Model outputs of surface PM2.5 concentration

Exposure to ambient PM2.5 (fine particulate matter with aerodynamic diameter less than 2.5 µm) can lead to adverse health effects. Air quality forecasting of PM2.5 is critical for informing the general public and decision makers to take preventive cautions. Air quality forecasting models of PM2.5 are subject to large uncertainties due to factors such as the incomplete representation of the physical and chemical processes. Here we develop a computationally efficient bias-correction framework to improve surface PM2.5 forecasts in the United States. We developed an ensemble-based Kalman filter (KF) technique focusing on the non-rural areas in the United States and apply the KF technique to outputs of three chemical transport models (GEOS-Chem, WRF-Chem and CMAQ) for the whole month of June 2012. All three models underestimate surface measured PM2.5 concentration by 20-50%, the KF technique is effective in improving the model forecasts by reducing the model bias.暴露于环境PM2.5（气动力学直径小于2.5μm的细颗粒物）可引发不良健康影响。PM2.5空气质量预报对于指导公众与决策者采取预防措施具有关键意义。受物理、化学过程表征不完备等因素影响，现有PM2.5空气质量预报模型存在较大不确定性。为此，本研究构建了一套计算高效的偏差校正框架，用于优化美国地区地表PM2.5预报结果。我们针对美国非农村区域开发了基于集合的卡尔曼滤波（KF）技术，并将该技术应用于2012年6月整月的三类化学传输模型（GEOS-Chem、WRF-Chem与CMAQ）的输出数据。三类模型均较实测地表PM2.5浓度低估20%至50%，而该卡尔曼滤波技术可通过降低模型偏差，有效改善模型预报性能。

搜集汇总

背景与挑战

背景概述

该数据集提供了2012年6月美国非农村地区地表PM2.5浓度的模型输出，基于GEOS-Chem和WRF-Chem等化学传输模型，并应用了卡尔曼滤波偏差校正技术以提高预测准确性。数据集由爱荷华大学研究人员开发，旨在支持空气质量预测和公共健康应用，包含开放获取的HDF格式文件。