BOUNDARY LAYER AEROSOL BACKSCATTERING

Transcription

1 BOUNDAY LAYE AEOSOL BACKSCATTEING AND ITS ELATIONSHIP TO ELATIVE HUMIDITY FOM A COMBINED AMAN-ELASTIC BACKSCATTE LIDA obert Tardif Program in Atmospheric and Oceanic Sciences University of Colorado at Boulder Class project for ATOC 5235 emote Sensing of the Atmosphere and Oceans April 2002

2 ABSTACT The role of aerosols on climate change, by their direct and indirect effects on the Earth s radiative budget, remains an elusive parameter in our quantitative assessment of the factors controlling our climate. One of the important factors is the change of aerosol optical properties in environments with variations in relative humidity (hygroscopic factor). As aerosols are subjected to variations in relative humidity, their size may increase, as well as changes to their refractive index. An increase in aerosol backscattering is then observed. Consequently, more radiative energy may be reflected back to space, creating an overall cooling effect. The possibility of determining this hygroscopic factor by using remote sensing methods is explored here. Data from the Atmospheric adiation Measurement, Southern Great Plains (AM-SGP) Clouds and adiation Testbed (CAT) aman lidar are used to assess the dependence of aerosol optical properties (backscatter) on relative humidity. High-resolution aman lidar profiles of aerosol backscatter, as well as profiles of relative humidity, taken under a boundary layer cloud deck are used. This scenario is particularly useful as constraints on the relative humidity profile can be applied to obtain a better estimate of the hygroscopic factor. esults have shown that the daytime relative humidity product of the aman lidar is not reliable enough to obtain a clear relationship between aerosol backscattering and relative humidity. Humidity profiles derived from surface-based measurements have been used in another analysis of the hygroscopic factor, yielding a relationship that is compared to other studies. 2

3 1. Introduction Sophisticated lidar (Light Detection and anging) remote sensing systems are now used to study many different aspects of the atmosphere and its components. For instance, lidars are used to study the properties of high-altitude cirrus clouds over equatorial regions (Omar, 2001), high-latitude polar stratospheric clouds (Santacesaria et al., 2001), stratospheric ozone (Douglass et al., 2001), along with stratospheric aerosols (Zuev et al., 1998) and tropospheric aerosols (Barnaba and Gobbi, 2001). A lidar is an active remote sensing instrument, meaning that it transmits electromagnetic radiation and measures the radiation that is scattered back (backscatter) to a receiver after interacting with various constituents of the atmosphere. Lidars use radiation in the ultraviolet, visible or infrared region of the electromagnetic spectrum. Different types of physical processes in the atmosphere are related to different types of light scattering. Choosing different types of scattering processes allows atmospheric composition, temperature and wind to be measured. Active remote sensing is an important tool to study atmospheric processes as it offers many advantages over passive remote sensing systems. One of the main advantages of active systems is the high vertical resolution that can be achieved. For lidar, the small divergence of the laser beam defines lidar volumes of typically only a few cubic meters at ranges of tens of kilometers (Stephens, 1994, p. 427). This feature makes lidar remote sensing systems very attractive for studying the atmospheric boundary layer (ABL), where high-resolution is necessary to capture variations in parameters of interest. As such, lidars have been used to study boundary layer aerosols (Devara and aj, 1991; Murayama et al., 1999) and boundary layer structure (aj and Devara, 1993; Menut et al., 1999). Other recent lidar applications for the ABL include the measurement of turbulent boundary layer flows (Mayor and Eloranta, 2001; Buttler et al., 2001) and instabilities in the stably stratified ABL (Newsom and Banta, 2002), as well as ABL water vapor distribution (Wulfmeyer, 1999; Eichinger et al.,2000). 3

4 The remote sensing of atmospheric properties from lidar all rely on the backscattering of light from particles (cloud hydrometeors, aerosols or gas molecules). The amount and characteristics of backscattered radiation depends on a number of factors. First and foremost, this backscattering depends on the refractive index of the particles. It also depends on the size distribution of the particles or scatterers. As the relative humidity of the environment increases, condensation of water vapor may take place on the aerosol particles depending on their chemical composition (Tang and Munkelwitz, 1993). This phenomenon leads to an increase of the size of the particles (hygroscopic growth) as well as to changes of the particle mean refractive index (Hänel, 1976, p.97). Consequently, significant variations in the lidar backscatter signal is expected when changes in relative humidity (H) is observed. This is particularly true for high H for which the hygroscopic growth of aerosols is more pronounced. This phenomenon was in fact identified quite a number of years ago (MacKinnon, 1969). An accurate description of this effect becomes important when using lidar to remotely sense properties in the boundary layer, where H experiences a significant diurnal cycle. For example, rapid changes in the boundary layer H during the evening transition will lead to significant changes in the observed lidar backscattering on a short time scale. Without an appropriate knowledge of the hygroscopic growth of the aerosols present in the lidar volume, it is impossible to differentiate if the increase in the measured backscattering is due to this effect or due to changes in aerosol concentration, which may be the result of an increase in static stability (less turbulent mixing) in the lower atmosphere. This fact can lead to ambiguous interpretation of lidar backscatter data in certain situations. Apart from the accurate interpretation of lidar backscatter, the determination of the backscattering coefficient of aerosols in various environmental conditions has a strong implication for the study of climate change. Indeed, the direct effect of aerosols on climate change is determined by the radiative forcing associated with the amount of solar energy absorbed and scattered back to space by these aerosols. An accurate quantitative estimation of the hygroscopic growth effect on the direct radiative effect of aerosols still eludes the research community. Experiments have been performed in the laboratory with 4

5 aerosols of known chemical composition (Tang and Munkelwitz, 1994; Tang, 1996). Also, in-situ experiments in the natural environment have been performed using nephelometers (Im et al., 2001). emote sensing using lidar is a powerful tool that can be used to gain more insights on the impact of the hygroscopic nature of aerosols on their optical properties in various natural environments. Lidar retrievals of aerosol backscattering coefficient over a wide range of relative humidities can help in acquiring a more global assessment of the aerosol impact on climate change. Here we report on the analysis of aerosol backscattering lidar measurements from a aman lidar, as a function of relative humidity, within the ABL at the Atmospheric adiation Measurement, Southern Great Plains (AM-SGP) site in Lamont Oklahoma. 2. Lidar remote sensing a. General description The basis for lidar remote sensing lies in the interaction of light with gas molecules and particulate matter in suspension in the atmosphere (aerosols). More particularly, a lidar uses a laser (emitter) to send a pulse of light into the atmosphere and a telescope (receiver) to measure the intensity scattered back (backscattered) to the lidar. By measuring the scattering and attenuation experienced by the incident pulse of light, one can investigate the properties of the scatterers (concentration of gaseous species, aerosol distribution and optical properties, cloud height) located in the atmosphere. The light scattered back to the detector comes from various distances, or ranges, with respect to the lidar. Because the light takes longer to return to the receiver from targets located farther away, the time delay of the return is converted into a distance (range) between the scatterers and the lidar, since the speed of light is a well-known quantity. By pointing the laser beam in various directions and at various angles with respect to the ground surface (scanning), a ground-based lidar system can gather information about the threedimensional distribution of aerosols in the atmosphere. 5

6 The backscattered radiation detected by a lidar is described by the lidar equation. In general terms, the received power is expressed as a function of range. For a simple backscatter lidar (measuring backscattered light at the same wavelength as the laser wavelength), the lidar equation is written as: (, ) C h β λ L Pr ( λl ) = O 2 ( ) exp 2 ke ( λl, r ') dr ' 2 4π, (1) 0 where P r is the power returned to the lidar at the laser wavelength ( λ L ), C is the lidar constant, is the range, h=c t p, where t p is the pulse duration and c the speed of light. The term O() describes the overlap between the laser beam and the receiver field of view. The term is equal to 1 for ranges where there is complete overlap of the laser beam and the receiver s field of view. Here, β ( λ, ) and k ( ) L e λ L, are the combined aerosol and molecular backscatter and extinction coefficients respectively, at the laser wavelength. The combined backscattering coefficient can be re-written as the sum of molecular and aerosol backscattering ( (, ) a m β λ β ( λ, ) β ( λ, ) = + ). For an elastic backscatter L L L (one wavelength) lidar, this combined backscattering can be obtained by solving the lidar equation following the method suggested by Fernald (1984). With a aman lidar, more information is available. Independent retrievals of aerosol backscatter and extinction can be obtained (Ansmann et al., 1990; Ansmann et al., 1992). A aman lidar is able to detect specific gaseous species (O 2, N 2 or H 2 O) by measuring the wavelength-shifted radiation returned to the lidar due to inelastic scattering by the gas molecules. The lidar equation describing the return at the aman-shifted wavelength ( λ ) is written as: 6

7 ( λ, ) C h β aman Pr ( λ ) = O 2 ( ) exp ke ( λl, r ') dr ' + ke ( λ, r ') dr ' 2 4π 0 0. (2) The first term in the exponential term describes the extinction of the laser beam (at laser wavelength) going up toward the target while the second term describes the extinction of the return signal back toward the lidar (at aman-shifted wavelength). The inelastic aman backscattering coefficient ( (, ) β λ ) is only associated to the inelastic aman molecular scattering and is not affected by aerosol scattering. eturns at the laser wavelength and at the aman-shifted wavelengths can be combined in various ways to obtain information about the aerosols and the water vapor content of the atmosphere. More details about the products available from the CAT aman lidar, as they pertain to this study, and how parameters are derived are provided hereafter. b. The CAT aman lidar A aman lidar designed for 24-hour automated operations has been making measurements at the Atmospheric adiation Measurement (AM) Southern Great Plains (SGP) Clouds and adiation Testbed (CAT) near Lamont Oklahoma for a few years now. It is a vertically pointing (non-scanning) lidar so it provides vertical profiles of various aerosol optical properties over the site, as well as profiles of water vapor mixing ratio (Turner et al., 2002). The CAT aman lidar uses a frequency tripled Nd:YAG (neodymium:yttrium/aluminium/garnet) laser transmitting 350 mj pulses of 355 nm light at 30Hz. The backscattered light is collected with a 61-cm telescope. The system measures backscattered light at the laser wavelength (355 nm), as well as at 387 and 408 nm wavelengths. These correspond to the aman-shifted nitrogen (N 2 ) and water vapor (H 2 O) wavelengths respectively. 7

8 Products available from the CAT lidar are listed in Table I. Various algorithms are used to obtain these products from the return signals measured at the three wavelengths. A typical vertical resolution of the products is of the order of 40m. Table I. Automated data products from the CAT aman lidar. Aerosol Water vapor Scattering ratio Backscatter coefficient Extinction coefficient Extinction-to-backscatter ratio Optical thickness Mixing ratio elative humidity Precipitable water The inversion algorithms are based on Ansmann et al. (1992). A brief overview of how some of these products are derived is given here. More detailed information is provided in the Appendix. The aerosol scattering ratio (AS) is defined as the ratio of the total (aerosol + molecular) backscattering to the molecular backscattering: m (, z) + (, z) m β ( λ, z) a β λ β λ AS =, (3) where a m β is the aerosol backscattering coefficient and β is the molecular (ayleigh) backscattering coefficient. The elastic return at the laser wavelength depends on both the ayleigh and Mie (molecular and aerosol) scattering, while the aman-shifted return is 8

9 only a function of molecular scattering. Therefore, the ratio of these return signals is proportional to the AS. Profiles of the AS are thus derived from the ratio of the signal detected at the laser wavelength to the signal of the N 2 aman channel (Ferrare et al., 2001). Corrections are applied to account for the difference between the atmospheric transmission of the return signal at the laser wavelength and the return signal at the aman N 2 wavelength (Ferrare et al.,1998, Ferrare et al. 2001). Another correction is applied to take into account that the laser beam is not fully within the detector field of view until a height of about 800m (Ferrare et al., 1998). m The molecular backscattering ( β ) can be estimated using air density profiles obtained from radiosondes or from a co-located ground-based Atmospheric Emitted adiance Interferometer (AEI). Then, the AS profile is used in conjunction with the molecular (ayleigh) backscattering profile to obtain the corresponding aerosol a backscattering ( β ) profile. aman lidars have the distinct advantage of providing profiles of water vapor in the same atmospheric volume as the aerosol measurements. The mass of water vapor to the mass of dry air (mixing ratio) is proportional to the ratio of the H 2 O aman signal and the N 2 aman signal (Turner et al., 2002). The same type of corrections for the wavelength dependence of transmission and the overlap function below 800m are applied here as in the case for aerosol retrieval. The relative humidity is then derived by combining the water vapor mixing ratio and temperature profiles measured either from radiosondes or the Atmospheric Emitted adiance Interferometer (AEI). The capabilities of the CAT aman lidar makes it an interesting tool to study the effect of hygroscopic aerosols on lidar backscatter. Data from the aman lidar are used here to assess the relationship between hygroscopic aerosol backscattering coefficient and relative humidity for continental aerosols over the AM-SGP CAT site. 9

10 3. Hygroscopic aerosols The swelling of hygroscopic particles with an increase in relative humidity generates haze particles that are significantly larger than their dry particle sizes. Some aerosols have a deliquescence relative humidity, over which they start picking up water spontaneously, while others like H 2 SO 4 are hygroscopic, meaning that their size increases in a continuous manner over a broad range of relative humidity values (Seinfeld and Pandis, 1998, p. 507). Figure 1 shows the increase in size, normalized to its dry size, of two aerosols of different chemical compositions as a function of relative humidity. It is observed that the rate of size increase with increasing relative humidity of various aerosols may be very different for a given range of relative humidity values. In the example shown, size may increase by a factor larger than 2 when the relative humidity is increased toward saturation. Hänel (1976) proposed simple models for the growth of hygroscopic aerosols as a function of relative humidity based on theoretical considerations and observational evidence. He has observed that the increase in particle size, as described by the ratio of the size of the particle over its dry size ( r r ), is a complicated function of relative o humidity (H), but can also be a function of the particle dry size ( r o ) as shown in Figure 2. From his experiments, Hänel has concluded that below a certain relative humidity the ratio of the particle size over its dry size ( r r ) is independent of the dry size of the o particles. Also, for moderate and large relative humidities, the ratio those particles with smaller sizes in their dry state. r r o is smaller for 10

11 Fig. 1. The growth of aerosols of various chemical compositions as a function of relative humidity (adapted from Seinfeld and Pandis, 1998). Fig. 2. Growth of urban aerosols of various dry sizes as a function of relative humidity. Based on measurements taken at Mainz, Germany in 1970 (adapted from Hänel (1976)). 11

12 Hänel (1976) proposed a simple model describing aerosol growth as a function of relative humidity. This model is expressed as: r r o ρ o 1+ µ ρw ( H ) 1 3 H, (4) 1 H where H is the relative humidity, and ρ o and ρ w are the densities of the dry particle and of pure water respectively. µ ( H ) is defined as the linear mass increase coefficient of the particle which depends on the aerosol composition. More sophisticated approaches exist (Feingold and Grund, 1994), but are not used here, since detailed modeling of aerosols is beyond the scope of the present paper. The relationship described by equation (3) is only used as an illustration of the types of relationships that can be used to characterize the aerosol hygroscopic growth. elatively good fits of observed data have been obtained with this relationship (Hänel, 1976). Apart from the change in size, hygroscopic aerosols experience a change in their refractive index as H increases. Generally, as the water uptake by the particles gets more and more important, the real and imaginary parts of their refractive index tend to decrease (Figure 3), as the real part of the refractive index of pure water is lower than the one associated to dry particles and its imaginary part is zero. This would suggest a decrease in aerosol backscattering and absorption as H increases. But as the scattering is a complex function of both refractive index and particle size, both effects need to be taken into account. In fact, variations in refractive index as H increases are not large enough to counteract the variation of the particles s cross-section due to size increase (r 2 dependence). So the size dependence dominates, leading to an increase in backscattering as H increases. 12

13 Fig. 3. Variations of the refractive index of urban aerosols as a function of relative humidity. Based on measurements taken at Mainz, Germany in 1970 (adapted from Hänel (1976)). 4. Lidar backscattering data analysis The effect of relative humidity on aerosol backscattering is studied by using aerosol backscattering and relative humidity data from a aman lidar, under a boundary layer cloud deck. In such a scenario, the sub-cloud layer is expected to be well-mixed due to the influence of shear-induced mixing but also due to top-down mixing related to the destabilization of the boundary layer associated to the cloud top radiative cooling. Thus, the relative humidity profile is expected to increase smoothly from its surface value up to 100% at cloud base. Also, aerosol concentration is more likely to be uniform with height in a neutrally stratified (well-mixed) boundary layer, in the absence of significant sources 13

14 and sinks. Thus, by measuring the aerosol backscatter as a function of height up until cloud base, the influence of the hygroscopic factor on aerosol optical properties can be isolated. Data from the Atmospheric adiation Measurement (AM) Southern Great Plains (SGP) Clouds and adiation Testbed (CAT) site for April 3 rd 1998 have been chosen. 4.1 Meteorological conditions On this day, a stratocumulus cloud layer formed a little after sunrise and persisted for the rest of the day. adiosondes launched at 2034 GMT and 2334 GMT show a cloud layer between 900 hpa up to 850 hpa approximately, with a well-mixed sub-cloud layer (Fig. 4). A ceilometer at the site detected a low cloud layer appearing at about 1130 UTC (Fig. 5). The initial cloud base height was about 500 m and increased to about 1000 m in six hours. After that period, cloud base height remained at its highest value. Temporal evolution of temperature, as measured at two different heights on an instrumented tower, shows a significant cooling over a period of 11 hours (Fig. 6). This cooling can be related to important radiative cooling of the surface but the significant increase in surface pressure after 1100 UTC (Fig. 7) suggests that the low cloud layer formation was triggered by low level shallow convection often observed in post-cold frontal air masses. 14

15 Fig. 4. Profiles of temperature and humidity at AM/SGP site from radiosondes launched at (a) 2034 UTC and (b) 2334 UTC on April 3 rd

16 Fig. 5. Observations of cloud base height from the AM/SGP ceilometer for April 3 rd Fig. 6. Observations of temperature from the AM/SGP instrumented tower for April 3 rd

17 Fig. 7. Observed temporal evolution of surface pressure at the AM/SGP site on April 3 rd Lidar measurements In this section, the 10-minute averaged data used in this study from the CAT aman lidar are described. Since we are interested in aerosol backscatter from the surface up until cloud base, the detection of the cloud by the lidar is very important. Figure 8 shows a comparison of the cloud base height as detected by the aman lidar and the ceilometer. The cloud detection algorithm for the aman lidar is based on the derived values of the aerosol scattering ratio (AS) defined previously. For heights below 5 km, clouds are identified if the AS is larger than 5.0. The comparison shows that the cloud base as seen by the lidar tracks well with the ceilometer results. Nevertheless, a difference of about 100m between the two sensors is observed, with the lidar results being lower. To make sure we are not including lidar backscattering data in volumes with 17

18 cloud drops, we are using the range just below lidar cloud base as the highest height for which lidar data are considered in our analysis. To gain an appreciation of the accuracy of the water vapor mixing ratio values retrieved from the aman lidar, water vapor measurements taken from a sensor located at a height of 60 m on an instrumented tower are compared to lidar estimates for the lowest possible range (60 m) (Figure 9). A relatively good agreement of the lidar retrievals with the in-situ measurements is observed, particularly during the nighttime hours during which very low values of water vapor mixing ratios were observed. Significant surface evaporation after sunrise most probably is the reason for the large increase in water vapor content within the lower atmosphere between 1000 UTC and 1200 UTC. Later in the day, the aman lidar humidity retrievals show significant temporal variability, which is not corroborated by the in-situ measurements. Also, the lidar values seem to overestimate the level of humidity by about 1 g/kg on average. This significant noise and bias in the lidar humidity can significantly affect our analysis of the aerosol hygroscopic factor. Fig. 8. Comparison of cloud base heights observed by the AM/SGP aman lidar and the ceilometer for April 3 rd

19 Fig. 9. Comparison of water vapor mixing ratios derived with the AM/SGP aman lidar and in situ measurements on the instrumented tower, for April 3 rd Figure 10 shows the temporal evolution of relative humidity (H) vertical structure retrieved from the lidar. Very low values are observed below 2 km during the night, with higher levels of humidity between 3 and 6 km heights. A significant increase in H in the early morning hours is observed in the boundary layer. Lidar retrievals of H within and above the cloud layer are not possible so values corresponding to these levels have been eliminated by the use of a mask. Focusing on the daytime humidity structure in the lowest atmosphere (Figure 11), it is observed that H generally increases with height in the sub-cloud layer as expected, with values between 60% and 70% near the surface increasing to values above 85% in the upper portion of the sub-cloud layer. The vertical structure of backscatter is shown in Figure 12. Large backscatter values correspond to times and heights where clouds were present. The development of a cloud-topped boundary layer during the daylight hours is clearly observed. A zoom on 19

20 the boundary layer (Figure 13) shows a distinct increase in aerosol backscatter below cloud base, as seen in the m layer from 1600 to 2000 UTC, and in the m from 2000 UTC onward. For these periods, we know from lidar cloud base data that this increase in backscattering must be related to aerosol swelling since cloud base is located higher up. Fig. 10. Time-height cross-section of relative humidity from the AM/SGP aman lidar on April 3 rd

21 Fig. 11. Time-height cross-section of daytime boundary layer relative humidity from the AM/SGP aman lidar on April 3 rd

22 Fig. 12. Time-height cross-section of aerosol backscatter from the AM/SGP aman lidar on April 3 rd

23 Fig. 13. Time-height cross-section of aerosol backscatter from the AM/SGP aman lidar within the daytime boundary layer on April 3 rd

24 Since profiles from radiosondes are available, a comparison with aman lidar data is possible. Figure 14 shows a comparison between lidar derived H and two available soundings taken during late afternoon and early evening. aman derived profiles of H corresponding to the 2034 UTC sounding shows a general agreement with the increase of H with height. The lidar profiles shown correspond to times 10-minute apart. Thus it can be concluded that lidar 10-minute averaged retrievals of H are characterized by an important temporal variability that is most likely unreal. This problem seem to be much less important in lidar profiles taken later in the day. A comparison with the 2334 UTC sounding shows a much better agreement. The rate at which H increases between 200 m and 700 m is very well represented by the lidar estimates. Nevertheless, there is a 5% positive bias in the lidar profiles compared to the sounding. But it should be mentioned that this is within the range of sounding accuracy. Hourly profiles of water vapor mixing ratio derived from the aman lidar are shown in Figure 15. esults suggest that the boundary layer is indeed well-mixed, as the mean mixing ratio is constant with height. The sub-cloud layer mixing ratio decreases from about 5 g/kg to 4.5 g/kg during the 1800 UTC to 2400 UTC time frame. It is observed that there is a fair amount of noise in the profiles. The corresponding profiles of H are shown in Figure 16. As expected, the general H increase with height is captured by the lidar, as well as the slight decrease in humidity levels within the boundary layer between 1800 UTC and 2400 UTC. Nevertheless there is a high level if noise in the data, particularly above 600 m. Hourly profiles of 10-minute averaged aerosol backscatter during the 1800 UTC to 2400 UTC period are shown in Figure 17. As with the other lidar profiles, only the portions located below cloud base, as detected by the lidar, are shown. An increase of backscatter with height is clearly represented. The increase is more pronounced above 600m, where H is above 85%. This is in general agreement with the aerosol size / H relationships presented in Figures 1 and 2. Thus, observed increase in lidar backscattering is most certainly related to the swelling of the aerosols as they are picking up water with increasing H. 24

25 Fig. 14. Profiles of relative humidity derived from the AM/SGP aman lidar on April 3 rd 1998, compared to relative humidity measured by radiosondes launched at (a) 2034 UTC and (b) 2334 UTC. 25

26 Fig. 15. Hourly profiles of water vapor mixing ratio derived from the AM/SGP aman lidar on April 3 rd Fig. 16. Hourly profiles of relative humidity derived from the AM/SGP aman lidar on April 3 rd

27 Fig. 17. Hourly profiles of aerosol backscatter from the AM/SGP aman lidar on April 3 rd Hygroscopic factor To better evaluate the changes in aerosol optical properties with increasing H, a scatterplot of aerosol backscattering versus relative humidity (humidogram) is shown in Figure 18. The data suggests a general increase of aerosol backscatter with relative humidity, but there is quite a large scatter in the data. Consequently, it is impossible to determine a relationship between backscatter and H with any statistical significance. 27

28 Fig. 18. Scatterplot of aerosol backscatter versus relative humidity from the AM/SGP aman lidar on April 3 rd 1998, for data collected from 1800 to 2400 UTC. To evaluate if this high variability in the results are related to the observed noise in the aman lidar derived H, another method is used to create H profiles below cloud base. In-situ water vapor mixing ratio, temperature and pressure measurements from sensors located at 60 m on the AM/SGP instrumented tower are used to build H profiles in the boundary layer. Assuming a well-mixed boundary layer, water vapor mixing ratio is assumed constant with height while temperature is assumed to decrease at the dry adiabatic lapse rate. The pressure profile is determined using the hydrostatic relation. Then the saturation water vapor pressure is determined from the temperature profile using a relationship proposed by Buck (1981) 28

29 e s ( T ) T = exp T, (5) where T is the temperature in o C. The saturation water vapor mixing ratio can then be determined with q sat ( T ) ( ) es ( T, p) = ε p e T s, (6) where p is the atmospheric pressure and ε is equal to The relative humidity is then determined with H q q obs =, (7) sat where qobs is the water vapor mixing ratio tower measurement. Thus H profiles are determined using 10-minute averaged tower measurements corresponding to times where lidar backscatter estimates are available. Tower water vapor mixing ratio observations were corrected by subtracting 0.4 g/kg in order to obtain a better agreement between heights at which the constructed H reach 100% and cloud base heights determined with the lidar. This is an acceptable correction as its magnitude is small enough to be considered within the acceptable level of calibration error of the humidity sensor. Examples of the estimated H profiles are shown in Figure 19. It can be seen that these profiles are much smoother than those derived using the lidar, and the variation of H with height agrees very well with the soundings. The derived H profiles show higher relative humidities than the soundings by about 5%. But H values of the soundings 29

30 seem to have a dry bias as saturation is not reached near cloud base. These differences are well within the possible calibration errors of the instruments. A new aerosol backscattering versus H humidogram is produced using H profiles determined from surface (tower) data (Figure 20). The resulting relationship is now much more compatible with results obtained by Wulfmeyer and Feingold (2000) in a marine boundary layer. A clearly defined increase in aerosol backscattering for H larger than 85% is now observed. Largest increases in backscatter occur for H larger than 95%. These results are in good qualitative agreement with theory (section 3) and the results of Wulfmeyer and Feingold (2000). Fig. 19. Profiles of relative humidity derived from tower measurements and assuming a well-mixed boundary layer, compared to available balloon soundings. AM/SGP site on April 3 rd

31 Fig. 20. Scatterplot of aerosol backscatter from the AM/SGP aman lidar versus relative humidity derived from tower measurements. April 3 rd 1998, data collected from 1800 to 2400 UTC. Even though the expected behavior of aerosol backscattering is more clearly observed in Figure 20, significant scatter of the data is again present. To minimize this scatter, the time window during which data are considered is reduced. When data from 2100 UTC to 2400 UTC are considered (instead of 1800 to 2400 UTC), the resulting humidogram is shown in Figure 21. An even clearer relationship between aerosol backscatter and relative humidity emerges, illustrating the important increase in aerosol backscatter for H close to saturation. 31

32 Fig. 21. Scatterplot of aerosol backscatter from the AM/SGP aman lidar versus relative humidity derived from tower measurements. April 3 rd 1998, data collected from 2100 to 2400 UTC. To gain a more qualitative assessment of the hygroscopic effect on aerosol backscatter, the normalized backscattering can be calculated by taking the ratio of aerosol backscatter over the mean backscatter observed for a reference relative humidity a ( β β a ref ). Here, a β ref is taken for a relative humidity of 70%. From the data, we determined that a β ref = (km-sr) -1. The backscatter data from Figure 21 is normalized with this value and the resulting normalized humidogram is shown in Figure 22. It is obsrved that aerosol backscatter increases by a factor larger than 3 for H>95% compared to backscatter values at 70%. Although the small number of data points considered in the regression for that range of relative humidities decreases the level of 32

33 confidence with which any conclusions can be drawn. Nevertheless, the results suggest a significant increase in aerosol backscatter for higher values of relative humidity. Fig. 22. Scatterplot of normalized aerosol backscatter from the AM/SGP aman lidar versus relative humidity derived from tower measurements. A best fit to the data is also shown, along with results adapted from Im et al. (2001). April 3 rd 1998, data collected from 2100 to 2400 UTC. As in Im et al. (2001), the data is fitted with a relationship of the form a β β a ref b H = a 1, (8)

34 The best fit to the data (in a least squares sense), yields values of the regression coefficients of a=0.43 and b=0.72, with an 2 equal to 0.85 indicating a good fit to the data. The resulting regression is shown in Figure 22 as the solid red line. Im et al. (2001) studied the hygroscopic growth factor over the East coast of the United States using humidified nephelometer data. They used a coefficient of a=1 in their fit to the data. There results were that the b coefficient seemed to be insensitive to the type of air masses affecting the region. They obtained b=0.38 for polluted continental, continental and maritime air masses. The difference between our results and theirs may be due to the fact that they used a reference relative humidity of 30%, compared to 70% use in our study. We could not reproduce the same analysis, as the lowest value of H present in our dataset is about 63%. The relationship obtained by Im et al., adjusted so that the normalized backscatter is close to 1.0 for H=70% but keeping the original exponent, is also shown on Figure 22 (solid green line). It is clear that our data suggest a much steeper relationship between aerosol backscatter and H as compared to Im et al. This difference may possibly be explained by the fact that the chemical composition of aerosols present in the eastern part of the United States is most likely different that those over the central part of the US. It is a well-known fact that aerosols in the eastern portion of the continent are composed of sulfates. Through a modeling study, Wulfmeyer and Feingold (2000) found that aerosols with larger mass fractions of soluble material exhibit a steeper increase in backscatter compared to partially soluble particles. This leads us to conclude that aerosols over the AM-SGP CAT site are most likely composed of material with a higher degree of solubility than what was observed by Im et al. (2001) over the East Coast of the US. Thus, the results obtained here can be used to gain more insights on the types of aerosols present over the CAT site. As in Wulfmeyer and Feingold (2000), models of aerosol growth and changes of refractive index could be used to determine more precisely which types of aerosols could produce the observed change in lidar backscatter as a function of relative humidity. This type of analysis is left for future efforts. 34

35 5. Summary and conclusions Data from the AM/SGP CAT site has been used to characterize the aerosol backscattering as a function of relative humidity. An analysis of the CAT aman lidar has shown that the relative humidity product for the particular day considered exhibited a fair amount of noise (large variations in time and in the vertical) that was detrimental to the analysis performed. This noise is due to background visible radiation present during the daytime (D. Turner, personal communication). Better results would have most probably been obtained with lidar data taken during the night. Also, uncertainties remain as to the well-mixed assumption about aerosol concentration. The recent passage of a cold front most likely disturbed the distribution of aerosols and it is unclear if the particles had time to settle back to a steady well-mixed (uniform concentration) state. But the well-mixed assumption seems reasonable as the water vapor profile exhibits a wellmixed profile. Nevertheless, a simple approach was used to determine the profiles of relative humidity in a well-mixed boundary layer to obtain more reliable (temporally and vertically) profiles. The analysis of aerosol backscatter as a function of this relative humidity yielded more consistent results when compared to theory and previous observations. An equation describing the hygroscopic factor on aerosol backscatter was derived by performing a regression on the data. Significant differences with results obtained by others are observed. A comparison with the results of Im et al. (2001) suggests that the aerosols over the AM-SGP CAT site were characterized by a larger fraction of soluble material than those sampled by the aforementioned authors. More detailed analysis of lidar data is required, in conjunction with modeling of aerosol behavior to better characterize the nature of the aerosols present in the boundary layer on the particular day studied. Nevertheless, the analysis performed in this study illustrates the difficulties, but also the possibilities, of detailed remote sensing of aerosol properties. This type of approach, using high-resolution lidar data, can prove to be a powerful tool in the quest to gain a better understanding of the role of aerosols on climate change at the local, regional and global scales. 35

36 Acknowledgments The author wishes to convey his deep appreciation to Dr. Graham Feingold of NOAA s Environmental Technology Laboratory (NOAA/ETL) for providing the initial motivation for this work, some data from the AM/SGP field site and much needed guidance on the analysis and interpretation of lidar data. Many thanks to Dr. Irina Sokolik for providing motivation and high quality lectures about remote sensing as part of her emote Sensing class in the PAOS program. Some of the data used in the present study have been obtained from the archives of the Atmospheric adiation Measurement (AM) program for the Southern Great Plains site (see Appendix A more detailed overview of how the return signals from the aman lidar are used to retrieve profiles of aerosol optical properties and atmospheric water vapor mixing ratio is provided here. The CAT aman lidar measures returned light at 355 nm (laser wavelength), as well as at 387 (N 2 aman) and 408 nm (H 2 O aman) wavelengths. The lidar equations describing the elastic and inelastic (aman) lidar returns can respectively be written as, where molecular and aerosol contributions are written explicitly: P r ( λ ) O ( ) L m (, ) + (, ) a C h β λl β λl = 2 2 4π, (A.1) a m exp 2 ( ke ( λl, r ') + ke ( λl, r ')) dr ' 0 36

37 P r ( λ ) O( ) ( λ, ) aman C h β = 2 2 4π.(A.2) a m a m exp ( ke ( λl, r ') + ke ( λl, r ')) dr ' + ( ke ( λ, r ') + ke ( λ, r ')) dr ' 0 0 For the elastic lidar equation (eq. A.1), P r ( λ L ) is the power returned to the lidar at the laser wavelength ( λ L ), C is the lidar constant, is the range, h=c t p, where t p is the pulse duration and c the speed of light. It should be noted that for a vertically pointing a m lidar, the range is equivalent to height z. Here, β ( λ, ) and (, ) L β λ are the range dependent aerosol and molecular backscatter coefficients respectively, at the laser a m wavelength. Similarly, k ( λ, ) and k (, ) e L e λ are the range dependent aerosol and molecular extinction coefficients at the laser wavelength. The term O() describes the overlap between the laser beam and the receiver field of view. The term is equal to 1 for ranges where there is complete overlap of the laser beam and the receiver s field of view. L For the inelastic (aman) lidar equation (eq. A.2), P r ( λ ) is the power returned to the lidar at the aman-shifted N 2 wavelength ( k m e (, ) a λ ). Similarly as before, k (, ) L e λ and λ are the range dependent aerosol and molecular extinction coefficients at the aman wavelength. It is pointed out that the aman return signal is independent of aerosol backscattering. The aman backscatter coefficient is linked to the differential aman backscatter cross section ( d σ ) of the gas and the molecule number density N d Ω following (Ansmann et al., 1990) ( ) aman dσ λ β ( λ ) = N( ) dω. (A.3) 37

38 It is observed that the elastic signal depends on both the extinction and backscattering due to the presence of aerosols. But the aman signal depends on aerosol extinction alone. This allows the independent determination of aerosol extinction and backscattering. A.1 Aerosol extinction The aerosol extinction is determined from measurements of the aman signal. By combining equations (A.2) and (A.3), and solving for the extinction coefficients, it follows that (Ansmann et al., 1990) ( ) ( ) 2 ( λ, ) a m a m d O N ke ( λl, ) + ke ( λl, ) + ke ( λ, ) + ke ( λ, ) = ln. (A.4) d P This can be re-written as ( ) ( ) 2 ( λ, ) a a d O N m m ke ( λl, ) + ke ( λ, ) = ln ke λl, ke λ, d P ( ) ( ). (A.5) The difference in atmospheric transmission between the return at the laser wavelength and the return at the aman N 2 wavelength has to be taken into account. This dependence comes mostly from the 4 λ wavelength dependence of molecular scattering. Assuming a power law wavelength dependence of aerosol extinction coefficients for the laser wavelength and the aman wavelength 38

39 k k ( λ ) ( λ ) a e L λ a e λl α =, (A.6) we re-write equation (A.5) as ( ) ( ) 2 ( λ, ) d O N m ln ke L, ke, d P a ke ( λl, ) = α λ L 1+ λ m ( λ ) ( λ ), (A.7) to obtain the unknown aerosol extinction coefficient profile. The only requirement to solve this equation is that the molecular density profile be known, so that N and the molecular extinction coefficients can be calculated. For the CAT site, this information can be obtained from temperature profiles from radiosondes or from the co-located Atmospheric Emitted adiance Interferometer (AEI). For the wavelength dependence of extinction coefficients, a value of α = 1 is commonly used (Ansmann et al., 1990), but corrections for this wavelength dependence can be evaluated by using density profiles from radiosondes or from the AEI to compute ayleigh scattering, along with values from the aman N 2 lidar returns. According the Ferrare et al. (1998), this correction can be calculated to within 1-2%. A.2 Aerosol backscatter The aerosol backscatter coefficient is determined by combining the return signal at the laser wavelength (elastic signal) and the inelastic (aman) signal and forming the ratio 39

40 ( λ, ) ( λ, ) ( λ, ) ( λ, ) P P Γ = r L r o. (A.8) P P r L o r P ( λ, ) and P (, ) r L o λ are the return signals measured at the laser and aman r o wavelengths respectively, for a reference range o. This range is chosen such that β m a ( ) β ( ) so that β m ( ) β a ( ) β m ( ) o o +. So an appropriate calibration o o o range is one with negligible aerosol-to-ayleigh extinction ratio. For the CAT aman lidar, this calibration is performed under clear skies using a reference range of 6 to 10 km above the ground surface. Using equations (A.1) and (A.2) and forming the ratio defined in equation (A.8), re-arranging the terms and using the reference range approximation defined above we get ( ) ( ) ( ) ( ) ( ) ( ) a m a Pr λl, Pr λ, o N λ, β ( λl, ) + β ( λl, ) = β ( λl, o ) P λ, P λ, N λ, r L o r o a m exp ke ( λ, r ') + ke ( λ, r ') dr '. (A.9) o a m exp ke ( λl, r ') + ke ( λl, r ') dr ' o The molecular density profile is determined from radiosondes of from the AEI profiles, and the molecular (ayleigh) backscattering calculated from density estimates. Also, the extinction coefficients have been computed in a previous step of the retrieval procedure so all required quantities are known so that equation (A.9) can be solved to obtain aerosol backscatter profiles. 40

41 A.2 Water vapor mixing ratio Profiles of water vapor mixing ratio are obtained from measurements of the water vapor (H 2 O aman) to reference (N 2 aman) signal ratio. Using equation (A.2), forming the ratio and rearranging the terms, we obtain an expression for the water vapor mixing ratio (q) ( λh, ) 2O ( λ, ) Pr q ( z) = A P r N2 o a m exp ke ( λn, r ') + k (, ') ' 2 e λn r dr, (A.10) 2 0 a m exp ke ( λh, ') (, ') ' 2O r + ke λh2o r dr 0 where A is a system constant which is deduced from known quantities. Again, the contribution to extinction from molecules (ayleigh) is evaluated using observed density profiles. Since we are now using the λ H2O wavelength, a correction has to be applied to the extinction coefficient derived with the N 2 aman signal as described in section A.1. A wavelength dependence of the extinction coefficient of 1 λ is assumed. 41

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