Please login first
Youhua Tang   Dr.  University Educator/Researcher 
Timeline See timeline
Youhua Tang published an article in December 2017.
Top co-authors See all
Daniel Q. Tong

39 shared publications

NOAA Air Resources Laboratory

Robert W. Pinder

31 shared publications

Environmental Protection Agency

Pius Lee

17 shared publications

NOAA Air Resources Air Resource Laboratory (ARL), NCWCP, College Park, USA

Annmarie Eldering

7 shared publications

NASA Jet Propulsion Laboratory/Caltech

Viney Aneja

3 shared publications

Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695, USA

8
Publications
13
Reads
2
Downloads
33
Citations
Publication Record
Distribution of Articles published per year 
(2007 - 2017)
Total number of journals
published in
 
7
 
Publications See all
Article 3 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< 2.5µm) 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 5 Reads 0 Citations 3D-Var versus Optimal Interpolation for Aerosol Assimilation: a Case Study over the Contiguous United States Youhua Tang, Mariusz Pagowski, Tianfeng Chai, Li Pan, Pius L... Published: 11 July 2017
Geoscientific Model Development Discussions, doi: 10.5194/gmd-2017-147
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. GSI results are compared with those obtained using the optimal interpolation (OI) method (Tang et al., 2015) for July, 2011 over CONUS. Both GSI and OI assimilate surface PM2.5 observations at 00, 06, 12, and 18 UTC, and MODIS AOD at 18 UTC. In the GSI experiments, assimilation of surface PM2.5 leads to stronger increments in surface PM2.5 compared to the MODIS AOD assimilation. In contrast, we find a stronger impact of MODIS AOD on surface aerosols at 18 UTC compared to the surface PM2.5 OI assimilation. The increments resulting from the OI assimilation are spread in 11×11 horizontal grid cells (12 km horizontal resolution) while the spatial distribution of GSI increments is controlled by its background error covariances, and the horizontal/vertical length scales. The assimilations of observations using both GSI and OI generally help reduce the prediction biases, and improve correlation between model predictions and observations. GSI produces smoother result and yields overall better correlation coefficient and root mean squared error (RMSE). In this study, OI uses the relatively big model uncertainties, which helps yield better mean biases, but sometimes causes the RMSE increase. We also examine and discuss the sensitivity of the assimilation experiments results to the AOD forward operators
CONFERENCE-ARTICLE 5 Reads 0 Citations Impact of Wildfires on Atmospheric Ammonia Concentrations in the US: Coupling Satellite and Ground Based Measurements Casey Bray, William Battye, Viney Aneja, Daniel Tong, Pius L... Published: 15 July 2016
The 1st International Electronic Conference on Atmospheric Sciences, doi: 10.3390/ecas2016-B001
DOI See at publisher website ABS Show/hide abstract

Gaseous ammonia plays a crucial role in the earth’s atmosphere. Major sources of atmospheric ammonia include agriculture and fires. As the climate continues to change, the pattern of fires across the US will also change, leading to changes in ammonia emissions. This study examines four major science questions using satellite and in-situ data from 2010–2014: (1) How have concentrations of ammonia changed across the US? (2) How have the strength and frequency of fires changed? (3) How has this change in fires impacted ammonia emissions? (4) How does the US EPA NEI compare with the calculated emissions? Satellite and in-situ data were used to evaluate the annual concentrations of ammonia and to calculate the total ammonia emissions across the continental US. The results of this study showed that ammonia concentrations have slightly increased over the five-year period. The total fire number and the average fire radiative power have decreased, while the total yearly burn area has increased. The calculated ammonia emissions from fires on a national scale show an increasing trend and when compared with the US EPA NEI for ammonia emissions from fires, annual ammonia emissions are, on average, a factor of 0.49 higher than the NEI.

BOOK-CHAPTER 0 Reads 1 Citation The Performance and Issues of a Regional Chemical Transport Model During Discover-AQ 2014 Aircraft Measurements Over Col... Youhua Tang, Li Pan, Pius Lee, Daniel Tong, Hyun-Cheol Kim, ... Published: 11 February 2016
Complex Systems Modeling and Simulation in Economics and Finance, doi: 10.1007/978-3-319-24478-5_103
DOI See at publisher website
Article 0 Reads 4 Citations Using optimal interpolation to assimilate surface measurements and satellite AOD for ozone and PM2.5: A case study for J... Youhua Tang, Tianfeng Chai, Li Pan, Pius Lee, Daniel Tong, H... Published: 19 June 2015
Journal of the Air & Waste Management Association, doi: 10.1080/10962247.2015.1062439
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
We employed an optimal interpolation (OI) method to assimilate AIRNow ozone/PM2.5 and MODIS (Moderate Resolution Imaging Spectroradiometer) aerosol optical depth (AOD) data into the Community Multi-scale Air Quality (CMAQ) model to improve the ozone and total aerosol concentration for the CMAQ simulation over the contiguous United States (CONUS). AIRNow data assimilation was applied to the boundary layer, and MODIS AOD data were used to adjust total column aerosol. Four OI cases were designed to examine the effects of uncertainty setting and assimilation time; two of these cases used uncertainties that varied in time and location, or "dynamic uncertainties." More frequent assimilation and higher model uncertainties pushed the modeled results closer to the observation. Our comparison over a 24-hr period showed that ozone and PM2.5 mean biases could be reduced from 2.54 ppbV to 1.06 ppbV and from -7.14 µg/m³ to -0.11 µg/m³, respectively, over CONUS, while their correlations were also improved. Comparison to DISCOVER-AQ 2011 aircraft measurement showed that surface ozone assimilation applied to the CMAQ simulation improves regional low-altitude (below 2 km) ozone simulation.
BOOK-CHAPTER 0 Reads 0 Citations Evaluating the Vertical Distribution of Ozone and Its Relationship to Pollution Events in Air Quality Models Using Satel... Jessica L. Neu, Gregory Osterman, Annmarie Eldering, Rob Pin... Published: 28 March 2014
Complex Systems Modeling and Simulation in Economics and Finance, doi: 10.1007/978-3-319-04379-1_95
DOI See at publisher website
Top