Ozone Exposure and Population Density in Harris County, Texas

Carroll, Robert J.; Chen, R.; George, Emily; Li, T. H.; Newton, H. Joseph; Schmiediche, Henrik; Wang, N.

doi:10.1080/01621459.1997.10473988

Cited by 71 publications

(41 citation statements)

References 16 publications

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“…Brown et al (1984), Haslett and Raftery (1989) and Carroll et al (1997), for instance, used normal distributions to model the square root of wind speed and atmospheric ozone concentration, respectively. To address the nonnegativity of the predictand, we replace the normal predictive distribution, N (µ, σ 2 ), by the cut-off normal predictive distribution, N 0 (µ, σ 2 ), with cumulative distribution function…”

Section: Cut-off Normal Predictive Distributionsmentioning

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: Cut-off Normal Predictive Distributionsmentioning

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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“…Space-time modeling of air pollutants, ground level ozone concentrations in particular, has attracted recent attention, see, e.g., Guttorp et al (1994), Carroll et al (1997). In recent years, hierarchical Bayesian approaches for spatial prediction of air pollution have been developed, see, e.g.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Space–Time Ozone Modeling for Assessing Trends

Sahu

Gelfand

Holland

2007

Journal of the American Statistical Association

100

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The assessment of air pollution regulatory programs designed to improve ground level ozone concentrations is a topic of considerable interest to environmental managers. To aid this assessment, it is necessary to model the space-time behavior of ozone for predicting summaries of ozone across spatial domains of interest and for the detection of long-term trends at monitoring sites. These trends, adjusted for the effects of meteorological variables, are needed for determining the effectiveness of pollution control programs in terms of their magnitude and uncertainties across space. This paper proposes a space-time model for daily 8-hour maximum ozone levels to provide input to regulatory activities: detection, evaluation, and analysis of spatial patterns of ozone summaries and temporal trends. The model is applied to analyzing data from the state of Ohio which has been chosen because it contains a mix of urban, suburban, and rural ozone monitoring sites in several large cities separated by large rural areas. The proposed space-time model is auto-regressive and incorporates the most important meteorological variables observed at a collection of ozone monitoring sites as well as at several weather stations where ozone levels have not been observed. This problem of misalignment of ozone and meteorological data is overcome by spatial modeling of the latter. In so doing we adopt an approach based on the successive daily increments in meteorological variables. With regard to modeling, the increment (or change-in-meteorology) process proves more attractive than working directly with the meteorology process, without sacrificing any desired inference. The full model is specified within a Bayesian framework and is fitted using MCMC techniques. Hence, full inference with regard to model unknowns is available as well as for predictions in time and space, evaluation of annual summaries and assessment of trends.

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“…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%

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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Ozone Exposure and Population Density in Harris County, Texas

Cited by 71 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

High-Resolution Space–Time Ozone Modeling for Assessing Trends

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

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