@article{jena_zhang_wang_campbell_2023, title={Decadal Application of WRF/Chem under Future Climate and Emission Scenarios: Impacts of Technology-Driven Climate and Emission Changes on Regional Meteorology and Air Quality}, volume={14}, ISSN={["2073-4433"]}, DOI={10.3390/atmos14020225}, abstractNote={This work presents new climate and emissions scenarios to investigate changes on future meteorology and air quality in the U.S. Here, we employ a dynamically downscaled Weather Research and Forecasting model coupled with chemistry (WRF/Chem) simulations that use two Intergovernmental Panel on Climate Change scenarios (i.e., A1B and B2) integrated with explicitly projected emissions from a novel Technology Driver Model (TDM). The projected 2046–2055 emissions show widespread reductions in most gas and aerosol species under both TDM/A1B and TDM/B2 scenarios over the U.S. The WRF/Chem simulations show that under the combined effects of the TDM/A1B climate and emission changes, the maximum daily average 8-h ozone (MDA8 h O3) increases by ~3 ppb across the U.S. mainly due to widespread increases in near-surface temperature and background methane concentrations, with some contributions from localized TDM emission changes near urban centers. For the TDM/B2 climate and emission changes, however, the MDA8 h O3 is widely decreased, except near urban centers where the relative TDM emission changes and O3 formation regimes leads to increased O3. The number of O3 exceedance days (i.e., MDA8 h O3 > 70 ppb) for the entire domain is significantly reduced by a grid cell maximum of up to 43 days (domain average ~0.5 days) and 62 days (domain average ~2 days) for the TDM/A1B and TDM/B2 scenarios, respectively, while in the western U.S., larger O3 increases lead to increases in nonattainment areas, especially for the TDM/A1B scenario. The combined effects of climate and emissions (for both A1B and B2 scenarios) will lead to widespread decreases in the daily 24-h average (DA24 h) PM2.5 concentrations, especially in the eastern U.S. (max decrease up to 93 µg m−3). The PM2.5 changes are dominated by decreases in anthropogenic emissions for both the TDM/A1B and TDM/B2 scenarios, with secondary effects on decreasing PM2.5 from climate change. The number of PM2.5 exceedance days (i.e., DA24 h PM2.5 > 35 µg m−3) is significantly reduced over the eastern U.S. under both TDM/A1B and B2 scenarios, which suggests that both climate and emission changes may synergistically lead to decreases in PM2.5 nonattainment areas in the future.}, number={2}, journal={ATMOSPHERE}, author={Jena, Chinmay and Zhang, Yang and Wang, Kai and Campbell, Patrick C.}, year={2023}, month={Feb} }
 @article{zhang_jena_wang_paton-walsh_guerette_utembe_silver_keywood_2019, title={Multiscale Applications of Two Online-Coupled Meteorology-Chemistry Models during Recent Field Campaigns in Australia, Part I: Model Description and WRF/Chem-ROMS Evaluation Using Surface and Satellite Data and Sensitivity to Spatial Grid Resolutions}, volume={10}, ISSN={["2073-4433"]}, DOI={10.3390/atmos10040189}, abstractNote={Air pollution and associated human exposure are important research areas in Greater Sydney, Australia. Several field campaigns were conducted to characterize the pollution sources and their impacts on ambient air quality including the Sydney Particle Study Stages 1 and 2 (SPS1 and SPS2), and the Measurements of Urban, Marine, and Biogenic Air (MUMBA). In this work, the Weather Research and Forecasting model with chemistry (WRF/Chem) and the coupled WRF/Chem with the Regional Ocean Model System (ROMS) (WRF/Chem-ROMS) are applied during these field campaigns to assess the models’ capability in reproducing atmospheric observations. The model simulations are performed over quadruple-nested domains at grid resolutions of 81-, 27-, 9-, and 3-km over Australia, an area in southeastern Australia, an area in New South Wales, and the Greater Sydney area, respectively. A comprehensive model evaluation is conducted using surface observations from these field campaigns, satellite retrievals, and other data. This paper evaluates the performance of WRF/Chem-ROMS and its sensitivity to spatial grid resolutions. The model generally performs well at 3-, 9-, and 27-km resolutions for sea-surface temperature and boundary layer meteorology in terms of performance statistics, seasonality, and daily variation. Moderate biases occur for temperature at 2-m and wind speed at 10-m in the mornings and evenings due to the inaccurate representation of the nocturnal boundary layer and surface heat fluxes. Larger underpredictions occur for total precipitation due to the limitations of the cloud microphysics scheme or cumulus parameterization. The model performs well at 3-, 9-, and 27-km resolutions for surface O3 in terms of statistics, spatial distributions, and diurnal and daily variations. The model underpredicts PM2.5 and PM10 during SPS1 and MUMBA but overpredicts PM2.5 and underpredicts PM10 during SPS2. These biases are attributed to inaccurate meteorology, precursor emissions, insufficient SO2 conversion to sulfate, inadequate dispersion at finer grid resolutions, and underprediction in secondary organic aerosol. The model gives moderate biases for net shortwave radiation and cloud condensation nuclei but large biases for other radiative and cloud variables. The performance of aerosol optical depth and latent/sensible heat flux varies for different simulation periods. Among all variables evaluated, wind speed at 10-m, precipitation, surface concentrations of CO, NO, NO2, SO2, O3, PM2.5, and PM10, aerosol optical depth, cloud optical thickness, cloud condensation nuclei, and column NO2 show moderate-to-strong sensitivity to spatial grid resolutions. The use of finer grid resolutions (3- or 9-km) can generally improve the performance for those variables. While the performance for most of these variables is consistent with that over the U.S. and East Asia, several differences along with future work are identified to pinpoint reasons for such differences.}, number={4}, journal={ATMOSPHERE}, author={Zhang, Yang and Jena, Chinmay and Wang, Kai and Paton-Walsh, Clare and Guerette, Elise-Andree and Utembe, Steven and Silver, Jeremy David and Keywood, Melita}, year={2019}, month={Apr} }
 @article{zhang_wang_jena_paton-walsh_guerette_utembe_silver_keywood_2019, title={Multiscale Applications of Two Online-Coupled Meteorology-Chemistry Models during Recent Field Campaigns in Australia, Part II: Comparison of WRF/Chem and WRF/Chem-ROMS and Impacts of Air-Sea Interactions and Boundary Conditions}, volume={10}, ISSN={["2073-4433"]}, DOI={10.3390/atmos10040210}, abstractNote={Air-sea interactions play an important role in atmospheric circulation and boundary layer conditions through changing convection processes and surface heat fluxes, particularly in coastal areas. These changes can affect the concentrations, distributions, and lifetimes of atmospheric pollutants. In this Part II paper, the performance of the Weather Research and Forecasting model with chemistry (WRF/Chem) and the coupled WRF/Chem with the Regional Ocean Model System (ROMS) (WRF/Chem-ROMS) are intercompared for their applications over quadruple-nested domains in Australia during the three following field campaigns: The Sydney Particle Study Stages 1 and 2 (SPS1 and SPS2) and the Measurements of Urban, Marine, and Biogenic Air (MUMBA). The results are used to evaluate the impact of air-sea interaction representation in WRF/Chem-ROMS on model predictions. At 3, 9, and 27 km resolutions, compared to WRF/Chem, the explicit air-sea interactions in WRF/Chem-ROMS lead to substantial improvements in simulated sea-surface temperature (SST), latent heat fluxes (LHF), and sensible heat fluxes (SHF) over the ocean, in terms of statistics and spatial distributions, during all three field campaigns. The use of finer grid resolutions (3 or 9 km) effectively reduces the biases in these variables during SPS1 and SPS2 by WRF/Chem-ROMS, whereas it further increases these biases for WRF/Chem during all field campaigns. The large differences in SST, LHF, and SHF between the two models lead to different radiative, cloud, meteorological, and chemical predictions. WRF/Chem-ROMS generally performs better in terms of statistics and temporal variations for temperature and relative humidity at 2 m, wind speed and direction at 10 m, and precipitation. The percentage differences in simulated surface concentrations between the two models are mostly in the range of ±10% for CO, OH, and O3, ±25% for HCHO, ±30% for NO2, ±35% for H2O2, ±50% for SO2, ±60% for isoprene and terpenes, ±15% for PM2.5, and ±12% for PM10. WRF/Chem-ROMS at 3 km resolution slightly improves the statistical performance of many surface and column concentrations. WRF/Chem simulations with satellite-constrained boundary conditions (BCONs) improve the spatial distributions and magnitudes of column CO for all field campaigns and slightly improve those of the column NO2 for SPS1 and SPS2, column HCHO for SPS1 and MUMBA, and column O3 for SPS2 at 3 km over the Greater Sydney area. The satellite-constrained chemical BCONs reduce the model biases of surface CO, NO, and O3 predictions at 3 km for all field campaigns, surface PM2.5 predictions at 3 km for SPS1 and MUMBA, and surface PM10 predictions at all grid resolutions for all field campaigns. A more important role of chemical BCONs in the Southern Hemisphere, compared to that in the Northern Hemisphere reported in this work, indicates a crucial need in developing more realistic chemical BCONs for O3 in the relatively clean SH.}, number={4}, journal={ATMOSPHERE}, author={Zhang, Yang and Wang, Kai and Jena, Chinmay and Paton-Walsh, Clare and Guerette, Elise-Andree and Utembe, Steven and Silver, Jeremy David and Keywood, Melita}, year={2019}, month={Apr} }
 @article{chate_waghmare_jena_gopalakrishnan_murugavel_ghude_kulkarni_devara_2018, title={Cloud condensation nuclei over the Bay of Bengal during the Indian summer monsoon}, volume={35}, ISSN={["1861-9533"]}, DOI={10.1007/s00376-017-6331-z}, number={2}, journal={ADVANCES IN ATMOSPHERIC SCIENCES}, author={Chate, D. M. and Waghmare, R. T. and Jena, C. K. and Gopalakrishnan, V. and Murugavel, P. and Ghude, Sachin D. and Kulkarni, Rachana and Devara, P. C. S.}, year={2018}, month={Feb}, pages={218–223} }
 @article{goldberg_gupta_wang_jena_zhang_lu_streets_2019, title={Using gap-filled MAIAC AOD and WRF-Chem to estimate daily PM2.5 concentrations at 1 km resolution in the Eastern United States}, volume={199}, ISSN={["1873-2844"]}, DOI={10.1016/j.atmosenv.2018.11.049}, abstractNote={To link short-term exposures of air pollutants to health outcomes, scientists must use high temporal and spatial resolution estimates of PM2.5 concentrations. In this work, we develop a daily PM2.5 product at 1 × 1 km2 spatial resolution across the eastern United States (east of 90° W) with the aid of 1 × 1 km2 MAIAC aerosol optical depth (AOD) data, 36 × 36 km2 WRF-Chem output, 1 × 1 km2 land-use type from the National Land Cover Database, and 0.125° × 0.125° ERA-Interim re-analysis meteorology. A gap-filling technique is applied to MAIAC AOD data to construct robust daily estimates of AOD when the satellite data are missing (e.g., areas obstructed by clouds or snow cover). The input data are incorporated into a multiple-linear regression model trained to surface observations of PM2.5 from the EPA Air Quality System (AQS) monitoring network. The model generates a high-fidelity estimate (r2 = 0.75 using a 10-fold random cross-validation) of daily PM2.5 throughout the eastern United States. Of the inputs to the statistical model, WRF-Chem output (r2 = 0.66) is the most important contributor to the skill of the model. MAIAC AOD is also a strong contributor (r2 = 0.52). Daily PM2.5 output from our statistical model can be easily integrated into county-level epidemiological studies. The novelty of this project is that we are able to simulate PM2.5 in a computationally efficient manner that is constrained to ground monitors, satellite data, and chemical transport model output at high spatial resolution (1 × 1 km2) without sacrificing the temporal resolution (daily) or spatial coverage (>2,000,000 km2).}, journal={ATMOSPHERIC ENVIRONMENT}, author={Goldberg, Daniel L. and Gupta, Pawan and Wang, Kai and Jena, Chinmay and Zhang, Yang and Lu, Zifeng and Streets, David G.}, year={2019}, month={Feb}, pages={443–452} }
 @article{zhang_wang_jena_2017, title={Impact of Projected Emission and Climate Changes on Air Quality in the US: from National to State Level}, volume={110}, ISSN={["1877-0509"]}, DOI={10.1016/j.procs.2017.06.074}, abstractNote={Future ambient air quality will respond to changes in anthropogenic and biogenic emissions as well as climate changes, which may vary at national and state levels in different regions of the world. In this work, we applied an advanced online-coupled meteorology and chemistry model, the Weather Research and Forecasting Model with Chemistry (WRF/Chem), to the continental U.S. for current (2001-2010) and future (2046-2055) decades under four climate scenarios including the Representative Concentration Pathways (RCP) 4.5 and 8.5 and the Technology Driver Model (TDM) A1B and B2. Our goal is to quantify the impact of projected changes in anthropogenic and biogenic emissions and climate on future air quality under various climate scenarios for policy analysis for emission control and mitigation of adverse climate change. The simulations are performed at 36-, 12-km, and 4-km over North America, Continental U.S., and selected states, respectively. A comprehensive evaluation has been performed for the current simulation period using available observations from surface networks and satellites and shows an overall good performance in reproducing climatic and chemical observations at all grid scales. Future air quality features greater reduction in PM2.5 by RCP 4.5/8.5 than TDM B2/A1B and decreased O3 over most areas in the U.S. by RCP4.5 and TDM B2, indicating the benefits of carbon policy and technology changes with greater emission reductions. Air quality responds differently to projected changes in anthropogenic emissions in different states and seasons, indicating a need to develop state-specific emission control strategies for different seasons.}, journal={14TH INTERNATIONAL CONFERENCE ON MOBILE SYSTEMS AND PERVASIVE COMPUTING (MOBISPC 2017) / 12TH INTERNATIONAL CONFERENCE ON FUTURE NETWORKS AND COMMUNICATIONS (FNC 2017) / AFFILIATED WORKSHOPS}, author={Zhang, Yang and Wang, Kai and Jena, Chinmay}, year={2017}, pages={167–173} }