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Observation Errors (observation + error)

Distribution by Scientific Domains

Life Sciences	20%
Engineering	20%

Selected Abstracts

Seeking a second opinion: uncertainty in disease ecology

ECOLOGY LETTERS, Issue 6 2010
Brett T. McClintock
Ecology Letters (2010) 13: 659,674 Abstract Analytical methods accounting for imperfect detection are often used to facilitate reliable inference in population and community ecology. We contend that similar approaches are needed in disease ecology because these complicated systems are inherently difficult to observe without error. For example, wildlife disease studies often designate individuals, populations, or spatial units to states (e.g., susceptible, infected, post-infected), but the uncertainty associated with these state assignments remains largely ignored or unaccounted for. We demonstrate how recent developments incorporating observation error through repeated sampling extend quite naturally to hierarchical spatial models of disease effects, prevalence, and dynamics in natural systems. A highly pathogenic strain of avian influenza virus in migratory waterfowl and a pathogenic fungus recently implicated in the global loss of amphibian biodiversity are used as motivating examples. Both show that relatively simple modifications to study designs can greatly improve our understanding of complex spatio-temporal disease dynamics by rigorously accounting for uncertainty at each level of the hierarchy. [source]

The Value of Subsidence Data in Ground Water Model Calibration

GROUND WATER, Issue 4 2008
Tingting Yan
The accurate estimation of aquifer parameters such as transmissivity and specific storage is often an important objective during a ground water modeling investigation or aquifer resource evaluation. Parameter estimation is often accomplished with changes in hydraulic head data as the key and most abundant type of observation. The availability and accessibility of global positioning system and interferometric synthetic aperture radar data in heavily pumped alluvial basins can provide important subsidence observations that can greatly aid parameter estimation. The aim of this investigation is to evaluate the value of spatial and temporal subsidence data for automatically estimating parameters with and without observation error using UCODE-2005 and MODFLOW-2000. A synthetic conceptual model (24 separate cases) containing seven transmissivity zones and three zones each for elastic and inelastic skeletal specific storage was used to simulate subsidence and drawdown in an aquifer with variably thick interbeds with delayed drainage. Five pumping wells of variable rates were used to stress the system for up to 15 years. Calibration results indicate that (1) the inverse of the square of the observation values is a reasonable way to weight the observations, (2) spatially abundant subsidence data typically produce superior parameter estimates under constant pumping even with observation error, (3) only a small number of subsidence observations are required to achieve accurate parameter estimates, and (4) for seasonal pumping, accurate parameter estimates for elastic skeletal specific storage values are largely dependent on the quantity of temporal observational data and less on the quantity of available spatial data. [source]

Overcompensatory population dynamic responses to environmental stochasticity

JOURNAL OF ANIMAL ECOLOGY, Issue 6 2008
James C. Bull
Summary 1To quantify the interactions between density-dependent, population regulation and density-independent limitation, we studied the time-series dynamics of an experimental laboratory insect microcosm system in which both environmental noise and resource limitation were manipulated. 2A hierarchical Bayesian state-space approach is presented through which it is feasible to capture all sources of uncertainty, including observation error to accurately quantify the density dependence operating on the dynamics. 3The regulatory processes underpinning the dynamics of two different bruchid beetles (Callosobruchus maculatus and Callosobruchus chinensis) are principally determined by environmental conditions, with fluctuations in abundance explained in terms of changes in overcompensatory dynamics and stochastic processes. 4A general, stochastic population model is developed to explore the link between abundance fluctuations and the interaction between density dependence and noise. Taking account of time-lags in population regulation can substantially increase predicted population fluctuations resulting from underlying noise processes. [source]

Estimates of spatial and interchannel observation-error characteristics for current sounder radiances for numerical weather prediction.

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 649 2010
I: Methods, application to ATOVS data
Abstract This is the first part of a two-part article that uses three methods to estimate observation errors and their correlations for clear-sky sounder radiances used in the European Centre for Medium-Range Weather Forecasts (ECMWF) assimilation system. The analysis is based on covariances derived from pairs of first-guess and analysis departures. The methods used are the so-called Hollingsworth/Lönnberg method, a method based on subtracting a scaled version of mapped assumed background errors from first-guess departure covariances and the Desroziers diagnostic. The present article reports the results for the three Advanced TIROS Operational Vertical Sounder (ATOVS) instruments: the Advanced Microwave Sounding Unit (AMSU)-A, High-Resolution Infrared Radiation Sounder (HIRS) and Microwave Humidity Sounder (MHS). The findings suggest that all AMSU-A sounding channels show little or no interchannel or spatial observation-error correlations, except for surface-sensitive channels over land. Estimates for the observation error are mostly close to the instrument noise. In contrast, HIRS temperature-sounding channels exhibit some interchannel error correlations, and these are stronger for surface-sensitive channels. There are also indications for stronger spatial-error correlations for the HIRS short-wave channels. There is good agreement between the estimates from the three methods for temperature-sounding channels. Estimating observation errors for humidity-sounding channels of MHS and HIRS appears more difficult. A considerable proportion of the observation error for humidity-sounding channels appears correlated spatially for short separation distances, as well as between channels. Observation error estimates for humidity channels are generally considerably larger than the instrument noise. Observation error estimates from this study are consistently lower than those assumed in the ECMWF assimilation system. As error correlations are small for AMSU-A, the study suggests that the current use of AMSU-A data in the ECMWF system in terms of observation-error or thinning-scale choices is fairly conservative. Copyright © 2010 Royal Meteorological Society [source]

Optimality for the linear quadratic non-Gaussian problem via the asymmetric Kalman filter

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 1 2004
Rosario Romera
Abstract In the linear non-Gaussian case, the classical solution of the linear quadratic Gaussian (LQG) control problem is known to provide the best solution in the class of linear transformations of the plant output if optimality refers to classical least-squares minimization criteria. In this paper, the adaptive linear quadratic control problem is solved with optimality based on asymmetric least-squares approach, which includes least-squares criteria as a special case. Our main result gives explicit solutions for this optimal quadratic control problem for partially observable dynamic linear systems with asymmetric observation errors. The main difficulty is to find the optimal state estimate. For this purpose, an asymmetric version of the Kalman filter based on asymmetric least-squares estimation is used. We illustrate the applicability of our approach with numerical results. Copyright © 2004 John Wiley & Sons, Ltd. [source]

The accuracy of matrix population model projections for coniferous trees in the Sierra Nevada, California

JOURNAL OF ECOLOGY, Issue 4 2005
PHILLIP J. VAN MANTGEM
Summary 1We assess the use of simple, size-based matrix population models for projecting population trends for six coniferous tree species in the Sierra Nevada, California. We used demographic data from 16 673 trees in 15 permanent plots to create 17 separate time-invariant, density-independent population projection models, and determined differences between trends projected from initial surveys with a 5-year interval and observed data during two subsequent 5-year time steps. 2We detected departures from the assumptions of the matrix modelling approach in terms of strong growth autocorrelations. We also found evidence of observation errors for measurements of tree growth and, to a more limited degree, recruitment. Loglinear analysis provided evidence of significant temporal variation in demographic rates for only two of the 17 populations. 3Total population sizes were strongly predicted by model projections, although population dynamics were dominated by carryover from the previous 5-year time step (i.e. there were few cases of recruitment or death). Fractional changes to overall population sizes were less well predicted. Compared with a null model and a simple demographic model lacking size structure, matrix model projections were better able to predict total population sizes, although the differences were not statistically significant. Matrix model projections were also able to predict short-term rates of survival, growth and recruitment. Mortality frequencies were not well predicted. 4Our results suggest that simple size-structured models can accurately project future short-term changes for some tree populations. However, not all populations were well predicted and these simple models would probably become more inaccurate over longer projection intervals. The predictive ability of these models would also be limited by disturbance or other events that destabilize demographic rates. [source]

Unbiased ensemble square root filters

PROCEEDINGS IN APPLIED MATHEMATICS & MECHANICS, Issue 1 2007
S. L. Dance
Ensemble square root filters are a method of data assimilation, where model forecasts are combined with observations to produce an improved state estimate, or analysis. There are a number of different algorithms in the literature and it is not clear which of these is the best for any given application. This work shows that in some implementations there can be a systematic bias in the analysis ensemble mean and consequently an accompanying shortfall in the spread of the analysis ensemble as expressed by the ensemble covariance matrix. We have established a set of necessary and sufficient conditions for the scheme to be unbiased. While these conditions are not a cure-all and cannot deal with independent sources of bias such as model and observation errors, they should be useful to designers of ensemble square root filters in the future. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

Can 4D-Var use dynamical information from targeted observations of a baroclinic structure?

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 651 2010
E. A. Irvine
Abstract Targeted observations are generally taken in regions of high baroclinicity, but often show little impact. One plausible explanation is that important dynamical information, such as upshear tilt, is not extracted from the targeted observations by the data assimilation scheme and used to correct initial condition error. This is investigated by generating pseudo targeted observations which contain a singular vector (SV) structure that is not present in the background field or routine observations, i.e. assuming that the background has an initial condition error with tilted growing structure. Experiments were performed for a single case-study with varying numbers of pseudo targeted observations. These were assimilated by the Met Office four-dimensional variational (4D-Var) data assimilation scheme, which uses a 6 h window for observations and background-error covariances calculated using the National Meteorological Centre (NMC) method. The forecasts were run using the operational Met Office Unified Model on a 24 km grid. The results presented clearly demonstrate that a 6 h window 4D-Var system is capable of extracting baroclinic information from a limited set of observations and using it to correct initial condition error. To capture the SV structure well (projection of 0.72 in total energy), 50 sondes over an area of 1×106 km2 were required. When the SV was represented by only eight sondes along an example targeting flight track covering a smaller area, the projection onto the SV structure was lower; the resulting forecast perturbations showed an SV structure with increased tilt and reduced initial energy. The total energy contained in the perturbations decreased as the SV structure was less well described by the set of observations (i.e. as fewer pseudo observations were assimilated). The assimilated perturbation had lower energy than the SV unless the pseudo observations were assimilated with the dropsonde observation errors halved from operational values. Copyright © 2010 Royal Meteorological Society [source]

Estimates of spatial and interchannel observation-error characteristics for current sounder radiances for numerical weather prediction.

Assimilation of satellite-derived soil moisture from ASCAT in a limited-area NWP model

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 648 2010
Jean-François Mahfouf
Abstract A simplified Extended Kalman Filter is developed for the assimilation of satellite-derived surface soil moisture from the Advanced Scatterometer (ASCAT) instrument (on board the polar-orbiting satellite METOP) in a limited-area NWP model where soil water vertical transfers are described by a force,restore method. An analytic formulation of the land surface scheme Jacobians is derived to simplify the coupling between land surface and atmospheric data assimilation systems. Various steps necessary before the assimilation of ASCAT products are defined: projection of satellite data on the model grid, screening based on various criteria, bias correction using a CDF matching technique, and specification of model and observation errors. Three-dimensional variational data assimilation experiments are then performed during a four-week period in May 2009 over western Europe. A control assimilation is also run where the soil moisture evolves freely. Forecasts from these analyses show that the assimilation of ASCAT data slightly reduces the daytime low-level relative humidity positive bias of the control run. Forecast skill scores with respect to other variables are rather neutral. A comparison of the control run with the operational system where soil moisture is corrected from short-range forecast errors of screen-level observations show similar improvements but are more pronounced. These differences come from the fact that the number of screen-level observations from the surface network over Europe is significantly larger than those provided by a polar-orbiting satellite. These results are consistent with those obtained at ECMWF using soil moisture products derived from other satellite instruments (X-band radiometer TMI and C-band scatterometer ERS). Several avenues for improving this preliminary methodology are proposed. Copyright © 2010 Royal Meteorological Society [source]

A robust formulation of the ensemble Kalman filter,

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 639 2009
S. J. Thomas
Abstract The ensemble Kalman filter (EnKF) can be interpreted in the more general context of linear regression theory. The recursive filter equations are equivalent to the normal equations for a weighted least-squares estimate that minimizes a quadratic functional. Solving the normal equations is numerically unreliable and subject to large errors when the problem is ill-conditioned. A numerically reliable and efficient algorithm is presented, based on the minimization of an alternative functional. The method relies on orthogonal rotations, is highly parallel and does not ,square' matrices in order to compute the analysis update. Computation of eigenvalue and singular-value decompositions is not required. The algorithm is formulated to process observations serially or in batches and therefore easily handles spatially correlated observation errors. Numerical results are presented for existing algorithms with a hierarchy of models characterized by chaotic dynamics. Under a range of conditions, which may include model error and sampling error, the new algorithm achieves the same or lower mean square errors as the serial Potter and ensemble adjustment Kalman filter (EAKF) algorithms. Published in 2009 by John Wiley and Sons, Ltd. [source]

The optimal density of atmospheric sounder observations in the Met Office NWP system

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 629 2007
M. L. Dando
Abstract Large numbers of satellite observations are discarded from the numerical weather prediction (NWP) process because high-density observations may have a negative impact on the analysis. In current assimilation schemes, the observation error covariance matrix R is usually represented as a diagonal matrix, which assumes there are no correlations in the observation errors and that each observation is an independent piece of information. This is not the case when there are strong error correlations and this can lead to a degraded analysis. The experiments conducted in this study were designed to identify the optimal density and to determine if there were circumstances when exceeding this density might be beneficial to forecast skill. The global optimal separation distance of Advanced TIROS Operational Vertical Sounder (ATOVS) observations was identified by comparing global forecast errors produced using different densities of ATOVS. The global average of the absolute forecast error produced by each different density was found for a 3-week period from December 2004 to January 2005. The results showed that, when using the Met Office NWP system with a horizontal model resolution of ,60 km, the lowest global forecast errors were produced when using separation distances of 115,154 km. However, localized regions of the atmosphere containing large gradients such as frontal regions may benefit from thinning distances as small as 40 km and therefore the global optimal separation distance is not necessarily applicable in these circumstances. Copyright © 2007 Royal Meteorological Society [source]

Adaptive thinning of atmospheric observations in data assimilation with vector quantization and filtering methods

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 613 2005
T. Ochotta
Abstract In data assimilation for numerical weather prediction, measurements of various observation systems are combined with background data to define initial states for the forecasts. Current and future observation systems, in particular satellite instruments, produce large numbers of measurements with high spatial and temporal density. Such datasets significantly increase the computational costs of the assimilation and, moreover, can violate the assumption of spatially independent observation errors. To ameliorate these problems, we propose two greedy thinning algorithms, which reduce the number of assimilated observations while retaining the essential information content of the data. In the first method, the number of points in the output set is increased iteratively. We use a clustering method with a distance metric that combines spatial distance with difference in observation values. In a second scheme, we iteratively estimate the redundancy of the current observation set and remove the most redundant data points. We evaluate the proposed methods with respect to a geometric error measure and compare them with a uniform sampling scheme. We obtain good representations of the original data with thinnings retaining only a small portion of observations. We also evaluate our thinnings of ATOVS satellite data using the assimilation system of the Deutscher Wetterdienst. Impact of the thinning on the analysed fields and on the subsequent forecasts is discussed. Copyright © 2005 Royal Meteorological Society [source]

An adaptive buddy check for observational quality control

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 577 2001
Dick P. Dee
Abstract An adaptive buddy-check algorithm is presented that adjusts tolerances for suspect observations, based on the variability of surrounding data. The algorithm derives from a statistical hypothesis test combined with maximum-likelihood covariance estimation. Its stability is shown to depend on the initial identification of outliers by a simple background check. The adaptive feature ensures that the final quality-control decisions are not very sensitive to prescribed statistics of first-guess and observation errors, nor on other approximations introduced into the algorithm. The implementation of the algorithm in a global atmospheric data assimilation is described. Its performance is contrasted with that of a non-adaptive buddy check, for the surface analysis of an extreme storm that took place over Europe on 27 December 1999. The adaptive algorithm allowed the inclusion of many important observations that differed greatly from the first guess and that would have been excluded on the basis of prescribed statistics. The analysis of the storm development was much improved as a result of these additional observations. [source]