Abstract

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... Show moreAccuracy 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. Show less