PhD Proposal: Zhifeng Yang
Location
Physics : 401
Date & Time
May 17, 2018, 2:00 pm – 4:00 pm
Description
ADVISOR: Dr. Belay Demoz
TITLE: Improving Air Quality Forecasts of Ozone and Particulate Matter: Modeling-Observation Integrated Study
ABSTRACT: In the context of improving the air quality forecasting especially for the big polluted event, this study focuses on the simulation of ozone and particulate matter over eastern US during the Canadian wildfire smoke event. By employing the mesoscale modeling, ground-based and satellite observations, and data assimilation technique, we are going to enhance the model air quality simulation performance from three perspectives. First, how does the Chesapeake Bay influence the local ozone pollution? Second, how the ozone and particulate matter mix within the boundary layer in terms of different PBL schemes. Third, With the help of data assimilation and observations, how well can we improve the model simulation? In order to answer these three questions, three sections are presented in this work. First, we employed Weather Research and Forecast model coupled with Chemistry package (WRF-Chem) with background emissions from National Emission Inventory 2011 (NEI-2011), and simulated the ozone near the Chesapeake Bay on June 3, 2015. We compared the difference between the simulation with the Bay and without the Bay by replacing the water over the Bay with land. After ozone formation over the Bay, the ozone was dispersed to the Baltimore-DC metropolis. We used the Environmental Protection Agency (EPA) airnow measurements to validate model performance on the surface and analyzed the ozone distribution from the south Bay to the north Bay. Second, by combining the fire inventory from National Center for Atmospheric Research (NCAR) (FINN) with the NEI-2011 emission, we simulated the Canadian wildfire event on June 6-13, 2015 and the transport of smoke from Canada to the eastern US. We evaluated the model on the smoke simulation by using the surface ozone lidar observations (Tropospheric Ozone Lidar NETwork, TOLNET), satellite observations (Moderate Resolution Imaging Spectroradiometer (MODIS), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)). The model captured the ozone transport and its diurnal variation pattern. Third, the Data Assimilation Research Testbed (DART) is going to be used to improve the smoke simulation by utilizing the EPA airnow ozone and particulate matter, TOLNET ozone vertical profile, MODIS aerosol optical thickness, and CALIOP aerosol extinction coefficient products. Through this study, we intend to explore a realistic and better way to improve the operational air quality forecasting.
TITLE: Improving Air Quality Forecasts of Ozone and Particulate Matter: Modeling-Observation Integrated Study
ABSTRACT: In the context of improving the air quality forecasting especially for the big polluted event, this study focuses on the simulation of ozone and particulate matter over eastern US during the Canadian wildfire smoke event. By employing the mesoscale modeling, ground-based and satellite observations, and data assimilation technique, we are going to enhance the model air quality simulation performance from three perspectives. First, how does the Chesapeake Bay influence the local ozone pollution? Second, how the ozone and particulate matter mix within the boundary layer in terms of different PBL schemes. Third, With the help of data assimilation and observations, how well can we improve the model simulation? In order to answer these three questions, three sections are presented in this work. First, we employed Weather Research and Forecast model coupled with Chemistry package (WRF-Chem) with background emissions from National Emission Inventory 2011 (NEI-2011), and simulated the ozone near the Chesapeake Bay on June 3, 2015. We compared the difference between the simulation with the Bay and without the Bay by replacing the water over the Bay with land. After ozone formation over the Bay, the ozone was dispersed to the Baltimore-DC metropolis. We used the Environmental Protection Agency (EPA) airnow measurements to validate model performance on the surface and analyzed the ozone distribution from the south Bay to the north Bay. Second, by combining the fire inventory from National Center for Atmospheric Research (NCAR) (FINN) with the NEI-2011 emission, we simulated the Canadian wildfire event on June 6-13, 2015 and the transport of smoke from Canada to the eastern US. We evaluated the model on the smoke simulation by using the surface ozone lidar observations (Tropospheric Ozone Lidar NETwork, TOLNET), satellite observations (Moderate Resolution Imaging Spectroradiometer (MODIS), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)). The model captured the ozone transport and its diurnal variation pattern. Third, the Data Assimilation Research Testbed (DART) is going to be used to improve the smoke simulation by utilizing the EPA airnow ozone and particulate matter, TOLNET ozone vertical profile, MODIS aerosol optical thickness, and CALIOP aerosol extinction coefficient products. Through this study, we intend to explore a realistic and better way to improve the operational air quality forecasting.
