Preprints
https://doi.org/10.5194/egusphere-2022-1125
https://doi.org/10.5194/egusphere-2022-1125
 
21 Nov 2022
21 Nov 2022
Status: this preprint is open for discussion.

Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes

Sepehr Fathi1,3, Mark Gordon2, and Yongsheng Chen2 Sepehr Fathi et al.
  • 1Physics and Astronomy Department, York University, Toronto, Canada
  • 2Earth and Space Science and Engineering Department,York University, Toronto, Canada
  • 3Air Quality Research Division, Environment and Climate Change Canada, Toronto, Canada

Abstract. Accurate "super-resolution'' (Δx < 250 m) atmospheric modelling is useful for several different sectors (e.g., renewable energy, natural disaster prediction), and essential for numerous applications such as downscaling of weather and climate information to finer resolutions. It can also be used to interpret environmental observations during top-down retrieval campaigns by providing complementary data that closely correspond to real-world atmospheric pollution transport and dispersion conditions. In top-down retrievals (e.g., aircraft-based), errors in estimates can arise from assumptions about atmospheric dispersion conditions, uncertainties in measurements, and data processing. As discussed in this work and in our companion paper (Fathi and Gordon, 2022), super-resolution numerical model simulations can be utilized to investigate these sources of uncertainty and optimize the retrievals. In order to conduct a thorough model-based study of the atmospheric dynamical processes that can affect top-down retrievals, model simulations at super-resolutions on the scale of measurement frequency are required: sufficient to resolve the dynamical and turbulent processes at the scale at which measurements are conducted. Here, in the context of our modelling case studies with WRF, we demonstrate a series of best practices for improved (realistic) modelling of atmospheric pollutant dispersion at super-resolutions. These include careful considerations for grid quality over complex terrain, sub-grid TKE parameterization at the scale of large eddies, and ensuring local and global tracer mass-conservation.

For this work, super-resolution (Δx ≤ 100 m, Δt ≤ 1 s) model simulations with Large-Eddy-Simulation sub-grid scale parameterization were developed and implemented using WRF-ARW. The objective was to resolve small dynamical processes inclusive of spatio-temporal scales of high-speed (e.g., 100 m/s) airborne measurements. This was achieved by down-scaling of reanalysis data from 31.25 km to 50 m through multi-domain model nesting in the horizontal and grid-refining in the vertical. 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 model performance within 5 % of observational data in terms of sea level pressure, temperature and humidity, and agreement within one standard deviation between modelled and observed wind fields. Model performance in terms of tracer mass conservation was within 2 % to 5 % of model input emissions.

Sepehr Fathi et al.

Status: open (until 16 Jan 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on egusphere-2022-1125', Astrid Kerkweg, 28 Nov 2022 reply

Sepehr Fathi et al.

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Short summary
We have combined various capabilities within WRF model to generate simulations of atmospheric pollutant dispersion at 50 m resolution. The objective was to resolve transport processes at the scale of measurements for assessing and optimizing aircraft-based emission rate retrievals. Model performance evaluation resulted in agreement within 5 % of observed meteorological, and within one standard deviation of observed wind fields. Mass was conserved in the model within 5 % of input emissions.