一个新的CMAQ-CNN混合模式预测每小时地面臭氧浓度提前14天
A Novel CMAQ-CNN Hybrid Model to Forecast Hourly Surface-Ozone Concentrations Fourteen Days in Advance
摘要：
有关空气质量和相关健康问题的问题促使了这项研究，该研究开发了一种准确，计算速度快，高效的混合建模系统，该系统结合了数值建模和机器学习来预测地表臭氧浓度。目前可用的用于空气质量预测的数值模拟系统（例如，CMAQ、NCEP EMP）可以提前24至48小时预报。在这项研究中，我们开发了一个基于卷积神经网络（CNN）模型的建模系统，该模型不仅速度快，而且覆盖了两周的时间段，分辨率小到一个小时，适用于255个站点。CNN模型使用天气研究和预报模型（由气象-化学界面处理器处理）预测的气象、社区多尺度空气质量模型（CMAQ）预测的空气质量以及各种可测量的空气质量参数的先前24小时浓度作为输入，并预测随后14天的每小时地面臭氧浓度。该模型实现了平均准确度为0.91的第一天和0.78的第十四天的一致性指数，而提前一天的预测从CMAQ的平均一致性指数为0.77。通过这项研究，我们打算融合数值建模的最佳特征（即，精细空间分辨率）和深度神经网络（即，计算速度和精度），以实现更准确的每小时臭氧浓度的时空预测。虽然这项研究的主要目的是预测每小时的臭氧浓度，该系统可以扩展到各种其他污染物。
英文摘要：
Issues regarding air quality and related health concerns have prompted this study, which develops an accurate and computationally fast, efficient hybrid modeling system that combines numerical modeling and machine learning for forecasting concentrations of surface ozone. Currently available numerical modeling systems for air quality predictions (e.g., CMAQ, NCEP EMP) can forecast 24 to 48 hours in advance. In this study, we develop a modeling system based on a convolutional neural network (CNN) model that is not only fast but covers a temporal period of two weeks with a resolution as small as a single hour for 255 stations. The CNN model uses forecasted meteorology from the Weather Research and Forecasting model (processed by the Meteorology-Chemistry Interface Processor), forecasted air quality from the Community Multi-scale Air Quality Model (CMAQ), and previous 24-hour concentrations of various measurable air quality parameters as inputs and predicts the following 14-day hourly surface ozone concentrations. The model achieves an average accuracy of 0.91 in terms of the index of agreement for the first day and 0.78 for the fourteenth day while the average index of agreement for one day ahead prediction from the CMAQ is 0.77. Through this study, we intend to amalgamate the best features of numerical modeling (i.e., fine spatial resolution) and a deep neural network (i.e., computation speed and accuracy) to achieve more accurate spatio-temporal predictions of hourly ozone concentrations. Although the primary purpose of this study is the prediction of hourly ozone concentrations, the system can be extended to various other pollutants.
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。
