Journal ArticleDOI
Statistical comparisons of methods for interpolating the output of a numerical air quality model
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In this article, the authors compare Models-3/Community Multiscale Air Quality (CMAQ) outputs at multiple resolutions by interpolating from coarse resolution to fine resolution and analyzing the interpolation difference.About:
This article is published in Journal of Statistical Planning and Inference.The article was published on 2007-07-01. It has received 16 citations till now. The article focuses on the topics: Bilinear interpolation & Nearest-neighbor interpolation.read more
Citations
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Journal ArticleDOI
Probabilistic forecasts of wind speed: ensemble model output statistics by using heteroscedastic censored regression
TL;DR: A novel way of statistically post-processing dynamical ensembles for wind speed by using heteroscedastic censored (tobit) regression, where location and spread derive from the ensemble, which results in a substantial improvement over the unprocessed ensemble or climatological reference forecasts.
Journal ArticleDOI
Spatial analysis to quantify numerical model bias and dependence: How many climate models are there?
TL;DR: In this paper, the authors present a statistical methodology to quantify whether climate models are indeed unbiased and whether and where model biases are correlated across models, and they consider the simulated mean state and the simulated trend over the period 1970-1999 for Northern Hemisphere summer and winter temperature.
Journal ArticleDOI
One way coupling of CMAQ and a road source dispersion model for fine scale air pollution predictions.
TL;DR: The current results show promise and demonstrate that the use of a combination of regional scale and local scale models can provide a practical modelling tool for policy development at intergovernmental, national and local authority level, as well as for use in epidemiological studies.
Journal ArticleDOI
Probabilistic Weather Forecasting for Winter Road Maintenance
TL;DR: In this article, the authors proposed two methods for forecasting the probability of ice formation, starting with deterministic numerical weather predictions, and model temperature and precipitation using distributions centered around the bias-corrected forecasts.
References
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Book
Statistics for spatial data
Noel A Cressie,Noel A Cressie +1 more
TL;DR: In this paper, the authors present a survey of statistics for spatial data in the field of geostatistics, including spatial point patterns and point patterns modeling objects, using Lattice Data and spatial models on lattices.
Journal ArticleDOI
5. Statistics for Spatial Data
Mike Rees,N. Cressie +1 more
TL;DR: Cressie et al. as discussed by the authors presented the Statistics for Spatial Data (SDS) for the first time in 1991, and used it for the purpose of statistical analysis of spatial data.
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