The core problem TRAILS tackles is what atmospheric scientists call the reception height limitation. Earlier automated source attribution tools, including a widely used framework developed by Martin Radenz and colleagues in 2021, assumed that an air parcel picks up the properties of the surface it passes over only when it travels close to the ground, typically within the planetary boundary layer or below a fixed threshold of two kilometers. That assumption works reasonably well for surface emissions, but smoke plume injection heights are notoriously variable. In extreme cases, fire-triggered thunderstorms known as pyrocumulonimbus can inject smoke into the upper troposphere and lower stratosphere in quantities comparable to volcanic eruptions, where particles may persist for months and circulate globally. A fixed reception height simply cannot capture plumes injected directly into the free troposphere at altitudes of five, eight, or even twelve kilometers.

TRAILS overcomes this by making the reception height dynamic. The framework integrates ten-day backward trajectories simulated with the FLEXPART Lagrangian particle dispersion model, which represents turbulence and atmospheric mixing more realistically than conventional mean-wind trajectory methods, with a multi-sensor satellite detection algorithm. For each altitude level, 500 air parcels are released and tracked at three-hour intervals, producing 40,000 potential parcel positions per level. The tool then calculates a vertically resolved Smoke Occurrence Fraction, or SOF, quantifying the likelihood of smoke influence based on the cumulative residence time of air parcels within smoke-affected regions along their trajectories. A higher fraction of parcels with long smoke plume residence times at a given altitude implies a greater probability of detectable smoke aerosols there.

A key innovation lies in how TRAILS determines where smoke actually sits in the vertical column. The researchers established a new, statistically significant linear relationship between the Ultraviolet Aerosol Index measured by the Ozone Mapping and Profiler Suite Nadir Mapper on the Suomi-NPP satellite and smoke plume height for fresh tropospheric smoke. This relationship was derived by collocating OMPS observations with vertically resolved smoke plume heights from the CALIOP lidar aboard the CALIPSO satellite. The UVAI is a residual parameter that quantifies the difference in spectral dependence between measured and calculated outgoing radiances at the top of the atmosphere. UV-absorbing aerosols such as smoke and dust produce positive UVAI values, while clouds produce nearly zero, making the technique remarkably robust against subpixel cloud contamination. Because absorbing aerosols intercept Rayleigh-scattered radiation from below, the higher the aerosol layer, the greater the amount of molecular radiation it can absorb, producing a nearly linear dependence on layer height that the team confirmed with a coefficient of determination of 0.50 and a p-value below 0.001.

Using this relationship, TRAILS estimates theoretical plume height boundaries for each air parcel position. Rather than assuming parcels acquire smoke properties only near the surface, the method allows the reception height to be vertically dynamic and spatially variable, explicitly defined by the observed UVAI intensity and its corresponding estimated plume altitude. The framework also incorporates active fire data from MODIS on the Aqua and Terra satellites and VIIRS on Suomi-NPP, flagging parcels positioned directly over sufficiently intense fires. Stratospheric smoke identification remains an experimental product, since the linear UVAI-height relationship is not generally valid for aged stratospheric plumes, and ten-day trajectories cannot attribute smoke older than that window. Dust filtering is handled conservatively: parcels passing over Africa or the global dust belt with elevated UVAI values are categorically labeled as dust-influenced and excluded from smoke attribution, a choice that minimizes false positives from the dominant North American smoke source region even if it underestimates smoke influence for trajectories crossing African biomass burning zones.

The team evaluated TRAILS against ground-based measurements from Leipzig during the record-breaking 2023 Canadian wildfire season, which burned over 15.6 million hectares of boreal forest, mixedwood, and peatland across Alberta, British Columbia, Quebec, Ontario, and Saskatchewan. Two complementary lidar systems provided the reference: MARTHA, a Multiwavelength Atmospheric Raman lidar for Temperature, Humidity and Aerosol profiling equipped since 2022 with a laser-induced fluorescence channel, and PollyXT, a portable automated Raman lidar operating continuously. Fluorescence detection is particularly powerful because biogenic and biomass burning aerosols fluoresce when excited by ultraviolet light, while water does not, allowing researchers to study fluorescent aerosols even in aerosol-cloud coexistence. The fluorescence capacity, the ratio of fluorescence backscatter to elastic backscatter at 532 nanometers, is up to fifty times higher for biomass burning smoke than for mineral dust, enabling discrimination between smoke, dust, pollen, and urban aerosols when combined with depolarization measurements.

In case studies from late May and early July 2023, TRAILS effectively reproduced the vertical distribution and temporal evolution of long-range smoke layers. During the 29 May to 2 June period, several fluorescent aerosol layers were detected throughout the troposphere, with the Smoke Occurrence Fraction showing enhanced values exceeding 15 percent on 29 and 30 May that coincided with lidar-observed layer altitudes. Trajectory analysis revealed that smoke-affected parcels originated in the mid-troposphere over the western Atlantic before being lofted within the warm conveyor belt of a mid-latitude cyclone south of Iceland, ascending at roughly one kilometer per day, a characteristic rate for frontal ascent. The July case demonstrated the tool’s capability to identify smoke even in the presence of mixed-phase clouds and coexisting dust, with a dust layer identified at ten kilometers altitude alongside smoke from fires in northern Quebec, underscoring that combining the SOF with lidar depolarization ratios enhances robustness in complex scenes.

Statistical validation against 23 fluorescence capacity profiles revealed a layer detection performance of 76 percent relative to fluorescent aerosol layers, with 62 of 82 fluorescence layers matched within a two-kilometer height tolerance. Matched layers showed a strong positive correlation between mean and peak heights, but TRAILS systematically underestimated layer heights by about 0.4 kilometers on average, with the discrepancy increasing to roughly one kilometer for layers in the upper troposphere and lower stratosphere. This systematic height mismatch provides indirect observational evidence for diabatic self-lofting, a process in which optically thick smoke layers absorb solar radiation, heat the ambient air, and generate buoyancy that causes the layer to ascend, reaching rates of up to one kilometer per day according to CALIOP observations following the 2017 Canadian wildfires. Because FLEXPART’s passive trajectory calculations do not represent post-injection self-lofting, the inferred ascent rate of 0.2 to 0.4 kilometers per day for upper-level layers offers a new observational constraint on this otherwise difficult-to-observe process.

The researchers are careful to note the framework’s limitations. TRAILS identifies where and at what altitudes smoke is present but does not automatically attribute these layers to specific fire sources; in the presented case studies, source attribution was performed by visually comparing the first smoke detection along trajectories with major active fire hotspots. The UVAI-plume height relationship was derived primarily from North American, Russian, and Australian smoke plumes, and its applicability to other fire regimes, such as Southern Hemisphere burns or different fuel types, may require recalibration of thresholds and carries additional uncertainties related to dust contamination. The FLEXPART setup also omits wet deposition, so removal of smoke through precipitation is not represented, and the coarse 50-kilometer spatial resolution of OMPS-NM introduces potential cloud contamination effects that the empirical relationship does not explicitly correct.

Despite these caveats, TRAILS provides a valuable, observationally constrained method for time-height-resolved smoke attribution, particularly at sites lacking advanced fluorescence lidars. The algorithm is computationally efficient and modular, relying on the longest continuous UVAI record available, extending back to 2012 and unaffected by the row anomaly that has degraded OMI observations since 2007. By delivering continuous, automated estimates of smoke influence, the tool can help disentangle the effects of smoke aerosols from other cloud condensation nuclei and ice-nucleating particle sources, enhancing investigations of aerosol-cloud interactions and the long-range transport of biomass burning aerosols. Future developments aim to automate the fire source attribution step, transforming TRAILS from a smoke detection tool into a complete source attribution system, and to extend its application to Southern Hemisphere biomass burning events, including the Australian bushfires of 2019-2020, testing the method’s robustness under different meteorological regimes and fire characteristics.

Subject of Research: TRAILS – a novel framework for time-height-resolved attribution of long-range transported wildfire smoke

Article Title: TRAILS – a novel framework for time-height-resolved attribution of long-range transported wildfire smoke

Article References: TRAILS – a novel framework for time-height-resolved attribution of long-range transported wildfire smoke. (n.d.). https://doi.org/10.5194/gmd-19-9905-2026

Image Credits: AI Generated

DOI: 10.5194/gmd-19-9905-2026

Keywords: wildfire smoke, atmospheric science, remote sensing, lidar, satellite observations, aerosol-cloud interactions, climate modeling, TRAILS, novel, framework, time-height-resolved, attribution