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Application of ensemble forecast and linear regression method in improving PM₁₀ forecast over Beijing areas

摘要：本研究将多模式集合预报和多元线性回归集成方法结合起来减小空气质量预报的不确定性.首先评估了北京空气质量多模式集合预报系统中3个模式成员(NAQPMS、CAMx、CMAQ)对北京地区PM₁₀日均浓度的预报性能,在此基础上引入多元线性回归将历史观测信息纳入进来对3个模式预报结果进行集成,并将集成预报结果与3个模式算术平均的预报结果进行比较.结果发现:1不同模式的预报结果差异较大,并没有一个模式的预报技巧完全优于其它两个模式,其中CMAQ对北京PM₁₀变化趋势的预报优于其它两个模式,NAQPMS预报的均方根误差整体低于其他两个模式.2多模式预报结果的算术平均在趋势预报和偏差两项指标上都低于部分单模式预报,并不能有效改进PM₁₀预报;基于分站点的模式和观测数据构建的多元线性回归集成预报模型能显著提高PM₁₀预报的准确率,选定合适的训练天数(36 d)后,28个站点PM₁₀日均值预报的均方根误差相对单模式预报或集合平均预报下降32%~43%,预报偏差大幅减小至5.8 μg·m^-3,总体预报技巧显著优于单模式和多模式算术平均的预报结果,并且采用线性回归集成方法大幅提高了对污染过程的预报能力.

Abstract：In this study, ensemble forecast combined with linear regression method is used to reduce the uncertainty in air quality models. Firstly, the PM₁₀ forecasts by three models (NAQPMS, CAMx and CMAQ) in EMS-Beijing are evaluated over Beijing areas. In order to improve the forecast performance, the linear regression method (REG) is used to combine the forecast results of the three models and is compared with the ensemble mean method. The results show that for single model forecast, great difference exists among different models and no model performs much better for all statistic indexes than the other two models. Overall, CMAQ performs better in tendency prediction, while NAQPMS has smaller root mean square errors than the other two models. Ensemble mean method presents poor performance in improving the PM₁₀ forecasts from the three models. On the other hand, REG brings significant improvement of the PM₁₀ forecast. When an appropriate training length (36 days) is applied, the root mean square errors of PM₁₀ forecast over 28 stations of Beijing is reduced by 32%~43% when using REG and the bias decreased considerably to 5.8 μg·m^-3. This result implies that REG can greatly improve forecast performance than single model and ensemble mean forecast. Furthermore, the REG also greatly improve capturing of pollution episode forecast.