Date of Award
Master of Science (MS)
John R. Mecikalski
Dust storms--United States--Simulation methods., Soil moisture--Remote sensing., Vegetation greenness--Remote sensing., Air quality.
Strong winds and dry surface conditions result in frequent occurrence of dusty conditions in the Southwestern United States (SW US) causing degraded air quality and harmful health effects. The Weather Research and Forecasting with Chemistry (WRF-Chem) model is often used to forecast such events. Biases in specifications of soil moisture and vegetation cover in the land surface component for initialization of WRF-Chem can result in systematic errors in forecasts. This study examines how WRF-Chem dust forecasts for the SW US can be improved through better prescriptions of soil and vegetation for the following cases: 27 April 2014, 23 March 2017, and 17 April 2018. Simulations of these events using the Global Forecast System (GFS) were compared against a set of simulations that used NASA Land Information System (LIS) and NESDIS green vegetation fraction (GVF) products to specify soil moisture and vegetation. Intercomparison with observations show that specification of vegetation and soil moisture improved dust forecasts with the latter having a larger impact.
Burrows, Erica, "Evaluating the impact of land surface properties on simulated dust emissions and air quality" (2020). Theses. 314.