Lidar data assimilation method based on CRTM and WRF-Chem models and its application in PM2.5 forecasts in Beijing
Lidar data assimilation method based on CRTM and WRF-Chem models and its application in PM2.5 forecasts in Beijing
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DOI：
10.1016/j.scitotenv.2019.05.186
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发表时间：
2019-09-10
影响因子：
9.8
通讯作者：
Yan, Peng
中科院分区：
文献类型：
--
作者：
Cheng, Xinghong;Liu, Yuelin;Yan, Peng
基于社区辐射传输模式（CRTM）和天气研究与预报耦合化学模式（WRF-Chem），提出了一种三维变分（3DVAR）激光雷达资料同化方法。建立了一个利用激光雷达消光系数观测资料的3DVAR数据同化系统，并采用WRF-Chem模式中的MOSAIC（ModelforSimulating Aerosol Interactions and Chemistry）机制。将北京地区4个站点2018年3月13日12：00 - 18：00 UTC的逐时激光雷达消光系数数据同化到WRF-Chem模式的初始场中，进行24 h PM2. 5浓度预报。结果表明，同化激光雷达资料可以有效地改善后续预报。未使用激光雷达DA的PM2. 5预报结果明显偏低，尤其是在重霾期间;相比之下，使用激光雷达DA的PM2. 5浓度预报结果更接近观测值，模式低偏差明显减小，北京PM2. 5浓度的垂直分布从地面到1200 m得到明显改善。在五种气溶胶中，NO3-的改善最为显著。与未使用DA的PM2.5浓度预报相比，使用激光雷达DA的PM2.5浓度预报与北京12个站点的观测值之间的相关系数提高了0.45，相应的平均RMSE降低了25 μ g.m（-3）。（C）2019 Elsevier B. V.版权所有。
A three-dimensional variational (3DVAR) lidar data assimilation method is developed based on the Community Radiative Transfer Model (CRTM) and Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) model. A 3DVAR data assimilation (DA) system using lidar extinction coefficient observation data is established, and variables from the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) mechanism of the WRF-Chem model are employed. Hourly lidar extinction coefficient data from 12:00 to 18:00 UTC on March 13, 2018 at four stations in Beijing are assimilated into the initial field of the WRF-Chem model; subsequently, a 24 h PM2.5 concentration forecast is made. Results indicate that assimilating lidar data can effectively improve the subsequent forecast. PM2.5 forecasts without using lidar DA are remarkably underestimated, particularly during heavy haze periods; in contrast, forecasts of PM2.5 concentrations with lidar DA are closer to observations, the model low bias is evidently reduced, and the vertical distribution of the PM2.5 concentration in Beijing is distinctly improved from the surface to 1200 m. Of the five aerosol species, improvements of NO3- are the most significant. The correlation coefficient between PM2.5 concentration forecasts with lidar DA and observations at 12 stations in Beijing is increased by 0.45, and the corresponding average RMSE is decreased by 25 mu g.m(-3), which respectively compared to those without DA. (C) 2019 Elsevier B.V. All rights reserved.