ABSTRACT

A bias correction scheme based on a Kalman filter (KF) method has been developed and implemented for the AIRPACT air quality forecast system which operates daily for the Pacific Northwest. The KF method was used to correct hourly rolling 24-h average PM_2.5 concentrations forecast at each monitoring site within the AIRPACT domain and the corrected forecasts were evaluated using observed daily PM_2.5 24-h average concentrations from 2017 to 2018. The evaluation showed that the KF method reduced mean daily bias from approximately −50% to ±6% on a monthly averaged basis, and the corrected results also exhibited much smaller mean absolute errors typically less than 20%. These improvements were also apparent for the top 10 worst PM_2.5 days during the 2017–2018 test period, including months with intensive wildfire events. Significant differences in AIRPACT performance among urban, suburban, and rural monitoring sites were greatly reduced in the KF bias correction forecasts. The daily 24-h average bias corrections for each monitoring site were interpolated to model grid points using three different interpolation schemes: cubic spline, Gaussian Kriging, and linear Kriging. The interpolated results were more accurate than the original AIRPACT forecasts, and both Kriging methods were better than the cubic spline method. The Gaussian method yielded smaller mean biases and the linear method yielded smaller absolute errors. The KF bias correction method has been implemented operationally using both Kriging interpolation methods for routine output on the AIRPACT website (http://lar.wsu.edu/airpact). This method is relatively easy to implement, but very effective to improve air quality forecast performance.

Implications: Current chemical transport models, including CMAQ, used for air quality forecasting can have large errors and uncertainties in simulated PM_2.5 concentrations. In this paper, we describe a relatively simple bias correction scheme applied to the AIRPACT air quality forecast system for the Pacific Northwest. The bias correction yields much more accurate and reliable PM_2.5 results compared to the normal forecast system. As such, the operational bias corrected forecasts will provide a much better basis for daily air quality management by agencies within the region. The bias corrected results also highlight issues to guide further improvements to the normal forecast system.

摘要

已经开发并实施了基于卡尔曼滤波器 (KF) 方法的偏差校正方案，用于太平洋西北地区每天运行的 AIRPACT 空气质量预报系统。KF 方法用于校正AIRPACT 域内每个监测点的每小时滚动 24 小时平均 PM _2.5浓度预报，并使用2017 年至 2018 年观测到的每日 PM _2.5 24 小时平均浓度对校正后的预报进行评估。评估表明， KF 方法将每月平均的平均每日偏差从大约 -50% 降低到 ±6%，并且校正后的结果也表现出更小的平均绝对误差，通常小于 20%。这些改进对于前 10 名最差 PM _2.5也很明显2017-2018 年测试期间的天数，包括发生密集野火事件的几个月。在 KF 偏差校正预测中，城市、郊区和农村监测点之间 AIRPACT 性能的显着差异大大减少。使用三种不同的插值方案将每个监测站点的每日 24 小时平均偏差校正值插值到模型网格点：三次样条、高斯克里金法和线性克里金法。插值结果比原始 AIRPACT 预测更准确，并且两种克里金方法都优于三次样条方法。高斯方法产生较小的平均偏差，而线性方法产生较小的绝对误差。KF 偏差校正方法已使用两种克里金插值方法在 AIRPACT 网站 (http://lar.wsu. edu/airpact）。这种方法实施起来相对容易，但对提高空气质量预报性能非常有效。

影响：当前用于空气质量预测的化学物质迁移模型（包括 CMAQ）在模拟 PM _2.5浓度中可能存在较大误差和不确定性。在本文中，我们描述了一种应用于太平洋西北地区 AIRPACT 空气质量预报系统的相对简单的偏差校正方案。与正常预测系统相比，偏差校正产生更准确和可靠的 PM _2.5结果。因此，经运营偏差修正的预测将为该地区各机构的日常空气质量管理提供更好的基础。偏差校正结果还突出了指导进一步改进正常预测系统的问题。