A spatiotemporal model for Mexico City ozone levels (original) (raw)
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There is an urgent need to provide accurate air quality information and forecasts to the general public and environmental health decision-makers. This paper develops a hierarchical space-time model for daily 8-h maximum ozone concentration (O 3 ) data covering much of the eastern United States. The model combines observed data and forecast output from a computer simulation model known as the Eta Community Multi-scale Air Quality (CMAQ) forecast model in a very flexible, yet computationally fast way, so that the next day forecasts can be computed in real-time operational mode. The model adjusts for spatio-temporal biases in the Eta CMAQ forecasts and avoids a change of support problem often encountered in data fusion settings where real data have been observed at point level monitoring sites, but the forecasts from the computer model are provided at grid cell levels. The model is validated with a large amount of set-aside data and is shown to provide much improved forecasts of daily O 3 concentrations in the eastern United States.
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A multivariate modeling approach is presented for ozone forecasting from meteorological variables. For each prediction, a separate optimized multivariate regression model is constructed. The optimization involves the determination of the size of the training set by an internal validation procedure. A grid search is used in order to determine how many observations the training set should contain. A straightforward ordinary least squares procedure leads to systematic positive or negative devia-Ž . tions between measured and predicted ozone concentrations. Partial least squares regression PLS in combination with training-set selection and variable selection, gave an overall correlation coefficient of 0.83 between observed and measured ozone levels. Appropriate weighting of the observations in the training sets improved the result to give an overall correlation coefficient between measured and predicted ozone levels of 0.86. The dependence of the optimal size of the training set on the number and location of missing data in the data matrix was also investigated. q 1998 Elsevier Science B.V. All rights reserved.
Spatial interpolation of urban air temperatures using satellite-derived predictors
Theoretical and Applied Climatology, 2020
Air temperatures in urban environments are usually obtained from sparse weather stations that provide limited information with regard to spatial patterns. Effective methods that predict air temperatures (T air) in urban areas are based on statistical models which utilize remotely sensed and geographic data. This work aims to compute T air predictions for diurnal and nocturnal time intervals using predictive models that do not exploit information on Land Surface Temperatures. The models are developed based on explanatory variables that describe the urban morphology, land cover and terrain, aggregated at 100 m × 100 m resolution, combined with in situ T air measurements from urban meteorological stations. The case study is the urban and per-urban area of Heraklion, Greece, where a dense meteorological station network is available since 2016. Moran's eigenvector filtering and an autoregressive moving average residual specification are implemented to account for spatial and temporal correlations. The statistical models display satisfactory predictive performance, with mean annual Mean Absolute Error (MAE) equal to 0.36°C, 0.34°C, 0.42°C and 0.54°C,