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Improving PM 2.5 Air Quality Model Forecasts in China Using a Bias-Correction Framework

机译：使用偏差校正框架改善中国的PM 2.5空气质量模型预测

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Chinese cities are experiencing severe air pollution in particular, with extremely high PM 2.5 levels observed in cold seasons. Accurate forecasting of occurrence of such air pollution events in advance can help the community to take action to abate emissions and would ultimately benefit the citizens. To improve the PM 2.5 air quality model forecasts in China, we proposed a bias-correction framework that utilized the historic relationship between the model biases and forecasted and observational variables to post-process the current forecasts. The framework consists of four components: (1) a feature selector that chooses the variables that are informative to model forecast bias based on historic data; (2) a classifier trained to efficiently determine the forecast analogs (clusters) based on clustering analysis, such as the distance-based method and the classification tree, etc.; (3) an error estimator, such as the Kalman filter, to predict model forecast errors at monitoring sites based on forecast analogs; and (4) a spatial interpolator to estimate the bias correction over the entire modeling domain. One or more methods were tested for each step. We applied five combinations of these methods to PM 2.5 forecasts in 2014–2016 over China from the operational AiMa air quality forecasting system using the Community Multiscale Air Quality (CMAQ) model. All five methods were able to improve forecast performance in terms of normalized mean error (NME) and root mean square error (RMSE), though to a relatively limited degree due to the rapid changing of emission rates in China. Among the five methods, the CART-LM-KF-AN (a Classification And Regression Trees-Linear Model-Kalman Filter-Analog combination) method appears to have the best overall performance for varied lead times. While the details of our study are specific to the forecast system, the bias-correction framework is likely applicable to the other air quality model forecast as well.
机译：特别是中国城市正在遭受严重的空气污染，在寒冷季节观测到的PM 2.5含量极高。提前准确预测此类空气污染事件的发生，可以帮助社区采取行动减少排放，最终使公民受益。为了改善中国的PM 2.5空气质量模型预报，我们提出了一个偏差校正框架，该模型利用模型偏差与预测变量和观测变量之间的历史关系来对当前预报进行后处理。该框架由四个部分组成：（1）一个特征选择器，它根据历史数据选择对预测偏差建模有用的变量; （2）经过分类训练的分类器，可以基于聚类分析（例如基于距离的方法和分类树等）有效地确定预测类似物（类）; （3）误差估计器，例如卡尔曼滤波器，用于基于预测类似物在监视站点预测模型预测误差; （4）空间插值器，用于估计整个建模域的偏差校正。每个步骤都测试一种或多种方法。我们使用社区多尺度空气质量（CMAQ）模型，从运行中的AiMa空气质量预报系统对2014-2016年中国PM 2.5预报应用了这些方法的五种组合。尽管由于中国排放率的快速变化，这五种方法均能在归一化均方误差（NME）和均方根误差（RMSE）方面提高预测性能。在这五种方法中，CART-LM-KF-AN（分类和回归树-线性模型-Kalman滤波器-模拟组合）方法似乎在不同的交货时间下具有最佳的整体性能。尽管我们的研究细节是特定于预报系统的，但偏差校正框架也可能适用于其他空气质量模型的预报。

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