为区域air pollution监测校准CAMS欧洲多模式空气质量预报
Calibrating the CAMS European multi-model air quality forecasts for regional air pollution monitoring
摘要：
CAMS空气质量多模型预测已根据利古里亚地区（意大利西北部）区域监测网在2019年和2020年收集的观测结果进行了PM10，PM2.5，O3，NO2和CO的评估和校准。在本工作中使用的校准策略有其根源，在完善的Ensemble模型输出统计（EMOS），通过该原始集合预报可以准确地转化为预测的概率密度函数，同时校正偏差和分散误差。该战略还提供了一个校准的预测模型的不确定性。作为我们的分析结果，污染物的实时观测的关键作用被摄入的校准策略显然出现，特别是在较短的前瞻预报小时。我们的动态校准策略被证明是优于其类似的实时数据不考虑在内。我们已经确定的最佳校准策略，使CAMS多模式预报系统更可靠比其他原始空气质量模式运行在更高的空间分辨率，利用更详细的信息，从库存排放。我们期望我们的研究对确定和建立可靠和经济的空气污染预警系统产生积极影响。
英文摘要：
The CAMS air quality multi-model forecasts have been assessed and calibrated for PM10, PM2.5, O3, NO2, and CO against observations collected by the Regional Monitoring Network of the Liguria region (northwestern Italy) in the years 2019 and 2020. The calibration strategy used in the present work has its roots in the well-established Ensemble Model Output Statistics (EMOS) through which a raw ensemble forecast can be accurately transformed into a predictive probability density function, with a simultaneous correction of biases and dispersion errors. The strategy also provides a calibrated forecast of model uncertainties. As a result of our analysis, the key role of pollutant real-time observations to be ingested in the calibration strategy clearly emerge especially in the shorter look-ahead forecast hours. Our dynamic calibration strategy turns out to be superior with respect to its analogous where real-time data are not taken into account. The best calibration strategy we have identified makes the CAMS multi-model forecast system more reliable than other raw air quality models running at higher spatial resolution which exploit more detailed information from inventory emission. We expect positive impacts of our research for identifying and set up reliable and economic air pollution early warning systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
