Abstract

Abstract. The Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument onboard the Cloud–Aerosol Lidar and Pathfinder Satellite Observations (CALIPSO) spacecraft has provided over 8 yr of nearly continuous vertical profiling of Earth's atmosphere. In this paper we investigate the V3.01 and V3.02 CALIOP 532 nm aerosol layer optical depth (AOD) product (i.e the AOD of individual layers) and the column AOD product (i.e., the sum AOD of the complete column) using an extensive database of coincident measurements. The CALIOP AOD measurements and AOD uncertainty estimates are compared with collocated AOD measurements collected with the NASA High Spectral Resolution Lidar (HSRL) in the North American and Caribbean regions. In addition, the CALIOP aerosol lidar ratios are investigated using the HSRL measurements. In general, compared with the HSRL values, the CALIOP layer AOD are biased high by less than 50% for AOD < 0.3 with higher errors for higher AOD. Less than 60% of the HSRL AOD measurements are encompassed within the CALIOP layer 1 SD uncertainty range (around the CALIOP layer AOD), so an error estimate is created to encompass 68% of the HSRL data. Using this new metric, the CALIOP layer AOD error is estimated using the HSRL layer AOD as ±0.035 ± 0.05 · (HSRL layer AOD) at night and ±0.05 ± 0.05 · (HSRL layer AOD) during the daytime. Furthermore, the CALIOP layer AOD error is found to correlate with aerosol loading as well as aerosol subtype, with the AODs in marine and dust layers agreeing most closely with the HSRL values. The lidar ratios used by CALIOP for polluted dust, polluted continental, and biomass burning layers are larger than the values measured by the HSRL in the CALIOP layers, and therefore the AODs for these types retrieved by CALIOP were generally too large. We estimated the CALIOP column AOD error can be expressed as ±0.05 ± 0.07 · (HSRL column AOD) at night and ±0.08 ± 0.1 · (HSRL column AOD) during the daytime. Multiple sources of error contribute to both positive and negative errors in the CALIOP column AOD, including multiple layers in the column of different aerosol types, lidar ratio errors, cloud misclassification, and undetected aerosol layers. The undetected layers were further investigated and we found that the layer detection algorithm works well at night, although undetected aerosols in the free troposphere introduce a mean underestimate of 0.02 in the column AOD in the data set examined. The decreased signal-to-noise ratio (SNR) during the daytime led to poorer performance of the layer detection. This caused the daytime CALIOP column AOD to be less accurate than during the nighttime, because CALIOP frequently does not detect optically thin aerosol layers with AOD < 0.1. Given that the median vertical extent of aerosol detected within any column was 1.6 km during the nighttime and 1.5 km during the daytime, we can estimate the minimum extinction detection threshold to be 0.012 km−1 at night and 0.067 km−1 during the daytime in a layer median sense. This extensive validation of level 2 CALIOP AOD products extends previous validation studies to nighttime lighting conditions and provides independent measurements of the lidar ratio; thus, allowing the assessment of the effect on the CALIOP AOD of using inappropriate lidar ratio values in the extinction retrieval.

Highlights

  • The role tropospheric aerosols play in Earth’s climate forcing is complex

  • A lidar ratio or some constraint (i.e., aerosol optical depth (AOD) or direct transmittance) must be used to retrieve extinction profiles and AOD from an elastic backscatter lidar (e.g., Young, 1995). This comparison of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP)’s lidar ratios with those measured in this study by the High Spectral Resolution Lidar (HSRL) shows the difficulty inherent in correctly determining an aerosol subtype using a classification algorithm and the consequences of AOD errors that can result from using a single lidar ratio value for each aerosol type

  • Studies, such as this one identifying systematic regional biases in the lidar ratio values currently used by CALIOP, can form a basis to improve the performance of the CALIOP algorithms in the future by accommodating these regional variations in the selection of lidar ratio values

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Summary

Introduction

The direct effect of scattering of incoming solar radiation by aerosols is well understood; the indirect effect of aerosols is less so (Quaas et al, 2009; Lohmann and Feichter, 2005). Aerosols and their optical properties vary greatly over space and time, and satellite remote-sensing observations are the only practical way to map out global distributions of aerosol optical properties pertinent to assessing the aerosol radiative forcing effect (Kaufman et al, 2002). The spatial and temporal coverage from the passive sensors do not completely characterize a scene because they typically provide little, if any, knowledge of the vertical distribution of aerosols in the atmosphere. The vertical distribution of aerosols, provided by lidar, is important for radiative forcing (e.g., Satheesh, 2002), and for other applications including air quality studies (e.g., Al-Saadi et al, 2005; Engel-Cox et al, 2006), and model validation (Dirksen et al, 2009; Koffi et al, 2012)

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