Modeling transport effects on ground‐level ozone using a non‐stationary space–time model

Huang, Hsin‐Cheng; Hsu, Nan‐Jung

doi:10.1002/env.639

Cited by 39 publications

(33 citation statements)

References 16 publications

(18 reference statements)

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“…The RST approach relies on two key ideas, the identification of distinct forecast regimes, and the use of geographically dispersed meteorological observations as off-site predictors. The regime switches address conditional spatio-temporal dependence structures that cannot be modeled by conventional vector time series techniques or geostatistical space-time methods, with the notable exception of the Huang and Hsu (2004) extension of the space-time model derived by Wikle and Cressie (1999) that allows for flow-dependent, non-stationary covariance structures. The computational requirements of the RST method are modest, and the technique can readily be implemented in real time.…”

Section: The Regime-switching Space-time (Rst) Methodsmentioning

confidence: 99%

Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching Space-Time (RST) Method

Gneiting¹,

Larson²,

Westrick³

et al. 2004

150

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With the global proliferation of wind power, accurate short-term forecasts of wind resources at wind energy sites are becoming paramount. Regime-switching space-time (RST) models merge meteorological and statistical expertise to obtain accurate and calibrated, fully probabilistic forecasts of wind speed and wind power. The model formulation is parsimonious, yet takes account of all the salient features of wind speed: alternating atmospheric regimes, temporal and spatial correlation, diurnal and seasonal non-stationarity, conditional heteroscedasticity, and non-Gaussianity. The RST method identifies forecast regimes at the wind energy site and fits a conditional predictive model for each regime. Geographically dispersed meteorological observations in the vicinity of the wind farm are used as off-site predictors.The RST technique was applied to 2-hour ahead forecasts of hourly average wind speed at the Stateline wind farm in the US Pacific Northwest. In July 2003, for instance, the RST forecasts had root-mean-square error (RMSE) 28.6% less than the persistence forecasts. For each month in the test period, the RST forecasts had lower RMSE than forecasts using state-of-the-art vector time series techniques. The RST method provides probabilistic forecasts in the form of predictive cumulative distribution functions, and those were well calibrated and sharp. The RST prediction intervals were substantially shorter on average than prediction intervals derived from univariate time series techniques. These results suggest that quality meteorological data from sites upwind of wind farms can be efficiently used to improve short-term forecasts of wind resources. It is anticipated that the RST technique can be successfully applied at wind energy sites all over the world. Report Documentation PageForm Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.

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Section: The Regime-switching Space-time (Rst) Methodsmentioning

confidence: 99%

Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching Space-Time (RST) Method

Gneiting¹,

Larson²,

Westrick³

et al. 2004

150

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“…Finally, the distribution of V can be updated dynamically according to the current state of the atmosphere. This option yields nonstationary, flow-dependent covariance structures similar to those posited by Riishøgaard (1998) and Huang and Hsu (2004). A related approach has been studied under the heading of Lagrangian kriging (Amani and Lebel 1997), which operates directly within the (moving) Lagrangian reference frame.…”

Section: Stationary Covariance Functions That Are Not Fully Symmetricmentioning

confidence: 96%

“…Atmospheric, environmental and geophysical processes are often under the influence of prevailing air or water flows, resulting in a lack of full symmetry. Transport effects of this type are well-known in the meteorological and hydrological literature and have recently been described by Gneiting (2002a), Stein (2005) and de Luna and Genton (2005), who considered the Irish wind data of Haslett and Raftery (1989), by Wan, Milligan and Parsons (2003) for wind power data, by Huang and Hsu (2004) for surface ozone levels and by Stein (2004a, 2004b) for atmospheric sulfate concentrations. Separability forms a special case of full symmetry.…”

Section: Introductionmentioning

confidence: 97%

“…In view of the prohibiting costs of spatially and temporally dense monitoring networks, one often aims to develop a statistical model in continuous space and time, based on observations at a limited number of monitoring stations. Examples include environmental monitoring and model assessment for surface ozone levels (Guttorp, Meiring and Sampson 1994;Carroll et al 1997;Meiring, Guttorp and Sampson 1998;Huang and Hsu 2004), precipitation forecasts (Amani and Lebel 1997) and the assessment of wind energy resources (Haslett and Raftery 1989). Geostatistical approaches model the observations as a partial realization of a spatio-temporal, typically Gaussian random function Z(s, t), (s, t) ∈ d × which is indexed in space by s ∈ d and in time by t ∈ .…”

Section: Introductionmentioning

confidence: 99%

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Geostatistical Space-Time Models, Stationarity, Separability, and Full Symmetry

Gneiting¹,

Genton²,

Guttorp³

2006

C&H/CRC Monographs on Statistics &Amp; Applied Probability

207

282

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Geostatistical approaches to modeling spatio-temporal data rely on parametric covariance models and rather stringent assumptions, such as stationarity, separability and full symmetry. This paper reviews recent advances in the literature on space-time covariance functions in light of the aforementioned notions, which are illustrated using wind data from Ireland. Experiments with time-forward kriging predictors suggest that the use of more complex and more realistic covariance models results in improved predictive performance.

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“…In recent years, there has been widespread attention in the statistical literature given to modeling space-time data (Gelfand et al, 1998;Kyriakidis and Journel, 1999;Cressie and Huang, 1999;;Gneiting, 2002;Stein, 2003;Huang and Hsu, 2004).…”

Section: Introductionmentioning

confidence: 99%

A Reparametrization Approach for Dynamic Space-Time Models

Lee¹,

Ghosh

2008

Journal of Statistical Theory and Practice

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Researchers in diverse areas such as environmental and health sciences are increasingly working with data collected across space and time. The space-time processes that are generally used in practice are often complicated in the sense that the auto-dependence structure across space and time is non-trivial, often non-separable and non-stationary in space and time. Moreover, the dimension of such data sets across both space and time can be very large leading to computational difficulties due to numerical instabilities. Hence, space-time modeling is a challenging task and in particular parameter estimation based on complex models can be problematic due to the curse of dimensionality. We propose a novel reparametrization approach to fit dynamic space-time models which allows the use of a very general form for the spatial covariance function. Our modeling contribution is to present an unconstrained reparametrization method for a covariance function within dynamic space-time models. A major benefit of the proposed unconstrained reparametrization method is that we are able to implement the modeling of a very high dimensional covariance matrix that automatically maintains the positive definiteness constraint. We demonstrate the applicability of our proposed reparametrized dynamic space-time models for a large data set of total nitrate concentrations.

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Modeling transport effects on ground‐level ozone using a non‐stationary space–time model

Cited by 39 publications

References 16 publications

Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching Space-Time (RST) Method

Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching Space-Time (RST) Method

Geostatistical Space-Time Models, Stationarity, Separability, and Full Symmetry

A Reparametrization Approach for Dynamic Space-Time Models

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