SIParCS 2021 - Fairuz Ishraque

Fairuz Ishraque

Fairuz Ishraque (he/they), Colgate University

Please stop and smell the tracers: Predicting tracer concentration behaviors in low-order models with data assimilation

Recorded Talk

Characterizing the source and behavior of airborne contaminants is an important problem in air-quality analysis and fighting air pollution. Identifying the location and strength of the source of a potentially harmful pollutant is often necessary to take appropriate actions for mitigation. At the very least, tracer releases from such sources need to be modeled to predict the damages they might cause. However, given the chaotic nature of atmospheric circulation, modeling airborne tracers with accuracy is a challenging task. To address this issue, a novel dynamical system is implemented to study the behavior of tracers by utilizing ensemble data assimilation.

Historically, data assimilation has been applied to make numerical weather predictions, combining model forecasts with weather observations to produce an analysis. Our method coupled the low order Lorenz-96 model with a Semi-Lagrangian scheme to advect model tracers on a circular array of sites (e.g., equally spaced sites along a latitude). We then assimilated synthetic observations with our model predictions using the ensemble adjustment Kalman Filter (eaKF) inside the Data Assimilation Research Testbed (DART). We were able to assimilate observations of Lorenz-96 state variables (wind) and tracer concentrations. Assimilating only wind observations improved predictions for both wind and tracer concentrations, but more interestingly, assimilating only tracer concentration observations noticeably improved the predictions for wind. Assimilating both wind and tracer concentrations resulted in the overall best predictions and produced interesting results in source characterization.

Mentors: Jeff Anderson & Helen Kershaw

Slides and poster