1.1 Wind-Blown Dust Forecasting using a Backward Lagrangian Particle Dispersion Model

Monday, 20 June 2016: 8:30 AM
Orion (Sheraton Salt Lake City Hotel)
Derek V. Mallia, University of Utah, Salt Lake City, UT; and A. K. Kochanski and J. C. Lin

Wind-blown dust forecasting using a backward Lagrangian particle dispersion model

Derek V. Mallia, Adam Kochanski, and John C. Lin

Previous work has shown that wind-blown dust can have considerable impacts on air quality and visibility, with adverse effects on human health, transportation, and aviation. As a result, there is a significant need for wind-blown dust forecasts to provide advance notice for the affected populations. Studies in the past have developed dust modeling frameworks from the Eulerian and forward Lagrangian perspective in order replicate wind-blown dust events. However, most of these studies relied upon reanalysis data, using computationally costly methods, rendering them cumbersome for operational forecasting. Here we present a high-resolution modeling framework using a backward Lagrangian particle dispersion model coupled with a dust emission model in order to reproduce two wind-blown dust events in Salt Lake City, Utah. The first event was characterized by strong southwesterly wind resulting in source emissions that originated from dry-lake beds in central Utah, while wind-blown dust from the second event originated from the western Utah playa as a result of strong westerly winds. First, model simulations were carried out for each case study using re-analysis data to test the validity of our modeling framework. Initial results showed that our model simulations running in hindcast mode were able to reproduce the duration and magnitude of two aforementioned dust events; however, there was a 1 to 2-hour discrepancy in the timing of these events. Each event was then simulated in forecast mode 24-, 36-, 48-, and 60-h prior to onset of wind-blown emissions across Utah to determine the predictability of each event. Initial results showed that each dust event was predictable 1- 2 ½ days in advance, with the model being able to capture the timing of each dust event.

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