资源环境科技发展态势分析平台

Accuracy of cloud predictions in numerical weather models can considerably impact ozone (O-3) forecast skill. This study assesses the benefits in surface O-3 predictions of using the Rapid Refresh (RAP) forecasting system that assimilates clouds as well as conventional meteorological variables at hourly time scales. We evaluate and compare the WRF-Chem simulations driven by RAP and the Global Forecast System (GFS) forecasts over the Contiguous United States (CONUS) for 2016 summer. The day 1 forecasts of surface O-3 and temperature driven by RAP are in better agreements with observations. Reductions of 5 ppb in O-3 mean bias error and 2.4 ppb in O-3 root-mean-square-error are obtained on average over CONUS with RAP compared to those with GFS. The WRF-Chem simulation driven by GFS shows a higher probability of capturing O-3 exceedances but exhibits more frequent false alarms, resulting from its tendency to overpredict O-3. The O-3 concentrations are found to respond mainly to the changes in boundary layer height that directly affects the mixing of O-3 and its precursors. The RAP data assimilation shows improvements in the cloud forecast skill during the initial forecast hours, which reduces O-3 forecast errors at the initial forecast hours especially under cloudy-sky conditions. Sensitivity simulations utilizing satellite clouds show that the WRF-Chem simulation with RAP produces too thick low-level clouds, which leads to O-3 underprediction in the boundary layer.