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Abstract
This paper describes advances in ground-based thermodynamic profiling of the lower troposphere through sensor synergy. The well-documented integrated profiling technique (IPT), which uses a microwave profiler, a cloud radar, and a ceilometer to simultaneously retrieve vertical profiles of temperature, humidity, and liquid water content (LWC) of nonprecipitating clouds, is further developed toward an enhanced performance in the boundary layer and lower troposphere. For a more accurate temperature profile, this is accomplished by including an elevation scanning measurement modus of the microwave profiler. Height-dependent RMS accuracies of temperature (humidity) ranging from ∼0.3 to 0.9 K (0.5–0.8 g m−3) in the boundary layer are derived from retrieval simulations and confirmed experimentally with measurements at distinct heights taken during the 2005 International Lindenberg Campaign for Assessment of Humidity and Cloud Profiling Systems and its Impact on High-Resolution Modeling (LAUNCH) of the German Weather Service. Temperature inversions, especially of the lower boundary layer, are captured in a very satisfactory way by using the elevation scanning mode. To improve the quality of liquid water content measurements in clouds the authors incorporate a sophisticated target classification scheme developed within the European cloud observing network CloudNet. It allows the detailed discrimination between different types of backscatterers detected by cloud radar and ceilometer. Finally, to allow IPT application also to drizzling cases, an LWC profiling method is integrated. This technique classifies the detected hydrometeors into three different size classes using certain thresholds determined by radar reflectivity and/or ceilometer extinction profiles. By inclusion into IPT, the retrieved profiles are made consistent with the measurements of the microwave profiler and an LWC a priori profile. Results of IPT application to 13 days of the LAUNCH campaign are analyzed, and the importance of integrated profiling for model evaluation is underlined.
Abstract
This paper describes advances in ground-based thermodynamic profiling of the lower troposphere through sensor synergy. The well-documented integrated profiling technique (IPT), which uses a microwave profiler, a cloud radar, and a ceilometer to simultaneously retrieve vertical profiles of temperature, humidity, and liquid water content (LWC) of nonprecipitating clouds, is further developed toward an enhanced performance in the boundary layer and lower troposphere. For a more accurate temperature profile, this is accomplished by including an elevation scanning measurement modus of the microwave profiler. Height-dependent RMS accuracies of temperature (humidity) ranging from ∼0.3 to 0.9 K (0.5–0.8 g m−3) in the boundary layer are derived from retrieval simulations and confirmed experimentally with measurements at distinct heights taken during the 2005 International Lindenberg Campaign for Assessment of Humidity and Cloud Profiling Systems and its Impact on High-Resolution Modeling (LAUNCH) of the German Weather Service. Temperature inversions, especially of the lower boundary layer, are captured in a very satisfactory way by using the elevation scanning mode. To improve the quality of liquid water content measurements in clouds the authors incorporate a sophisticated target classification scheme developed within the European cloud observing network CloudNet. It allows the detailed discrimination between different types of backscatterers detected by cloud radar and ceilometer. Finally, to allow IPT application also to drizzling cases, an LWC profiling method is integrated. This technique classifies the detected hydrometeors into three different size classes using certain thresholds determined by radar reflectivity and/or ceilometer extinction profiles. By inclusion into IPT, the retrieved profiles are made consistent with the measurements of the microwave profiler and an LWC a priori profile. Results of IPT application to 13 days of the LAUNCH campaign are analyzed, and the importance of integrated profiling for model evaluation is underlined.
Abstract
Airborne Leandre II differential absorption lidar (DIAL), S-band dual-polarization Doppler radar (S-Pol), and Goddard Lidar Observatory for Winds (GLOW) Doppler lidar data are used, in conjunction with surface mesonet and special sounding data, to derive the structure and dynamics of a bore and associated solitary wave train (soliton) that were generated in southwestern Kansas during the International H20 Project (IHOP_2002). Vertical cross sections of S-Pol reflectivity, S-Pol radial velocity, and DIAL water vapor mixing ratio show a stunning amplitude-ordered train of trapped solitary waves. DIAL data reveal that the leading wave in the soliton increasingly flattened with time as the soliton dissipated.
A method is developed for using the GLOW Doppler winds to obtain the complex two-dimensional vertical circulation accompanying the dissipating soliton. The results show multiple circulations identical in number to the oscillations seen in the S-Pol and DIAL data. The leading updraft occurred precisely at the time that the bore passed over the GLOW facility, as well as when the photon count values suddenly ramped up (suggesting lifting of the low-level inversion by the bore). Additional evidence in support of the validity of the results is provided by the fact that layer displacements computed using the derived vertical motions agree well with those implied by the changes in height of the DIAL mixing ratio surfaces.
The depth and speed of propagation of the bore seen in the DIAL and surface mesoanalyses were shown to be consistent with the predictions from bore hydraulic theory. Analysis of National Center for Atmospheric Research (NCAR) Integrated Sounding System (ISS) data shows that a highly pronounced curvature in the profile of bore-relative winds, related to the existence of a very strong low-level jet, effectively trapped the upward leakage of solitary wave energy below 3 km. This finding explains the trapped lee wave–type structures seen in the DIAL, GLOW, and S-Pol data.
Abstract
Airborne Leandre II differential absorption lidar (DIAL), S-band dual-polarization Doppler radar (S-Pol), and Goddard Lidar Observatory for Winds (GLOW) Doppler lidar data are used, in conjunction with surface mesonet and special sounding data, to derive the structure and dynamics of a bore and associated solitary wave train (soliton) that were generated in southwestern Kansas during the International H20 Project (IHOP_2002). Vertical cross sections of S-Pol reflectivity, S-Pol radial velocity, and DIAL water vapor mixing ratio show a stunning amplitude-ordered train of trapped solitary waves. DIAL data reveal that the leading wave in the soliton increasingly flattened with time as the soliton dissipated.
A method is developed for using the GLOW Doppler winds to obtain the complex two-dimensional vertical circulation accompanying the dissipating soliton. The results show multiple circulations identical in number to the oscillations seen in the S-Pol and DIAL data. The leading updraft occurred precisely at the time that the bore passed over the GLOW facility, as well as when the photon count values suddenly ramped up (suggesting lifting of the low-level inversion by the bore). Additional evidence in support of the validity of the results is provided by the fact that layer displacements computed using the derived vertical motions agree well with those implied by the changes in height of the DIAL mixing ratio surfaces.
The depth and speed of propagation of the bore seen in the DIAL and surface mesoanalyses were shown to be consistent with the predictions from bore hydraulic theory. Analysis of National Center for Atmospheric Research (NCAR) Integrated Sounding System (ISS) data shows that a highly pronounced curvature in the profile of bore-relative winds, related to the existence of a very strong low-level jet, effectively trapped the upward leakage of solitary wave energy below 3 km. This finding explains the trapped lee wave–type structures seen in the DIAL, GLOW, and S-Pol data.
Abstract
A previous study showed success in determining the convective boundary layer depth with radar wind-profiling radars using fuzzy logic methods, and improvements to the earlier work are discussed. The improved method uses the Vaisala multipeak picking (MPP) procedure to identify the atmospheric signal in radar spectra in place of a fuzzy logic peak picking procedure that was previously used. The method then applies fuzzy logic techniques to calculate the depth of the convective boundary layer. The planetary boundary layer depth algorithm is improved with respect to the one used in the previous study in that it adds information obtained from the small-scale turbulence (vertical profiles of the spectral width of the vertical velocity), while also still using vertical profiles of the radar-derived refractive index structure parameter C 2 n and the variance of vertical velocity. Modifications to the fuzzy logic rules (especially to those using vertical velocity data) that improve the algorithm’s accuracy in cloudy boundary layers are incorporated. In addition, a reliability threshold value to the fuzzy logic–derived score is applied to eliminate PBL depth data values with low score values. These low score values correspond to periods when the PBL structure does not match the conceptual model of the convective PBL built into the algorithm. Also, as a final step, an optional temporal continuity test on boundary layer depth has been developed that helps improve the algorithm’s skill. A comparison with independent boundary layer depth estimations made “by eye” by meteorologists at two radar wind-profiler sites, significantly different in their characteristics, shows that the new improved method gives significantly more accurate estimates of the boundary layer depth than does the previous method, and also much better estimates than the simpler “standard” method of selecting the peak of C 2 n . The new method produces an absolute error of the mixing-depth estimates comparable to the vertical range resolution of the profilers.
Abstract
A previous study showed success in determining the convective boundary layer depth with radar wind-profiling radars using fuzzy logic methods, and improvements to the earlier work are discussed. The improved method uses the Vaisala multipeak picking (MPP) procedure to identify the atmospheric signal in radar spectra in place of a fuzzy logic peak picking procedure that was previously used. The method then applies fuzzy logic techniques to calculate the depth of the convective boundary layer. The planetary boundary layer depth algorithm is improved with respect to the one used in the previous study in that it adds information obtained from the small-scale turbulence (vertical profiles of the spectral width of the vertical velocity), while also still using vertical profiles of the radar-derived refractive index structure parameter C 2 n and the variance of vertical velocity. Modifications to the fuzzy logic rules (especially to those using vertical velocity data) that improve the algorithm’s accuracy in cloudy boundary layers are incorporated. In addition, a reliability threshold value to the fuzzy logic–derived score is applied to eliminate PBL depth data values with low score values. These low score values correspond to periods when the PBL structure does not match the conceptual model of the convective PBL built into the algorithm. Also, as a final step, an optional temporal continuity test on boundary layer depth has been developed that helps improve the algorithm’s skill. A comparison with independent boundary layer depth estimations made “by eye” by meteorologists at two radar wind-profiler sites, significantly different in their characteristics, shows that the new improved method gives significantly more accurate estimates of the boundary layer depth than does the previous method, and also much better estimates than the simpler “standard” method of selecting the peak of C 2 n . The new method produces an absolute error of the mixing-depth estimates comparable to the vertical range resolution of the profilers.
Abstract
The performance of the boundary determination of fog and low stratiform cloud layers with data from a frequency-modulated continuous-wave (FMCW) cloud radar and a Vaisala ceilometer is assessed. During wintertime stable episodes, fog and low stratiform cloud layers often occur in the Swiss Plateau, where the aerological station of Payerne, Switzerland, is located. During the international COST 720 Temperature, Humidity, and Cloud (TUC) profiling experiment in winter 2003/04, both a cloud radar and a ceilometer were operated in parallel, among other profiling instruments. Human eye observations (“synops”) and temperature and humidity profiles from radiosoundings were used as reference for the validation. In addition, two case studies were chosen to demonstrate the possibilities and limitations of such ground-based remote sensing systems in determining low clouds. In these case studies the cloud boundaries determined by ceilometer and cloud radar were furthermore compared with wind profiler signal-to-noise ratio time series. Under dry conditions, cloud-base and -top detection was possible in 59% and 69% of the cases for low stratus clouds and fog situations, respectively. When cases with any form of precipitation were included, performances were reduced with detection rates of 41% and 63%, respectively. The combination of ceilometer and cloud radar has the potential for providing the base and top of a cloud layer with optimal efficiency in the continuous operational mode. The cloud-top height determination by the cloud radar was compared with cloud-top heights detected using radiosounding humidity profiles. The average height difference between the radiosounding and cloud radar determination of the cloud upper boundary is 53 ± 32 m.
Abstract
The performance of the boundary determination of fog and low stratiform cloud layers with data from a frequency-modulated continuous-wave (FMCW) cloud radar and a Vaisala ceilometer is assessed. During wintertime stable episodes, fog and low stratiform cloud layers often occur in the Swiss Plateau, where the aerological station of Payerne, Switzerland, is located. During the international COST 720 Temperature, Humidity, and Cloud (TUC) profiling experiment in winter 2003/04, both a cloud radar and a ceilometer were operated in parallel, among other profiling instruments. Human eye observations (“synops”) and temperature and humidity profiles from radiosoundings were used as reference for the validation. In addition, two case studies were chosen to demonstrate the possibilities and limitations of such ground-based remote sensing systems in determining low clouds. In these case studies the cloud boundaries determined by ceilometer and cloud radar were furthermore compared with wind profiler signal-to-noise ratio time series. Under dry conditions, cloud-base and -top detection was possible in 59% and 69% of the cases for low stratus clouds and fog situations, respectively. When cases with any form of precipitation were included, performances were reduced with detection rates of 41% and 63%, respectively. The combination of ceilometer and cloud radar has the potential for providing the base and top of a cloud layer with optimal efficiency in the continuous operational mode. The cloud-top height determination by the cloud radar was compared with cloud-top heights detected using radiosounding humidity profiles. The average height difference between the radiosounding and cloud radar determination of the cloud upper boundary is 53 ± 32 m.
Abstract
In this work, the accuracy of the Doppler beam-swinging (DBS) technique for wind measurements is studied using an imaging radar—the turbulent eddy profiler (TEP) developed by the University of Massachusetts, with data collected in summer 2003. With up to 64 independent receivers, and using coherent radar imaging (CRI), several hundred partially independent beams can be formed simultaneously within the volume defined by the transmit beam. By selecting a subset of these beams, an unprecedented number of DBS configurations with varying zenith angle, azimuth angle, and number of beams can be investigated. The angular distributions of echo power and radial velocity obtained by CRI provide a unique opportunity to validate the inherent assumption in the DBS method of homogeneity across the region defined by the beam directions. Through comparison with a reference wind field, calculated as the optimal uniform wind field derived from all CRI beams with sufficient signal-to-noise ratio (SNR), the accuracy of the wind estimates for various DBS configurations is statistically analyzed. It is shown that for a three-beam DBS configuration, although the validity of the homogeneity assumption is enhanced at smaller zenith angles, the root-mean-square (RMS) error increases because of the ill-conditioned matrix in the DBS algorithm. As expected, inhomogeneities in the wind field produce large bias for the three-beam DBS configuration for large zenith angles. An optimal zenith angle, in terms of RMS error, of approximately 9°–10° was estimated. It is further shown that RMS error can be significantly reduced by increasing the number of off-vertical beams used for the DBS processing.
Abstract
In this work, the accuracy of the Doppler beam-swinging (DBS) technique for wind measurements is studied using an imaging radar—the turbulent eddy profiler (TEP) developed by the University of Massachusetts, with data collected in summer 2003. With up to 64 independent receivers, and using coherent radar imaging (CRI), several hundred partially independent beams can be formed simultaneously within the volume defined by the transmit beam. By selecting a subset of these beams, an unprecedented number of DBS configurations with varying zenith angle, azimuth angle, and number of beams can be investigated. The angular distributions of echo power and radial velocity obtained by CRI provide a unique opportunity to validate the inherent assumption in the DBS method of homogeneity across the region defined by the beam directions. Through comparison with a reference wind field, calculated as the optimal uniform wind field derived from all CRI beams with sufficient signal-to-noise ratio (SNR), the accuracy of the wind estimates for various DBS configurations is statistically analyzed. It is shown that for a three-beam DBS configuration, although the validity of the homogeneity assumption is enhanced at smaller zenith angles, the root-mean-square (RMS) error increases because of the ill-conditioned matrix in the DBS algorithm. As expected, inhomogeneities in the wind field produce large bias for the three-beam DBS configuration for large zenith angles. An optimal zenith angle, in terms of RMS error, of approximately 9°–10° was estimated. It is further shown that RMS error can be significantly reduced by increasing the number of off-vertical beams used for the DBS processing.
Abstract
Using radar observations to quantify precipitation intensity requires the intervention of the radar equation, which converts the precipitation signal into reflectivity units. This equation generally assumes that the reflectivity is uniform within each sampling gate and that the sidelobes of the antenna pattern are negligible. The purpose here is to provide a more realistic approach that eliminates these assumptions when computing profiles of precipitation intensity (by using a height-variable reflectivity and antenna pattern of significant sidelobes to compute profiles of a radar reflectivity factor). To achieve this, simultaneous observations of collocated vertically pointing radars operating in the VHF and X bands were obtained. Raindrop measurements were used to correct for attenuation in the precipitation signal at the X band. Then the precipitation signal in the VHF radar was simulated by combining this X-band signal and the VHF antenna pattern into a general version of the radar equation. The simulated precipitation signal at VHF compares well with actual measurements of the rain signal (range gates centered at 2.5, 3.0, and 3.5 km) by the VHF radar, and this validates the analysis methods. In conclusion, the analysis indicates that VHF reflectivity at gates above the melting layer is artificially enhanced by the precipitation signal collected in the sidelobe direction. Similar enhancement will be expected wherever there is a strong vertical gradient of reflectivity (i.e., on the order of 10 dB km−1 or larger).
Abstract
Using radar observations to quantify precipitation intensity requires the intervention of the radar equation, which converts the precipitation signal into reflectivity units. This equation generally assumes that the reflectivity is uniform within each sampling gate and that the sidelobes of the antenna pattern are negligible. The purpose here is to provide a more realistic approach that eliminates these assumptions when computing profiles of precipitation intensity (by using a height-variable reflectivity and antenna pattern of significant sidelobes to compute profiles of a radar reflectivity factor). To achieve this, simultaneous observations of collocated vertically pointing radars operating in the VHF and X bands were obtained. Raindrop measurements were used to correct for attenuation in the precipitation signal at the X band. Then the precipitation signal in the VHF radar was simulated by combining this X-band signal and the VHF antenna pattern into a general version of the radar equation. The simulated precipitation signal at VHF compares well with actual measurements of the rain signal (range gates centered at 2.5, 3.0, and 3.5 km) by the VHF radar, and this validates the analysis methods. In conclusion, the analysis indicates that VHF reflectivity at gates above the melting layer is artificially enhanced by the precipitation signal collected in the sidelobe direction. Similar enhancement will be expected wherever there is a strong vertical gradient of reflectivity (i.e., on the order of 10 dB km−1 or larger).
Abstract
The daytime atmospheric convective boundary layer (CBL) is characterized by strong turbulence that is primarily caused by buoyancy forced from the heated underlying surface. The present study considers a combination of a virtual radar and large eddy simulation (LES) techniques to characterize the CBL. Data representative of a daytime CBL with wind shear were generated by LES and used in the virtual boundary layer radar (BLR) with both vertical and multiple off-vertical beams and frequencies. To evaluate the virtual radar, a multiple radar experiment (MRE) was conducted using five virtual radars with common resolution volumes at two different altitudes. Three-dimensional wind fields were retrieved from the virtual radar data and compared with the LES output. It is shown that data produced from the virtual BLR are representative of what one expects to retrieve using a real BLR and the measured wind fields match those of the LES. Additionally, results from a frequency domain interferometry (FDI) comparison are presented, with the ultimate goal of enhancing the resolution of conventional radar measurements. The virtual BLR produces measurements consistent with the LES data fields and provides a suitable platform for validating radar signal processing algorithms.
Abstract
The daytime atmospheric convective boundary layer (CBL) is characterized by strong turbulence that is primarily caused by buoyancy forced from the heated underlying surface. The present study considers a combination of a virtual radar and large eddy simulation (LES) techniques to characterize the CBL. Data representative of a daytime CBL with wind shear were generated by LES and used in the virtual boundary layer radar (BLR) with both vertical and multiple off-vertical beams and frequencies. To evaluate the virtual radar, a multiple radar experiment (MRE) was conducted using five virtual radars with common resolution volumes at two different altitudes. Three-dimensional wind fields were retrieved from the virtual radar data and compared with the LES output. It is shown that data produced from the virtual BLR are representative of what one expects to retrieve using a real BLR and the measured wind fields match those of the LES. Additionally, results from a frequency domain interferometry (FDI) comparison are presented, with the ultimate goal of enhancing the resolution of conventional radar measurements. The virtual BLR produces measurements consistent with the LES data fields and provides a suitable platform for validating radar signal processing algorithms.
Abstract
A new water vapor Raman lidar was recently built at the Table Mountain Facility (TMF) of the Jet Propulsion Laboratory (JPL) in California and more than a year of routine 2-h-long nighttime measurements 4–5 times per week have been completed. The lidar was designed to reach accuracies better than 5% anywhere up to 12-km altitude, and with the capability to measure water vapor mixing ratios as low as 1 to 10 ppmv near the tropopause and in the lower stratosphere. The current system is not yet fully optimized but has already shown promising results as water vapor profiles have been retrieved up to 18-km altitude. Comparisons with Vaisala RS92K radiosondes exhibit very good agreement up to at least 10 km. They also revealed a wet bias in the lidar profiles (or a dry bias in the radiosonde profiles), increasing with altitude and becoming significant near 10 km and large when approaching the tropopause. This bias cannot be explained solely by well-known too-dry measurements of the RS92K in the upper troposphere and therefore must partly originate in the lidar measurements. Excess signal due to residual fluorescence in the lidar receiver components is among the most likely candidates and is subject to ongoing investigation.
Abstract
A new water vapor Raman lidar was recently built at the Table Mountain Facility (TMF) of the Jet Propulsion Laboratory (JPL) in California and more than a year of routine 2-h-long nighttime measurements 4–5 times per week have been completed. The lidar was designed to reach accuracies better than 5% anywhere up to 12-km altitude, and with the capability to measure water vapor mixing ratios as low as 1 to 10 ppmv near the tropopause and in the lower stratosphere. The current system is not yet fully optimized but has already shown promising results as water vapor profiles have been retrieved up to 18-km altitude. Comparisons with Vaisala RS92K radiosondes exhibit very good agreement up to at least 10 km. They also revealed a wet bias in the lidar profiles (or a dry bias in the radiosonde profiles), increasing with altitude and becoming significant near 10 km and large when approaching the tropopause. This bias cannot be explained solely by well-known too-dry measurements of the RS92K in the upper troposphere and therefore must partly originate in the lidar measurements. Excess signal due to residual fluorescence in the lidar receiver components is among the most likely candidates and is subject to ongoing investigation.
Abstract
The impact of water vapor observations on mesoscale initial fields provided by a triangle of Raman lidar systems covering an area of about 200 km × 200 km is investigated. A test case during the Lindenberg Campaign for Assessment of Humidity and Cloud Profiling Systems and its Impact on High-Resolution Modeling (LAUNCH-2005) was chosen. Evaluation of initial water vapor fields derived from ECMWF analysis revealed that in the model the highly variable vertical structure of water vapor profiles was not recovered and vertical gradients were smoothed out. Using a 3-h data assimilation window and a resolution of 10–30 min, continuous water vapor data from these observations were assimilated in the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) by means of a four-dimensional variational data analysis (4DVAR). A strong correction of the vertical structure and the absolute values of the initial water vapor field of the order of 1 g kg−1 was found. This occurred mainly upstream of the lidar systems within an area, which was comparable with the domain covered by the lidar systems. The correction of the water vapor field was validated using independent global positioning system (GPS) sensors. Much better agreement to GPS zenith wet delay was achieved with the initial water vapor field after 4DVAR. The impact region was transported with the mean wind and was still visible after 4 h of free forecast time.
Abstract
The impact of water vapor observations on mesoscale initial fields provided by a triangle of Raman lidar systems covering an area of about 200 km × 200 km is investigated. A test case during the Lindenberg Campaign for Assessment of Humidity and Cloud Profiling Systems and its Impact on High-Resolution Modeling (LAUNCH-2005) was chosen. Evaluation of initial water vapor fields derived from ECMWF analysis revealed that in the model the highly variable vertical structure of water vapor profiles was not recovered and vertical gradients were smoothed out. Using a 3-h data assimilation window and a resolution of 10–30 min, continuous water vapor data from these observations were assimilated in the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) by means of a four-dimensional variational data analysis (4DVAR). A strong correction of the vertical structure and the absolute values of the initial water vapor field of the order of 1 g kg−1 was found. This occurred mainly upstream of the lidar systems within an area, which was comparable with the domain covered by the lidar systems. The correction of the water vapor field was validated using independent global positioning system (GPS) sensors. Much better agreement to GPS zenith wet delay was achieved with the initial water vapor field after 4DVAR. The impact region was transported with the mean wind and was still visible after 4 h of free forecast time.
Abstract
A backscattering lidar system, the first of this kind in Brazil, has been used to provide the vertical profile of the aerosol backscatter coefficient at 532 nm up to an altitude of 4–6 km above sea level (ASL), in a suburban area in the city of São Paulo. The lidar system has been operational since September 2001. The lidar data products were obtained in a 4-yr period (2001–04) and concerned the aerosol optical thickness (AOT), the aerosol backscattering and extinction coefficients at 532 nm, cloud properties (cloud base, thickness), planetary boundary layer (PBL) heights, aerosol layering, and the structure and dynamics of the lower troposphere. The lidar data are presented and analyzed in synergy with AOT measurements obtained by a Cimel sun-tracking photometer in the visible spectral region, not only to validate the lidar data but also to provide an input value of the so-called extinction-to-backscatter ratio [lidar ratio (LR)]. A correlation between the lidar data and the data obtained by a Cimel sun-tracking photometer [belonging to the Aerosol Robotic Network (AERONET)] is being made to set a temporal database of those data that were collected concomitantly and to cross correlate the information gathered by each instrument. The sun photometer data are used to provide AOT values at selected wavelengths and thus to derive the Ångström exponent (AE) values, single scattering albedo (SSA) and phase function values, and LR values. The analysis of these data showed an important trend in the seasonal signature of the LR indicating a change of the predominant type of aerosol between the dry and wet seasons. Thus, during the wet season the LR lidar values are greater (50–60 sr), which indicates that larger absorption by the aerosols takes place during this period. The corresponding AE values range between 1.3 and 2 for both periods.
Abstract
A backscattering lidar system, the first of this kind in Brazil, has been used to provide the vertical profile of the aerosol backscatter coefficient at 532 nm up to an altitude of 4–6 km above sea level (ASL), in a suburban area in the city of São Paulo. The lidar system has been operational since September 2001. The lidar data products were obtained in a 4-yr period (2001–04) and concerned the aerosol optical thickness (AOT), the aerosol backscattering and extinction coefficients at 532 nm, cloud properties (cloud base, thickness), planetary boundary layer (PBL) heights, aerosol layering, and the structure and dynamics of the lower troposphere. The lidar data are presented and analyzed in synergy with AOT measurements obtained by a Cimel sun-tracking photometer in the visible spectral region, not only to validate the lidar data but also to provide an input value of the so-called extinction-to-backscatter ratio [lidar ratio (LR)]. A correlation between the lidar data and the data obtained by a Cimel sun-tracking photometer [belonging to the Aerosol Robotic Network (AERONET)] is being made to set a temporal database of those data that were collected concomitantly and to cross correlate the information gathered by each instrument. The sun photometer data are used to provide AOT values at selected wavelengths and thus to derive the Ångström exponent (AE) values, single scattering albedo (SSA) and phase function values, and LR values. The analysis of these data showed an important trend in the seasonal signature of the LR indicating a change of the predominant type of aerosol between the dry and wet seasons. Thus, during the wet season the LR lidar values are greater (50–60 sr), which indicates that larger absorption by the aerosols takes place during this period. The corresponding AE values range between 1.3 and 2 for both periods.
