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Youhua Tang   Dr.  University Educator/Researcher 
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Youhua Tang published an article in August 2018.
Top co-authors See all
Annmarie Eldering

53 shared publications

Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

Daniel Tong

53 shared publications

Center for Spatial Information Science and Systems; George Mason University; Fairfax Virginia USA

Pius Lee

51 shared publications

NOAA Air Resources Laboratory, Silver Spring, Maryland

Robert W. Pinder

40 shared publications

Environmental Protection Agency

Viney P. Aneja

14 shared publications

Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC, USA

20
Publications
46
Reads
2
Downloads
61
Citations
Publication Record
Distribution of Articles published per year 
(2007 - 2018)
Total number of journals
published in
 
11
 
Publications See all
Article 2 Reads 0 Citations Ammonia emissions from biomass burning in the continental United States Casey D. Bray, William Battye, Viney P. Aneja, Daniel Q. Ton... Published: 01 August 2018
Atmospheric Environment, doi: 10.1016/j.atmosenv.2018.05.052
DOI See at publisher website
Article 5 Reads 0 Citations A case study of aerosol data assimilation with the Community Multi-scale Air Quality Model over the contiguous United St... Youhua Tang, Mariusz Pagowski, Tianfeng Chai, Li Pan, Pius L... Published: 22 December 2017
Geoscientific Model Development, doi: 10.5194/gmd-10-4743-2017
DOI See at publisher website ABS Show/hide abstract
This study applies the Gridpoint Statistical Interpolation (GSI) 3D-Var assimilation tool originally developed by the National Centers for Environmental Prediction (NCEP), to improve surface PM2.5 predictions over the contiguous United States (CONUS) by assimilating aerosol optical depth (AOD) and surface PM2.5 in version 5.1 of the Community Multi-scale Air Quality (CMAQ) modeling system. An optimal interpolation (OI) method implemented earlier (Tang et al., 2015) for the CMAQ modeling system is also tested for the same period (July 2011) over the same CONUS. Both GSI and OI methods assimilate surface PM2.5 observations at 00:00, 06:00, 12:00 and 18:00UTC, and MODIS AOD at 18:00UTC. The assimilations of observations using both GSI and OI generally help reduce the prediction biases and improve correlation between model predictions and observations. In the GSI experiments, assimilation of surface PM2.5 (particle matter with diameter< 2.5┬Ám) leads to stronger increments in surface PM2.5 compared to its MODIS AOD assimilation at the 550nm wavelength. In contrast, we find a stronger OI impact of the MODIS AOD on surface aerosols at 18:00UTC compared to the surface PM2.5 OI method. GSI produces smoother result and yields overall better correlation coefficient and root mean squared error (RMSE). It should be noted that the 3D-Var and OI methods used here have several big differences besides the data assimilation schemes. For instance, the OI uses relatively big model uncertainties, which helps yield smaller mean biases, but sometimes causes the RMSE to increase. We also examine and discuss the sensitivity of the assimilation experiments' results to the AOD forward operators.
Article 1 Read 0 Citations Evaluating a fire smoke simulation algorithm in the National Air Quality Forecast Capability (NAQFC) by using multiple o... Li Pan, Hyun Cheol Kim, Pius Lee, Rick Saylor, Youhua Tang, ... Published: 06 November 2017
Geoscientific Model Development Discussions, doi: 10.5194/gmd-2017-207
DOI See at publisher website ABS Show/hide abstract
Multiple observation data sets, including Interagency Monitoring of Protected Visual Environments (IMPROVE) network data, Automated Smoke Detection and Tracking Algorithm (ASDTA), Hazard Mapping System (HMS) smoke plume shapefiles and aircraft acetonitrile (CH3CN) measurements from the NOAA Southeast Nexus (SENEX) field campaign are used to evaluate the HMS-BlueSky-SMOKE-CMAQ fire emissions and smoke plume prediction system. A similar configuration is used in the National Air Quality Forecasting Capability (NAQFC). The system was found to capture signatures of most of the observed fire signals. Use of HMS-detected fire hotspots and smoke plume information are valuable for both initiating fire emissions and evaluating model simulations. However, we also found that the current system does not include fire contributions through lateral boundary condition and missed fires that are not associated with visible smoke plumes resulting in significant simulation uncertainties. In this study we focused not only on model evaluation but also on evaluation methods. We discuss how to use observational data correctly to filter out fire signals and synergistic use of multiple data sets together. We also address the limitations of each of the observation data sets and of the evaluation methods.
BOOK-CHAPTER 4 Reads 0 Citations Dynamic Coupling of the NMMB and CMAQ Models Through the U.S. National Unified Operational Prediction Capability (NUOPC) Pius Lee, Barry Baker, Daniel Tong, Li Pan, Dusan Jovic, Mar... Published: 15 September 2017
Air Pollution Modeling and its Application XXIII, doi: 10.1007/978-3-319-57645-9_31
DOI See at publisher website
BOOK-CHAPTER 3 Reads 0 Citations Toward a Unified National Dust Modeling Capability Pius Lee, Daniel Tong, Youhua Tang, Li Pan Published: 15 September 2017
Air Pollution Modeling and its Application XXIII, doi: 10.1007/978-3-319-57645-9_56
DOI See at publisher website
Article 3 Reads 3 Citations Evaluating ammonia (NH3) predictions in the NOAA National Air Quality Forecast Capability (NAQFC) using in-situ aircraft... Casey D. Bray, William Battye, Viney P. Aneja, Daniel Tong, ... Published: 01 August 2017
Atmospheric Environment, doi: 10.1016/j.atmosenv.2017.05.032
DOI See at publisher website
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