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

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dc.contributor.advisorGordon, Mark
dc.contributor.authorFathi, Sepehr
dc.date.accessioned2022-12-14T16:27:20Z
dc.date.available2022-12-14T16:27:20Z
dc.date.copyright2022-07-13
dc.date.issued2022-12-14
dc.date.updated2022-12-14T16:27:20Z
dc.degree.disciplinePhysics And Astronomy
dc.degree.levelDoctoral
dc.degree.namePhD - Doctor of Philosophy
dc.description.abstractA 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.
dc.identifier.urihttp://hdl.handle.net/10315/40663
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectAtmospheric sciences
dc.subjectEnvironmental science
dc.subjectPhysics
dc.subject.keywordsAtmospheric dispersion modelling
dc.subject.keywordsNumerical modelling
dc.subject.keywordsHigh resolution numerical modelling
dc.subject.keywordsSuper resolution numerical modelling
dc.subject.keywordsComputational fluid dynamics
dc.subject.keywordsLarge eddy simulation
dc.subject.keywordsComputer simulations
dc.subject.keywordsAtmospheric boundary layer
dc.subject.keywordsPlanetary boundary layer
dc.subject.keywordsWeather Research and Forecasting
dc.subject.keywordsWRF
dc.subject.keywordsGlobal Environmental Multiscale-Modeling Air-Quality and Chemistry
dc.subject.keywordsGEM-MACH
dc.subject.keywordsEnvironment and Climate Change Canada
dc.subject.keywordsECCC
dc.subject.keywordsAir quality
dc.subject.keywordsEmission retrieval
dc.subject.keywordsEmission rate retrieval
dc.subject.keywordsEmission rate estimation
dc.subject.keywordsTop-down retrieval
dc.subject.keywordsTop-down estimation
dc.subject.keywordsMass-balance estimation
dc.subject.keywordsMass-balance technique
dc.subject.keywordsVirtual sampling
dc.subject.keywordsVirtual airborne sampling
dc.subject.keywordsVirtual aircraft-based sampling
dc.subject.keywordsModel-based study
dc.subject.keywordsModelling
dc.subject.keywordsAnthropocentric pollution
dc.subject.keywordsAnthropocentric emissions
dc.subject.keywordsOil sands
dc.subject.keywordsCanadian oil sands
dc.subject.keywordsAlberta
dc.subject.keywordsAthabasca
dc.subject.keywordsOil sands region
dc.subject.keywordsRemote sensing
dc.subject.keywordsRemote measurements
dc.subject.keywordsAircraft-based remote measurements
dc.subject.keywordsLIDAR profiling
dc.subject.keywordsLIDAR measurements
dc.subject.keywordsAirborne LIDAR measurements
dc.subject.keywordsAirborne LIDAR profiling
dc.subject.keywordsAircraft in-situ measurements
dc.subject.keywordsTurbulence
dc.subject.keywordsTurbulent transport
dc.subject.keywordsTurbulent resolving modelling
dc.subject.keywordsTurbulent eddies
dc.subject.keywordsEddy diffusivity
dc.subject.keywordsDiffusion
dc.subject.keywordsAdvection
dc.subject.keywordsConvection
dc.subject.keywordsStability
dc.subject.keywordsAtmospheric stability
dc.subject.keywordsBox flights
dc.subject.keywordsDownwind flights
dc.subject.keywordsBuoyancy
dc.subject.keywordsBuoyant plumes
dc.subject.keywordsSmoke plumes
dc.subject.keywordsPlume
dc.subject.keywordsPlumes
dc.subject.keywordsConcentrations
dc.subject.keywordsWind
dc.subject.keywordsWind fields
dc.subject.keywordsMixing
dc.subject.keywordsAtmospheric mixing
dc.subject.keywordsMixing layer
dc.subject.keywordsInversion
dc.subject.keywordsMass-balance analysis
dc.subject.keywordsNested modelling
dc.subject.keywordsGrid refinement
dc.subject.keywordsPassive tracers
dc.subject.keywordsTracer emissions
dc.subject.keywordsSurface emissions
dc.subject.keywordsStack emissions
dc.subject.keywordsMining
dc.subject.keywordsSurface mining
dc.subject.keywordsExcavation area
dc.subject.keywordsRoad
dc.subject.keywordsHighway
dc.subject.keywordsPollution
dc.subject.keywordsIndustry
dc.subject.keywordsIndustrial
dc.subject.keywordsOil and gas
dc.subject.keywordsUpgrading
dc.subject.keywordsMining and upgrading
dc.subject.keywordsRefinement
dc.subject.keywordsSuper computer
dc.subject.keywordsSuper computers
dc.subject.keywordsHigh performance computing
dc.subject.keywordsHPC
dc.subject.keywordsHigh performance computation
dc.subject.keywordsParallel computation
dc.subject.keywordsParallel computing
dc.subject.keywordsConcurrent nesting
dc.subject.keywordsSerial nesting
dc.subject.keywordsRegional reanalysis data
dc.subject.keywordsBoundary conditions
dc.subject.keywordsInitial conditions
dc.subject.keywordsEnvironmental studies
dc.subject.keywordsEnvironmental science
dc.subject.keywordsAtmospheric science
dc.subject.keywordsAtmospheric physics
dc.subject.keywordsMass transfer
dc.subject.keywordsAtmospheric mass transfer
dc.subject.keywordsMass transport
dc.subject.keywordsAtmospheric mass transport
dc.subject.keywordsComplex topography
dc.subject.keywordsEmission rate calculation
dc.subject.keywordsAirborne emission estimation
dc.subject.keywordsAircraft-based emission rate estimation
dc.titleOptimizing Top-down Airborne Retrievals through High and Super-Resolution Numerical Modelling
dc.typeElectronic Thesis or Dissertation

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