Lidar data assimilation method based on CRTM and WRF-Chem models and its application in PM 2.5 forecasts in Beijing.

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 PM _2.5 concentration forecast is made. Results indicate that assimilating lidar data can effectively improve the subsequent forecast. PM _2.5 forecasts without using lidar DA are remarkably underestimated, particularly during heavy haze periods; in contrast, forecasts of PM _2.5 concentrations with lidar DA are closer to observations, the model low bias is evidently reduced, and the vertical distribution of the PM _2.5 concentration in Beijing is distinctly improved from the surface to 1200 m. Of the five aerosol species, improvements of NO ₃ ^- are the most significant. The correlation coefficient between PM _2.5 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 μg·m ^-3 , which respectively compared to those without DA.
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