Modeling extremely cold stable boundary layers over interior Alaska using a WRF FDDA system

Stable boundary layers with very shallow depths (approaching tens of meters) and extremely strong capping inversions (40 degrees C per 100 m) present very challenging air-quality and meteorological modeling problems over interior Alaska in the winter months. These extremely stable layers, which are often accompanied by ice fogs, and complex-terrain forcing, produce conditions where new advanced modeling techniques using even finer horizontal and vertical model resolutions, upgraded model physics and innovative data assimilation methods will be investigated. This paper presents some modeling results during these strong inversion conditions, when the concentrations of fine particulate matter (PM2.5) can reach very high levels in the Fairbanks region due to emissions by stationary sources (e.g., power plants), motor vehicle traffic, and space heating. However, at other times during the winter the fine particulate concentration is much lower. Also, concentrations in the Fairbanks area can show high spatial variability due to the influence of the surrounding ridges and lowlands on the local near-surface wind flow. Thus a combined field measurement and modeling study is underway to investigate and better understand the impact of these extremely cold stable boundary layers.

The Advanced Research version of the Weather Research and Forecasting (WRF) model, known as WRF-ARW, is used to simulate a couple of high-concentration periods during the 2007-2008 winter season in the Fairbanks region. The meteorological output from the WRF-ARW simulations will be used to drive the Community Multiscale Air Quality (CMAQ) model. The baseline meteorological model configuration uses three nested model domains of 12-km, 4-km, and 1.333 km horizontal grid spacing (a finer 444-m grid is also included in some simulations), and 7 layers in the lowest 50 m above ground level with vertical grid spacing of only 4 m near the surface.

A multi-scale four-dimensional data assimilation (FDDA) procedure, newly developed for the WRF-ARW model, is used to provide the lateral boundary conditions for the finer-resolution domains used to study the physical interactions associated with these stable layers. Sensitivity tests are performed to determine the added value of various model physics parameterizations and options (e.g., land surface models, boundary layer parameterizations, and moist microphysics) for predicting these high-latitude stable boundary layers. The model results are subjectively and objectively evaluated against available quality-controlled observations. The best combination of mesoscale data assimilation methods and model physics for predicting these very stable boundary layers, so important for modeling air quality in these very cold environments, will be discussed.