Global space–time models for climate ensembles

Castruccio, Stefano; Stein, Michael L.

doi:10.1214/13-aoas656

Cited by 75 publications

(126 citation statements)

References 23 publications

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“…The first equation assumes an exponential decay of the coherence across latitude modulated by ξ j and an exponential decay across wavenumbers modulated by ν j . The second equation has been shown to be reasonable for data at this time scale (Castruccio and Stein, 2013).…”

Section: Step 3: Multiple Latitudesmentioning

confidence: 84%

“…In this regard, the statistical model can be regarded as an emulator of an initial condition ensemble, under the assumption that runs are independent for different initial conditions. This is, to our knowledge, the first time an emulator is used in this context, as it is traditionally used for calibration and sensitivity analysis (Sansó et al, 2008;Sansó and Forest, 2009;Bhat et al, 2012;Drignei et al, 2008;Chang et al, 2015) or scenario extrapolation (Holden and Edwards, 2010;Castruccio and Stein, 2013;Holden et al, 2013;Castruccio et al, 2014). The key difference with traditional emulators is that we do not assume correlation among inputs, as different initial conditions sensibly sampled from the spin-up run generate effectively independent runs.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…This idea was first introduced by Castruccio and Stein (2013) for temperatures at the Earth's surface under a single scenario.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…We do not report the proof since it is a straightforward generalization of that in Castruccio and Stein (2013). In this work, all the four steps of the model we present in the next section estimate the parameters by maximizing (2).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Equation (3) assumes that an AR(2) structure is sufficient to capture the temporal features of the data (see supplements for diagnostic and discussion). Moreover, (3) assumes no temporal cross-correlation between neighboring points (see the supplements) and stationarity across time, discussed in a similar context in Castruccio and Stein (2013). If the assumption of the temporal structure (3) is not adequate, the parameter estimates are not informative and therefore the statistical model would not be able to compress effectively the data.…”

Section: Step 1: Temporal Partmentioning

confidence: 99%

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Compressing an Ensemble With Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature

Castruccio

Genton

2016

Technometrics

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One of the main challenges when working with modern climate model ensembles is the increasingly larger size of the data produced, and the consequent difficulty in storing large amounts of spatio-temporally resolved information. Many compression algorithms can be used to mitigate this problem, but since they are designed to compress generic scientific data sets, they do not account for the nature of climate model output and they compress only individual simulations. In this work, we propose a different, statistics-based approach that explicitly accounts for the space-time dependence of the data for annual global three-dimensional temperature fields in an initial condition ensemble. The set of estimated parameters is small (compared to the data size) and can be regarded as a summary of the essential structure of the ensemble output; therefore, it can be used to instantaneously reproduce the temperature fields in an ensemble with a substantial saving in storage and time. The statistical model exploits the gridded geometry of the data and parallelization across processors. It is therefore computationally convenient and allows to fit a non-trivial model to a data set of one billion data points with a covariance matrix comprising of 10 18 entries.

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Section: Step 3: Multiple Latitudesmentioning

confidence: 84%

Section: Accepted Manuscriptmentioning

confidence: 99%

“…This idea was first introduced by Castruccio and Stein (2013) for temperatures at the Earth's surface under a single scenario.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

Section: Step 1: Temporal Partmentioning

confidence: 99%

See 3 more Smart Citations

Compressing an Ensemble With Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature

Castruccio

Genton

2016

Technometrics

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A PDE‐regularized smoothing method for space–time data over manifolds with application to medical data

Ponti

Perotto

Sangalli

2022

Numer Methods Biomed Eng

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We propose an innovative statistical-numerical method to model spatiotemporal data, observed over a generic two-dimensional Riemanian manifold.The proposed approach consists of a regression model completed with a regularizing term based on the heat equation. The model is discretized through a finite element scheme set on the manifold, and solved by resorting to a fixed point-based iterative algorithm. This choice leads to a procedure which is highly efficient when compared with a monolithic approach, and which allows us to deal with massive datasets. After a preliminary assessment on simulation study cases, we investigate the performance of the new estimation tool in practical contexts, by dealing with neuroimaging and hemodynamic data.finite elements, fixed point algorithm, hemodynamics, neuroimaging, regularized regression | INTRODUCTIONThis work proposes a statistical-numerical methodology to analyze spatio-temporal data measured on general twodimensional Riemanian manifold domains. These kinds of data are very common in diverse contexts, from Engineering to Applied Sciences. In an Engineering design process, for instance, it is standard to study time-as well as space-varying quantities of interest observed over the surface of a three-dimensional prototype in order to optimize the design pipeline (e.g., the aerodynamic forces exerted on the surface of an airfoil when dealing with the design of an airplane). In Environmental Science, it is of paramount importance to accurately model space-time data distributed over regions characterized by a complex orography, for example, in order to better understand the Earth processes, or to control pollution or global climate changes, or to optimize the exploitation of natural resources. In this paper, we focus on some applications which arise from Life Science. Figure 1 refers to one of the analyzed contexts. The panel on the left shows the mesh approximating the cortical surface of a brain, on which the hemodynamic signal induced by the neuronal activity on the cortical surface, shown in the right panel, has been observed at a certain time. Standard spatio-temporal techniques, that rely on the Euclidean distance, are not suited, in general, to handle data such as the ones in Figure 1. Due to folded geometry of the domain, such methods can yield highly inaccurate estimates, by incorrectly identifying as close, data locations that actually are far apart on the real geometry. Thus, values observed over two distinct gyri could be artificially linked each other, although, in practice, separated by a sulcus. As a consequence, in order to obtain accurate estimates on complex manifolds, it becomes mandatory to appropriately comply with the complex geometry of the domain.

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Using covariates to model dependence in nonstationary, high‐frequency meteorological processes

Poppick

Stein

2014

Environmetrics

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Meteorological processes that are measured at high temporal frequencies require nonstandard statistical models to adequately characterize their observed behavior. We examine this problem through the specific example of a nonstationary, bivariate process—the high‐frequency changes in surface temperature and dew point—conditionally on the average hourly level of relative humidity, magnitude of minute‐to‐minute changes in wind direction, and presence of sunlight. One particularly interesting aspect of this process is that dew point is bounded above by temperature, and so a part of the modeler's challenge is to characterize the joint behavior as temperature approaches dew point (or equivalently as relative humidity approaches 100%). The data analyzed are from early May from the years 2003 through 2012 at the Atmospheric Radiation Measurement Program's Southern Great Plains site in Northern Oklahoma, at the central facility near Lamont, Oklahoma. Our model gives a parametric description of how the spectral matrix of the process varies with covariates over blocks of time. The spectral approach allows for convenient and interpretable models of bivariate processes in time, and our model captures many of the observed changes in the behavior of the process. Copyright © 2014 John Wiley & Sons, Ltd.

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Global space–time models for climate ensembles

Cited by 75 publications

References 23 publications

Compressing an Ensemble With Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature

Compressing an Ensemble With Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature

A PDE‐regularized smoothing method for space–time data over manifolds with application to medical data

Using covariates to model dependence in nonstationary, high‐frequency meteorological processes

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