Improving Air Quality Forecasts of Ozone and Particulate Matter: Modeling-Observation Integrated Study
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Date
2021-01-01
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Department
Physics
Program
Physics, Atmospheric
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Distribution Rights granted to UMBC by the author.
Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan thorugh a local library, pending author/copyright holder's permission.
Abstract
This research includes three parts to investigate the contribution to the ozone (O3) and particle pollution in the Mid-Atlantic region, U.S. and improve the O3 forecast by employing data assimilation techniques. The contributions are from the local O3 source from the Chesapeake Bay (CB) and smoke transported from the Canadian wildfire. Both observations and model are employed. The model used is the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). The data assimilation technique employed is the WRF- Chem/Data Assimilation Research Testbed (WRF-Chem/DART).First, this study investigates the dynamical influence of CB on the local O3 pollution through weather modeling. WRF-Chem was employed to simulate the O3 production and transportation near CB. One baseline experiment and one sensitivity experiment were carried out by changing the surface types over CB from water to land (loam). Due to the presence of CB, the O3 mixing ratio increased during both day and night, resulting from bay breeze circulation. In addition, the bay breeze transported O3 from CB to the western shore and increased the O3 mixing ratio over the downwind regions of onshore winds. The model for the June 3 2015 case overestimated spatially averaged surface O3 by about 20-30 %, surface O3 concentration mean increased by up to 10 % at night and 5 % during the day because of the bay dynamics effect. Furthermore, the boundary layer height over the northern CB was higher during daytime due to the higher surface temperature and active vertical convection than that in the southern side. O3 was produced, mixed and diluted up to 1.2 km over the northern CB in the day, while that height dropped to 0.4 km at night, due to the emergence of the stable nocturnal boundary layer. The large increase of O3 over the southern CB stemmed from the Atlantic Ocean. This large water body is associated with large thermal and moisture contrast to that in CB. It rendered stronger bay breeze circulation and more water vapor, which resulted in more O3 production over the southern CB.
Second, the integration of observations and models can improve air quality forecasts (in particular O3 and particulate matter (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. We use the WRF-Chem model and various measurements from both ground-based and spaceborne observations, including the U.S. Environmental Protection Agency (EPA) AirNow data, the National Aeronautics and Space Administration (NASA) operated TROPospheric OZone lidar (TROPOZ), wind radar, ceilometer, Moderate Resolution Imaging Spectroradiometer (MODIS), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The objective is to understand the physics of the Planetary Boundary Layer (PBL) and its role on the O3 and PM forecast. The model captured the O3 diurnal variation and PM spatial distribution when comparing with EPA AirNow and MODIS/CALIOP observations, respectively. Wildfire smoke was transported from central Canada through Lake Michigan, passing the Ohio River Valley and down to the Baltimore-Washington D.C. metropolis. The night-time O3 mixing ratio reached 30 ppbv, while the daytime O3 mixing ratio approached larger than 100 ppbv near AirNow stations in Maryland, due to the mixing of the transported smoke into the PBL. The NASA TROPOZ lidar at Beltsville resolved the O3 vertical profile and ceilometer identified the smoke intrusion at altitudes above 3.5 km, but later mixed down into the PBL and surface. Model simulations as well as ceilometer and O3 lidar measurements revealing this "mixing- down" are presented and discussed in Chapter 3.
Third, this study uses the WRF-Chem/DART chemical transport forecasting/data assimilation system, to assimilate EPA AirNow surface and ground-based lidar vertical profile O3 observations over the eastern U.S. to study the impact of smoke intrusion from a Canadian wildfire event in June 2015. The positive systematic bias of the operational surface O3 forecasts motivated this work. Additionally, in the absence of the assimilation of in situ O3 observations, WRF-Chem performed poorly producing positive biases near the surface ranging from 5 ppbv to 15 ppbv based on the AirNow observations, especially during the night-day transition period. Higher in the troposphere, between the surface and 1.5 km, WRF-Chem performed well with biases of 5 ppbv to 10 ppbv, but from 1.5 to 2.5 km it produced positive biases ranging from 10 ppbv to 20 ppbv based on comparison with the Tropospheric Ozone Lidar Network (TOLNet) observations. Due to the unsatisfying model performance, we propose to improve the model simulations by using the ensemble adjustment Kalman filter of Anderson (2001) to constrain the O3 forecasts with surface and profile O3 observations. The WRF-Chem/DART system is described by Mizzi et al. (2016; 2018). It uses the WRF-Chem model described by Grell et al. (2005) and the DART ensemble data assimilation system described by Anderson et al. (2009). For this study, we initialize the WRF-Chem meteorological fields with the Global Forecast System (GFS), and we initialize the chemistry fields with the output from the Model for OZone And Related chemical Tracers (MOZART-4). The WRF-Chem simulation uses various chemical emissions: (i) anthropogenic emissions from the National Emission Inventory 2011 (NEI 2011); (ii) biogenic emissions calculated during model integration by the Model of Emissions of Gases and Aerosols from Nature (MEGAN); and (iii) fire emissions from the Fire INventory from NCAR (FINN). We employ several different sources of observation datasets in this study. To constrain the WRF-Chem O3 forecasts we assimilated EPA AirNow surface O3 mixing ratio observations and Goddard Space Flight Center TROPospheric OZone differential absorption (GSFC TROPOZ - one of the instruments used in the TOLNet) O3 lidar observations. To verify the forecast results, we use balloon-borne electrochemical concentration cell (ECC) ozonesonde vertical profile observations. We present results from two experiments: (i) a control experiment where only model simulation is considered without any data assimilation, and (ii) a chemical data assimilation experiment where we employ data assimilation of the above-mentioned observations.