A fast Bayesian method for updating and forecasting hourly ozone levels |
| |
Authors: | Sujit K Sahu Stan Yip David M Holland |
| |
Institution: | (1) Atkins Water, Warrington, Cheshire, UK; |
| |
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. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|