From June 2006 until August 2023, the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite has provided the scientific community with continuous, vertically resolved data on the global distribution of atmospheric aerosols. The elastic-backscatter lidar features three measurement channels. Linearly polarized light pulses at 532 and 1064 nm are transmitted by the laser system. The detectors measure both cross- and co-polarized backscatter components at 532 nm and the total (cross + co-polarized) signal at 1064 nm. Here, “cross” and “co” refer to backscattered light polarized orthogonally and parallel, respectively, to the transmitted laser's polarization plane. A summary of the retrieval algorithms is provided by . The basic measurement products include the attenuated backscatter coefficients at 532 and 1064 nm, and the volume linear depolarization ratio (defined as the ratio of the cross-polarized to co-polarized signal components). Figure  shows an overview of CALIOP observations from 18 July 2014. Enhanced backscatter coefficients reveal a smoke plume originating from British Columbia wildfires, along with cirrus clouds.

Within the Cloud and Aerosol Discrimination (CAD) algorithm of the CALIOP data processing sequence, clouds are distinguished from aerosols using multidimensional probability density functions derived from an extensive training set of CALIOP measurements . Subsequently, aerosol types are classified using predefined lidar ratios from the Lidar Level 2 Aerosol Profile (Version 4.51) product as detailed in . Although the CAD algorithm (Versions 3–4) improved smoke identification, we observe misclassification of smoke plumes as clouds, which is a known issue reported by . This arises from overlapping attenuated backscatter color ratios (0.5–0.9) and attenuated backscatter coefficients (0.004–0.01 m⁻¹ sr⁻¹) between dense smoke and clouds (Fig. c, d, e), preventing an unambiguous classification . The color ratio, which is the ratio of the backscatter coefficient at two wavelengths, serves as a proxy for particle size, with higher values typically indicating the presence of larger particles. For smoke, rapid signal attenuation at 532 nm (vs. 1064 nm) results in larger layer-averaged attenuated color ratios, further biasing classification toward clouds. This effect is evident in Fig. c, where below the smoke plume at approximately 5 km, the color ratio increases rapidly from 0.5 to above 1.5. Consequently, the known limitations of the standard CAD algorithm can lead to underestimation of smoke. To resolve this for our analysis, we implemented a custom classification scheme that allows the accurate identification of plume altitude for our case studies. These smoke plume altitude boundaries are then used to derive an altitude relationship with Suomi-NPP OMPS UVAI in Sect. .

To identify aerosol and cloud features, we utilize the attenuated backscatter coefficient at 532 nm (β532) from the CALIOP Level 1B Profile data product (Version 4.51) . Boundaries of aerosol plumes and clouds are enhanced using denoising and Gaussian filtering techniques to isolate coherent features. A percentile cutoff converts the smoothed backscatter into a binary mask, preserving only the strongest aerosol and cloud structures. This method maintains the original Level 1B resolution of 333 m. Object-based feature detection scikit-image identifies separated clusters, outputting a labeled array where each distinct plume is assigned a unique integer. Here, a “cluster” is defined as a contiguous region of pixels classified as smoke, identified via connected-component labeling. Each distinct cluster is assigned a unique integer label, while multiple clusters may belong to the same smoke layer if the layer is horizontally fragmented. To discriminate smoke plumes from clouds, marine aerosol, dust, and volcanic plumes, we use the extinction-to-backscatter ratio also known as lidar ratio at 532 nm (S532) from the Lidar Level 2 Aerosol Profile (Version 4.51) data product and the particle depolarization ratio δ532 that can be estimated from individual profiles of the cross-polarized (⟂β532) and total backscatter coefficients as: 1δ532⟂/∥=⟂β532∥β532-⟂β532 Each cluster is classified based on different thresholds for the cluster mean particle depolarization ratio δ532,mean and the maximum lidar ratio S532,max summarized in Table . The classification thresholds applied were derived empirically by testing a range of values for δ532,mean and S532,max across multiple CALIPSO overpasses and selecting the values that best reproduced the smoke boundaries identified through visual inspection. Smoke plumes are further separated into lower troposphere (LT) or upper troposphere-lower stratosphere (UTLS) regions using tropopause height data from the CALIOP L2 dataset. Note: This classification extracts boundaries for distinct smoke features, and does not intend to characterize complex aerosol mixtures (e.g. does not differentiate between smoke and polluted dust – see Fig. f).

Compared to the original CALIPSO atmospheric feature classification Fig. d), the smoke plume between 4–5 km is correctly identified by our classification method in panel (f). Moreover, our method shows similar mixed features (e.g., dust and smoke) compared to CALIPSO's aerosol classification (panel e). We classify features as smoke-cloud mixtures when parts of the feature meet the smoke lidar ratio threshold (S532≥55sr) and the feature-mean depolarization exceeds 35 %. This accounts for cirrus clouds' high depolarization (from non-spherical ice crystals).

The Ozone Mapping and Profiler Suite (OMPS) Nadir Mapper (NM), flown aboard the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite, provides measurements of the ultraviolet Aerosol Index (UVAI). The concept of UVAI was introduced by and and has since become an invaluable tool for tracking the long-range transport of absorbing aerosols globally. Here we utilize UVAI generated by the NMMIEAI-L2 algorithm (version 2.1.1) . OMPS-NM has a nominal at-launch spatial resolution of about 50 km × 50 km.

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 (TOA). For the calculation of radiances, Rayleigh scattering is assumed, and cloud scattering effects are parameterized. Global climatological data sets of Lambertian surface reflectivity are used to account for surface effects in the algorithm. Further information on the derivation of UVAI can be found in the README document in and in . For OMPS-NM, the UVAI is retrieved from the 340 and 378.5 nm wavelength channels . UV-absorbing aerosols, particularly smoke from biomass burning and desert dust, produce positive UVAI values. Non-absorbing aerosols in the UV produce negative UVAI values. A unique strength of the UVAI technique is that clouds produce nearly zero UVAI, and the presence of subpixel clouds does not significantly affect the detection of aerosols . Standard practice for filtering out cloudy pixels can be achieved using a UVAI threshold below 0.7 .

The UVAI of absorbing aerosols depends especially on the height of the aerosol layer and the aerosol optical depth (AOD). The AOD is influenced by the aerosol single scattering albedo, which depends on the imaginary refractive index and the particle size distribution . Aerosols that produce positive UVAI strongly absorb the molecular radiation coming from below the aerosol layer. Consequently, the higher the aerosol layer, the greater the amount of affected Rayleigh-scattered radiation that can be absorbed. This dependence is nearly linear and was established by , . The UVAI also increases linearly with AOD, with a slope proportional to the single-scattering albedo . For the 340 and 380 nm wavelength pair and a single layer of strongly absorbing aerosols, the linear increase with AOD continues until a value of approximately 2 . For AOD larger than 2, further increases in UVAI depend mainly on aerosol layer height . This effect diminishes with increasing single scattering albedo of the aerosol layer. Simulations of the UVAI sensitivities are described in , . Model simulations show that the UVAI is further enhanced when the absorbing aerosol layer overlies a bright underlying scene. This occurs, for example, when liquid or ice clouds are located beneath or are embedded within the aerosol layer. The magnitude of the change in UVAI depends particularly on the cloud and aerosol optical depth and the aerosol single scattering albedo . Moreover, the sensitivity of the UVAI to aerosol layer height is reduced when underlying clouds are present .

demonstrated that the UVAI, as measured by OMI, increases with plume height for fresh tropospheric and stratospheric smoke plumes exhibiting UVAI > 7. They pointed out that for young smoke plumes, which maintain sufficient optical thickness and stable single-scattering albedo (SSA), the primary driving factor of UVAI magnitude is plume height. They showed that UVAI is reduced in aged stratospheric plumes (4–5 d old), where dilution through mixing with clean air reduces aerosol optical depth and consequently UVAI. The authors further proposed that particle-aging processes play only a minor role in UVAI reduction, noting that the chemical and physical evolution of smoke occurs rapidly within the first hour after emission, with properties stabilizing thereafter for extended periods (hours to days) .

report that UVAI values (for Suomi-NPP OMPS-NM) exceeding 15 typically indicate fresh stratospheric smoke plumes from extreme pyroCb events. For the Canadian wildfires in 2023, values exceeding 15 of the UVAI measured by OMPS-NM appeared in 5 cases between May and September, primarily between August–September when pyroCb activity was surprisingly low . Studies of such events, e.g., , indicate that UVAI values larger than 10 are a strong indicator of pyroCb activity or smoke in the upper troposphere and lower stratosphere (UTLS).

Satellite-based wildfire and smoke detection is illustrated with an example case from the boreal summer of 2014. Strong wildfire activity was observed in the western part of North America (State of Washington and Northeast British Columbia) in July and August 2014. Active fires detected by MODIS (Aqua and Terra combined) between 16–18 July 2014 and the resulting wildfire smoke over Alberta and Montana on 18 July 2014 are evident from UVAI values exceeding 8 in Fig. . CALIOP backscatter coefficients (Fig. ) indicate a smoke plume at approximately 5 km altitude during the overpass, marked by the blue dashed line in Fig. . We extract the respective grid points of the UVAI at the time of the CALIPSO overpass using the overpass time difference to Suomi-NPP combined with information about the 3D-wind field from ERA5 hourly data on the respective pressure levels of the smoke plume altitudes . The extracted values of the UVAI can be seen in Fig.  together with the extracted smoke plume height from the CALIOP measurements.

The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board National Aeronautics and Space Administration (NASA)'s Earth Observing System (EOS) Aqua and Terra satellites has operationally detected active fires globally since 2000. The Aqua and Terra MODIS sensors are aging and nearing the end of their lives. Since 2017, the new generation moderate-resolution instrument called the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi-NPP satellite provides continuity with MODIS .

We obtain daily global active fire data from the MODIS MCD14ML product (Aqua and Terra combined) and the VIIRS 375 m active fire product VNP14IMGML , both available via the Fire Information for Resource Management System (FIRMS). The VNP14IMGML product is built on the modified active fire detection algorithm of the MODIS Collection 6 combined active fire product , with modifications to separate fire pixels from background clear-land pixels . A comparison between MODIS and VIIRS active fire data used in this study can be found in . The active fires detected in British Columbia and Washington State for the 18 July 2014 case are shown in Fig. .

In 2022, a discrete fluorescence channel was incorporated into the Multiwavelength Atmospheric Raman Lidar for Temperature, Humidity, and Aerosol Profiling (MARTHA) system at the Leibniz Institute for Tropospheric Research (TROPOS) in Leipzig . The MARTHA system emits electromagnetic radiation at three wavelengths (355, 532, and 1064 nm) with an overall pulse energy of approximately 1.2 J at a repetition rate of 30 Hz. A detailed description of the system is provided in , , . The fluorescence channel added in the MARTHA lidar system is designed around a 44 nm wide interference filter centered at 466 nm (excitation wavelength: 355 nm). Further technical details on the setup can be found in .

Generally, when excited by ultraviolet light, biogenic aerosol particles can be detected by measuring their laser-induced fluorescence emissions. Atmospheric fluorescent aerosols include biological particles such as pollen, viruses, fungi, bacteria, and spores, as well as non-biological compounds from biomass burning and fossil fuel combustion . Mineral dust has also been found to produce a fluorescence signal, though it is relatively weak compared to that of biomass burning aerosols . The fluorescence spectrum varies depending on the type and composition of the aerosol. Because water does not fluoresce, fluorescent aerosols can be studied even in aerosol-cloud coexistence . However, an increase in relative humidity affects the fluorescence of water-soluble aerosol particles through hygroscopic growth, as well as that of water-insoluble particles due to the droplet lens effect. This complicates the retrieval of quantitative information about aerosol properties .

The ability of certain aerosol particles to fluoresce may be expressed as the fluorescence capacity GF 2GF=βFβ532

which is the ratio of the fluorescence backscatter coefficient (βF) to the elastic particle backscatter coefficient at 532 nm (β532). The spectral fluorescence capacity with respect to the 355 nm wavelength GF355 can be expressed as 3GF355=βFβ355dIF where dIF is the bandwidth (44 nm) of the interference filter in the fluorescence channel. Pollen and smoke exhibit significantly higher fluorescence capacity values compared to urban aerosol and mineral dust. Thus, when combined with information on the linear depolarization ratio, discrimination between smoke, mineral dust, pollen, and urban aerosol is possible .

Since 2022, profiles of the fluorescence backscatter coefficient have been obtained for a limited number of measurements, as the MARTHA lidar requires manual operation. Due to the broad bandwidth of the fluorescence channel and the low intensity of the fluorescence signal, measurements were only feasible during nighttime.

During daytime, when MARTHA measurements are unavailable, we use observations from the PortabLe Lidar sYstem with eXTended capabilities (Polly^XT) lidar to interpret the air mass source estimate. Polly^XT is a Raman lidar that emits light at three wavelength channels. The technical setup of Polly^XT allows the measurement of the backscatter coefficient at 1064, 532, and 355 nm and the extinction coefficient at the two shorter wavelengths (532 and 355 nm). Additionally, Polly^XT is equipped with two polarization channels, providing measurements of the particle linear depolarization ratio at 532 and 355 nm . From these measurements, aerosol characterization is possible by comparing the respective extinction-to-backscatter ratio (lidar ratio) . A detailed description of the Polly lidar system can be found in , .

Attribution of smoke within the troposphere is the main goal of this study. Stratospheric smoke detection using the UVAI is treated as an experimental product, since the linear relationship between UVAI and aerosol properties is not generally valid for stratospheric plumes , particularly once they have aged . Smoke can be injected into the UTLS by intense pyroconvective events, where it can circulate globally and persist for several months to years . Since our analysis is based on ten-day backward trajectories, stratospheric smoke plumes older than ten days cannot be attributed to their sources. Consequently, our approach is likely to miss aged or long-range transported smoke especially in the UTLS region. As shown in Fig. , the algorithm initializes at the air parcel position at step i=80 (corresponding to the 10th day backward in the trajectory). For each air parcel, FLEXPART provides the particle position and tropopause height (z_tropo) at 3 h time steps. If the air parcel's altitude (z_p) is located above the tropopause, potential stratospheric smoke influence is investigated (see Fig. ). As an input, the daily maximum global UVAI field is used. Air parcels located above grid points where the UVAI exceeds the threshold UVAI_fresh=10 are classified as being influenced by fresh stratospheric smoke. While prior work has suggested that UVAI values greater than 15 indicate fresh stratospheric smoke , our adopted threshold is comparatively lower. Further details on the sensitivity analysis and the discussion on the choice for this threshold are given in Appendix . Once stratospheric smoke influence is detected, this information is retained and propagated along the trajectory. Subsequent air parcel positions are flagged as stratospheric if they are located above grid points where the UVAI exceeds the UVAI_aged threshold, applied to identify aged smoke plumes. For example, reported that plumes older than two days exhibited UVAI values between 5 and 10 at altitudes of 14–20 km. According to theoretical calculations describing the relationship between UVAI and AOD at a specified aerosol layer height , such plumes must have been optically thin (AOD <2).

To prevent unrealistic attribution, the algorithm imposes an upper altitude limit for stratospheric smoke identification. This limit is derived from the empirical relationship between UVAI and plume height established for tropospheric smoke (see Sect.  and Fig. ). For a given UVAI value, the maximum reception height hmax,s is calculated. A stratospheric air parcel is only considered smoke-influenced if its altitude z_p lies between the tropopause and this dynamic altitude limit. In the default version of TRAILS we increase hmax,s by 4 km, which would equal a maximum altitude of 13.3 km for UVAI_fresh = 15 and 11.3 km for UVAI_fresh =10 (default version). However, grid points with UVAI values below 15 may either reflect an optically thin stratospheric smoke layer or an optically thick aerosol layer within the troposphere. Thus, we acknowledge that a complete separation between tropospheric and stratospheric smoke plumes cannot always be achieved. Moreover, stratospheric smoke might be mixed into the troposphere and therefore introduce another uncertainty in the smoke attribution. This limitation is particularly relevant because the decay phase of stratospheric smoke varies strongly between events. Gravitational settling is likely the dominant removal process, resulting in a slow and continuous decay of the smoke. Nevertheless, transport from the stratosphere to the troposphere can still occur during tropopause fold events . These events, however, are rare and challenging to trace. Over the Arctic, a strong polar vortex may enhance vertical air mass exchange linked to the Brewer–Dobson circulation, facilitating downward transport of stratospheric smoke . Furthermore, stratospheric smoke removal within the more isolated Southern Hemisphere might be more slow compared to the Northern Hemisphere, adding hemispheric variability to the detectability of aged smoke layers in the UTLS. Aged smoke can influence the evolution of cirrus clouds. Its subsequent removal by falling ice crystals further modifies the vertical distribution of smoke .

Taken together, these factors show that stratosphere–troposphere exchange processes further complicate smoke attribution. The UVAI-based identification of stratospheric smoke should therefore be interpreted with caution. We expect that our ten-day trajectory approach likely underestimates the occurrence, persistence, and vertical transport of stratospheric smoke signatures. Nonetheless, the focus of this study lies on tropospheric smoke attribution, and the final data product stores stratospheric- and tropospheric-affected air parcels separately. The attribution of stratospheric smoke therefore remains experimental, but it provides a practical means to minimize biases and to isolate tropospheric aerosol–UVAI relationships. A sensitivity analysis of the UVAI thresholds is presented in the Appendix.

While long-range transported dust is typically confined to the troposphere, deep convective systems can episodically inject it into the UTLS . As our algorithm specifically targets tropospheric smoke plume detection, we omit a dedicated stratospheric dust identification to maintain simplicity and focus. For the tropospheric component of the analysis, potential dust influence must be excluded. Consequently, if the altitude (z_p) of the air parcel is located below the tropopause (see Fig.), it is screened for dust influence. To mitigate false positives from dust, air parcels over Africa with UVAI > UVAI_cloud and z_p < z_tropo are categorically labeled as dust-influenced. This approach, however, also excludes African biomass burning aerosols and mixed dust–smoke scenarios. In practice, this means that all subsequent air parcel positions along a trajectory are excluded from smoke identification once the trajectory enters a dust source region. We acknowledge that this leads to an underestimation of smoke influence for trajectories that pass over Africa. However, for the scope and location of this study, which focus on smoke transported to Leipzig, Germany (51° N) from May to October 2023, this bias is minimized. The primary biomass burning seasons in Northern Hemisphere Africa typically occur from December to April , outside our analysis period. Therefore, while the dust filter is coarse, its impact on the quantitative results for wildfire smoke arriving over Leipzig, Germany, is likely small. We opt for this conservative approach as it robustly increases the true positive rate for smoke identification from the dominant North American source region, which is the primary focus. Future applications of TRAILS in other regions or seasons may benefit from a more sophisticated dust-fire discrimination method.

Dust is identified when a trajectory passes either over Africa (defined by 35° S to 37° N, 20° W to 55° E) or within the global dust belt (30° S–10° N) . As with stratospheric smoke, dust attribution is stored along the trajectory so that downstream positions can be consistently flagged as dust-influenced, even if they extend beyond Africa or the dust belt. Previous studies have shown that UVAI values exceeding 4 are indicative of elevated dust during severe dust storms . In cases where dust was previously identified and the UVAI subsequently falls below the maximum dust threshold (UVAI_dust), the air parcel remains classified as dust-affected below an experimental altitude threshold (z_dust). While z_dust remains preliminary, its refinement lies beyond the scope of this study, which focuses primarily on tropospheric smoke identification. As a default, z_dust is set to 6 km (above mean sea level), representing the maximum dust layer altitude for extreme Saharan dust outbreaks near the west coast of Africa . Future work will address a more systematic evaluation of this parameter.

Dust storms outside the dust belt or Africa are also considered. For example, small-scale events in the Aralkum Desert, Kazakhstan, have been observed to produce UVAI values exceeding 3 . For such localized storms, dust is identified for air parcels located below an experimental altitude threshold (z_dust) above grid points with UVAI values lower than UVAI_dust. As input, we use the daily global mean UVAI field together with MODIS land surface classifications to identify deserts (category Barren) . Once dust is identified along a trajectory, all subsequent air parcel positions are excluded from potential smoke attribution, ensuring that dust contamination does not bias smoke identification. This is particularly important given the spectral overlap between dust and smoke in UVAI, which often complicates attribution in mixed aerosol regions.

Air parcels affected by tropospheric smoke plumes are identified based on their location relative to grid points with enhanced UVAI values and their altitude. A new relationship between UVAI and plume height has been established for the Suomi-NPP UV Aerosol Index, extending previous work by on OMI-derived UVAI. As shown by , UVAI exhibits a linear relationship with aerosol layer height for young tropospheric and stratospheric smoke plumes. Figure  confirms a pronounced linear dependence of UVAI on plume height for young tropospheric smoke in the Suomi-NPP UVAI product, with a statistically significant correlation (R2=0.50, p<0.001). Our analysis indicates that approximately half of the observed UVAI variability can be attributed to changes in plume altitude, with a standard error of 0.017.

The systematically lower UVAI values (UVAI < 7) observed for Russian smoke plumes compared to North American and Australian counterparts likely result from several interrelated factors. First, boreal forest fires in Russia typically burn at lower intensities than those in North America , with surface fires accounting for approximately 75 % of burned area in Siberia. In contrast, crown fires dominate the more intense North American forest fires. These differences are largely driven by divergent plant functional types between Siberian and North American boreal forests . It has been shown that increased fire intensity enhances particle emission and coarse-mode aerosol production . Consequently, the lower UVAI for Russian plumes could result from differences in particle size distribution, composition, and resultant AOD. reported lower pyroCb activity in Russia compared to North America, resulting in fewer high-altitude smoke injections. Over North America, storm systems from the Pacific Ocean provide the atmospheric instability and moisture necessary for pyroCb formation . In contrast, South-East Siberia frequently experiences dry, anticyclonic conditions influenced by monsoon circulation , which often do not fulfill the necessary ingredients for pyroCb development . Consequently, Russian plumes remain predominantly in the mid-troposphere where UVAI sensitivity to height is reduced.

The spread in plume heights across all regions likely results from a combination of factors, including differences in satellite overpass times, variations in aerosol optical depth, and heterogeneity in smoke composition. Additionally, the potential presence of underlying cloud contamination due to the coarse spatial resolution of OMPS-NM (50 km × 50 km) is likely another source of uncertainty. As our empirical relationship (Eq. ) does not explicitly account for this effect, cloud-contaminated pixels could bias the derived plume height upward. This potential positive height bias may partly offset the underestimation in plume height from optically thin or aged smoke plumes, which are expected to produce a weaker UVAI response for a given height . However, as aerosol optical depth decreases, the sensitivity to enhanced scene reflectance also decreases . We do not attempt to disentangle or explicitly correct for either effect. Consequently, the retrieved plume-height relationship is expected to be especially reliable for optically thick, young tropospheric smoke plumes over low-reflective underlying scenes (e.g. cloud-free conditions). It should be noted that our objective is not to retrieve exact layer heights but rather to constrain the horizontal and vertical extent within which smoke may occur. An overview of wildfire source regions and extracted data points for each year, serving as the basis for Fig. , is provided in Table .

For air parcels near fires that do not meet stratospheric or dust conditions and reside below the tropopause (z_p < z_tropo), tropospheric smoke identification requires the parcel altitude to fall within theoretical plume height boundaries (h_min < z_p < h_max), estimated from the UVAI–plume height relationship as follows: 5h=0.51⋅UVAI+2.2km from which the maximum and minimum reception heights hmax,t=h+2 km and hmin,t=h-1 km (default version) are derived.

This dynamic definition of plume height boundaries directly addresses the “reception height” problem highlighted in the introduction. Instead of assuming that air parcels only acquire smoke properties near the surface (e.g., within the PBL or below a fixed reception height of 2 km altitude), our method allows the reception height to be vertically dynamic and spatially variable. It is explicitly defined by the observed intensity of the UVAI and its corresponding estimated plume altitude. This represents a fundamental advancement over the previous tool introduced by , enabling the attribution of smoke influence for elevated plumes that have been injected directly into the free troposphere, thereby providing a more accurate and physically representative source attribution for wildfire emissions.

This criterion is applied under two key assumptions. First, continuous wildfire emissions near the source maintain stable UVAI–plume height relationships, despite temporal offsets between the Suomi-NPP overpass time and the FLEXPART air parcel positions. Second, we assume that smoke plumes within the troposphere will dilute if they are not lifted into the upper troposphere by processes such as WCBs within the first few days after emission. Consequently, we circumvent the challenge of directly identifying aged, diffuse tropospheric smoke. We argue that sustained high UVAI values in aged plumes originate from optically thick, condensed plumes that have been elevated into the UTLS. Therefore, we classify a plume as aged smoke if it is detected along an air parcel trajectory where smoke was previously identified. Similar to the method for aged stratospheric smoke, once a tropospheric smoke detection is made, it is retained and propagated along the calculated trajectory. Subsequent air parcel positions are then flagged as containing tropospheric smoke if they pass over grid points where the UVAI exceeds the predefined UVAI_aged threshold used to identify aged plumes. By propagating the detection along trajectories in this manner, the method inherently accounts for the advection and lofting of smoke over time. This approach ensures a realistic representation of slower atmospheric uplift mechanisms, such as those driven by WCBs. These processes typically require 2–4 d to transport smoke-laden air masses from the lower troposphere to the UTLS region, as demonstrated by .

Additionally, if an air parcel is located above a grid cell in the daily one-degree averaged MODIS FRP grid and the cell exceeds the threshold of (FRP_thresh), the parcel is identified as smoke-influenced from the surface up to hmax. This condition ensures that air parcels positioned directly over a fire and with a height z_p below hmin are identified as smoke-influenced, provided the fire is sufficiently large and persistent to maintain burning over a daily period.

We set FRP_thresh = 30 MW based on detection reliability, not fire behaviour. This threshold sits at the upper end of the peak in fire detection across all world regions and well above the sensor's detection limit (9–11 MW ). Consequently, reaching 30 MW on average across a grid cell requires a robust and spatially consistent fire signal. This is different from the FRP level at which fire size stops growing, which show varies widely by region (roughly 10 to 280 MW). Since our goal is to filter out unreliable low-intensity detections rather than to match a specific fire regime, a single global value based on detection statistics fits better than a region-specific one.

The period from 29 May to 2 June 2023 over Leipzig was characterized by the presence of extensive biomass burning aerosol layers. Figure  illustrates their temporal evolution, as captured by the complementary MARTHA (fluorescence, nightly) (panel a) and Polly^XT (continuous) (panels c–d) lidar systems. Smoke occurrence fractions retrieved from the airmass attribution tool are shown in panel (b). Several fluorescent aerosol layers were detected throughout the troposphere. These layers are partly visible in the Polly^XT measurements of the 1064 nm attenuated backscatter coefficient shown in panel (b), although they are most prominent at altitudes below 6 km. The evolution of cirrus clouds with ice virga between 7 to 12 km on the afternoons of 29 and 31 May can be recognized by increased values of the attenuated backscatter coefficient and the depolarization ratio, which are typical for irregularly shaped ice particles (panel c). Likely, ice nucleation was initiated at heights >10 km and at temperatures of below -50 °C according to model data from the ECMWF Integrated Forecast System (IFS). A detailed analysis of the fluorescence measurements from 29 May 2023, including potential smoke-cirrus interactions, is presented in . For the aerosol layers below 6 km the depolarisation ratio in panel (d) shows values below 7 %. These values are in agreement with those found in aged tropospheric canadian smoke plumes . The signal-to-noise ratio in the Polly^XT measurements is lower during day, making aerosol features less distinct above 4 km.

Layered aerosol features identified as smoke by our retrieval show enhanced SOF values exceeding 15 % on 29 and 30 May 2023. This stratified structure coincides with the altitudes of aerosol layers detected by both lidars. The observed wave-like patterns are consistent with the passage of a warm front, which transported smoke along tilted isentropic surfaces between approximately 4–8 km. Trajectory analysis indicates that smoke identified air parcels originated in the mid-troposphere (at around 5 km) over the western Atlantic. These air parcels were then lofted within the warm conveyor belt (WCB) of a mid-latitude cyclone located south of Iceland over the North Atlantic. Such frontal ascent provides a typical pathway for transporting polluted air and smoke from the lower to the upper troposphere . From around 40° N, 60° W, the air parcels moved northward, ascending to about 7 km within one day before their trajectories turned anticyclonically over Western Europe. This transport pathway and its timescale (1 kmd-1) are characteristic of air mass ascent within warm conveyor belts . The upper portion of the frontal zone arrived near 09:00 UTC on 29 May 2023, coinciding with observed enhancements in SOF near 8 km and the presence of cirrus clouds, which are typical of warm-frontal overrunning. The lower portion of the front passed the site around 03:00 UTC the following day. Subsequently, subsidence led to further downward mixing of the smoke layer. This subsidence is evident in the MARTHA lidar observations in Fig. a), which show a gradual descent of thin aerosol layers. The associated large-scale downward motion also suppressed cloud formation during the surface warm-front passage. The overall synoptic situation closely resembles conditions described by , featuring a stationary anticyclone over the North Atlantic that promoted large-scale descent through combined cold-air advection and negative vorticity advection.

Figure  shows MARTHA and Polly^XT lidar 1 h mean (centered at the full hour) vertical profiles between 30 May and 1 June 2023 averaged over 500 m height bins to match the resolution of the TRAILS output. The SOF is shown against the fluorescence backscatter coefficient together with the MARTHA fluorescence capacity (panels a–c) and against the Polly^XT elastic-backscatter coefficient at 1064 nm and depolarisation ratio at 532 nm in panels (d)–(f). Profiles at 21:00 UTC on 30 May 2023 (panels a and d), at 00:00 UTC during the night of 30–31 May 2023 (panels b and e), and at 21:00 UTC on 1 June 2023 (panels c and f) over Leipzig were selected, as they correspond to cloud-free periods where a reduction in fluorescence backscatter through effects such as fluorescence quenching or aerosol scavenging within cirrus clouds are negligible. Moreover, MARTHA lidar fluorescence measurements are only available during the night, with hourly mean profiles centered on the full hour (requiring at least 50 % data availability), whereas the SOF has a time resolution of 3 h (21:00, 00:00, 03:00 UTC, etc.). Consequently, only the profiles at 21:00 and 00:00 UTC can be used for evaluating the SOF against the fluorescence measurements.

The fluorescence measurements (particularly the fluorescence capacity GF) reveal multiple elevated aerosol layers, with mean layer heights between 2–12 km. On 30 May at 21:00 UTC, three distinct layers were detected (3.5, 8, and 11 km height), splitting up into five during the night of the 30 to 31 May at 00:00 UTC. By 1 June at 21:00 UTC, these five layers were present again, with particularly high fluorescence capacity in the lower troposphere (up to 3.8 ×10-4), consistent with previously reported values for wildfire smoke over Europe . In comparison, the Polly^XT backscatter coefficient profiles showed fewer and less distinct layers. For example, on 30 May only four broad layers were identified, with substantially weaker particle backscatter coefficients at 11 km. On 31 May, five layers were detected, but the UTLS layer remained faint. Overall, fluorescence lidar provides higher sensitivity to elevated aerosol layers, especially in the UTLS, whereas Polly^XT backscatter coefficient at 1064 nm captures the broader layer structure but struggles to resolve weak layers at high altitudes. This finding aligns with previous observations by . In general, the particle depolarisation ratio increases steadily from 0.5 % at 2 km towards 6 % at 10 km. The observed increase in the depolarisation ratio with altitude indicates reduced condensation of water vapor, which suppresses the formation of a spherical coating on the aerosol particles in the drier upper troposphere, leading to more irregular shapes .

The SOF profiles between 30 May at 21:00 UTC and 31 May at 00:00 UTC reveal four smoke layers (2–11 km height), with peak intensities decreasing from 3.4 % in the lower troposphere to about 1 % near 8 km. The detection of the highest layer required lowering the threshold to 0.1 %, indicating a reduced detection performance at these altitudes. Using our layer-matching procedure, three SOF layers (2.25, 7.5, and 10.5 km) correspond to layers in the GF profiles, with an average altitude difference of -300 m and slightly larger thicknesses (+100 m). Layer thickness was determined by calculating the distance between the points where the peak intersects a specified reference level (50 % of the peaks prominence height). This requires predefining the peak threshold and peak prominence, which are summarized in Sect. . On 1 June at 21:00 UTC, however, fewer layers were detected with SOF: the UTLS layer at 12 km is missing, and two mid-tropospheric GF layers (3.75 and 6.25 km) are merged into a single broader SOF layer at 5.25 km. Only the lower (1.75 km) and upper-tropospheric (8.25 km) layers showed good agreement with GF. These results demonstrate that while SOF generally captures the main smoke structures and mean layer heights agree well with GF profile, it tends to underestimate thin layers, particularly in the UTLS. This highlights the benefit of independent fluorescence measurements.

Interestingly, smoke-affected trajectories for detected layers in the SOF profiles originated from wildfires in British Columbia, Alberta, the Northwest Territories, and Saskatchewan between 21 and 23 May 2023. According to , fires in Alberta that started in mid-May were mostly crown and surface fires caused by lightning. Burned area in May in Alberta was predominantly forested with conifer and mixedwood fuel. The dominant conifer fuel type reported is the C-2 Boreal Spruce fuel type of the Canadian Forest Fire Behaviour Prediction (FBP) System . The C-2 Boreal Spruce is moderately well-stocked, with tree crowns extending nearly to the ground . It remains an open question, however, how the optical and microphysical properties of such aged smoke aerosols are influenced by this specific fuel type.

An example of wildfire smoke in the presence of mixed-phase clouds is shown in Fig. . Between 20:00 and 23:00 UTC on 9 July 2023, the fluorescence backscatter reveals an aerosol layer between 4 and 6 km altitude, also captured by the Polly^XT lidar. Prior to 20:00 UTC, a second aerosol layer is detected by the Polly^XT lidar between 10 and 12 km. Throughout the night, precipitating mixed-phase clouds developed in the vicinity of the smoke layers. Cirrus clouds (top height at around 12 km at temperatures <-60 °C) are also visible, identified by elevated backscatter coefficients in panel (c) and relatively high depolarization ratios (>20 % – indicative of non-spherical ice crystals) in panel (d). By 23:30 UTC, attenuation in the lidar signal at 5 km suggests the development of liquid layers, with the mixed-phase cloud system reaching a top height of about 8 km (cloud-top temperature ∼-30 °C). Precipitation onset was observed on 10 July at 00:00 UTC by the Parsivel2 disdrometer and lasted until 00:25 UTC. A second mixed-phase cloud produced precipitation between 04:15–04:40 and 05:00–05:20 UTC. These rain events are clearly visible in the Polly^XT volume depolarization ratio in panel (d), which increased to 10 %–15 % below 4 km at these times. Interestingly, after 06:00 UTC, enhanced particle backscatter reappeared between 4 and 6 km, with the layers appearing optically thicker than before precipitation. The temporal evolution of the SOF closely matches the lidar observations, supporting the interpretation of the aerosol layers as wildfire smoke. In particular, the correspondence of enhanced SOF values with the lower-tropospheric smoke layers after precipitation exemplifies the detection performance of our tool.

Looking at the hourly averaged lidar profiles on 9 July 2023 at 21:00 UTC in Fig. a), the fluorescence lidar detects two fluorescent layers at 5.5 and 13 km. In contrast, the SOF profile identifies layers at 3.5, 10.25, and 13.75 km. In short, the SOF profile at 21:00 UTC does not include the layer at 5.5 km. However, in the SOF profile at 18:00 UTC (Fig. c), the SOF shows a peak at 5.5 km layer, illustrating that our tool does not always capture the time evolution of the layers precisely, possibly due to the 3 h time resolution. It is likely that the middle smoke layer at 5.5 km was mixed downwards between 18:00 and 21:00 UTC, such that the SOF layer at 3.5 km (peak at 4 km) at 21:00 UTC actually corresponds to the lidar-observed fluorescent layer at 5.5 km. Interestingly, the smoke layer between 4–6 km primarily originated from wildfires which occurred between 1–3 July 2023 in Northern Quebec (Nunavik), Canada, with a smaller fraction of trajectories passing through the Rocky Mountain Trench wildfire region. Both areas are characterized by boreal forests dominated by black spruce and pine species.

The SOF profile shows a distinct increase at a layer centered around 10.25 km at 21:00 UTC while the fluorescence capacity at this altitude remains weak. At 21:00 UTC between 8 to 12 km (at 18:00 UTC between 11 and 12 km), enhanced backscatter coefficient and particle depolarisation ratio (>30 %), indicate the presence of cirrus clouds. Enhanced Polly^XT depolarisation values (at 18:00 UTC: >15 % between 7 and 10.5 km indicate the presence of nonspherical particles like dust . However, depolarisation ratios of biomass burning aerosol exceeding 15 % at 532 nm have also been previously observed in the UTLS region . Ten-day back trajectories support the interpretation of dust presence: some trajectories arriving at 10 km originated near the Pinacate and Gran Desierto de Altar (Mexico), part of the larger Sonoran Desert system extending into the U.S. (Yuma Desert). Air parcels located below 5 km were tracked over grid points with UVAI values up to 5 and MODIS land cover classified as “Barren” (1° resolution). The resulting dust occurrence fraction (DOF) over Leipzig on 9–10 July 2023 is included in Fig. b and c. For both times, a dust layer was identified at 10 km with DOF >1.9 and DOF >3.5.

Taken together, these results demonstrate that the enhanced SOF at 10 km cannot be caused by false smoke attribution. Instead, the evidence points to either uncertainties in trajectory calculations or, more plausibly, the coexistence of dust and smoke in the same altitude range, i.e., a mixed dust–smoke layer at 10 km. This comparison demonstrates that relying on the SOF alone may lead to ambiguities in distinguishing smoke from dust. Incorporating measurements of the particle depolarisation ratio alongside the SOF therefore enhances the robustness of our tool, ensuring more reliable identification of aerosol type in complex scenes with coexisting smoke and dust layers.