PhD Defense: Zhifeng Yang
Location
Off Campus : via WebEx
Date & Time
July 20, 2021, 10:00 am – 1:00 pm
Description
ADVISOR: Belay Demoz
TITLE: Improving Air Quality Forecasts of Ozone and Particulate Matter: Modeling-Observation Integrated Study
ABSTRACT: This research investigates both local and long-range transported smoke contributions to the ozone (O3) and particulate matter (PM) pollution in the Mid-Atlantic region, U.S. The study employs data assimilation techniques of remotely sensed data to improve the O3 forecast. The work discusses the local O3 source and its modification by the Chesapeake Bay (CB) and smoke transported from the Canadian wildfire and its role in local pollution. The study integrates observations and models. Observations include ground-based and satellite measurements. The model used is the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). The data assimilation system employed is the WRF-Chem/Data Assimilation Research Testbed (WRF-Chem/DART). This study demonstrates the dynamical influence of CB on local O3 pollution using a conceptual modification of CB in the WRF-Chem modeling. WRF-Chem was employed to simulate the O3 production and transportation near CB based on the interaction of the land-water surface and prevailing weather dynamics. One baseline experiment and one sensitivity experiment were carried out by changing the surface types over CB from water to land (loam). The differences between these two experiments were analyzed to quantify the change. Second, the integration of observations and models can improve air quality forecasts (in particular O3 and PM) for extreme events (i.e., wildfires). This work is on a Canadian fire event on 6-12 June 2015 that impacted the air quality in the Mid-Atlantic region in the U.S. Third, this study uses the WRF-Chem/DART chemical transport forecasting/data assimilation system, to assimilate EPA AirNow surface and ground-based lidar O3 vertical profile observations over the eastern U.S. to study the impact of smoke intrusion from a Canadian wildfire event in June 2015. The results show that both meteorological and chemical observations were successfully assimilated into the WRF-Chem model and improved the O3 forecast while comparing with independent O3 observations from ozonesonde.
Defense will be held using WebEx.
TITLE: Improving Air Quality Forecasts of Ozone and Particulate Matter: Modeling-Observation Integrated Study
ABSTRACT: This research investigates both local and long-range transported smoke contributions to the ozone (O3) and particulate matter (PM) pollution in the Mid-Atlantic region, U.S. The study employs data assimilation techniques of remotely sensed data to improve the O3 forecast. The work discusses the local O3 source and its modification by the Chesapeake Bay (CB) and smoke transported from the Canadian wildfire and its role in local pollution. The study integrates observations and models. Observations include ground-based and satellite measurements. The model used is the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). The data assimilation system employed is the WRF-Chem/Data Assimilation Research Testbed (WRF-Chem/DART). This study demonstrates the dynamical influence of CB on local O3 pollution using a conceptual modification of CB in the WRF-Chem modeling. WRF-Chem was employed to simulate the O3 production and transportation near CB based on the interaction of the land-water surface and prevailing weather dynamics. One baseline experiment and one sensitivity experiment were carried out by changing the surface types over CB from water to land (loam). The differences between these two experiments were analyzed to quantify the change. Second, the integration of observations and models can improve air quality forecasts (in particular O3 and PM) for extreme events (i.e., wildfires). This work is on a Canadian fire event on 6-12 June 2015 that impacted the air quality in the Mid-Atlantic region in the U.S. Third, this study uses the WRF-Chem/DART chemical transport forecasting/data assimilation system, to assimilate EPA AirNow surface and ground-based lidar O3 vertical profile observations over the eastern U.S. to study the impact of smoke intrusion from a Canadian wildfire event in June 2015. The results show that both meteorological and chemical observations were successfully assimilated into the WRF-Chem model and improved the O3 forecast while comparing with independent O3 observations from ozonesonde.
Defense will be held using WebEx.
