Optimizing Top-down Airborne Retrievals through High and Super-Resolution Numerical Modelling

A multi-scale-modelling study of conventional top-down source emission-rate estimation methodologies was conducted. Two modelling systems were employed: Environment and Climate Change Canada's regional air quality model GEM-MACH at 2.5km resolution (high-resolution), and Weather Research and Forecasting (WRF) with ARW dynamical core at 50m resolution (super-resolution).

Using GEM-MACH, high-resolution air-quality model simulations were conducted for the period of an airborne campaign in 2013 over the Canadian oil sands facilities. Modelling products from these simulations were analyzed to investigate the application of the mass-balance technique in aircraft-based retrievals. The focus was on exploring the theoretical aspects and the underlying assumptions of the mass-balance technique. An extensive range of realistic meteorological and source emission conditions were considered. It was demonstrated how temporal variability in meteorology/emission conditions can give rise to storage-and-release events, where mass-balancing using only aircraft measurements can result in significant under-/over-estimates.

Using WRF-ARW, super-resolution (<100 m) model simulations with Large-Eddy-Simulation (LES) subgrid-parameterization were developed/implemented. The objective was to resolve smaller dynamical processes at the spatio-temporal scales of the airborne measurements. This was achieved by multi-domain model nesting in the horizontal, grid-refining in the vertical, and down-scaling of reanalysis data from 31.25 km to 50 m. Further, WRF dynamical-solver source code was modified to simulate passive-tracer emissions within the finest resolution domain. Different meteorological case studies and several tracer emission sources were considered. Model-generated fields were evaluated against observational data and also in terms of tracer mass-conservation, results indicated high model performance.

Using the model output from the WRF super-resolution simulations, conventional aircraft-based retrievals were simulated/evaluated. It was shown that conventional methods can result in estimates with 30-50% uncertainty/error. Two major sources of uncertainty were identified: (a) the spatio-temporal variability in the sampled fields, and (b) the gap of information below the flight level. Optimal flight-time around one hour and sampling-distance between 10-15 km, were shown to minimize the uncertainty arising from (a)-(b). Finally, a new sampling/retrieval strategy is introduced where aircraft-based in-situ and remote measurements can be combined to improve the accuracy of top-down estimates by up to 30%. This method utilizes remote sensing to fill the information gap below flight level, characterize temporal trends in the environmental fields during flight-time (to estimate storage-rate), while reducing the required flight-time for more accurate source emission rate estimates.