The University of Southampton
University of Southampton Institutional Repository

A fast Bayesian method for updating and forecasting hourly ozone levels

Sahu, Sujit K., Yip, Stan and Holland, David M. (2009) A fast Bayesian method for updating and forecasting hourly ozone levels Environmental and Ecological Statistics, 18, (1), pp. 185-207. (doi:10.1007/s10651-009-0127-y).

Record type: Article

Abstract

A Bayesian hierarchical space-time model is proposed by combining information from real-time ambient AIRNow air monitoring data, and output from a computer simulation model known as the Community Multi-scale Air Quality (Eta-CMAQ) forecast model. A model validation analysis shows that the model predicted maps are more accurate than the maps based solely on the Eta-CMAQ forecast data for a 2 week test period. These out-of sample spatial predictions and temporal forecasts also outperform those from regression models with independent Gaussian errors. The method is fully Bayesian and is able to instantly update the map for the current hour (upon receiving monitor data for the current hour) and forecast the map for several hours ahead. In particular, the 8 h average map which is the average of the past 4 h, current hour and 3 h ahead is instantly obtained at the current hour. Based on our validation, the exact Bayesian method is preferable to more complex models in a real-time updating and forecasting environment.

PDF sahuyipholland_env_eco_09.pdf - Other
Restricted to Repository staff only
Download (1MB)

More information

Published date: 9 November 2009
Keywords: bayesian inference, eta-cmaq model, space-time forecasting, hierarchical model, separable models, spatial interpolation
Organisations: Statistics

Identifiers

Local EPrints ID: 147821
URI: http://eprints.soton.ac.uk/id/eprint/147821
ISSN: 1352-8505
PURE UUID: 75e1b843-5ce2-48fc-96e6-53a1fff9123c
ORCID for Sujit K. Sahu: ORCID iD orcid.org/0000-0003-2315-3598

Catalogue record

Date deposited: 26 Apr 2010 13:41
Last modified: 18 Jul 2017 19:33

Export record

Altmetrics

Contributors

Author: Sujit K. Sahu ORCID iD
Author: Stan Yip
Author: David M. Holland

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×