ABSTRACT

This study develops an aerosol data assimilation and forecast system using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and the three-dimensional variational (3D-VAR) data assimilation method. The system assimilates the aerosol optical depth (AOD) from the Geostationary Ocean Color Imager (GOCI) satellite and surface particulate matter (PM) observations. The simulation domain covers Northeast Asia at 15 km horizontal resolution, and the assimilation and forecast skill is evaluated for the Korea–US Air Quality (KORUS-AQ) intensive observing period. Observing system experiments (OSEs) are conducted to examine the changes in quality of assimilation and forecast skills sensitive to the assimilated observational input data. The baseline model simulation underestimates AOD and surface PM concentration in most regions, in which the assimilation of satellite and in-situ data improves the mean biases and spatial distribution. Moreover, it improves the forecast skill of the surface concentration of PM₁₀ and PM_2.5. The results from the OSEs indicate that the assimilation of GOCI AOD only slightly enhances the forecast skill. However, most of the skill improvement comes from the surface PM assimilation, showing a practically useful level of skill until 12 hours from the initial state. The marginal improvement in the PM₁₀ forecasts by the GOCI AOD suggests the non-negligible difference between column-representing AOD and the surface PM concentration.

摘要

本研究使用天气研究和预报模型结合化学 (WRF-Chem) 和三维变分 (3D-VAR) 数据同化方法开发了气溶胶数据同化和预报系统。该系统同化来自地球同步海洋彩色成像仪 (GOCI) 卫星和表面颗粒物 (PM) 观测的气溶胶光学深度 (AOD)。模拟域覆盖东北亚，水平分辨率为 15 公里，对韩美空气质量 (KORUS-AQ) 密集观测期的同化和预报技能进行评估。进行观测系统实验 (OSE) 以检查同化质量的变化和对同化观测输入数据敏感的预测技能。基线模型模拟低估了大部分地区的 AOD 和地表 PM 浓度，其中卫星和原位数据的同化改善了平均偏差和空间分布。此外，它提高了 PM 表面浓度的预测技巧₁₀和下午_2.5。来自 OSE 的结果表明 GOCI AOD 的同化仅略微增强了预测技能。然而，大部分技能提升来自表面 PM 同化，显示出实际有用的技能水平，直到从初始状态开始 12 小时。GOCI AOD对 PM ₁₀预测的边际改善表明，代表柱的 AOD 与地表 PM 浓度之间存在不可忽视的差异。