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Youhua Tang   Dr.  University Educator/Researcher 
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Youhua Tang published an article in December 2017.
Top co-authors See all
Anne M. Thompson

511 shared publications

Earth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, MD, USA

David Streets

176 shared publications

Energy Systems Division, Argonne National Laboratory, Argonne, Illinois 60439, United States

Louisa Emmons

161 shared publications

National Center for Atmospheric Research Boulder, Colorado, USA

Oliver Wild

124 shared publications

Lancaster Environment Centre, Lancaster University, Lancaster, UK

Edward V. Browell

121 shared publications

STARSS-III Affiliate; NASA Langley Research Center; Hampton VA USA

34
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227
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Publication Record
Distribution of Articles published per year 
(1970 - 2017)
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Article 2 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
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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 2 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
Article 2 Reads 3 Citations Multi-year downscaling application of two-way coupled WRF v3.4 and CMAQ v5.0.2 over east Asia for regional climate and a... Chaopeng Hong, Qiang Zhang, Yang Zhang, Youhua Tang, Daniel ... Published: 29 June 2017
Geoscientific Model Development, doi: 10.5194/gmd-10-2447-2017
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In this study, a regional coupled climate–chemistry modeling system using the dynamical downscaling technique was established by linking the global Community Earth System Model (CESM) and the regional two-way coupled Weather Research and Forecasting – Community Multi-scale Air Quality (WRF-CMAQ) model for the purpose of comprehensive assessments of regional climate change and air quality and their interactions within one modeling framework. The modeling system was applied over east Asia for a multi-year climatological application during 2006–2010, driven with CESM downscaling data under Representative Concentration Pathways 4.5 (RCP4.5), along with a short-term air quality application in representative months in 2013 that was driven with a reanalysis dataset. A comprehensive model evaluation was conducted against observations from surface networks and satellite observations to assess the model's performance. This study presents the first application and evaluation of the two-way coupled WRF-CMAQ model for climatological simulations using the dynamical downscaling technique. The model was able to satisfactorily predict major meteorological variables. The improved statistical performance for the 2m temperature (T2) in this study (with a mean bias of −0.6°C) compared with the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-models might be related to the use of the regional model WRF and the bias-correction technique applied for CESM downscaling. The model showed good ability to predict PM2. 5 in winter (with a normalized mean bias (NMB) of 6.4% in 2013) and O3 in summer (with an NMB of 18.2% in 2013) in terms of statistical performance and spatial distributions. Compared with global models that tend to underpredict PM2. 5 concentrations in China, WRF-CMAQ was able to capture the high PM2. 5 concentrations in urban areas. In general, the two-way coupled WRF-CMAQ model performed well for both climatological and air quality applications. The coupled modeling system with direct aerosol feedbacks predicted aerosol optical depth relatively well and significantly reduced the overprediction in downward shortwave radiation at the surface (SWDOWN) over polluted regions in China. The performance of cloud variables was not as good as other meteorological variables, and underpredictions of cloud fraction resulted in overpredictions of SWDOWN and underpredictions of shortwave and longwave cloud forcing. The importance of climate–chemistry interactions was demonstrated via the impacts of aerosol direct effects on climate and air quality. The aerosol effects on climate and air quality in east Asia (e.g., SWDOWN and T2 decreased by 21.8Wm−2 and 0.45°C, respectively, and most pollutant concentrations increased by 4.8–9.5% in January over China's major cities) were more significant than in other regions because of higher aerosol loadings that resulted from severe regional pollution, which indicates the need for applying online-coupled...
Article 2 Reads 0 Citations Multi-year Downscaling Application of Online Coupled WRFCMAQ over East Asia for Regional Climate and Air Quality Modelin... Chaopeng Hong, Qiang Zhang, Youhua Tang, Daniel Tong, Kebin ... Published: 07 December 2016
Geoscientific Model Development Discussions, doi: 10.5194/gmd-2016-261
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In this study, a regional coupled climate-chemistry modeling system using the dynamical downscaling technique was established by linking the global Community Earth System Model (CESM) and the regional online coupled Weather Research and Forecasting – Community Multiscale Air Quality (WRF-CMAQ) model for the purpose of comprehensive assessments of regional climate change and air quality and their interactions within one modeling framework. The modeling system was applied over East Asia for a multiyear climatological application during 2006–2010 driven with CESM downscaling data under Representative Concentration Pathway 4.5 (RCP 4.5) as well as a short-term air quality application in representative months in 2013 driven with a reanalysis dataset. A comprehensive model evaluation was conducted against observations from surface networks and satellite observations to assess the model's performance. This study presents the first application and evaluation of the online coupled WRF-CMAQ model for climatological simulations using the dynamical downscaling technique. The model was able to satisfactorily predict major meteorological variables. The improved statistical performance for the 2-m temperature (T2) in this study compared with the Coupled Model Inter-comparison Project Phase 5 (CMIP5) multi-models might be related to the use of the regional model WRF and the bias-correction technique applied for CESM downscaling. The model showed good ability to predict PM2.5 in winter and O3 in summer in terms of statistical performance and spatial distributions. Compared with global models that tend to underpredict PM2.5 concentrations in China, WRF-CMAQ was able to capture the high PM2.5 concentrations in urban areas. In general, the online coupled WRF-CMAQ model performed well for both climatological and air quality applications. The coupled modeling system with direct aerosol feedbacks predicted aerosol optical depth relatively well and significantly reduced the overprediction in downward shortwave radiation at the surface (SWDOWN) over polluted regions in China. The performance of cloud variables was not as good as other meteorological variables, and underpredictions of cloud fraction resulted in overpredictions of SWDOWN and underpredictions of shortwave and longwave cloud forcing. The importance of climate-chemistry interactions was demonstrated via the impacts of aerosol direct effects on climate and air quality. The aerosol effects on climate and air quality in East Asia were more significant than in other regions because of higher aerosol loadings that resulted from severe regional pollution, which indicates the need for applying online-coupled models over East Asia for regional climate and air quality modeling and to study the important climate-chemistry interactions.
Article 2 Reads 1 Citation Impact of the 2008 Global Recession on air quality over the United States: Implications for surface ozone levels from ch... Daniel Tong, Li Pan, Weiwei Chen, Lok Lamsal, Pius Lee, Youh... Published: 05 September 2016
Geophysical Research Letters, doi: 10.1002/2016gl069885
DOI See at publisher website
CONFERENCE-ARTICLE 4 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
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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.

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