Wednesday, 9 January 2019
Hall 4 (Phoenix Convention Center - West and North Buildings)
Accurate cloud representation in air quality models plays a key role in photochemical production of ozone (O3) and thus O3 forecast skills. We present two approaches to assess errors related to clouds in O3 predictions. First, we use GOES satellite cloud products to constrain radiation fields for photochemistry in WRF-Chem and evaluate the effects of satellite cloud-constrained radiation on summertime O3 formation over CONUS. O3 simulations for 2013 summer over CONUS are carried out using reanalysis data, and the results show that the average difference in summer time surface O3 concentrations derived from the modeled clouds and satellite clouds ranges from 1 to 5 ppb for maximum daily 8-h average O3 (MDA8 O3) over CONUS. The cloud bias accounts for up to ~40% of the total MDA8 O3 bias under cloudy conditions. Second, we assess the benefits of cloud assimilation on O3 production and forecast skills using two different initial meteorological conditions i.e. the Global Forecast System (GFS) and Rapid Refresh (RAP) in WRF-Chem simulations during 2016 summer. RAP assimilates METAR observations and satellite-retrieved cloud top pressure to modify background hydrometeor fields. The results will be discussed in terms of ozone mean concentrations and ozone exceedances. For instance, the RAP simulations perform better for the next-day O3 forecasts than the GFS simulations as O3 in GFS simulations is generally overpredicted. The mean bias error of MDA8 O3 over CONUS is smaller by ~3 ppb under cloudy conditions in RAP simulations than in GFS simulations. The differences in predictability of next-day cloud cover between RAP and GFS simulations are also analyzed, and indicate that the predictability of cloud cover is the highest for 1-h forecasts using RAP and gradually decreases with the forecast length. The impact of other meteorological parameters that are assimilated within RAP on O3 forecast will also be discussed.
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