Sensitivity of Meteorological Skill to Selection of WRF‐Chem Physical Parameterizations and Impact on Ozone Prediction During the Lake Michigan Ozone Study (LMOS)

Abstract

Ozone concentrations in excess of health‐based standards occur along the coastline of Lake Michigan. A complex pattern of ozone precursor emissions interfaces with a complex meteorological environment, presenting a challenge for air quality management and simulation. Precursors are transported into a shallow, stable boundary layer over the lake. This is followed by ozone formation and transport back onshore through a combination of synoptic and lake breeze winds. In this study, we use measurements during the Lake Michigan Ozone Study 2017 (LMOS) to quantitatively evaluate the Weather Research and Forecasting with Chemistry (WRF‐Chem) model at 4 km horizontal resolution for key features of high ozone episodes over Southern Lake Michigan, with a focus on meteorological performance. WRF‐Chem showed good performance and successful reproduction of meteorological fields and clouds. Lake breeze model skill was inconsistent, with both good and poor performance depending on site and day. The combination of Noah land surface model and High‐Resolution Rapid Refresh meteorology gave the best performance with the mean bias of −0.5 °C for temperature, −0.6 °C for dewpoint temperature, and −0.3 m/s for wind speed along the western coast of Lake Michigan during the daytime. For ozone, WRF‐Chem was biased low (−4.4 ppb mean bias for daytime ozone) and underestimated hourly peak ozone. In some cases, ozone bias can be attributed to transport and lake breeze errors. Average ozone concentration showed minor (<2 ppb) sensitivity to changes to meteorology initial and boundary conditions or the land surface model.

Overview of the Lake Michigan Ozone Study 2017

“…NO2 is overpredicted in NAM-CMAQ relative to aircraft observations below 500 m by a factor of 2-4 (0.2-1 ppb interquartile range in observation, and 1-3.5 ppb interquartile range in model). Performance of modeled NO2 compared to aircraft in situ profiles, reported in Abdi-Oskouei et al (2019), is similar to that of NAM-CMAQ. Additionally, both modeling systems underpredict NO2 at altitudes above about 2 km.…”

Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms

“…Meanwhile, sensitivity analysis focus on determining the modification in an output data of technique that leads from modest modifications in model input parameter data (Noori et al, 2010). Consequently, a sensitivity analysis determines the prediction model output's fluctuation or changes in a localized region of the inputs' space (Abdi-Oskouei et al, 2020). As in this research, however, uncertainty analyses and sensitivity analyses of the same set of data can be performed.…”

Improving regional air quality predictions in the Indo-Gangetic Plain – case study of an intensive pollution episode in November 2017

“…The Indo-Gangetic Plain (IGP) experiences high levels of air pollution during the post-monsoon season (October to early December) due to stagnant meteorological conditions and higher air pollution emissions (Adhikary et al, 2007;Marrapu et al, 2014). Figure 1a shows the averaged aerosol optical depth (AOD) retrieved from the Visible Infrared Imaging Radiometer Suite (VIIRS) remote sensing instrument during November 2017 over northern India.…”