PBL data assimilation with the high vertical-resolution observations during the T-POMDA field campaign and its impact on meteorological analysis and air-quality simulation

This study investigates the impact of assimilating the high vertical-resolution observations on the Planetary Boundary Layer (PBL) analysis, forecast, and air quality prediction during the Taiwan Air Pollution Modeling and Data assimilation (T-POMDA) experiment. The observations include wind profilers, unmanned aerial vehicles (UAV), and Aerosond. This study focuses on two highly polluted events, 16-17 March 2021 (IOP1) and 28 Feb to 01 Marc 2023 (IOP3). Under the condition of weak wind and stable stability, the leeside vortex can cause serious air pollution in western Taiwan. For these two IOPs, we perform PBL data assimilation experiments using the Weather Research and Forecasting model-Local Ensemble Transform Kalman Filter (WRF-LETKF) with rapid update cycles. Variable localization and adaptive observation error variance inflation (AOEI) are adopted and optimized with IOP1 to highlight the impact of assimilating the PBL observations with a high vertical resolution.

For IOP1, our results suggest that assimilating the wind observations of wind profilers and Aerosond can improve the PBL dynamic structure over central Taiwan. Applying variable error covariance localization is crucial to maintain the impact of wind assimilation and avoid the negative impact on the thermodynamic structure. Due to the significant temperature variation and cold bias, the large innovation occurred easily in the PBL. Assimilating the UAV temperature observation using AOEI alleviates the overly large temperature increment and prevents the unrealistic flow in the analysis, further improving the thermal-direct land-sea breeze simulation. The WRF-LETKF analysis is used to drive the Community Multiscale Air Quality Model (CMAQ) to obtain the air quality simulation. The assimilation of wind data with high vertical resolution corrects the structure of vertical circulation near the terrain and thus better represents the vertical transportation of the pollutants. In addition, the assimilation of UAV temperature corrects the model overcooling at night, which help to suppress the unrealistic strong land breeze and prevent the pollutants from diffusing offshore. In summary, assimilating the PBL observation with the high vertical resolution is beneficial for improving the air-quality simulation related to local emission issues.

The optimized assimilation strategies are further applied to conduct the PBL data assimilation for the IOP3 event. Results with show that assimilating both the PBL wind and temperature data over the complex terrain is important to correctly represent the local circulation (leeside vortex) and the transport of air pollutants.