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A fast Bayesian method for updating and forecasting hourly ozone levels

机译：快速的贝叶斯方法来更新和预测每小时的臭氧水平

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摘要

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.
机译：通过结合实时AIRNow实时环境监测数据中的信息，并从称为社区多尺度空气质量（Eta-CMAQ）预测模型的计算机仿真模型中输出，提出了贝叶斯分层时空模型。模型验证分析表明，在2周的测试期内，模型预测的地图比仅基于Eta-CMAQ预测数据的地图更为准确。这些样本外空间预测和时间预测也优于具有独立高斯误差的回归模型的预测。该方法完全是贝叶斯方法，能够即时更新当前小时的地图（在接收到当前小时的监视数据之后）并预测未来数小时的地图。特别是，在当前时刻立即获得过去4小时，当前小时和提前3小时的平均值的8小时平均图。根据我们的验证，在实时更新和预测环境中，精确的贝叶斯方法优于更复杂的模型。

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