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. 2018 Mar;11(3):1459-1479.
doi: 10.5194/amt-11-1459-2018. Epub 2018 Mar 14.

CALIPSO Lidar Calibration at 532 nm: Version 4 Nighttime Algorithm

Jayanta Kar  1   2 , Mark A Vaughan  2 , Kam-Pui Lee  1   2 , Jason L Tackett  1   2 , Melody A Avery  2 , Anne Garnier  1 , Brian J Getzewich  1   2 , William H Hunt  1   2 , Damien Josset  1   2 , Zhaoyan Liu  2 , Patricia L Lucker  1   2 , Brian Magill  1   2 , Ali H Omar  2 , Jacques Pelon  3 , Raymond R Rogers  2 , Travis D Toth  2   4 , Charles R Trepte  2 , Jean-Paul Vernier  1   2 , David M Winker  2 , Stuart A Young  1
Affiliations

CALIPSO Lidar Calibration at 532 nm: Version 4 Nighttime Algorithm

Jayanta Kar et al. Atmos Meas Tech. 2018 Mar.

Abstract

Data products from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on board Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) were recently updated following the implementation of new (version 4) calibration algorithms for all of the level 1 attenuated backscatter measurements. In this work we present the motivation for and the implementation of the version 4 nighttime 532 nm parallel channel calibration. The nighttime 532 nm calibration is the most fundamental calibration of CALIOP data, since all of CALIOP's other radiometric calibration procedures - i.e., the 532 nm daytime calibration and the 1064 nm calibrations during both nighttime and daytime - depend either directly or indirectly on the 532 nm nighttime calibration. The accuracy of the 532 nm nighttime calibration has been significantly improved by raising the molecular normalization altitude from 30-34 km to 36-39 km to substantially reduce stratospheric aerosol contamination. Due to the greatly reduced molecular number density and consequently reduced signal-to-noise ratio (SNR) at these higher altitudes, the signal is now averaged over a larger number of samples using data from multiple adjacent granules. As well, an enhanced strategy for filtering the radiation-induced noise from high energy particles was adopted. Further, the meteorological model used in the earlier versions has been replaced by the improved MERRA-2 model. An aerosol scattering ratio of 1.01 ± 0.01 is now explicitly used for the calibration altitude. These modifications lead to globally revised calibration coefficients which are, on average, 2-3% lower than in previous data releases. Further, the new calibration procedure is shown to eliminate biases at high altitudes that were present in earlier versions and consequently leads to an improved representation of stratospheric aerosols. Validation results using airborne lidar measurements are also presented. Biases relative to collocated measurements acquired by the Langley Research Center (LaRC) airborne high spectral resolution lidar (HSRL) are reduced from 3.6% ± 2.2% in the version 3 data set to 1.6% ± 2.4 % in the version 4 release.

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Figures

Figure 1.
Figure 1.
Zonally averaged time-latitude cross section of the adjusted calibration coefficient obtained using the CALIOP version 2 data (reproduced from Vernier et al., 2009, copyright 2009 by the American Geophysical Union, with permission from John Wiley and Sons).
Figure 2.
Figure 2.
Scattering ratio at 30–34 km and 36–39 km at 532 nm (top) from SAGE II and GOMOS for the years 2002–2005 and (bottom) from GOMOS for the years 2006–2009.
Figure 3.
Figure 3.
12 SNR profiles from CALIOP measurements, calculated between 30 km and the maximum CALIOP measurement altitude of 40 km, and representing various latitudes and seasons. The thick red line is the mean profile.
Figure 4.
Figure 4.
Mean 532 nm calibration coefficients (km3 sr J−1 count) in V3 (computed at 30–34 km) as a function of orbit along-track distance computed for all nighttime data acquired on 30 October 2014. The peaks in the curve at ~8250 km and ~12000 km are the result of aerosol contamination in the 30– 34 km calibration region. The marked drop-off beginning just after 15000 km is attributed to thermal beam steering caused by warming as the satellite first enters the sunlit portion of the orbits. For nighttime, the orbit starts in the north and the starting point of the along track distance is at the day-night terminator.
Figure 5.
Figure 5.
The NSR thresholds employed in V4 algorithm for various months (same for all years) as a function of granule elapsed time (left panel) and latitude (right panel). The granule elapsed time starts in the northern hemisphere at the day-to-night terminator.
Figure 6.
Figure 6.
Noise-to-signal ratio (blue diamonds) for a single granule (orbit track shown in the left panel) showing the effect of V3 (blue dashed line) and V4 (red line) thresholds. Also plotted are the molecular number densities, averaged over 36–39 km, along the orbit (in magenta). Extreme outliers beyond NSR of 10 and negative values have not been plotted. The granule elapsed time starts at the day-to-night terminator in the northern hemisphere.
Figure 7.
Figure 7.
Spatial distribution of the calibration success rates for V3 and V4 for the month of July 2010. The data are binned in 2° x 2° in latitude and longitude. The bottom panel shows the difference (V3 – V4) in the success rates between the two versions.
Figure 8.
Figure 8.
Time series of the granule-averaged V3 and V4 532 nm CALIOP nighttime parallel channel calibration coefficient, smoothed over 10 consecutive granules. The values have been normalized by 6.1483 × 1010 km3 sr J−1 count. Letters indicate a subset of most significant instrument events that affect the calibration: (B) -- boresight alignment, (E) – etalon temperature adjustment, (L) – laser switch and (N) – off-nadir angle change. Not all events are marked
Figure 9.
Figure 9.
Spatial distribution of the 532 nm nighttime calibration coefficient for October 2010, (left) from V3 and (right) from V4.
Figure 10.
Figure 10.
The fractional change from V3 to V4, (V4-V3)/V3 in the zonally averaged 532 nm calibration coefficient for 4 months in 2010 (top panel) and the zonally averaged relative uncertainty (ΔC532/C532) in the V4 calibration coefficient for the same months (bottom panel).
Figure 11.
Figure 11.
Clear air attenuated scattering ratios at 8–12 km as a function of latitude for the month of October 2010 for V3 (left panel) and V4 (right panel). The thick red lines are median values calculated over 2° latitude bins.
Figure 12.
Figure 12.
Zonally and vertically (over 30–34 km) averaged R′ calculated from V4 CALIOP attenuated backscatter data for January, April, July and October 2009. The data are binned over 2° in latitude. The SAA region and bins with less than 50 points were not included.
Figure 13.
Figure 13.
(left) Means and standard deviations of the zonally averaged 532 nm calibration coefficients normalized by 6.1483 × 1010 km3 sr J−1 count and (right) mean and standard deviation of R′ averaged over 30–34 km. Both time series were calculated using 2 weeks’ worth of data before (February 1–14, 2009) and after (March 18–31, 2009) the laser switch. R′ profiles were calculated over 2° latitude intervals from each granule and then averaged over all granules for the latitude bin, with a minimum number of 50 R′ profiles required in each bin. Data over the SAA were not included.
Figure 14.
Figure 14.
As in Figure 13, using data before (August 4–20, 2007), during (August 22-September 6, 2007) and after (September 8–24, 2007) the off-nadir laser pointing test.
Figure 15.
Figure 15.
Same as in Figure 13 using data before (November 21-December 6, 2009) and after (December 8–23, 2009) the boresight alignment procedure on December 7, 2009.
Figure 16.
Figure 16.
Zonally averaged height latitude cross sections of R′ calculated using V3 and V4 level 1 data for November 2007 (top two panels) and for May 2009 (bottom two panels). The contour lines shown are for R′=1.
Figure 17.
Figure 17.
Difference between HSRL and CALIOP attenuated backscatter measurements for nighttime clear-air profiles as a function of latitude. The data are colored by the season of measurement. V3 differences are shown as filled diamonds and the corresponding V4 differences are shown as open circles. The error bars for each point represent the standard deviation of the mean.
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