Date of Award

January 2020

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Atmospheric Sciences

First Advisor

Jianglong Zhang

Abstract

When unaccounted for in numerical weather prediction (NWP) models, heavy aerosol events can cause significant unrealized biases in forecasted meteorological parameters such as surface temperature. A novel concept is proposed in this study to dynamically downscale aerosol fields from a global chemical transport model into a higher resolution NWP model to improve the near-surface forecasting accuracies during heavy aerosol events like biomass burning events. This concept is tested for a major biomass burning aerosol event over the Northern Great Plains region of the United States that occurred from 28 June – 4 July 2015. Aerosol analyses from the Navy Aerosol Analysis and Prediction System (NAAPS) are used as initial and boundary conditions for Weather Research and Forecasting with Chemistry (WRF-Chem) simulations. Through incorporating more realistic aerosol direct effects into the WRF-Chem simulations, errors in WRF-Chem simulated surface downward shortwave radiation, near-surface temperature, and near-surface wind speed are reduced compared with surface-based observations. This study confirms the ability to dynamically downscale analyses and forecasts from a global transport model to decrease aerosol direct effect induced biases for regional NWP forecasts during high-impact continental-scale aerosol events.

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