Toward a Better Regional Ozone Forecast Over CONUS Using Rapid Data Assimilation of Clouds and Meteorology in WRF‐Chem

Accuracy of cloud predictions in numerical weather models can considerably impact ozone (O3) forecast skill. This study assesses the benefits in surface O3predictions 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 O3and temperature driven by RAP are in better agreements with observations. Reductions of 5 ppb in O3mean bias error and 2.4 ppb in O3root‐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 O3exceedances but exhibits more frequent false alarms, resulting from its tendency to overpredict O3. The O3concentrations are found to respond mainly to the changes in boundary layer height that directly affects the mixing of O3and its precursors. The RAP data assimilation shows improvements in the cloud forecast skill during the initial forecast hours, which reduces O3forecast 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 O3underprediction in the boundary layer. The performance for day 1 O3forecasts driven by RAP is generally improved than that driven by GFSThe reduction in day 1 O3forecast errors is mainly associated with the boundary layer heightThe cloud assimilation improves cloud forecasts for the first few hours and reduces errors in 1‐hr O3forecast under cloudy conditions