| 30 Oct 2025
Optimizing Airborne Emission Rate Retrievals with Sub-Hectometre Resolution Numerical Modelling
Abstract. A comprehensive model-based study is designed to provide optimal flight paths for airborne top-down emission rate retrieval methodologies. The meteorology and plume dispersion were modelled using the Weather Research and Forecasting (WRF) modelling platform with the Advanced Research WRF (ARW) dynamical core at 50-m resolution. Multiple flight path designs and parameters were investigated to determine emission rate retrieval accuracy as a function of downwind distance and transect spacing, which are ultimately related to flight time and cost. Three unique source types (multiple smokestack plumes, small area sources, and a large area source) were investigated for 4 summer afternoon flight cases over 2 days. The results demonstrate that emissions estimate uncertainty is primarily due to storage and release. The average advective flux estimates are within 12 % of the known emissions for downwind distance of D ≥ 4 km. Variability between flights decreases with D. For stack sources the variability near D = 10 km is approximately half that at D = 4 km. For small area sources, there is less reduction with D, and for the large area source, variability reaches a minimum at D = 8 km. For stack sources, transect spacing is optimized at 100 m, while for area sources, a spacing of 50 m reduces uncertainty. Error due to extrapolation below the lowest flight path is less than 20 % for stack sources and less than 30 % for area sources for non-dimensionalized downwind distance of D' ≥ 3. Results demonstrate the need for surface sampling coincident with the flights to reduce extrapolation error, and the use of modeling with reanalysis data to account for storage and release effects.
This study investigates sources of error in typical aircraft mass balance experiments and provides guidance for experiment design. It is well written and easy to follow, with clear conclusions: storage leads to random error in E_H/E_S, whereas extrapolation below the lowest transect can be an important source of systematic error. This is consistent with previous studies, but the thorough investigation presented here provides valuable insight for planning and evaluating future real-world data. I suggest that this paper is suitable for publication with only minor revisions. I think it would benefit from a slightly expanded discussion on the points below.
There is currently very little mention of the background concentrations. I understand that this is not a factor in the simulated data, as all the tracers released in the model come from sources within the domain. However, in real world examples variability in the background can be an important source of error, so at least some discussion of this is required. In particular, it impacts statements such as that in L368-370. Going further downwind may reduce the sources of random error addressed here, but there is a trade-off in terms of signal-to-noise above background.
It is interesting that the kriging interpolation resulted in an overestimation of the instantaneous screen (L375). It would be great to see some more investigation of this. Was anisotropy in the variogram considered? I wonder if the variogram becomes more isotropic as you move further from the source? That would make intuitive sense to me. Were other functions (i.e. other than the spherical function mentioned) tested when fitting the variogram? It could also be interesting to see if this choice impacts the overestimation, although I appreciate that it is hard to draw general conclusions because the best function will always be specific to an individual flight. The same goes for the area source flights – L487 points to the kriging as a potentially significant error source so it would be good to see this case investigated too.
The investigation of the vertical transect spacing is interesting but the results are hard to interpret. The hypothesis that plume movement could be responsible for the changes seen in the Sep 2 case seems plausible, but it would be nice to see this tested. Seeing as we are dealing with simulated flights, could a test be done where the order of the transects is changed?
L213/216 – refers to the known emissions as Es but I don’t think this has been defined yet
L225 – in some cases even faster than 2 Hz. I know the UK FAAM aircraft has a CO2/CH4 LGR with a data acquisition rate of 10 Hz, although the cell turnover time means that the effective frequency of measurement is less than this (more like 7 Hz I believe).
L360 – it might be worth rephrasing this to clarify that it is E_H/E_S which is lower in the non-instantaneous cases (i.e. the underestimation is worse). A “lower underestimation” could perhaps be misinterpreted.
L413 – typo “sometimes”
Figure 9 – formatting error on some axes labels
L576-577 – it makes qualitative sense that more information below the lowest transect would help. Could this be tested? At least for the case of a mobile vehicle you could presumably add an extra transect at z=0 with a typical vehicle speed and see what difference this makes