Comparison of aerosol optical depth between CALIOP and MODIS‐Aqua for CALIOP aerosol subtypes over the ocean

Kim, Man‐Hae; Kim, Sang Woo; Yoon, Soon‐Chang; Omar, Ali

doi:10.1002/2013jd019527

Cited by 71 publications

(56 citation statements)

References 67 publications

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“…The CALIOP lidar ratio for dust (40 sr) is on the low end of the typical range of lidar ratios (40-60 sr) measured in Europe or Africa (Mattis et al, 2002;Tesche et al, , 2011Papayannis et al, 2008;Müller et al 2012;Esselborn et al, 2009), but is consistent with recent measurements of lidar ratio for dust from the Arabian Peninsula (Mamouri et al, 2013), and with earlier estimates based on AERONET observations (e.g., Cattrall et al, 2005). Recent studies have demonstrated that considering the source region of the dust, and any changes in its properties during transport, along with any other aerosol type it mixes with would likely provide a better estimate of lidar ratio (Schuster et al, 2012;Kim et al, 2014). However, accounting for these additional factors would require additional measurements and/or sources of information (e.g., back trajectories) that are not currently incorporated into the CALIOP data analysis scheme.…”

Section: Dustsupporting

confidence: 85%

“…Schuster et al (2012) found CALIOP to agree within 13 % of AERONET with better agreement, within 3 %, if dust is excluded from the analysis. Kim et al (2014) used MODIS (Moderate Resolution Imaging Spectroradiometer) to evaluate CALIOP, finding CALIOP to be 63 % lower than MODIS. However, one limitation common to all previous CALIOP AOD investigations is that the comparisons used only total column AOD measured during daytime, when the CALIOP signal-to-noise ratio (SNR) is the lowest.…”

Section: Introductionmentioning

confidence: 99%

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Looking through the haze: evaluating the CALIPSO level 2 aerosol optical depth using airborne high spectral resolution lidar data

et al. 2014

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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 valida...

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Section: Dustsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Looking through the haze: evaluating the CALIPSO level 2 aerosol optical depth using airborne high spectral resolution lidar data

et al. 2014

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“…This suggests that differences in AE estimates from MODIS and CALIOP largely explain the discrepancy between two aerosol products. Previous studies indicate that MODIS and CALIOP AOD are poorly correlated (e.g., Costantino and Bréon, 2010;Kim et al, 2013;Kittaka et al, 2011;Ma et al, 2013). Our results suggest that differences in AOD retrievals can lead to differences in AE estimates and further affect AI and precipitation susceptibly estimates.…”

Section: S X_ai From Different Aerosol Productsmentioning

confidence: 65%

“…Unlike MODIS AE, which is directly reported in aerosol products, AE measurement for CALIOP is calculated based on AOD at 1.064 and 0.532 µm from the CAL_LID_L2_05kmALay product (Bréon et al, 2011). Our data screening for CAL_LID_L2_05kmALay follows a previous study by Kim et al (2013).…”

Section: Ai and Cdncmentioning

confidence: 99%

Estimating precipitation susceptibility in warm marine clouds using multi-sensor aerosol and cloud products from A-Train satellites

et al. 2018

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Abstract. Precipitation susceptibility to aerosol perturbation plays a key role in understanding aerosol-cloud interactions and constraining aerosol indirect effects. However, large discrepancies exist in the previous satellite estimates of precipitation susceptibility. In this paper, multi-sensor aerosol and cloud products, including those from the CloudAerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), CloudSat, Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) from June 2006 to April 2011 are analyzed to estimate precipitation frequency susceptibility S POP , precipitation intensity susceptibility S I , and precipitation rate susceptibility S R in warm marine clouds. We find that S POP strongly depends on atmospheric stability, with larger values under more stable environments. Our results show that precipitation susceptibility for drizzle (with a −15 dBZ rainfall threshold) is significantly different than that for rain (with a 0 dBZ rainfall threshold). Onset of drizzle is not as readily suppressed in warm clouds as rainfall while precipitation intensity susceptibility is generally smaller for rain than for drizzle. We find that S POP derived with respect to aerosol index (AI) is about one-third of S POP derived with respect to cloud droplet number concentration (CDNC). Overall, S POP demonstrates relatively robust features throughout independent liquid water path (LWP) products and diverse rain products. In contrast, the behaviors of S I and S R are subject to LWP or rain products used to derive them. Recommendations are further made for how to better use these metrics to quantify aerosolcloud-precipitation interactions in observations and models.

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“…This may be different for different regions and time of year, thus adding to the uncertainty in the comparison. The CALIOP analysis may also misclassify aerosol as discussed by Kim et al (2013). The latter is largely avoided in this study by focusing on dust aerosol which has a relatively large depolarization ratio.…”

Section: Discussionmentioning

confidence: 99%

Comparison of dust-layer heights from active and passive satellite sensors

et al. 2018

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Abstract. Aerosol-layer height is essential for understanding the impact of aerosols on the climate system. As part of the European Space Agency Aerosol_cci project, aerosollayer height as derived from passive thermal and solar satellite sensors measurements have been compared with aerosollayer heights estimated from CALIOP measurements. The Aerosol_cci project targeted dust-type aerosol for this study. This ensures relatively unambiguous aerosol identification by the CALIOP processing chain. Dust-layer height was estimated from thermal IASI measurements using four different algorithms (from BIRA-IASB, DLR, LMD, LISA) and from solar GOME-2 (KNMI) and SCIAMACHY (IUP) measurements. Due to differences in overpass time of the various satellites, a trajectory model was used to move the CALIOPderived dust heights in space and time to the IASI, GOME-2 and SCIAMACHY dust height pixels. It is not possible to construct a unique dust-layer height from the CALIOP data. Thus two CALIOP-derived layer heights were used: the cumulative extinction height defined as the height where the CALIOP extinction column is half of the total extinction column, and the geometric mean height, which is defined as the geometrical mean of the top and bottom heights of the dust layer. In statistical average over all IASI data there is a general tendency to a positive bias of 0.5-0.8 km against CALIOP extinction-weighted height for three of the four algorithms assessed, while the fourth algorithm has almost no bias. When comparing geometric mean height there is a shift of −0.5 km for all algorithms (getting close to zero for the three algorithms and turning negative for the fourth). The standard deviation of all algorithms is quite similar and ranges between 1.0 and 1.3 km. When looking at different conditions (day, night, land, ocean), there is more detail in variabilities (e.g. all algorithms overestimate more at night than during the day). For the solar sensors it is found that on average SCIAMACHY data are lower by −1.097 km (−0.961 km) compared to the CALIOP geometric mean (cumulative extinction) height, and GOME-2 data are lower by −1.393 km (−0.818 km).

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Comparison of aerosol optical depth between CALIOP and MODIS‐Aqua for CALIOP aerosol subtypes over the ocean

Cited by 71 publications

References 67 publications

Looking through the haze: evaluating the CALIPSO level 2 aerosol optical depth using airborne high spectral resolution lidar data

Looking through the haze: evaluating the CALIPSO level 2 aerosol optical depth using airborne high spectral resolution lidar data

Estimating precipitation susceptibility in warm marine clouds using multi-sensor aerosol and cloud products from A-Train satellites

Comparison of dust-layer heights from active and passive satellite sensors

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