Impact of varying lidar measurement and data processing techniques in evaluating cirrus cloud and aerosol direct radiative effects

Abstract

Abstract. In the past 2 decades, ground-based lidar networks have drastically increased in scope and relevance, thanks primarily to the advent of lidar observations from space and their need for validation. Lidar observations of aerosol and cloud geometrical, optical and microphysical atmospheric properties are subsequently used to evaluate their direct radiative effects on climate. However, the retrievals are strongly dependent on the lidar instrument measurement technique and subsequent data processing methodologies. In this paper, we evaluate the discrepancies between the use of Raman and elastic lidar measurement techniques and corresponding data processing methods for two aerosol layers in the free troposphere and for two cirrus clouds with different optical depths. Results show that the different lidar techniques are responsible for discrepancies in the model-derived direct radiative effects for biomass burning (0.05 W m−2 at surface and 0.007 W m−2 at top of the atmosphere) and dust aerosol layers (0.7 W m−2 at surface and 0.85 W m−2 at top of the atmosphere). Data processing is further responsible for discrepancies in both thin (0.55 W m−2 at surface and 2.7 W m−2 at top of the atmosphere) and opaque (7.7 W m−2 at surface and 11.8 W m−2 at top of the atmosphere) cirrus clouds. Direct radiative effect discrepancies can be attributed to the larger variability of the lidar ratio for aerosols (20–150 sr) than for clouds (20–35 sr). For this reason, the influence of the applied lidar technique plays a more fundamental role in aerosol monitoring because the lidar ratio must be retrieved with relatively high accuracy. In contrast, for cirrus clouds, with the lidar ratio being much less variable, the data processing is critical because smoothing it modifies the aerosol and cloud vertically resolved extinction profile that is used as input to compute direct radiative effect calculations.

Impact of mineral dust on shortwave and longwave radiation: evaluation of different vertically resolved parameterizations in 1-D radiative transfer computations

“…The AOD values presented here (included in Table 4) are obtained by integrating the α aer profiles at 550 nm from the surface up to the considered top of the aerosol layer (4.3 km on 16 June and 4.7 km on 17 June). In GRASP retrieved α aer profiles, values above this top of the aerosol layer are slightly larger than zero since GRASP takes into account stratospheric aerosols by an exponential decay (Lopatin et al, 2013), thus the approach used here to calculate the AOD leads to lower values compared to the column-integrated AOD provided by the sun photometer. Differences among the three datasets are more noticeable on 16 June, when the AOD for DS1 is 0.05 lower than for DS2 and DS3; whereas on 17 June the maximum difference is 0.03, obtained between DS1 and DS2.…”

“…In our case, GRASP was used to invert simultaneously coincident lidar data (range-corrected signal, RCS, at 355, 532, and 1064 nm) and sun/sky photometer measurements (AOD and sky radiances both from AERONET at 440, 675, 870, and 1020 nm) providing a detailed characterization of the aerosol properties, both column-integrated and vertically resolved. It is worthy to note that this GRASP scheme, based on Lopatin et al (2013), presents the main advantage that it allows for retrieving aerosol optical and microphysical properties for two distinct aerosol modes, namely fine and coarse. The α aer , β aer , SSA (all at 355, 440, 532, 675, 870, 1020, and 1064 nm), and aerosol volume concentration (VC) profiles obtained as output from GRASP will be used as input to GAME in the present study, together with the column-integrated PSD properties (namely r eff and σ for fine and coarse modes).…”

Multispectral Pansharpening with Radiative Transfer-Based Detail-Injection Modeling for Preserving Changes in Vegetation Cover

“…The Fu-Liou-Gu (FLG) radiative transfer model [36] requires acquisition year, month, day, local time, longitude, latitude and possibly the type of landscape for setting aerosols [59,60] (advected [61,62] or local) both in the boundary layer [63,64] or upper troposphere. The content of water vapor may be inferred from the presence of cirrus clouds in the visible bands [65,66]. Such a model directly yields values of path radiance in predefined bands, roughly corresponding to those of MS scanners, like Landsat 8 OLI.…”

Vertically Resolved Precipitation Intensity Retrieved through a Synergy between the Ground-Based NASA MPLNET Lidar Network Measurements, Surface Disdrometer Datasets and an Analytical Model Solution

“…The total power, P, is reconstructed as P = P co + 2P cr [16,17]). The signal, P, multiplied by the squared range is the basis for retrieving all of the different Level 2 cloud and aerosol products [18,19]. Since the P-MPL is a single wavelength lidar, however, the retrieval of the vertically-resolved microphysical and optical aerosol properties are subject to stronger assumptions with respect to multi-wavelength lidars [20].…”