| 1. |
- Ailliot, Pierre, et al.
(author)
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Space-time models for moving fields with an application to significant wave height fields
- 2011
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In: Environmetrics. - : Wiley. - 1099-095X .- 1180-4009. ; 22:3, s. 354-369
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Journal article (peer-reviewed)abstract
- The surface of the ocean, and so such quantities as the significant wave height, $H-s$, can be thought of as a random surface that develops over time. In this paper, we explore certain types of random fields in space and time, with and without dynamics that may or may not be driven by a physical law, as models for the significant wave height. Reanalysis data is used to estimate the sea-state motion which is modeled as a hidden Markov chain in a state space framework by means of an AR(1) process or in the presence of the dispersion relation. Parametric covariance models with and without dynamics are fitted to reanalysis and satellite data and compared to the empirical covariance functions. The derived models have been validated against satellite and buoy data. © 2010 John Wiley & Sons, Ltd.
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| 2. |
- Baxevani, Anastassia, 1969, et al.
(author)
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Spatio-temporal statistical modelling of significant wave height
- 2009
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In: Environmetrics. - : Wiley. - 1180-4009 .- 1099-095X. ; 20:1, s. 14-31
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Journal article (peer-reviewed)abstract
- In this paper. we construct a homogeneous spatio-temporal model to describe the variability of significant wave height over small regions of the sea and over short periods of time. Then. the model is extended to a non-homogeneous one that is valid over larger areas of the sea and for time periods of up to 10 h. To validate the proposed model. we reconstruct the significant wave height surface under different scenarios and then compare it to satellite measurements and the C-ERA-40 field. Copyright (C) 2008 John Wiley & Sons, Ltd.
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| 3. |
- Bojarova, Jelena, et al.
(author)
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Non-Gaussian state space models in decomposition of ice core time series in long and short time-scales
- 2010
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In: Environmetrics. - : Wiley. - 1180-4009 .- 1099-095X. ; 21:6, s. 562-587
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Journal article (peer-reviewed)abstract
- Statistical modelling of six time series of geological ice core chemical data from Greenland is discussed. We decompose the total variation into long time-scale (trend) and short time-scale variations (fluctuations around the trend), and a pure noise component. Too heavy tails of the short-term variation makes a standard time-invariant linear Gaussian model inadequate. We try non-Gaussian state space models, which can be efficiently approximated by time-dependent Gaussian models. In essence, these time-dependent Gaussian models result in a local smoothing, in contrast to the global smoothing provided by the time-invariant model. To describe the mechanism of this local smoothing, we utilise the concept of a local variance function derived from a heavy-tailed density. The time-dependent error variance expresses the uncertainty about the dynamical development of the model state, and it controls the influence of observations on the estimates of the model state components. The great advantage of the derived time-dependent Gaussian model is that the Kalman filter and the Kalman smoother can be used as efficient computational tools for performing the variation decomposition. One of the main objectives of the study is to investigate how the distributional assumption on the model error component of the short time-scale variation affects the decomposition.
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| 4. |
- Dahlén, Unn, et al.
(author)
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Spatiotemporal reconstructions of global CO2-fluxes using Gaussian Markov random fields
- 2020
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In: Environmetrics. - : Wiley. - 1180-4009 .- 1099-095X. ; 31:4
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Journal article (peer-reviewed)abstract
- Atmospheric inverse modeling is a method for reconstructing historical fluxes of green-house gas between land and atmosphere, using observed atmospheric concentrations and an atmospheric tracer transport model. The small number of observed atmospheric concentrations in relation to the number of unknown flux components makes the inverse problem ill-conditioned, and assumptions on the fluxes are needed to constrain the solution. A common practice is to model the fluxes using latent Gaussian fields with a mean structure based on estimated fluxes from combinations of process modeling (natural fluxes) and statistical bookkeeping (anthropogenic emissions). Here, we reconstruct global CO2 flux fields by modeling fluxes using Gaussian Markov random fields (GMRFs), resulting in a flexible and computational beneficial model with a Matérn-like spatial covariance and a temporal covariance arriving from an autoregressive model in time domain. In contrast to previous inversions, the flux is defined on a spatially continuous domain, and the traditionally discrete flux representation is replaced by integrated fluxes at the resolution specified by the transport model. This formulation removes aggregation errors in the flux covariance, due to the traditional representation of area integrals by fluxes at discrete points, and provides a model closer resembling real-life space–time continuous fluxes.
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| 5. |
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| 6. |
- Dimberg, Peter H., 1985-
(author)
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Defining a new parameter for regression models with aggregated data in aquatic science
- 2014
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In: Environmetrics. - : Wiley. - 1180-4009 .- 1099-095X. ; 25:2, s. 97-106
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Journal article (peer-reviewed)abstract
- In aquatic ecosystem analysis, it is common to create regression models of aggregated data. There are several published papers on regression models that produce high values for the coefficient of determination (r2) and low p-values but that have nevertheless failed to predict responses in individual lakes. There appears, therefore, to be a need for a descriptive parameter that can be used to determine the certainty in aggregated regression models. To explore the applicability of a new parameter, the aim of this study was to develop a new parameter to detect the reliability of aggregated data in regression analysis. This parameter was tested using three different examples of empirical data from Himmerfjärden bay (Sweden) and one example of 111 Swedish lakes. The results showed that even for a high r2 and a low p-value, it is possible that the aggregated data are too highly variable to make correct conclusions about causality. To investigate this, the new parameter should be used to indicate if r2 can demonstrate a causality relationship. However, if the parameter rejects r2 as valid, it does not mean that there is no causality; it indicates that the uncertainty in the aggregated data is too high to draw conclusions regarding causality. In such cases, more effort needs to be made to decrease uncertainty in the variables.
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| 7. |
- Grafström, Anton, et al.
(author)
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A sample coordination method to monitor totals of environmental variables
- 2020
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In: Environmetrics. - : Wiley. - 1180-4009 .- 1099-095X. ; 31
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Journal article (peer-reviewed)abstract
- A new sampling strategy for design-based environmental monitoring is proposed. It has the potential to produce superior estimators for totals of environmental variables and their changes over time. In the strategy, we combine two concepts known as spatially balanced sampling and coordination of samples. Spatially balanced sampling can provide superior estimators of totals, while coordination of samples over time is often used to improve estimators of change. Compared with reference strategies, we show that the new strategy can improve the precision of the estimators of totals and their change simultaneously. A forest inventory application is used to illustrate the new strategy and the results can be summarized as (i) using auxiliary information to spread the sample can improve the estimators of totals; (ii) a positive coordination of the samples reduced the variance of the estimator of change by more than 37% compared with independent samples; and (iii) a high overlap between successive samples does not guarantee a good estimator of change. The presented strategy can be used to develop more efficient environmental monitoring programs.
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| 8. |
- Grafström, Anton
(author)
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Doubly balanced spatial sampling with spreading and restitution of auxiliary totals
- 2013
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In: Environmetrics. - : Wiley. - 1180-4009 .- 1099-095X. ; 24, s. 120-131
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Journal article (peer-reviewed)abstract
- A new spatial sampling method is proposed in order to achieve a double property of balancing. The sample is spatially balanced or well spread so as to avoid selecting neighbouring units. Moreover, the method also enables to satisfy balancing equations on auxiliary variables available on all the sampling units because the HorvitzThompson estimator is almost equal to the population totals for these variables. The method works with any definition of distance in a multidimensional space and supports the use of unequal inclusion probabilities. The algorithm is simple and fast. Examples show that the method succeeds in using more information than the local pivotal method, the cube method and the Generalized Random-Tessellation Stratified sampling method, and thus performs better. An estimator of the variance for this sampling design is proposed in order to lead to an inference that takes the effect of the sampling design into account. Copyright (c) 2012 John Wiley & Sons, Ltd.
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| 9. |
- Grafström, Anton, et al.
(author)
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The continuous population approach to forest inventories and use of information in the design
- 2017
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In: Environmetrics. - : Wiley. - 1180-4009 .- 1099-095X. ; 28
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Journal article (peer-reviewed)abstract
- An extended theoretical framework for the continuous population approach to forest inventories is derived. Here, we treat a simultaneous selection of sample points with any prescribed sampling intensity over a continuous population. Different ways to use available auxiliary information, for example, from remote sensing, by selection of approximately balanced or spatially balanced samples are considered. A large data set of spatially continuous individual tree-level data is used to demonstrate the potential of these theoretical approaches. This study shows new ways to integrate remote sensing information in designs for forest inventory applications, which can significantly reduce the variance of the Horvitz-Thompson estimator for target variables related to the auxiliary information.
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| 10. |
- Grimvall, Anders, et al.
(author)
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Semiparametric smoothers for trend assessment of multiple time series of environmental quality data
- 2008
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In: Environmetrics. - 1180-4009 .- 1099-095X.
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Journal article (other academic/artistic)abstract
- Multiple time series of environmental quality data with similar, but not necessarily identical, trends call for multivariate methods for trend detection and adjustment for covariates. Here, we show how an additive model in which the multivariate trend function is specified in a nonparametric fashion (and the adjustment for covariates is based on a parametric expression) can be used to estimate how the human impact on an ecosystem varies with time and across components of the observed vector time series. More specifically, we demonstrate how a roughness penalty approach can be utilized to impose different types of smoothness on the function surface that describes trends in environmental quality as a function of time and vector component. Compared to other tools used for this purpose, such as Gaussian smoothers and thin plate splines, an advantage of our approach is that the smoothing pattern can easily be tailored to different types of relationships between the vector components. We give explicit roughness penalty expressions for data collected over several seasons or representing several classes on a linear or circular scale. In addition, we define a general separable smoothing method. A new resampling technique that preserves statistical dependencies over time and across vector components enables realistic calculations of confidence and prediction intervals.
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