the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Age of smoke sampled by aircraft during FIREX-AQ: methods and critical evaluation
Abstract. The age of smoke, meaning the time elapsed since it was produced in a fire, is an important parameter for interpreting measurements of evolving smoke composition. This study describes the smoke age estimates developed for large plumes sampled in the 2019 NASA-NOAA FIREX-AQ field experiment. Smoke ages are computed using two methods and applied to observations from two aircraft: the NASA DC-8 and a NOAA Twin Otter. The first method uses measurements of mean horizontal wind speed, as observed by the sampling aircraft, and distance to the fire to provide a single age estimate for each plume-crossing performed by the aircraft. While this "mean-wind method" uses accurate wind measurements, it can be systematically biased by assumptions that plume rise time is negligible and that winds are homogeneous horizontally and in time during the plume transport. Wind inhomogeneities due to terrain effects and day-to-night transition, among other factors, affected some plumes during FIREX-AQ. The mean-wind method therefore performs best for short-range transport over level terrain with steady winds. The second method relies on upwind air parcel trajectories and plume rise computed with multiple high-resolution meteorological datasets. This "trajectory-based method" quantifies age uncertainty from the meteorological ensemble, plume rise speed, wind speed errors, and fire location. The second method also resolves age differences from the center to edge of a transect. Still, it is susceptible to errors in the meteorological model. With careful comparison of the simulated trajectories to smoke transport observed from geostationary satellite imagery described here, we filter out many trajectory errors and improve the smoke age estimates. The two age methods are strongly correlated (R = 0.93) for the periods during FIREX-AQ when both ages are available. The mean-wind age is systematically 14 % younger than the trajectory-based age and the median absolute difference between them is 19 % (23 % for mean). The favorable agreement between the two age methods reflects that the mean-wind method was selectively applied to plumes with little wind variability. Trajectory-based ages are available for more of the FIREX-AQ smoke observations than the mean-wind ages. The median trajectory-based age uncertainty during FIREX-AQ is 24 % and the mean uncertainty is 37 %, due to a long-tailed distribution. The main source of age uncertainty is spread within the meteorological ensemble, followed by discrepancy between measured and modeled wind speed, then other factors like plume rise. The age uncertainty variable enables the user to identify periods with high or low confidence in the age estimate, thereby informing studies of smoke aging.
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Status: open (until 16 Aug 2025)
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RC1: 'Comment on essd-2025-307', Anonymous Referee #1, 01 Jul 2025
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Notes
This paper develops two methods to estimate the age of smoke plume particles downwind from the source, and applies them to fires during the FIREX-AQ campaign. One uses the mean wind speed measured downstream combined with the distance from the source, the other is based on plume rise and trajectory transport modeling. Given the inherent uncertainties in these approaches, the authors are clear about the limitations of each, and they critically assess the results. Their dataset includes 339 plume transects for the NASA DC8 and 266 transects for the NOAA Twin Otter, so robust statistics were obtained for assessing these methods. This is valuable work, and deserves publication in ESSD.
Below are a few suggestions, for your consideration:
Introduction and/or Conclusions Sections. Regarding plume-age estimation generally, it might be helpful to provide some context by discussing ‘how good is good enough’ for different applications. This would provide an important perspective on the overall results, for example on how much accuracy you really need in the plume-rise time estimate part of the trajectory method, and also on the value of any of the method-related suggestions I’ve included below. For plume chemical evolution, I’m thinking plume age as a function of distance from the source might be of particular interest. (Such data are implicit in your work, at least with the trajectory method, but they don’t seem to be reported explicitly.)
Lines 141-145. I assume you chose to use only upwind trajectories with HYSPLIT because they are guaranteed to “end” at the plume observation point. However, because these age estimates are at the heart of the current study, and especially with HYSPLIT, it might be interesting to run the model again in the forward direction from the fire source location to see how the result compares, at least in some representative cases. (The comparison might be most interpretable where HYSPLIT identifies a source that is clearly associated with the downwind observation point.)
Lines 185-188. Similar to the note on Line 141, the variability in the aircraft-derived wind speed across a transect, or among several nearby transects of the same plume at similar points along the plume cross-section, might yield further confidence in the aircraft-derived values. I’m thinking these uncertainties might make a larger contribution to the overall plume age estimates than the uncertainty in the plume-rise time (~5 min) that are assessed so carefully, as discussed toward the end of Section 2.4.
Lines 206 ff. Another thought on procedure, in case it is of use. For very long trajectories, there might be some value in assessing the “mean-wind” advection time by dividing the plume into at least a couple of segments, making age assessments for each individual segment, adding them together, and comparing with the trajectory advection-time estimates, especially if the plume curves or has otherwise complex downwind horizontal structure. (This approach might be helpful, for example, in addressing the issues raised on Lines 314-315 and Lines 319-320.) Similarly, for long plumes that change direction along the way, you might obtain different results from the trajectory method by running the model separately for significantly different segments.
Lines 268-269. You do a careful job of accounting for parallax related to surface topography in the GOES ABI imagery. Would a further correction be needed for plume elevation, especially for plumes that reside in the free troposphere, much above the boundary layer? (I know there are relatively few of those in your dataset.)
Lines 305-310. This seems important. As I understand, the plume-rise times are explicitly not included in the statistics for the mean-wind method. (The distance used is from the fire horizontal location, not the surface, and besides, the vertical velocity is governed by factors other than the mean wind at plume elevation.) So, when comparing with the trajectory approach, I’d think adding the plume-rise time from the trajectory estimate would be appropriate (as highlighted by the issue raised on Lines 345-348). I see you were thinking about this by Line 309...
Another note: If your data set happens to include the Williams Flats fire plume on August 09, 2019, you might consider comparing your results with plume ages estimated based on motion vectors at plume elevation, assessed along the entire plume, that were derived from MISR multi-angle imagery (Junghenn Noyes et al., 2020, doi:10.3390/rs12223823). You obviously won’t get statistics from a single case (there are many others, though not during FIREX, e.g., doi:10.5194/acp-22-10267-2022), but the method is entirely different and quite robust, as it is based on the geometry of the observations.
Citation: https://doi.org/10.5194/essd-2025-307-RC1
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