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This study introduces a new chemistry option in the Weather Research and Forecasting model data assimilation (WRFDA) system, coupled with the WRF‐Chem model (Version 4.4.1), to incorporate aqueous chemistry (AQCHEM) in the assimilation of ground‐level chemical measurements. The new DA capability includes the integration of aqueous‐phase aerosols from the Regional Atmospheric Chemistry Mechanism (RACM) gas chemistry, the Modal Aerosol Dynamics Model for Europe (MADE) aerosol chemistry, and the Volatility Basis Set (VBS) for secondary organic aerosol production. The RACM‐MADE‐VBS‐AQCHEM scheme facilitates aerosol‐cloud‐precipitation interactions by activating aerosol particles in cloud water during the model simulation. With the goal of enhancing air quality forecasting in cloudy conditions, this new implementation is demonstrated in the weakly coupled three‐dimensional variational data assimilation (3D‐Var) system through regional air quality cycling over East Asia. Surface particulate matter (PM) concentrations and four gas species (SO₂, NO₂, O₃, and CO) are assimilated every 6 hr for the month of March 2019. The results show that including aqueous‐phase aerosols in both the analysis and forecast can represent aerosol wet removal processes associated with cloud development and rainfall production. During a pollution event with high cloud cover, simulations without aerosols defined in cloud water exhibit significantly higher values for liquid water path, and surface PM₁₀(PM_2.5) concentrations are overestimated by a factor of 10 (3) when wet scavenging processes dominate. On the contrary, AQCHEM proves to be helpful in simulating the wet deposition of aerosols, accurately predicting the evolution of surface PM concentrations without such overestimation.

 

ABSTRACT To explore the various couplings across space and time and between ecosystems in a consistent manner, atmospheric modeling is moving away from the fractured limited-scale modeling strategy of the past toward a unification of the range of scales inherent in the Earth system. This paper describes the forward-looking Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA), which is intended to become the next-generation community infrastructure for research involving atmospheric chemistry and aerosols. MUSICA will be developed collaboratively by the National Center for Atmospheric Research (NCAR) and university and government researchers, with the goal of serving the international research and applications communities. The capability of unifying various spatiotemporal scales, coupling to other Earth system components, and process-level modularization will allow advances in both fundamental and applied research in atmospheric composition, air quality, and climate and is also envisioned to become a platform that addresses the needs of policy makers and stakeholders. 

Climate change is expected to have many impacts on the environment, including changes in ozone concentrations at the surface level. A key public health concern is the potential increase in ozone-related summertime mortality if surface ozone concentrations rise in response to climate change. Although ozone formation depends partly on summertime weather, which exhibits considerable inter-annual variability, previous health impact studies have not incorporated the variability of ozone into their prediction models. A major source of uncertainty in the health impacts is the variability of the modeled ozone concentrations. We propose a Bayesian model and Monte Carlo estimation method for quantifying health effects of future ozone. An advantage of this approach is that we include the uncertainty in both the health effect association and the modeled ozone concentrations. Using our proposed approach, we quantify the expected change in ozone-related summertime mortality in the contiguous United States between 2000 and 2050 under a changing climate. The mortality estimates show regional patterns in the expected degree of impact. We also illustrate the results when using a common technique in previous work that averages ozone to reduce the size of the data, and contrast these findings with our own. Our analysis yields more realistic inferences, providing clearer interpretation for decision making regarding the impacts of climate change.

 

We use the extensive set of aircraft and ground‐based observations from the NSF/National Center for Atmospheric Research (NCAR) and State of Colorado Front Range Air Pollution and Photochemistry Éxperiment and the NASA DISCOVER‐AQ experiments in summer 2014 together with the regional chemical transport model Weather Research and Forecast Model with Chemistry (WRF‐Chem) to study the ozone production and chemical regimes in the Northern Colorado Front Range (NFR). We apply the model's Integrated Reaction Rate capability and chemical tendencies diagnostics and present results from an in‐depth analysis of the ozone formation in various NFR regions for a case study of 12 August 2014. We further apply these diagnostics along a WRF online trajectory to assess the chemical evolution of an airmass during transport. The results show efficient ozone production within the NFR driven by the availability of NO_xand an abundance of highly reactive volatile organic compound and also continued ozone production during the transport into the mountains. We identify CO, formaldehyde, higher alkanes, acetaldehyde, and isoprene among the volatile organic compound species with the highest efficiency in ozone production. Formaldehyde and acetaldehyde concentrations in the NFR have a significant contribution from photochemical production, which in turn is linked back to methane oxidation and to emissions of higher alkanes, isoprene, ethane, and propane. This study provides valuable policy information into the chemical fingerprint of surface ozone in the NFR, an area that is in nonattainment of the U.S. EPA ozone health standards and demonstrates the capability of the newly added diagnostic tool in WRF‐Chem to address the drivers behind secondary production of pollutants in greater detail.

 

We analyze the effects of the diurnal cycle of fire emissions (DCFE) and plume rise on U.S. air quality using the MUSICAv0 (Multi‐Scale Infrastructure for Chemistry and Aerosols Version 0) model during the FIREX‐AQ (Fire Influence on Regional to Global Environments and Air Quality) and WE‐CAN (Western wildfire Experiment for Cloud chemistry, Aerosol absorption and Nitrogen) field campaigns. To include DCFE in the model, we employ two approaches: a DCFE climatology and DCFE derived from a satellite fire radiative power product. We also implemented two sets of plume‐rise climatologies, and two plume‐rise parameterizations. We evaluate the model performance with airborne measurements, U.S. EPA Air Quality System surface measurements, and satellite products. Overall, including plume rise improves model agreement with observations such as aircraft observations of CO and NO_xfor FIREX‐AQ and WE‐CAN. Applying DCFE also improves model performance, such as for surface PM_2.5in fire‐impacted regions. The impact of plume rise is larger than the impact of DCFE. Plume rise can greatly enhance modeled long‐range transport of fire‐emitted pollutants. The simulations with plume‐rise parameterizations generally perform better than the simulations with plume‐rise climatologies during FIREX‐AQ, but not for WE‐CAN. The 2019 Williams Flats Fire case study demonstrates that DCFE and plume rise change fire impacts because fire emissions are subject to different meteorology and chemistry when emitted at different times of a day and altitudes. Moreover, DCFE and plume rise also impact local‐to‐regional meteorology and chemical reaction rates. DCFE and plume rise will be included in future MUSICA versions.