Assessment of Planetary Boundary Layer Parameterizations and Urban Heat Island Comparison: Impacts and Implications for Tracer Transport

Abstract

Accurate simulation of planetary boundary layer height (PBLH) is key to greenhouse gas emission estimation, air quality prediction and weather forecasting. This manuscript describes an extensive performance assessment of several Weather Research and Forecasting (WRF) model configurations where novel observations from ceilometers, surface stations and a flux tower were used to study their ability to reproduce planetary boundary layer heights (PBLH) and the impact that the urban heat island (UHI) has on the modeled PBLHs in the greater Washington, D.C. area. In addition, CO2 measurements at two urban towers were compared to tracer transport simulations. The ensemble of models used 4 PBL parameterizations, 2 sources of initial and boundary conditions and 1 configuration including the building energy parameterization (BEP) urban canopy model. Results have shown low biases over the whole domain and period for wind speed, wind direction and temperature with no drastic differences between meteorological drivers. We find that PBLH errors are mostly positively correlated with sensible heat flux errors, and that modeled positive UHI intensities are associated with deeper modeled PBLs over the urban areas. In addition, we find that modeled PBLHs are typically biased low during nighttime for most of the configurations with the exception of those using the MYNN parametrization and that these biases directly translate to tracer biases. Overall, the configurations using MYNN scheme performed the best, reproducing the PBLH and CO2 molar fractions reasonably well during all hours, thus opening the door to future nighttime inverse modeling.

Background conditions for an urban greenhouse gas network in the Washington, DC, and Baltimore metropolitan region

“…We also note that in the EPA or EDGAR emissions do not include emissions from wetlands, which may explain some of the poor performance especially when winds are from the east. The small negative biases of the upwind aft and upwind col backgrounds over the year (3 and 4 ppb, respectively) are to be expected if emissions inside the domain are lower than the EPA 2012 inventory, which previous work suggests is the case (Lopez-Coto et al, 2020b).…”

“…We use meteorological fields from the Weather Research and Forecast (WRF) model to drive the Stochastic Time-Inverted Lagrangian Model (STILT; Lin et al, 2003). Following Lopez-Coto et al (2020a), WRF is configured with the RRTMG radiation scheme (Mlawer et al, 1997), Thompson microphysics scheme (Thompson et al, 2004(Thompson et al, , 2008, Noah land surface model (Chen and Dudhia, 2001), Kain-Fritsch cumulus scheme (for the 9 km domain only) (Kain, 2004), 1.5order closure scheme MYNN Niino, 2004, 2006) with the eddy mass-flux option (Olson et al, and the land-use classification from the 2011 National Land Cover Database (Homer et al, 2015). Three nested domains are used (9, 3 and 1 km), with the innermost domain covering the urban area of interest, with 60 vertical levels with monotonically increasing thickness from the surface (34 levels below 3 km) and driven by initial and boundary conditions from the North American Regional Reanalysis (NARR) 3-hourly data (Mesinger et al, 2006).…”

Influence of the transported Canadian wildfire smoke on the ozone and particle pollution over the Mid-Atlantic United States

“…This UCM is the only urban canopy scheme option available in WRF-Chem version 3.9, since the PBL scheme used is Yonsei University scheme (YSU, Hong et al, 1996Hong et al, , 2006). An extended discussion of the different model PBL parameterization schemes and their success in comparison with lidar observed PBL data in this study region is reported by Lopez-Coto et al (2020). The land-surface model scheme is the Noah Land Surface Model (Chen and Dudhia, 2001).…”

Comment on acp-2020-1256

“…We use the meteorological fields from the Weather Research and Forecast (WRF) model to drive the Stochastic Time-Inverted Lagrangian Model (STILT; (Lin et al, 2003)). Following Lopez-Coto et al (2020a), WRF is configured with the RRTMG radiation scheme (Mlawer et al, 1997), Thompson microphysics scheme (Thompson et al, 2004;Thompson et al, 2008), Noah land surface model (Chen and Dudhia, 2001), the Kain-Fritsch cumulus scheme (for the 9 km domain only) (Kain, 2004), the 1.5-order closure scheme MYNN Niino, 2004, 2006) with the eddy mass-flux option (Olson et al, 2019) and the land-use classification from the 2011 National Land Cover Database (Homer et al, 2015). Three nested domains are used (9 km, 3 km and 1 km), with the innermost domain covering the urban area of interest, with 60 vertical levels with monotonically increasing thickness from the surface (34 levels below 3 km) and driven by initial and boundary conditions from the North American Regional Reanalysis (NARR) three-hourly data (Mesinger et al, 2006).…”