Citation:

Gilliam, R., K. Fahey, G. Pouliot, H. Pye, Nicole Briggs, S. Farrell, D. Huff, W. Simpson, AND M. Meeta Cesler-Maloney. Modeling wintertime meteorology for the 2022 Alaskan Layered Pollution and Chemical Analysis (ALPACA) campaign - AMS 2023. 103rd American Meteorological Society Conference and 25th Conference on Atmospheric Chemistry, Denver, CO, January 08 - 12, 2023.

Impact/Purpose:

Fairbanks, Alaska is a nonattainment area for the 24-hour PM2.5 National Ambient Air Quality Standards (NAAQS). Violations of the NAAQS typically occur in winter when the cold conditions are associated with strong temperature inversions and air stagnation that are often difficult to simulate. These weather regimes in urban areas of higher emissions (i.e.; residential wood combustion, mobile sources and energy production) result in a buildup of particulate pollution at the surface. The Alaskan Layered Pollution and Chemical Analysis (ALPACA) field campaign was conducted in January and February of 2022 to address some of the knowledge gaps with a focus on better understanding emissions, meteorology, and atmospheric chemistry.   This presentation details the meteorological modeling component of ALPACA, a principal input to the Community Multiscale Air Quality (CMAQ) model that is being used to characterize the atmospheric chemistry and transport of pollutants in and around Fairbanks. We employ the Weather Research and Forecasting (WRF) model to simulate meteorology at a grid scale of 1.33 km. More specifically, we will cover the WRF configuration including physics and data assimilation for this complex subarctic, mid-winter, problem as well as an evaluation that focuses on several extreme cold periods where observed PM2.5 was well above the NAAQS. Results of the preliminary evaluation indicate that WRF can simulate near-surface meteorology and vertical temperature and moisture gradients around Fairbanks with high confidence considering the complex meteorology of the area. This is accomplished with four-dimensional data assimilation using global model analyses, observational nudging of standard surface observation networks, mesonet and above-surface rawinsonde soundings in combination with the selection of land-surface and boundary layer physics options. The final modeling platform will incorporate the latest scientific understanding to provide an improved modeling tool for the state of Alaska to use in its air pollution program in Fairbanks.

Description:

Fairbanks, Alaska is a nonattainment area for the 24-hour PM2.5 National Ambient Air Quality Standards (NAAQS). Violations of the NAAQS typically occur in winter when the cold conditions are associated with strong temperature inversions and air stagnation. This leads to a buildup of particulate pollution at the surface, exacerbated by elevated emissions from residential wood combustion. The Alaskan Layered Pollution and Chemical Analysis (ALPACA) field campaign was conducted in January and February of 2022 to address some of the knowledge gaps via an intensive field campaign focused on better understanding emissions, meteorology, and atmospheric chemistry. This presentation details the meteorological modeling component that is a principal input to the Community Multiscale Air Quality (CMAQ) modeling that is being used to model the atmospheric chemistry and transport of the ALPACA field campaign period. We use the Weather Research and Forecasting (WRF) model to simulate meteorology at a grid scale of 1.33 km. Subjects covered are the details of the WRF configuration including physics and data assimilation for this complex problem as well as an evaluation that focuses on several extreme cold periods where observed PM2.5 was well above the NAAQS. The results indicate WRF can simulate near-surface meteorology and vertical temperature and moisture gradients around Fairbanks with high confidence considering the complex meteorology of the area. This is accomplished with data assimilation using global model analyses, standard weather networks, local mesonet and ALPACA field campaign observations and soundings in combination with the selection of land-surface and boundary layer physics options. Results are presented in three phases. Phase 1 confirmed that the original simulation that was based off a observation nudging configuration developed in a prior evaluation performed reasonably well. A confirmation that the data assimilation was working as expected given the observation inputs. Phase 2 was an evaluation of this original simulation and three additional sensitivity tests using ALPACA field campaign observations that were independent of the observation nudging. This effort found that observation nudging worked well close to the surface in terms of temperature, but not as well aloft with respect to winds. This testing found some reduction in error above the surface when four dimensional data assimilation was used in combination with observation nudging. Phase 3 was an effort to leverage ALAPCA observation in the data assimilation along with standard available observation datasets. Also  a chance to test relevant, but more obscure observation nudging settings. This testing found dramatic improvements in model performance in the lower 200 m of the atmosphere including near-surface temperature and wind. The final modeling platform will incorporate the latest scientific understanding to provide an improved modeling tool for the state of Alaska to use in its air pollution program in Fairbanks.  

Record Details: