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Effect of aerosols on the infrared transmission in Lakiala, Finland
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Atmospheric Environment 42 (2008) 2603–2610 www.elsevier.com/locate/atmosenv
Effect of aerosols on the infrared transmission in Lakiala, Finland T. Mielonena,, T. Kaurilab, A. Arolaa, H. Lihavainenc, M. Komppulac, K.E.J. Lehtinena,d a
Finnish Meteorological Institute, P.O. Box 1627, FIN-70211 Kuopio, Finland Finnish Defence Forces Technical Research Centre, P.O. Box 5, FIN-34111 Lakiala, Finland c Finnish Meteorological Institute, P.O. Box 503, FIN-00101 Helsinki, Finland d Department of Physics, University of Kuopio, P.O. Box 1627, FIN-70211 Kuopio, Finland
b
Received 17 January 2007; received in revised form 21 August 2007; accepted 6 September 2007
Abstract Effects of atmospheric aerosols on radiative transfer are mainly studied because of their role in climate change. Aerosols also affect some technological applications by deteriorating range performance of electro-optical systems. This study focuses on the aerosol-induced attenuation of infrared radiation along a horizontal path. Measured attenuation values are compared with modeled ones and an attempt is made to understand the differences. Similar measurements have been done earlier; however, those measurements have not usually contained any information about the prevailing aerosol size distributions. Both measured and modeled aerosol extinction coefficients are studied as a function of different weather parameters (visibility, relative humidity and temperature). Measured size distributions are also investigated and they are compared with the size distribution assumed in MODTRAN4. Because measurements and extinction calculations contain some error sources, e.g. instrument errors and errors in aerosol growth factors, whose magnitudes are not exactly known, the total uncertainty was difficult to assess. Despite the uncertainty in the measurements, differences between measured size distributions and model size distributions were found. It appears that weather parameters do not offer the most feasible input data to model the aerosol extinction. In addition, aerosol extinction coefficients calculated from the measured size distributions were much lower than the measured and modeled values. The continuation of aerosol attenuation measurements in the future is of vital importance to obtain enough data for the analysis. Moreover, the accuracy of the measuring equipments should be comprehensively assessed. r 2007 Elsevier Ltd. All rights reserved. PACS: 42.68.J; 42.25.B; 42.68.A Keywords: Optical properties of aerosols; Visibility; Measurement campaigns
Corresponding author. Tel.: +358 17 162 305; fax: +358 17 162 301. E-mail addresses: tero.mielonen@fmi.fi (T. Mielonen), timo.kaurila@mil.fi (T. Kaurila), antti.arola@fmi.fi (A. Arola), heikki.lihavainen@fmi.fi (H. Lihavainen), mika.komppula@fmi.fi (M. Komppula), kari.lehtinen@fmi.fi (K.E.J. Lehtinen).
1352-2310/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2007.09.038
1. Introduction Effects of atmospheric aerosols on radiative transfer are complex to model, because aerosol size distributions vary strongly both temporally and
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spatially. Moreover, the optical properties of aerosols are not fully known (Bond and Bergstrom, 2006). Patchy sources, sinks and the short atmospheric lifetime (days) of tropospheric aerosols result in local effects and regional differences in aerosol properties (Delene and Ogren, 2002). According to Anderson et al. (2003) aerosol concentrations are typically coherent for timescales and space scales less than 10 h and 200 km, respectively. Algorithms used in retrieving satellite data assume that aerosol properties are constant and therefore the actual variability in aerosol optical properties can cause uncertainties in satellite-retrieved parameters. Despite these difficulties, the modeling of the interaction between aerosols and radiation should be reliable enough that the effect of aerosols on climate change can be assessed. However, at the present moment the effect of aerosols seems to be the greatest uncertainty in climate change modeling. It partly derives from the fact that aerosols affect radiation in two distinct ways: (1) aerosols themselves scatter and absorb solar and thermal infrared radiation (direct effect) and (2) aerosols modify the microphysical and hence the radiative properties and amount of clouds (indirect effect). Direct radiative forcing appears to have a cooling effect, but with relatively large uncertainties. Moreover, indirect radiative forcing is even more difficult to estimate (IPCC, 2001). Aerosols also affect number of technological applications, for example, electro-optical sensors and satellite imaging. Finnish Defence Forces (FDF) has been using MODerate spectral resolution atmospheric TRANsmittance algorithm (MODTRAN4 v3, R1) for modeling the effect of aerosols on radiative transfer. This software uses weather parameters and aerosol models created by Shettle and Fenn (1979) as input values. FDF has also measured aerosol-induced attenuation with an OLAF-transmissometer. They have found that modeled attenuation values can differ greatly from the measured ones. Aerosol-induced attenuation of infrared radiation along a horizontal path has not been measured and studied in Finland before, while in Sweden (Persson and Kaurila, 2002; Nilsson, 1979), United States (Zeisse et al., 2000; Doss-Hammel et al., 2002) and Canada (Theriault et al., 1990) corresponding studies have been made. Persson and Kaurila (2002) compared measured and modeled values, but they did not have coexistent size distribution measurements. Our purpose is to extend this type of
approach by including aerosol size distribution measurements, the focus being on particle sizes around and above 1 mm because those particles have the biggest impact on infrared radiation. 2. Data and methodology The data discussed in this paper consists of two different measurement periods. Continuous measurements were done in Lakiala between 01/10/2004 and 31/05/2005; moreover an inter comparison campaign was carried out between 08/11/2005 and 29/11/2005. Lakiala is a rural location in southern Finland, approximately 20 km north of Tampere, which is the third largest city in Finland with a population of about 200 000. The measurement site in Lakiala is an old logging area in a mixed forest (see Fig. 1). 2.1. Continuous measurements Continuous aerosol attenuation measurements were conducted with a modernized OLAF-transmissometer which was constructed by Swedish Defence Research Agency (Ha˚ga˚rd and Persson, 1992) and aerosol size distribution measurements with an Electrical Low Pressure Impactor (ELPI) at Lakiala by FDF. The OLAF-transmissometer measures attenuation of infrared radiation in three bands: 0.96–1.08, 3.4–4.3 and 7.7–16:5 mm. It includes a transmitter–receiver unit, a reflector, control electronics and a computer. A measurement beam through the atmosphere and a reference beam inside the device are generated from a 1500 K Globar radiation source. The measurement beam
Fig. 1. The measurement site in Lakiala, Finland.
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travels 495 m to the reflector and back so that the total path length in the atmosphere is 990 m. The path distance to the ground varies between 2 and 5 m. By comparing electronically the detected power values of these two beams, the value for the attenuation in the atmosphere can be determined. Since OLAF measures the total attenuation in the atmosphere, the attenuation caused by aerosols is obtained by subtracting the part caused by atmospheric gases. Only carbon dioxide and water vapor have a significant impact on the radiative transfer at the OLAF wavelength bands. The effect of these gases is calculated with MODTRAN4. The extinction coefficient bext can be estimated from the transmittance t according to Beer–Lambert’s equation t ¼ ebext L ,
(1)
where L is the path length of the radiation in the atmosphere. The OLAF-transmissometer and its measurement accuracy have been described in detail by Kaurila et al. (2006). OLAF’s relative error has a parabolic dependence on measured extinction coefficient in all wavelength bands. For the longest wavelength band (7.7–16:5 mm) the relative error is less than 10% when the measured extinction coefficient is between 0.23 and 3:5 km1 . When the measured extinction coefficient is between 0.11 and 0:23 km1 or between 3.5 and 4:4 km1 the relative error is less than 20%. The accuracy of the OLAF-transmissometer is confined especially by Globar’s quite low radiant exitance as wavelength increases, the noise in the pyroelectric detector and the deviation of the system transmission. ELPI is a cascade impactor which measures the electric charge of the charged particles that hit the collector stages. It has 12 collecting stages and can measure particle sizes (aerodynamic radius) from 15 nm to 5 mm with less than 5 s response time (Marjama¨ki et al., 2000). The instrument used in these measurements was an Outdoor Air ELPI manufactured by Dekati Ltd., Finland. The inlet of the Outdoor Air ELPI consists of a PM10 nozzle Digitel DPM10/1.8/00, a stainless steel tube with a heater and a nafion dryer PD-200T-24SS. The inlet nozzle was about 5 m above ground and 2 m above the roof of the measurement container. Weather measurements were done with an automatic Vaisala Weather Sensor FD12P. This instrument has an optical forward-scatter sensor that senses fog and distinguishes between precipitation
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types, an analog capacitive surface sensor that senses the amount of water falling on it, a temperature sensor and it calculates present weather and visibility values using data collected from all the sensors. More extensive introduction to FD12P has been published by Merenti-Va¨lima¨ki et al. (2001). Aerosol attenuation was modeled with MODTRAN4 v3, R1. It calculates atmospheric transmittance and radiance for frequencies from 0 to 50 000 cm1 (0.2–10 000 mm) at a moderate spectral resolution, primarily 2 cm1 . The software uses aerosol data bases created by Shettle and Fenn (1979) and weather parameters (visibility, relative humidity, temperature, air pressure) as an input. Since it gives transmittance t values as an output, we have to convert them to correspond with measured extinction coefficients bext according to Eq. (1). 2.2. Measurement campaign An inter-comparison campaign was organized, in which aerosol size distributions were measured with two additional instruments: forward scattering spectrometer probe (FSSP) and aerodynamic particle sizer (APS). The total particle concentration was measured with a TSI CPC 3010 (condensation particle counter) which detects all particles larger than 10 nm. The APS is a laser-based particle sizing instrument which measures aerosol size distributions from 0.5 to 34 mm (aerodynamic radius) by determining the transit time of a particle between two laser beams downstream of a flow accelerating nozzle. Particles with a larger inertia will accelerate slower than the particles with a smaller inertia. Hence, particles can be divided into different size bins (APS has 60 size bins) based on their time-of-flight. The inlet to the APS and CPC was a stainless steel tube about the same height as the inlet of the ELPI. The sample was dried by heating the sample air from outside temperature to temperature of about 30 C prior to measurement. This ensured that the measured sample air had relative humidity below 40%. Interested reader finds more information about APS in Stein et al. (2002), Tsai et al. (2004) and Volckens and Peters (2005). The FSSP is an optical instrument that detects the scattered light from individual particles which pass through a thin laser beam. The intensity of the scattered light is related to the particle size but it also depends on particle shape and complex
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refractive index. FSSP measures optical radius from 0.25 to 4 mm and it has 30 size bins. The FSSP was installed on the ground so that its inlet without any filtering was about 1 m above the ground level. Brenguier (1989), Coelho et al. (2005), Kim and Boatman (1990) and Strapp et al. (1992) have published more detailed introductions to FSSP. 2.3. Data analysis Both measured and MODTRAN-based aerosol extinction coefficients were studied as a function of different weather parameters (visibility, relative humidity and temperature). Moreover, measured aerosol size distributions were compared with the default size distributions used in the MODTRAN4. The comparisons discussed in this paper were calculated with rural size distributions. In Shettle and Fenn (1979) it is assumed that rural aerosols are composed of a mixture of 70% of water soluble substance (ammonium and calcium sulfate and also organic compounds) and 30% dust-like aerosols. Aerosol extinction coefficients were also calculated using the data from the different size distribution measurements by using Mie-scattering theory and the results were compared with attenuation values measured by OLAF. Mie-scattering calculations were done with LibRadtran (library for radiative transfer calculations) software (Mayer and Kylling, 2005) which includes a Mie-scattering program by Bohren and Huffman (1983). In these calculations we assumed that the measured aerosols had the refractive indices of the rural model. Since all the instruments have different measurement methods, direct comparison of measured size distributions was not possible. We transformed all measured sizes, except optical size measured by FSSP, to Stokes sizes and grew the particles according to ambient relative humidity and temperature in the atmosphere by using the approach described in the work of Nilsson (1979). Optical size was assumed to be equal to the Stokes size, although this is not exactly true. However, arguably the error due to this simplification is small enough not to hinder our analysis of the FSSP measurements. The relationship between visibility, or meteorological range, and aerosol extinction coefficient is given by the Koschmieder equation V¼
lnð0:02Þ , bextð0:55Þ þ 0:01159
(2)
where V is visibility (km), bextð0:55Þ is the aerosol extinction coefficient at the wavelength 0:55 mm ðkm1 Þ and 0.01159 ðkm1 Þ is the surface Rayleigh scattering coefficient at 0:55 mm. Eq. (2) gives 2% contrast resolution visibility. Usually extinction due to Rayleigh scattering is very small compared to aerosol extinction at the near-infrared and infrared wavelengths. FD12P visibility measurements are based on 5% contrast resolution, thus we converted them to the 2% contrast resolution. Our analysis of the OLAF measurements concentrated on the 1 mm band because the measured extinction values at the other two bands fall very often below OLAF’s accuracy limit. 3. Results 3.1. Results from continuous measurements The continuous measurements were averaged for every 10 minutes. Only data of clear weather (Vaisala FD12P weather code was zero) were included in our data analysis, resulting in approximately 7000 measurements. First, we compared the measured and modeled extinction coefficients. Fig. 2 shows such a comparison for the 1 mm band. The correlation is clearly very poor; correlation coefficient is only 0.28. Occasionally OLAF measured unrealistically high values compared to measured visibility. This is very likely due to the fact that the FD12P measures visibility at one point, whereas the OLAF measures extinction across a 495 m path. For clarity, Fig. 2 does not show these highest values which are between 1.2 and 3:14 km1 . Band 1 - RURAL Modeled extinction coefficient [1/km]
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0.25
corr.coeff= 0.28
0.2 0.15 0.1 0.05 0 0
0.2
0.4
0.6
0.8
1
1.2
Measured extinction coefficient [1/km] Fig. 2. The correlation between measured and modeled extinction coefficients ðkm1 Þ in the 1 mm band. The 1:1 line denotes perfect agreement.
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When the measured values that are high ð0:5 km1 Þ or below OLAF’s measurement accuracy ð0:05 km1 Þ are excluded from the data, the number of measurements decreases to 2700 and the correlation coefficient increases to 0.34. Moreover, there was no clear relationship between the measured attenuation values and the weather parameters (relative humidity, visibility and temperature). As an example, in Figs. 3 and 4 the difference between measured and modeled aerosol extinction coefficients (in percentage) in the 1 mm band is plotted as a function of relative humidity and visibility, respectively. When relative humidity is near 50% the mean difference is between 70% and 60%. As relative humidity increases, the mean difference decreases quasi-linearly reaching 10% in relative humidities over 90%. In the visibilities below 20 km the modeled values are too high, while the situation turns opposite when the visibility increases. The mean difference increases quasi-linearly from 10% to 70% as the visibility increases from 10 to 65 km. These results suggest that the weather parameters do not make the most suitable input data for modeling the aerosol extinction. MODTRAN’s sensitivity for the input values was studied to find out the influence of the uncertainty in weather parameter measurements in the calculation of aerosol extinction. According to manufacturers the
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error in relative humidity measurements is approximately 2% units when relative humidity is below 90% and approximately 3% units when relative humidity is over 90%. In the visibility measurements the error is approximately 20% when the visibility is better than 10 km. Error of 2% in relative humidity measurements can cause 8% error in the calculated extinction coefficients when ambient relative humidity is near 85%. If the ambient relative humidity is 96% and the error of relative humidity measurements is 3%, the error in the calculated extinction coefficients increases to 44%. Twenty percent error in visibility measurements can cause an error of 25–33% in the calculated extinction coefficients. Thus, the errors in weather parameter measurements can cause a cumulative error up to 130% in the calculations in the worst case. This may explain partly the mean differences presented in Figs. 3 and 4, indicating that the errors in weather parameters can cause errors in calculated aerosol extinction. However, the measured extinction coefficients also have a considerable natural random variability. 3.2. Results from the measurement campaign In the inter-comparison campaign, measurements were also averaged for every 10 min. ELPI was the
Band 1 - RURAL 100
50
Difference [%]
0
-50
-100
-150
mean= 63.3 std = 2.5 c.c = N n=7
mean = 70.1 std = 15.5 c.c = 0.4 n = 26
mean = 62.5 std = 14.6 c.c = 0.5 n = 85
mean = 52.7 std = 29.8 c.c = 0.4 n = 111
mean = 37.7 std = 27.3 c.c = 0.6 n = 172
mean = 20.5 std = 40.1 c.c = 0.4 n = 813
mean = 9.9 std = 38.5 c.c = 0.4 n = 1455
-200 30
40
50
60
70
80
90
100
Relative humidity [%] Fig. 3. The difference (%) between measured and modeled aerosol extinction coefficients ðkm1 Þ as a function of relative humidity in the 1 mm band. The figure has been divided into seven sections and values for mean difference (mean), standard deviation of the difference (std), correlation coefficient between measured and modeled values (c.c) and number of the measurements (n) have been presented for each section. Rural aerosol model was used in the modeling.
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Band 1 - RURAL 100
50
Difference [%]
0
-50
-100
-150
mean = -9.8 std = 37.6 c.c = 0.2 n =1017
mean = 15.4 std = 28.5 c.c = 0.1 n = 697
mean = 36.4 std = 16.9 c.c = -0.0 n = 350
mean = 51.4 std = 11.9 c.c = 0.0 n = 173
mean = 61.7 std = 8.2 c.c = -0.0 n = 160
mean = 68.7 std = 7.6 c.c = -0.2 n = 272
-200 10
20
30
40
50
60
70
Visibility [km] Fig. 4. The difference (%) between measured and modeled aerosol extinction coefficients ðkm1 Þ as a function of visibility in the 1 mm band. The figure has been divided into six sections and values for mean difference (mean), standard deviation of the difference (std), correlation coefficient between measured and modeled values (c.c) and number of the measurements (n) have been presented for each section. Rural aerosol model was used in the modeling.
1e+06 CPC Shettle Shettle fssp elpi aps
10000 100 n (r) [1/cm3/µm]
only instrument which did not have gaps in measurements due to instrument problems or maintenance. Data sets from ELPI, FSSP, OLAF and APS contained 3077, 2462, 1603 and 1274 measurements, respectively. The rural size distribution of Shettle and Fenn (1979) was compared to the measured size distributions and to a modified model distribution, the number density of which was averaged from the CPC measurements. An example of this kind of comparison is shown in Fig. 5. Since APS and FSSP do not measure small particles (dry aerodynamic radius below 0:5 mm) reliably, we combined small particles from the ELPI data with the APS and FSSP data. Thus, the seven smallest size bins in all size distributions are from ELPI measurements, although in the following, the names of the size distributions are those of the instrument used for the measurement of larger particles. The model size distribution was calculated for rural aerosol model with 13 km visibility and 95% relative humidity. The mean ELPI, FSSP and APS size distributions were averages from 170, 156 and 83 measurements, respectively. In these measurements the relative humidity varied between 91% and 98% and the visibility between 11 and 15 km. The ELPI, FSSP and APS measurements were from 17, 16 and 12
1 0.01 1e-04 1e-06 1e-08 1e-10 0.01
0.1
1
10
100
r [µm]
Fig. 5. Corresponding size distributions ðcm3 mm1 Þ from ELPI, APS and FSSP measurements and the model created by Shettle and Fenn (1979) for 95% relative humidity and 13 km visibility.
different days, respectively. It is apparent that the measured size distributions are much smaller than the one used in MODTRAN. ELPI and APS measurements are very similar, while FSSP has typically measured less particles in the size range of 0.9–1:3 mm and more particles in the size range of 2–4 mm. APS and ELPI measurements differ when particle radii are in the size range of 1–4 mm and larger than 6 mm. The CPC had measured 2200
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particles per cm3 in average for these weather conditions, while the model distribution assumed 13 289 particles per cm3 ; hence the modified model distribution is in better agreement with the measured size distributions. The aerosol extinction coefficients were calculated from these size distributions using LibRadtran software. The same refractive indices of rural aerosols of Shettle and Fenn (1979) were used for all the size distributions. In Fig. 6, in addition to the calculated extinction spectra, extinction coefficients measured with OLAF and modeled with MODTRAN4 are shown. Values from OLAF and MODTRAN are average values from 99 measurements at similar weather conditions. The point in the graph, labeled ‘FD12P’, shows the extinction coefficient for 0:55 mm as a mean from the visibility measurements. Evidently, the extinction coefficients calculated from the measured size distributions are much smaller than the measured ones. MODTRAN-based aerosol extinction values compare better with the OLAF measurements than the values calculated from measured size distributions. It also appears that large particles (radius 44 mm) do not affect extinction coefficient spectrum as much as the particles with a radii near 1 mm. This can be seen by comparing Figs. 5 and 6. APS has measured greater amount of large particles than ELPI and still their extinction coefficient spectra are very similar, particularly in the wavelength region below 3 mm. On the other hand, extinction coefficient spectra based on FSSP measurements differ greatly from both. A small drop in the FSSP size distribution
Extinction coefficient [1/km]
1
RH= 91.80 - 98.00 T= -3.20 - 8.40
Vis= 11.97 - 14.92
0.1
OLAF MODTRAN FD12P fssp elpi aps CPC Shettle
1
10
near 1 mm radius causes extinction in the wavelengths below 1 mm to decrease, while the large number of particles in the size range of 2–4 mm increases extinction in the wavelengths over 2 mm. Even though APS has measured much larger particles than FSSP (radius 44 mm), they do not influence extinction as much as the smaller particles. Extinction coefficient spectrum calculated from the model size distribution modified with CPC number concentration is in the same size range as the spectra calculated from the ELPI, APS and FSSP data. Overall, it was difficult to make definite conclusions about the different measurements and their relations, in particular, because every instrument had significant measurement variability even during apparently stable atmospheric conditions. The variability was largest with the OLAF measurements. One likely reason for this is that there is inherent spatial averaging in OLAF measurements, while all the other measurements are point measurements. For example, in the measurements presented in Fig. 6, the coefficient of variation (standard deviation/mean) for ELPI ranges from 0.49 to 0.68 in the studied wavelength region (0.2–40 mm), while for APS and FSSP it was between 0.49–0.67 and 1.25–5.95, respectively. Coefficients of variation for the OLAF 1, 4 and 10 mm measurement bands were 0.66, 1.35 and 1.87, respectively. For comparison, aerosol attenuation values based on MODTRAN calculations, for corresponding wavelength bands, had coefficients of variation of 0.07, 0.03 and 0.02, thus in the 1 mm band OLAF measurements had almost an order of magnitude larger deviation if compared to the modeled values. 4. Conclusions
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0.001 0.1
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Wavelength [µm] Fig. 6. Aerosol extinction coefficients ðkm1 Þ from OLAF, ELPI, APS, FSSP, FD12P, MODTRAN and the model created by Shettle and Fenn (1979) for 95% relative humidity and 13 km visibility.
Aerosol-induced attenuation of infrared radiation was studied along a horizontal path. Continuous measurements from the year 2004 were used to compare extinction coefficients measured with an OLAF-transmissometer to values modeled with MODTRAN4. The most important results from this comparison are as follows: (1) OLAF measurements and modeled extinction coefficients do not always match very well; (2) weather parameters do not offer the most feasible input data to model the aerosol extinction and (3) uncertainties in weather measurements can cause cumulative errors in calculated aerosol extinction. We organized an inter-comparison campaign in 2005 to study aerosol size distributions in more
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detail. In addition to the continuous measurements performed with ELPI, we measured aerosol size distributions with APS and FSSP. The measured size distributions were then used as an input in the calculation of aerosol extinction coefficients. Data analysis showed that: (1) size distributions measured with ELPI and APS are very similar while FSSP measurements differ considerably from both; (2) aerosol extinction coefficients calculated from the measured size distributions are much lower than the values measured with OLAF or modeled with MODTRAN4; (3) standard deviations of the measurements are significant in certain weather conditions; (4) the model size distribution modified with the CPC number concentration is closer to the measured size distributions than the original model and their extinction coefficient spectra agree much better. Thus, further inter-comparisons and data quality assurance are required if aerosol size distribution measurements are used for transmission calculations based on Mie theory. Chemical properties of the aerosols should also be measured in order to have measurement-based estimates for the refractive indices. Acknowledgments The authors would like to thank Scientific Advisory Board for Defence (MATINE) for supporting this research. References Anderson, T.L., Charlson, R.J., Winker, D.M., Ogren, J.A., Holme´n, K., 2003. Mesoscale variations of tropospheric aerosols. Journal of the Atmospheric Sciences 60, 119–136. Bohren, C.T., Huffman, D.R., 1983. Absorption and Scattering of Light by Small Particles. Wiley, New York. Bond, T., Bergstrom, R., 2006. Light absorption by carbonaceous particles: an investigative review. Aerosol Science and Technology 40, 27–67. Brenguier, J., 1989. Coincidence and dead-time corrections for particle counters. Part II: high concentration measurements with an FSSP. Journal of Atmospheric and Oceanic Technology 6, 585–598. Coelho, A., Brenguier, J.-L., Perrin, T., 2005. Droplet spectra measurements with the FSSP-100. Part I: low droplet concentration measurements. Journal of Atmospheric and Oceanic Technology 22, 1748–1755. Delene, D.J., Ogren, J.A., 2002. Variability of aerosol optical properties at four North American surface monitoring sites. Journal of the Atmospheric Sciences 59, 1135–1150.
Doss-Hammel, S., Zeisse, C., Barrios, A., de Leeuw, G., Moerman, M., de Jong, A., Frederickson, P., Davidson, K., 2002. Low-altitude infrared propagation in a coastal zone: refraction and scattering. Applied Optics 41, 3706–3724. Ha˚ga˚rd, A., Persson, R., 1992. Infrared transmission measurement in the atmosphere. In: Infrared Technology XVIII, vol. 1762, SPIE. IPCC-Intergovernmental Panel on Climate Change, 2001. The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, New York. Kaurila, T., Ha˚ga˚rd, A., Persson, R., 2006. Aerosol extinction models based on measurements at two sites in Sweden. Applied Optics, 45, 6750–6761. Kim, Y., Boatman, J., 1990. Size calibrations for the forward scattering spectrometer probe (FSSP) for measurement of atmospheric aerosols of different refractive indices. Journal of Atmospheric and Oceanic Technology 7, 681–688. Marjama¨ki, M., Keskinen, J., Chen, D.-R., Pui, D., 2000. Performance evaluation of the electrical low-pressure impactor (ELPI). Journal of Aerosol Science 31, 249–261. Mayer, B., Kylling, A., 2005. Technical note: the libRadtran software package for radiative transfer calculations—description and examples of use. Atmospheric Chemistry and Physics 5, 1855–1877. Merenti-Va¨lima¨ki, H.-L., Lo¨nnqvist, J., Laininen, P., 2001. Present weather: comparing human observations and one type of automated sensor. Meteorological Applications 8, 491–496. Nilsson, B., 1979. Meteorological influence on aerosol extinction in the 0.2–40 mm wavelength range. Applied Optics 18, 3457–3473. Persson, R., Kaurila, T., 2002. Aerosolda¨mpningsmodell fo¨r skandinavisk miljo¨—baserad pa˚ ma¨tningar vid Lo¨vsa¨ttra i Uppland. Teknisk rapport FOI-R–0689-SE. Shettle, E.P., Fenn, R.W., 1979. Models for the Aerosols of the Lower Atmosphere and the Effects of Humidity Variations on their Optical Properties. Air Force Geophysics Laboratory. Stein, T., Gabrio, B., Oberreit, D., Hairston, P., Myrdal, P., Beck, T., 2002. An evaluation of mass-weighted size distribution measurements with the model 3320 aerodynamic particle sizer. Aerosol Science and Technology 36, 845–854. Strapp, J., Leaitch, W., Liu, P., 1992. Hydrated and dried aerosol-size-distribution measurements from the particle measuring systems FSSP-300 probe and the deiced PCASP100X probe. Journal of Atmospheric and Oceanic Technology 9, 548–555. Theriault, J.-M., Roney, P., Reid, F., 1990. Atmospheric transmission in the 2.8–5:5 mm region: description of the Fourier interferometric transmissometer and typical result at low temperatures. Applied Optics 29, 3654–3666. Tsai, C.-J., Chen, S.-C., Huang, C.-H., Chen, D.-R., 2004. A universal calibration curve for the TSI aerodynamic particle sizer. Aerosol Science and Technology 38, 467–474. Volckens, J., Peters, T., 2005. Counting and particle transmission efficiency of the aerodynamic particle sizer. Journal of Aerosol Science 36, 1400–1408. Zeisse, C., Nener, B., Dewees, R., 2000. Measurement of lowaltitude infrared propagation. Applied Optics 39, 873–886.
Atmospheric Environment 42 (2008) 2603–2610 www.elsevier.com/locate/atmosenv
Effect of aerosols on the infrared transmission in Lakiala, Finland T. Mielonena,, T. Kaurilab, A. Arolaa, H. Lihavainenc, M. Komppulac, K.E.J. Lehtinena,d a
Finnish Meteorological Institute, P.O. Box 1627, FIN-70211 Kuopio, Finland Finnish Defence Forces Technical Research Centre, P.O. Box 5, FIN-34111 Lakiala, Finland c Finnish Meteorological Institute, P.O. Box 503, FIN-00101 Helsinki, Finland d Department of Physics, University of Kuopio, P.O. Box 1627, FIN-70211 Kuopio, Finland
b
Received 17 January 2007; received in revised form 21 August 2007; accepted 6 September 2007
Abstract Effects of atmospheric aerosols on radiative transfer are mainly studied because of their role in climate change. Aerosols also affect some technological applications by deteriorating range performance of electro-optical systems. This study focuses on the aerosol-induced attenuation of infrared radiation along a horizontal path. Measured attenuation values are compared with modeled ones and an attempt is made to understand the differences. Similar measurements have been done earlier; however, those measurements have not usually contained any information about the prevailing aerosol size distributions. Both measured and modeled aerosol extinction coefficients are studied as a function of different weather parameters (visibility, relative humidity and temperature). Measured size distributions are also investigated and they are compared with the size distribution assumed in MODTRAN4. Because measurements and extinction calculations contain some error sources, e.g. instrument errors and errors in aerosol growth factors, whose magnitudes are not exactly known, the total uncertainty was difficult to assess. Despite the uncertainty in the measurements, differences between measured size distributions and model size distributions were found. It appears that weather parameters do not offer the most feasible input data to model the aerosol extinction. In addition, aerosol extinction coefficients calculated from the measured size distributions were much lower than the measured and modeled values. The continuation of aerosol attenuation measurements in the future is of vital importance to obtain enough data for the analysis. Moreover, the accuracy of the measuring equipments should be comprehensively assessed. r 2007 Elsevier Ltd. All rights reserved. PACS: 42.68.J; 42.25.B; 42.68.A Keywords: Optical properties of aerosols; Visibility; Measurement campaigns
Corresponding author. Tel.: +358 17 162 305; fax: +358 17 162 301. E-mail addresses: tero.mielonen@fmi.fi (T. Mielonen), timo.kaurila@mil.fi (T. Kaurila), antti.arola@fmi.fi (A. Arola), heikki.lihavainen@fmi.fi (H. Lihavainen), mika.komppula@fmi.fi (M. Komppula), kari.lehtinen@fmi.fi (K.E.J. Lehtinen).
1352-2310/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2007.09.038
1. Introduction Effects of atmospheric aerosols on radiative transfer are complex to model, because aerosol size distributions vary strongly both temporally and
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spatially. Moreover, the optical properties of aerosols are not fully known (Bond and Bergstrom, 2006). Patchy sources, sinks and the short atmospheric lifetime (days) of tropospheric aerosols result in local effects and regional differences in aerosol properties (Delene and Ogren, 2002). According to Anderson et al. (2003) aerosol concentrations are typically coherent for timescales and space scales less than 10 h and 200 km, respectively. Algorithms used in retrieving satellite data assume that aerosol properties are constant and therefore the actual variability in aerosol optical properties can cause uncertainties in satellite-retrieved parameters. Despite these difficulties, the modeling of the interaction between aerosols and radiation should be reliable enough that the effect of aerosols on climate change can be assessed. However, at the present moment the effect of aerosols seems to be the greatest uncertainty in climate change modeling. It partly derives from the fact that aerosols affect radiation in two distinct ways: (1) aerosols themselves scatter and absorb solar and thermal infrared radiation (direct effect) and (2) aerosols modify the microphysical and hence the radiative properties and amount of clouds (indirect effect). Direct radiative forcing appears to have a cooling effect, but with relatively large uncertainties. Moreover, indirect radiative forcing is even more difficult to estimate (IPCC, 2001). Aerosols also affect number of technological applications, for example, electro-optical sensors and satellite imaging. Finnish Defence Forces (FDF) has been using MODerate spectral resolution atmospheric TRANsmittance algorithm (MODTRAN4 v3, R1) for modeling the effect of aerosols on radiative transfer. This software uses weather parameters and aerosol models created by Shettle and Fenn (1979) as input values. FDF has also measured aerosol-induced attenuation with an OLAF-transmissometer. They have found that modeled attenuation values can differ greatly from the measured ones. Aerosol-induced attenuation of infrared radiation along a horizontal path has not been measured and studied in Finland before, while in Sweden (Persson and Kaurila, 2002; Nilsson, 1979), United States (Zeisse et al., 2000; Doss-Hammel et al., 2002) and Canada (Theriault et al., 1990) corresponding studies have been made. Persson and Kaurila (2002) compared measured and modeled values, but they did not have coexistent size distribution measurements. Our purpose is to extend this type of
approach by including aerosol size distribution measurements, the focus being on particle sizes around and above 1 mm because those particles have the biggest impact on infrared radiation. 2. Data and methodology The data discussed in this paper consists of two different measurement periods. Continuous measurements were done in Lakiala between 01/10/2004 and 31/05/2005; moreover an inter comparison campaign was carried out between 08/11/2005 and 29/11/2005. Lakiala is a rural location in southern Finland, approximately 20 km north of Tampere, which is the third largest city in Finland with a population of about 200 000. The measurement site in Lakiala is an old logging area in a mixed forest (see Fig. 1). 2.1. Continuous measurements Continuous aerosol attenuation measurements were conducted with a modernized OLAF-transmissometer which was constructed by Swedish Defence Research Agency (Ha˚ga˚rd and Persson, 1992) and aerosol size distribution measurements with an Electrical Low Pressure Impactor (ELPI) at Lakiala by FDF. The OLAF-transmissometer measures attenuation of infrared radiation in three bands: 0.96–1.08, 3.4–4.3 and 7.7–16:5 mm. It includes a transmitter–receiver unit, a reflector, control electronics and a computer. A measurement beam through the atmosphere and a reference beam inside the device are generated from a 1500 K Globar radiation source. The measurement beam
Fig. 1. The measurement site in Lakiala, Finland.
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travels 495 m to the reflector and back so that the total path length in the atmosphere is 990 m. The path distance to the ground varies between 2 and 5 m. By comparing electronically the detected power values of these two beams, the value for the attenuation in the atmosphere can be determined. Since OLAF measures the total attenuation in the atmosphere, the attenuation caused by aerosols is obtained by subtracting the part caused by atmospheric gases. Only carbon dioxide and water vapor have a significant impact on the radiative transfer at the OLAF wavelength bands. The effect of these gases is calculated with MODTRAN4. The extinction coefficient bext can be estimated from the transmittance t according to Beer–Lambert’s equation t ¼ ebext L ,
(1)
where L is the path length of the radiation in the atmosphere. The OLAF-transmissometer and its measurement accuracy have been described in detail by Kaurila et al. (2006). OLAF’s relative error has a parabolic dependence on measured extinction coefficient in all wavelength bands. For the longest wavelength band (7.7–16:5 mm) the relative error is less than 10% when the measured extinction coefficient is between 0.23 and 3:5 km1 . When the measured extinction coefficient is between 0.11 and 0:23 km1 or between 3.5 and 4:4 km1 the relative error is less than 20%. The accuracy of the OLAF-transmissometer is confined especially by Globar’s quite low radiant exitance as wavelength increases, the noise in the pyroelectric detector and the deviation of the system transmission. ELPI is a cascade impactor which measures the electric charge of the charged particles that hit the collector stages. It has 12 collecting stages and can measure particle sizes (aerodynamic radius) from 15 nm to 5 mm with less than 5 s response time (Marjama¨ki et al., 2000). The instrument used in these measurements was an Outdoor Air ELPI manufactured by Dekati Ltd., Finland. The inlet of the Outdoor Air ELPI consists of a PM10 nozzle Digitel DPM10/1.8/00, a stainless steel tube with a heater and a nafion dryer PD-200T-24SS. The inlet nozzle was about 5 m above ground and 2 m above the roof of the measurement container. Weather measurements were done with an automatic Vaisala Weather Sensor FD12P. This instrument has an optical forward-scatter sensor that senses fog and distinguishes between precipitation
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types, an analog capacitive surface sensor that senses the amount of water falling on it, a temperature sensor and it calculates present weather and visibility values using data collected from all the sensors. More extensive introduction to FD12P has been published by Merenti-Va¨lima¨ki et al. (2001). Aerosol attenuation was modeled with MODTRAN4 v3, R1. It calculates atmospheric transmittance and radiance for frequencies from 0 to 50 000 cm1 (0.2–10 000 mm) at a moderate spectral resolution, primarily 2 cm1 . The software uses aerosol data bases created by Shettle and Fenn (1979) and weather parameters (visibility, relative humidity, temperature, air pressure) as an input. Since it gives transmittance t values as an output, we have to convert them to correspond with measured extinction coefficients bext according to Eq. (1). 2.2. Measurement campaign An inter-comparison campaign was organized, in which aerosol size distributions were measured with two additional instruments: forward scattering spectrometer probe (FSSP) and aerodynamic particle sizer (APS). The total particle concentration was measured with a TSI CPC 3010 (condensation particle counter) which detects all particles larger than 10 nm. The APS is a laser-based particle sizing instrument which measures aerosol size distributions from 0.5 to 34 mm (aerodynamic radius) by determining the transit time of a particle between two laser beams downstream of a flow accelerating nozzle. Particles with a larger inertia will accelerate slower than the particles with a smaller inertia. Hence, particles can be divided into different size bins (APS has 60 size bins) based on their time-of-flight. The inlet to the APS and CPC was a stainless steel tube about the same height as the inlet of the ELPI. The sample was dried by heating the sample air from outside temperature to temperature of about 30 C prior to measurement. This ensured that the measured sample air had relative humidity below 40%. Interested reader finds more information about APS in Stein et al. (2002), Tsai et al. (2004) and Volckens and Peters (2005). The FSSP is an optical instrument that detects the scattered light from individual particles which pass through a thin laser beam. The intensity of the scattered light is related to the particle size but it also depends on particle shape and complex
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refractive index. FSSP measures optical radius from 0.25 to 4 mm and it has 30 size bins. The FSSP was installed on the ground so that its inlet without any filtering was about 1 m above the ground level. Brenguier (1989), Coelho et al. (2005), Kim and Boatman (1990) and Strapp et al. (1992) have published more detailed introductions to FSSP. 2.3. Data analysis Both measured and MODTRAN-based aerosol extinction coefficients were studied as a function of different weather parameters (visibility, relative humidity and temperature). Moreover, measured aerosol size distributions were compared with the default size distributions used in the MODTRAN4. The comparisons discussed in this paper were calculated with rural size distributions. In Shettle and Fenn (1979) it is assumed that rural aerosols are composed of a mixture of 70% of water soluble substance (ammonium and calcium sulfate and also organic compounds) and 30% dust-like aerosols. Aerosol extinction coefficients were also calculated using the data from the different size distribution measurements by using Mie-scattering theory and the results were compared with attenuation values measured by OLAF. Mie-scattering calculations were done with LibRadtran (library for radiative transfer calculations) software (Mayer and Kylling, 2005) which includes a Mie-scattering program by Bohren and Huffman (1983). In these calculations we assumed that the measured aerosols had the refractive indices of the rural model. Since all the instruments have different measurement methods, direct comparison of measured size distributions was not possible. We transformed all measured sizes, except optical size measured by FSSP, to Stokes sizes and grew the particles according to ambient relative humidity and temperature in the atmosphere by using the approach described in the work of Nilsson (1979). Optical size was assumed to be equal to the Stokes size, although this is not exactly true. However, arguably the error due to this simplification is small enough not to hinder our analysis of the FSSP measurements. The relationship between visibility, or meteorological range, and aerosol extinction coefficient is given by the Koschmieder equation V¼
lnð0:02Þ , bextð0:55Þ þ 0:01159
(2)
where V is visibility (km), bextð0:55Þ is the aerosol extinction coefficient at the wavelength 0:55 mm ðkm1 Þ and 0.01159 ðkm1 Þ is the surface Rayleigh scattering coefficient at 0:55 mm. Eq. (2) gives 2% contrast resolution visibility. Usually extinction due to Rayleigh scattering is very small compared to aerosol extinction at the near-infrared and infrared wavelengths. FD12P visibility measurements are based on 5% contrast resolution, thus we converted them to the 2% contrast resolution. Our analysis of the OLAF measurements concentrated on the 1 mm band because the measured extinction values at the other two bands fall very often below OLAF’s accuracy limit. 3. Results 3.1. Results from continuous measurements The continuous measurements were averaged for every 10 minutes. Only data of clear weather (Vaisala FD12P weather code was zero) were included in our data analysis, resulting in approximately 7000 measurements. First, we compared the measured and modeled extinction coefficients. Fig. 2 shows such a comparison for the 1 mm band. The correlation is clearly very poor; correlation coefficient is only 0.28. Occasionally OLAF measured unrealistically high values compared to measured visibility. This is very likely due to the fact that the FD12P measures visibility at one point, whereas the OLAF measures extinction across a 495 m path. For clarity, Fig. 2 does not show these highest values which are between 1.2 and 3:14 km1 . Band 1 - RURAL Modeled extinction coefficient [1/km]
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0.25
corr.coeff= 0.28
0.2 0.15 0.1 0.05 0 0
0.2
0.4
0.6
0.8
1
1.2
Measured extinction coefficient [1/km] Fig. 2. The correlation between measured and modeled extinction coefficients ðkm1 Þ in the 1 mm band. The 1:1 line denotes perfect agreement.
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When the measured values that are high ð0:5 km1 Þ or below OLAF’s measurement accuracy ð0:05 km1 Þ are excluded from the data, the number of measurements decreases to 2700 and the correlation coefficient increases to 0.34. Moreover, there was no clear relationship between the measured attenuation values and the weather parameters (relative humidity, visibility and temperature). As an example, in Figs. 3 and 4 the difference between measured and modeled aerosol extinction coefficients (in percentage) in the 1 mm band is plotted as a function of relative humidity and visibility, respectively. When relative humidity is near 50% the mean difference is between 70% and 60%. As relative humidity increases, the mean difference decreases quasi-linearly reaching 10% in relative humidities over 90%. In the visibilities below 20 km the modeled values are too high, while the situation turns opposite when the visibility increases. The mean difference increases quasi-linearly from 10% to 70% as the visibility increases from 10 to 65 km. These results suggest that the weather parameters do not make the most suitable input data for modeling the aerosol extinction. MODTRAN’s sensitivity for the input values was studied to find out the influence of the uncertainty in weather parameter measurements in the calculation of aerosol extinction. According to manufacturers the
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error in relative humidity measurements is approximately 2% units when relative humidity is below 90% and approximately 3% units when relative humidity is over 90%. In the visibility measurements the error is approximately 20% when the visibility is better than 10 km. Error of 2% in relative humidity measurements can cause 8% error in the calculated extinction coefficients when ambient relative humidity is near 85%. If the ambient relative humidity is 96% and the error of relative humidity measurements is 3%, the error in the calculated extinction coefficients increases to 44%. Twenty percent error in visibility measurements can cause an error of 25–33% in the calculated extinction coefficients. Thus, the errors in weather parameter measurements can cause a cumulative error up to 130% in the calculations in the worst case. This may explain partly the mean differences presented in Figs. 3 and 4, indicating that the errors in weather parameters can cause errors in calculated aerosol extinction. However, the measured extinction coefficients also have a considerable natural random variability. 3.2. Results from the measurement campaign In the inter-comparison campaign, measurements were also averaged for every 10 min. ELPI was the
Band 1 - RURAL 100
50
Difference [%]
0
-50
-100
-150
mean= 63.3 std = 2.5 c.c = N n=7
mean = 70.1 std = 15.5 c.c = 0.4 n = 26
mean = 62.5 std = 14.6 c.c = 0.5 n = 85
mean = 52.7 std = 29.8 c.c = 0.4 n = 111
mean = 37.7 std = 27.3 c.c = 0.6 n = 172
mean = 20.5 std = 40.1 c.c = 0.4 n = 813
mean = 9.9 std = 38.5 c.c = 0.4 n = 1455
-200 30
40
50
60
70
80
90
100
Relative humidity [%] Fig. 3. The difference (%) between measured and modeled aerosol extinction coefficients ðkm1 Þ as a function of relative humidity in the 1 mm band. The figure has been divided into seven sections and values for mean difference (mean), standard deviation of the difference (std), correlation coefficient between measured and modeled values (c.c) and number of the measurements (n) have been presented for each section. Rural aerosol model was used in the modeling.
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Band 1 - RURAL 100
50
Difference [%]
0
-50
-100
-150
mean = -9.8 std = 37.6 c.c = 0.2 n =1017
mean = 15.4 std = 28.5 c.c = 0.1 n = 697
mean = 36.4 std = 16.9 c.c = -0.0 n = 350
mean = 51.4 std = 11.9 c.c = 0.0 n = 173
mean = 61.7 std = 8.2 c.c = -0.0 n = 160
mean = 68.7 std = 7.6 c.c = -0.2 n = 272
-200 10
20
30
40
50
60
70
Visibility [km] Fig. 4. The difference (%) between measured and modeled aerosol extinction coefficients ðkm1 Þ as a function of visibility in the 1 mm band. The figure has been divided into six sections and values for mean difference (mean), standard deviation of the difference (std), correlation coefficient between measured and modeled values (c.c) and number of the measurements (n) have been presented for each section. Rural aerosol model was used in the modeling.
1e+06 CPC Shettle Shettle fssp elpi aps
10000 100 n (r) [1/cm3/µm]
only instrument which did not have gaps in measurements due to instrument problems or maintenance. Data sets from ELPI, FSSP, OLAF and APS contained 3077, 2462, 1603 and 1274 measurements, respectively. The rural size distribution of Shettle and Fenn (1979) was compared to the measured size distributions and to a modified model distribution, the number density of which was averaged from the CPC measurements. An example of this kind of comparison is shown in Fig. 5. Since APS and FSSP do not measure small particles (dry aerodynamic radius below 0:5 mm) reliably, we combined small particles from the ELPI data with the APS and FSSP data. Thus, the seven smallest size bins in all size distributions are from ELPI measurements, although in the following, the names of the size distributions are those of the instrument used for the measurement of larger particles. The model size distribution was calculated for rural aerosol model with 13 km visibility and 95% relative humidity. The mean ELPI, FSSP and APS size distributions were averages from 170, 156 and 83 measurements, respectively. In these measurements the relative humidity varied between 91% and 98% and the visibility between 11 and 15 km. The ELPI, FSSP and APS measurements were from 17, 16 and 12
1 0.01 1e-04 1e-06 1e-08 1e-10 0.01
0.1
1
10
100
r [µm]
Fig. 5. Corresponding size distributions ðcm3 mm1 Þ from ELPI, APS and FSSP measurements and the model created by Shettle and Fenn (1979) for 95% relative humidity and 13 km visibility.
different days, respectively. It is apparent that the measured size distributions are much smaller than the one used in MODTRAN. ELPI and APS measurements are very similar, while FSSP has typically measured less particles in the size range of 0.9–1:3 mm and more particles in the size range of 2–4 mm. APS and ELPI measurements differ when particle radii are in the size range of 1–4 mm and larger than 6 mm. The CPC had measured 2200
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particles per cm3 in average for these weather conditions, while the model distribution assumed 13 289 particles per cm3 ; hence the modified model distribution is in better agreement with the measured size distributions. The aerosol extinction coefficients were calculated from these size distributions using LibRadtran software. The same refractive indices of rural aerosols of Shettle and Fenn (1979) were used for all the size distributions. In Fig. 6, in addition to the calculated extinction spectra, extinction coefficients measured with OLAF and modeled with MODTRAN4 are shown. Values from OLAF and MODTRAN are average values from 99 measurements at similar weather conditions. The point in the graph, labeled ‘FD12P’, shows the extinction coefficient for 0:55 mm as a mean from the visibility measurements. Evidently, the extinction coefficients calculated from the measured size distributions are much smaller than the measured ones. MODTRAN-based aerosol extinction values compare better with the OLAF measurements than the values calculated from measured size distributions. It also appears that large particles (radius 44 mm) do not affect extinction coefficient spectrum as much as the particles with a radii near 1 mm. This can be seen by comparing Figs. 5 and 6. APS has measured greater amount of large particles than ELPI and still their extinction coefficient spectra are very similar, particularly in the wavelength region below 3 mm. On the other hand, extinction coefficient spectra based on FSSP measurements differ greatly from both. A small drop in the FSSP size distribution
Extinction coefficient [1/km]
1
RH= 91.80 - 98.00 T= -3.20 - 8.40
Vis= 11.97 - 14.92
0.1
OLAF MODTRAN FD12P fssp elpi aps CPC Shettle
1
10
near 1 mm radius causes extinction in the wavelengths below 1 mm to decrease, while the large number of particles in the size range of 2–4 mm increases extinction in the wavelengths over 2 mm. Even though APS has measured much larger particles than FSSP (radius 44 mm), they do not influence extinction as much as the smaller particles. Extinction coefficient spectrum calculated from the model size distribution modified with CPC number concentration is in the same size range as the spectra calculated from the ELPI, APS and FSSP data. Overall, it was difficult to make definite conclusions about the different measurements and their relations, in particular, because every instrument had significant measurement variability even during apparently stable atmospheric conditions. The variability was largest with the OLAF measurements. One likely reason for this is that there is inherent spatial averaging in OLAF measurements, while all the other measurements are point measurements. For example, in the measurements presented in Fig. 6, the coefficient of variation (standard deviation/mean) for ELPI ranges from 0.49 to 0.68 in the studied wavelength region (0.2–40 mm), while for APS and FSSP it was between 0.49–0.67 and 1.25–5.95, respectively. Coefficients of variation for the OLAF 1, 4 and 10 mm measurement bands were 0.66, 1.35 and 1.87, respectively. For comparison, aerosol attenuation values based on MODTRAN calculations, for corresponding wavelength bands, had coefficients of variation of 0.07, 0.03 and 0.02, thus in the 1 mm band OLAF measurements had almost an order of magnitude larger deviation if compared to the modeled values. 4. Conclusions
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0.001 0.1
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Wavelength [µm] Fig. 6. Aerosol extinction coefficients ðkm1 Þ from OLAF, ELPI, APS, FSSP, FD12P, MODTRAN and the model created by Shettle and Fenn (1979) for 95% relative humidity and 13 km visibility.
Aerosol-induced attenuation of infrared radiation was studied along a horizontal path. Continuous measurements from the year 2004 were used to compare extinction coefficients measured with an OLAF-transmissometer to values modeled with MODTRAN4. The most important results from this comparison are as follows: (1) OLAF measurements and modeled extinction coefficients do not always match very well; (2) weather parameters do not offer the most feasible input data to model the aerosol extinction and (3) uncertainties in weather measurements can cause cumulative errors in calculated aerosol extinction. We organized an inter-comparison campaign in 2005 to study aerosol size distributions in more
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detail. In addition to the continuous measurements performed with ELPI, we measured aerosol size distributions with APS and FSSP. The measured size distributions were then used as an input in the calculation of aerosol extinction coefficients. Data analysis showed that: (1) size distributions measured with ELPI and APS are very similar while FSSP measurements differ considerably from both; (2) aerosol extinction coefficients calculated from the measured size distributions are much lower than the values measured with OLAF or modeled with MODTRAN4; (3) standard deviations of the measurements are significant in certain weather conditions; (4) the model size distribution modified with the CPC number concentration is closer to the measured size distributions than the original model and their extinction coefficient spectra agree much better. Thus, further inter-comparisons and data quality assurance are required if aerosol size distribution measurements are used for transmission calculations based on Mie theory. Chemical properties of the aerosols should also be measured in order to have measurement-based estimates for the refractive indices. Acknowledgments The authors would like to thank Scientific Advisory Board for Defence (MATINE) for supporting this research. References Anderson, T.L., Charlson, R.J., Winker, D.M., Ogren, J.A., Holme´n, K., 2003. Mesoscale variations of tropospheric aerosols. Journal of the Atmospheric Sciences 60, 119–136. Bohren, C.T., Huffman, D.R., 1983. Absorption and Scattering of Light by Small Particles. Wiley, New York. Bond, T., Bergstrom, R., 2006. Light absorption by carbonaceous particles: an investigative review. Aerosol Science and Technology 40, 27–67. Brenguier, J., 1989. Coincidence and dead-time corrections for particle counters. Part II: high concentration measurements with an FSSP. Journal of Atmospheric and Oceanic Technology 6, 585–598. Coelho, A., Brenguier, J.-L., Perrin, T., 2005. Droplet spectra measurements with the FSSP-100. Part I: low droplet concentration measurements. Journal of Atmospheric and Oceanic Technology 22, 1748–1755. Delene, D.J., Ogren, J.A., 2002. Variability of aerosol optical properties at four North American surface monitoring sites. Journal of the Atmospheric Sciences 59, 1135–1150.
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