse

Nouri, Hamideh; Glenn, Edward P.; Beecham, Simon; Chavoshi Boroujeni, Sattar; Sutton, Paul; Alaghmand, Sina; Nagler, Pamela L.; Noori, Behnaz

2016-01-01

Despite being the driest inhabited continent, Australia has one of the highest per capita water consumptions in the world. In addition, instead of having fit-for-purpose water supplies (using different qualities of water for different applications), highly treated drinking water is used for nearly all of Australia’s urban water supply needs, including landscape irrigation. The water requirement of urban landscapes, and particularly urban parklands, is of growing concern. The estimation of ET and subsequently plant water requirements in urban vegetation needs to consider the heterogeneity of plants, soils, water and climate characteristics. Accurate estimation of evapotranspiration (ET), which is the main component of a plant’s water requirement, in urban parks is highly desirable because this water maintains the health of green infrastructure and this in turn provides essential ecosystem services. This research contributes to a broader effort to establish sustainable irrigation practices within the Adelaide Parklands in Adelaide, South Australia.

Using climate models to estimate the quality of global observational data sets.

PubMed

Massonnet, François; Bellprat, Omar; Guemas, Virginie; Doblas-Reyes, Francisco J

2016-10-28

Observational estimates of the climate system are essential to monitoring and understanding ongoing climate change and to assessing the quality of climate models used to produce near- and long-term climate information. This study poses the dual and unconventional question: Can climate models be used to assess the quality of observational references? We show that this question not only rests on solid theoretical grounds but also offers insightful applications in practice. By comparing four observational products of sea surface temperature with a large multimodel climate forecast ensemble, we find compelling evidence that models systematically score better against the most recent, advanced, but also most independent product. These results call for generalized procedures of model-observation comparison and provide guidance for a more objective observational data set selection. Copyright © 2016, American Association for the Advancement of Science.

Determining the Uncertainties in Prescribed Burn Emissions Through Comparison of Satellite Estimates to Ground-based Estimates and Air Quality Model Evaluations in Southeastern US

NASA Astrophysics Data System (ADS)

Odman, M. T.; Hu, Y.; Russell, A. G.

2016-12-01

Prescribed burning is practiced throughout the US, and most widely in the Southeast, for the purpose of maintaining and improving the ecosystem, and reducing the wildfire risk. However, prescribed burn emissions contribute significantly to the of trace gas and particulate matter loads in the atmosphere. In places where air quality is already stressed by other anthropogenic emissions, prescribed burns can lead to major health and environmental problems. Air quality modeling efforts are under way to assess the impacts of prescribed burn emissions. Operational forecasts of the impacts are also emerging for use in dynamic management of air quality as well as the burns. Unfortunately, large uncertainties exist in the process of estimating prescribed burn emissions and these uncertainties limit the accuracy of the burn impact predictions. Prescribed burn emissions are estimated by using either ground-based information or satellite observations. When there is sufficient local information about the burn area, the types of fuels, their consumption amounts, and the progression of the fire, ground-based estimates are more accurate. In the absence of such information satellites remain as the only reliable source for emission estimation. To determine the level of uncertainty in prescribed burn emissions, we compared estimates derived from a burn permit database and other ground-based information to the estimates by the Biomass Burning Emissions Product derived from a constellation of NOAA and NASA satellites. Using these emissions estimates we conducted simulations with the Community Multiscale Air Quality (CMAQ) model and predicted trace gas and particulate matter concentrations throughout the Southeast for two consecutive burn seasons (2015 and 2016). In this presentation, we will compare model predicted concentrations to measurements at monitoring stations and evaluate if the differences are commensurate with our emission uncertainty estimates. We will also investigate if