Biomass burning (BB) is one of the key sources of aerosol emission. During large forest fires particles can be injected into the upper troposphere and transported over long distances affecting air quality in the subsidence regions. The processes of smoke particle production and transport on local and regional scales have been much studied over the last decade (Reid et al., 2005a, b; Adler et al., 2011; Pratt et al., 2011; Miller et al., 2011). Optical properties of the smoke particles near the source region may vary over a wide range depending on vegetation type, atmospheric conditions and combustion phase (see, for example, Reid et al., 2005a, b). The particles are modified during transport (so-called “smoke aging”) due to different mechanisms, including hygroscopic water uptake (Hobbs et al., 1997), and coagulation (Fiebig et al., 2003), and are mixed with local anthropogenic emissions. To characterize the smoke particle properties, different kinds of observational techniques – such as satellite imaging (Miller et al., 2011), in situ aircraft probes (Fiebig et al., 2002, 2003) sun photometry (Dubovik et al., 2002; O'Neill et al., 2002) and star photometry (Pérez-Ramírez et al., 2008) – have been used. However, the general problem of passive sensors is that the results are appropriate for the particle column properties; thus the separate contributions of smoke layers, which may be above the planetary boundary layer (PBL), and aerosols within the PBL cannot be separated in a straightforward way. Aircraft sampling can be used to probe isolated aerosol layers but is too expensive to be done regularly. Meanwhile, active sensors, such as lidars, have the ability to profile the vertical structure of smoke plumes on a regular basis.

Previous multi-wavelength lidar measurements performed within the European lidar network (EARLINET), as well as measurements at other stations, have provided important information about intensive smoke particle parameters, such as lidar ratio and Ångström exponent (e.g., Müller et al., 2005, 2007a, b; Ansmann et al., 2009; Tesche et al., 2009; Amiridis et al., 2009; Murayama et al., 2004; Nicolae et al., 2013; Giannaki et al., 2010; Burton et al., 2012). In particular, analysis of parameters obtained for relatively fresh and aged smoke demonstrates a general trend of increasing lidar ratio as the particle ages. Thus lidar ratios at 355 nm (LR355) for smoke after 8 and 16 days, respectively, were about 45 and 80 sr (Amiridis et al., 2009). The aging process leads also to a decrease of the Ångström exponent from approximately 1.8 (for 1-day-old smoke) to almost 0 for 20-day-old smoke (Müller et al., 2007a), which points to an increase of the particle size. Another interesting finding is that for aged smoke the lidar ratio at 532 nm generally exceeds LR355 (Müller et al., 2005, 2007a; Nicolae et al., 2013).

Moreover, measurements of aerosol backscattering and extinction at multiple wavelengths can be inverted to particle microphysical properties, e.g., effective radius, volume density and the complex refractive index (CRI) (Ansmann and Müller, 2005). Though multi-wavelength Raman lidar normally provides only five independent observations (three backscattering and two extinction coefficients), which is insufficient to obtain a unique solution, the use of reasonable constraints in the inversions permits a reasonable estimate of the particle characteristics to be made. Numerous approaches have been suggested for such inversion, with promising results being obtained (Müller et al., 1999; Böckmann et al., 2005; Veselovskii et al., 2002, 2012, 2013). Multi-wavelength Raman lidar retrievals of BB aerosol have already been presented by several groups for smoke of tropical, North American and Russian origin (e.g., Müller et al., 2005, 2007a; Ansmann et al., 2009; Tesche et al., 2009; Amiridis et al., 2011; Murayama et al., 2004; Baars et al., 2012; Nicolae et al., 2013). These measurements have shown that the characteristics of aged smoke particles and their vertical distribution are highly variable, even when particles of the same origin are considered. Thus additional independent measurements are desirable, particularly in North America, where multi-wavelength inversions of aerosol properties are fairly rare.

The earlier studies have focused on the long-range transport of Canadian forest-fire-generated aerosol and air quality impact on the east coast of the USA (Colarco et al., 2004; Miller et al., 2011). The current study considers the impact of forest wildfires in the western USA in August of 2013 as detected on the east coast of the USA by multi-wavelength Raman lidar at NASA Goddard Space Flight Center (GSFC). This paper focuses on the retrieval methodology providing vertical profiles of particle microphysical parameters from lidar measurements. The second (coming) part of this study, briefly summarized in the next section, should give a complex event characterization combining analysis of aerosol retrievals from MODIS with aerosol source attribution from back trajectories and the Goddard Chemistry, Aerosol, Radiation and Transport module in a global Earth system model framework (GEOS-5 GOCART) (Bian et al., 2013; Chin et al., 2002, 2014; Colarco et al., 2010).

On 27 August the complete set of 3β+2α data was available, so the particle microphysical properties – such as volume density V, effective radius reff and complex refractive index – could be estimated. The example of such a data set for the time interval 03:20–05:20 UTC is presented in Fig. 11; the optical data are averaged over a height interval varying from 100 to 250 m. Figure 12 shows the vertical profiles of reff and mR obtained for the same three temporal intervals as in Fig. 7 using the LE algorithm. The corresponding profiles of backscattering coefficients at 355 nm from Fig. 7 are also shown without scale in the figure for convenience of comparing the vertical variation of aerosol scattering. As mentioned in Sect. 2.1, the uncertainties of the retrieved values of reff and mR are estimated as 25 % and ±0.05, respectively, but relative changes in these quantities can be revealed with significantly lower uncertainty (Veselovskii et al., 2013). This permits the vertical variation of the particle parameters to be studied. During the first temporal interval, the effective radius decreases with height above 1500 m from 0.275 to 0.15 µm at 2750 m. In the center of the smoke layer at 3250 m, however, the effective radius increases slightly up to 0.2 µm. The decrease of reff is accompanied by an increase of the real part of the CRI from ∼ 1.4 at 1250 to ∼ 1.5 at 3500 m, which is a typical value for biomass burning aerosol (Dubovik et al., 2002). For the second and third temporal intervals the height variation of the effective radius is similar, but the minimum value of reff in the smoke layer increases with a value during the third interval of approximately 0.2 µm. The enhancement of the real part of the CRI above 2000 m becomes smaller with time, and during the 03:20–05:20 UTC time interval a maximum value of mR is approximately 1.43. Based on these results, we infer that during the interval of 23:20–01:20 UTC the smoke layer was the least influenced by external mixing, while for the other two temporal intervals partial mixing of the smoke with local aerosols may have occurred. The imaginary part of CRI in the smoke layer was estimated to be less than 0.0075, but the high uncertainty of its retrieval in general does not permit evaluating the vertical distribution of mI.

On 27 August AERONET provides just a single Level 2.0 inversion at 13:44 UTC. The real part of the refractive index varies from 1.44 at 439 nm to 1.41 at 1020 nm, while the imaginary part is below 0.004 for all wavelengths. These values are consistent with the lidar retrieval, given that the main contribution to the columnar properties was provided by the PBL particles. The effective radius provided by AERONET is 0.258 µm, which again is quite close to the lidar-retrieved values inside the PBL.

The vertical profiles of the particle volume density V for these three temporal intervals are given in Fig. 13. The backscattering coefficient maximum shown in Fig. 4 moves upward with time, and the profiles of the volume density behave similarly. During the first temporal interval the maximum of V is below 1000 m, but during the third interval the maximum value of V=50µm3cm-3 is observed at 1500 m height. From Fig. 13 we conclude that most of the particle volume is contained inside the PBL and that the region associated with the smoke contributes much less to the columnar particle volume.

The regularization technique permits the main features of the particle size distribution (PSD) to be retrieved (Müller et al., 1999; Veselovskii et al., 2002). Figure 14 shows the volume distribution dV / d ln r obtained from the 3β+2α lidar measurements during the interval of 01:20–03:20 UTC for the height layers centered at 1500, 2486 and 2816 m. The width of the layers was 250 m. In all three PSDs the fine mode dominates, which is typical for rural and biomass burning aerosols (Dubovik et al., 2002). Inside the PBL the maximum is achieved for r≈0.3 µm, while at higher altitudes it shifts toward smaller radii reaching r≈0.15 µm at 2486 m. However in the center of the smoke layer at 2816 m, the maximum is again at approximately r≈0.25 µm. The coarse mode is practically absent. As discussed in our earlier publication (Veselovskii et al., 2009), it may be due to the lower sensitivity of the lidar technique to big particles (maximal available wavelength is only 1064 nm). For comparison Fig. 15 shows the PSDs provided by AERONET. On 26 August the contribution of the smoke layer to the columnar properties appears to have been more important, so the effective radius of the fine mode is rather small, about 0.15 µm at 13:11 UTC. On 27 August the effective radius of the fine mode is larger (about 0.19 µm), because the main contribution to the columnar reff is provided by the PBL particles. Thus at least for the fine mode the lidar and AERONET retrievals are in reasonable agreement.

As demonstrated previously, the linear estimation technique is fast and robust and permits large volumes of lidar measurements to be processed to provide time–height distributions of particle parameters (Veselovskii et al., 2012, 2013). Here we apply the same technique to study the evolution of the particle parameters on the night of 27 August. Figure 16 shows the particle extinction at 355 nm calculated by the Raman method. In this calculation the vertical resolution varied with height from 75 m (at 1000 m) up to 200 m (at 6000 m). The lidar signals were also smoothed in the temporal domain by using a sliding average of three profiles. The effective temporal resolution of the result was, therefore, approximately 4 min. Time–height distributions such as these were generated for all five input data to permit the particle parameters to be retrieved. The quality of the input data can be characterized by the discrepancy ρ, which is the difference between the measured input optical data and the optical data recalculated from the solution obtained using Mie kernels. The details of the ρ calculation for the LE technique are given in Veselovskii et al. (2012). The time–height distribution of the discrepancy for the night of 27 August is shown in Fig. 17. Assuming that the uncertainties of the input data εi are independent, we can expect that for ρεi2 the retrieval becomes unreliable, which in our case (εi∼10%) corresponds to a value ρ∼20 %, so we limit the inversion to heights below 4000 m. We should mention also that the increase in ρ in the 1000–1200 m layer toward the end of the measurement period could be partly explained by overlap function variation during long-term lidar operation.

The maps of volume density, effective radius and the real part of the refractive index retrieved from 3β+2α measurements are shown in Fig. 18. The time–height distribution of volume concentration follows the corresponding distribution of the particle extinction, and we can observe upward movement with time of the layer with enhanced particle volume from ∼ 1000 to almost 2000 m height. The effective radius is largest at low altitudes and then decreases above an altitude of approximately 2000 m. During the night the region containing larger particles moves to higher altitudes. Above a height of 2000 m, the real part of the refractive index is also enhanced, indicating the presence of smoke. The retrieval of effective radius is especially sensitive to the noise in α532 (Pérez-Ramírez et al., 2013), so oscillations in the calculated extinction at 532 nm are the main source of small-scale oscillation on the map for the effective radius. As shown previously, the exclusion of α532 from the input data removes this oscillation, thus making the maps smoother (Veselovskii et al., 2013). However, the reduced data set is less sensitive to the radius variation, especially for small particles, so some information about the vertical structure may be lost by using a reduced data set. Thus, improving the signal-to-noise quality of the α532 measurements is one of the priorities in upgrading the current version of the GSFC multi-wavelength Raman lidar.

In numerous publications the backscatter-related Ångström exponent (BAE) γβ is considered to characterize the smoke particle properties, especially when γα is not available (Giannakaki et al., 2010; Amiridis et al., 2009). Tripled Nd:YAG-based lidar allows the calculation of the BAE for the wavelength intervals of 355–532 and 532–1064 nm (we will denote these as γβ(355–532) and γβ(532–1064)), thus increasing the amount of available information. However, while the EAE does not depend significantly on the particle refractive index and thus characterizes mainly the particle size, the BAE depends on both particle size and refractive index. Thus relationships between γα and γβ are quite complicated (Su et al., 2008); to understand these relationships numerical simulation was performed. Extinction and backscatter Ångström exponents were calculated using Mie theory for a bimodal particle size distribution: dn(r)dln⁡(r)=∑i=f,cNi(2π)1/2ln⁡σiexp⁡-(ln⁡r-ln⁡ri)22(ln⁡σi)2. Here Nf,c is particle number density in the fine (f) and the coarse (c) mode. Each mode is represented by a lognormal distribution with modal radius rf,c and dispersion ln⁡σf,c. In all simulations the value ln⁡σf,c=0.4 was used. The computation results are given in Fig. 19, showing γβ(355–532) and γβ(532–1064) vs. γα(355–532). In our study we considered both the effects of varying the modal radii and effects of varying the relative contribution of the fine mode to the PSD. The particle parameters used in the simulations were chosen to match typical properties of BB aerosols in order to aid interpretation of our observations. The results presented in Fig. 19 are obtained for the ratio of number density in the fine- and the coarse-mode RN=NfNc varying from 102 to 104, while the fine-mode radius varies in the range of 0.075-0.2 µm. The radius of the coarse mode is fixed at rc=1.2 µm, and the particle refractive index is m=1.45–i0.005. The contribution of the fine mode to the total volume for ratio RN= 102 and 104 is 16 and 95 %, respectively, for rf=0.15 µm. In the first case the fine mode dominates in the PSD, while in the second case the main particle volume is contained in the coarse mode.

The solid lines in Fig. 19 show results for the fixed ratios RN=104 (upper curves) and 102 (lower curves). At RN=104 and considering the radius range rf of 0.2–0.075 µm, γα ranges from 0 to 1.8, while γβ for both wavelength pairs varies inside the interval of 0.8 < γβ < 1.5. Hence, for fine-mode predominance, the BAE is less sensitive to variation in the fine-mode radius than the EAE. The dashed lines illustrate the effect of varying the ratio of fine and coarse concentrations RN for four chosen values of the fine-mode radius rf=0.075,0.1,0.125 and 0.15 µm. Increasing the coarse-mode contribution leads to a rapid decrease of γβ. As stated, these plots use a coarse-mode radius rc=1.2 µm; however computations performed for rc=1.0 and 1.5 µm lead to very similar results. Thus, the variation of the fine-mode radius rf and the ratio RN affects γβ and γα in different ways.

The plots in Fig. 19 were obtained for a fixed value of the refractive index. The backscatter Ångström exponent is sensitive to variations in m, however, thus complicating interpretation of the results. The influence of the real part of CRI on γβ and γα dependence is illustrated by Fig. 20, showing plots for mR=1.35,1.45 and 1.55. The imaginary part of CRI is mI=0.005 and ratio RN=104 for all plots. Just like in Fig. 19 the radius of the fine-mode rf varies from 0.075 to 0.25 µm, while rc=1.2 µm. From Fig. 20 we can also conclude that in the range 0.5 < γα < 1.5, which is typical for BB aerosols, γβ(355–532) is more sensitive to variations of mR than γβ(532–1064). As was demonstrated by the retrievals in Fig. 12, the particles inside the PBL are characterized by low mR and relatively high reff, while in the smoke layer the rise of the real part is accompanied by a decrease of the effective radius. To illustrate this evolution, in Fig. 20 the open stars mark two particle states. The first state with rf=0.18 µm (reff=0.325 µm) and mR=1.35 corresponds to the PBL particle parameters, while the second state represents the smoke layer particles with rf=0.085 µm (reff=0.185 µm) and mR=1.55. Thus γβ and γα values calculated for the total vertical profile should provide the pattern corresponding to transition from “state 1” to “state 2”.

To compare the simulation results with actual measurements, Fig. 21 shows vertical profiles of the backscatter Ångström exponents γβ(355–532) and γβ(532–1064) together with γα(355–532) measured on 27–28 August during 23:20–01:20 UTC. The general trend of increasing γα with height is accompanied by the rise of γβ(355–532), while γβ(532–1064) decreases. A consideration of these results along with those in Fig. 12 indicates that the BAE γβ(355–532) is highly sensitive to variations of mR. For example, an increase of the real part of CRI in the smoke layer up to a value of mR=1.5 (considering the retrievals in Fig. 8a) increases the value of γβ(355–532) to 1.7 at a height of 3250 m, while γα decreases to 1.1 at this same height. It is interesting that the decrease of γβ(355–532) from 0.9 to 0.4 in the height layer 1500–2000 m matches the decrease of mR in the same layer as shown in Fig. 12a. At the same time, γβ(532–1064) decreases gradually with height from 1.6 to 1.0, indicating low sensitivity to the real part of CRI variation, which again agrees with the simulation results in Fig. 20.

The dependence of γβ(355–532) and γβ(532–1064) on γα(355–532) for 27–28 August is illustrated by Fig. 22, showing corresponding values for the same three time intervals as in Fig. 7. The patterns provided by γβ(355–532) and γβ(532–1064) are essentially different: γβ(355–532) in general increases with γα, presenting strong data scattering due to high sensitivity to the refractive index; meanwhile γβ(532–1064) decreases with γα and data scattering is significantly lower. Such behavior is in agreement with patterns predicted by Fig. 20 (marked with stars).

Thus the results presented demonstrate that the backscatter Ångström exponents for the two wavelength pairs considered contain additional information about the height variation of aerosol size and refractive index. Such information can be used for preliminary analysis of lidar observations. For more complete information about the particle microphysical parameters, the full inversion of all 3β+2α measurements should be performed as described in the previous sections.

The multi-wavelength lidar technique was applied to the study of a forest fire smoke event observed near Washington, DC on 26–27 August 2013. Satellite observations combined with a transport model indicate that the smoke plume had its origin from Wyoming/Idaho forest fires and its transport to Washington, DC took about 5 days. This case was observed with the NASA GSFC multi-wavelength Mie–Raman lidar in order to accomplish two goals. The first goal was to get information about smoke particle intensive parameters such as lidar ratios and Ångström exponents at different wavelengths. The second goal was to test whether the multi-wavelength lidar technique is capable of resolving the height variation of particle microphysical parameters through the PBL and the region containing the smoke.

The intensive smoke particle parameters obtained are in agreement with findings of other authors, though when making such comparisons we should keep in mind that these parameters depend on many factors such as combustion phase, age and transport. As a result, the reported values present significant scattering. In our measurements, the lidar ratio at 355 nm in the unmixed smoke layer (on 26–27 August) was found to be 50 ± 10 sr, and it was significantly higher than the value measured inside the PBL, where LR355 was found to be 90 ± 10 sr. One of the specific features of smoke particles reported by several authors (Müller et al., 2007a, Nicolae et al., 2013) is that higher values of LR532 are obtained as compared to LR355. In our observations on 27 August the ratio of LR355 / LR532 in the region containing the smoke was normally above 1, and only in the strong smoke layer at 3000–3750 m height was the situation reversed. This may be an indication that, by the time of the observation, the smoke plume had been significantly diluted by local aerosols during transport.

The extinction and backscatter Ångström exponents are important parameters for particle characterization. The extinction Ångström exponent depends mainly on particle size, so interpretation of observations is quite straightforward. In our measurements on 27–28 August γα(355–532) increased with height from approximately 0.5 at 1000 m to 1.2–1.7 in the smoke-containing region at ∼ 2800 m, indicating that the smoke particles were rather small. The behavior of the backscatter Ångström exponents over the wavelength intervals of 355–532 and 532–1064 nm was quite different: γβ(355–532) grew strongly in the smoke layer, while γβ(532–1064) gradually decreased with height. Numerical simulations confirmed that, for the aerosol with dominating fine mode, the Ångström exponents γβ(355–532) and γβ(532–1064) have different dependence on the particle size and refractive index. The lidar-determined dependencies of γβ and γα were in agreement with the simulated results. The Ångström exponent γβ(355–532) is very sensitive to the refractive index, while γβ(532–1064) is less affected by variation of mR, so joint consideration of all Ångström exponents provides useful information for preliminary analysis of lidar observations. However, γβ is not unambiguously related to particle size, so it should be used with care; to get information about the particle size and refractive index, the full 3β+2α data set should be inverted.

The inversion of lidar measurements on 27 August revealed that the particle effective radius decreased with height from approximately 0.27 µm inside the PBL to 0.15 µm in the elevated smoke layer. Simultaneously the real part of the CRI in the smoke layer increased to mR≈1.5. The retrievals demonstrate also that the fine mode is predominant in the particle size distribution, and that a decrease of the effective radius with height is due to the shift of the fine mode toward smaller radii. This fine-mode shift is consistent with AERONET column-integrated observations.

The results presented demonstrate that inversion of multi-wavelength lidar measurements provides information about the height variation of particle volume, effective radius and the real part of CRI through the PBL and smoke layer. However, to perform such inversion with better spatial and temporal resolution, further improvement of the lidar system is needed. The main limitation is posed by the statistical uncertainties in the measurement of α532. The retrieval of effective radius is the most sensitive to the errors in extinction coefficients (Pérez-Ramírez et al., 2013), and the vibrational Raman backscatter at 608 nm is rather weak compared, for example, to that due to rotational Raman scattering at 532 nm. Therefore, our upcoming plans are to augment the lidar instrument to measure rotational Raman scattering at 532 nm to supply the visible extinction coefficient needed for multi-wavelength aerosol inversions.