Detailed Data Quality Summary for the CALIPSO Lidar Level 2 PSC Mask Version 2.00 Data Product

Created: March 04, 2022 - Updated: March 09, 2022

PSC Detection

As described in Pitts et al. (2018), PSCs are detected using a successive horizontal averaging (5, 15, 45, 135 km) procedure similar to the approach used in the standard CALIOP feature detection algorithm (SIBYL). This approach ensures that optically thicker clouds (e.g., ice and fully developed supercooled ternary solution, STS) are found at the finest possible spatial resolution while also enhancing the detection of tenuous PSCs (e.g., low number density NAT mixtures) that are found only through additional averaging. Prior to PSC detection, CALIOP data between 8.3 and 30.1 km are averaged to a common 5-km horizontal by 180-m vertical grid to account for the change in spatial resolution of the Level 1B data products at the 20.2 km altitude. PSCs are then identified as statistical outliers in either 532-nm scattering ratio, R′₅₃₂, or 532-nm perpendicular attenuated backscatter, Β′_⊥, relative to the background stratospheric aerosol population. The inclusion of Β′_⊥ is a significant enhancement over the detection approach used in SIBYL that increases the sensitivity to depolarizing, low scattering ratio PSCs and results in an increase in total PSC area of 15% (Pitts et al., 2009). Individual profiles of R′₅₃₂ and Β′_⊥ are scanned from the top altitude (~ 30.1 km) downward, and a CALIOP observation is assumed to be a PSC if either R′₅₃₂ or Β′_⊥ exceeds a statistical threshold. The thresholds for the background aerosol - assumed to be those data at MERRA-2 temperatures above 200 K - are defined as the daily median plus one median absolute deviation of Β′_⊥ and R′₅₃₂. These are computed in overlapping 100 K-thick potential temperature (θ) layers over the range from θ = 250-750 K. Then for a candidate CALIOP data point to be identified as a PSC, its value of Β′_⊥ (or R′₅₃₂) must exceed the background aerosol threshold by at least its uncertainty, u(Β′_⊥) (or u(R′₅₃₂)). We also impose a spatial coherence test that requires that more than 11 of the points in a 5-point horizontal by 3-point vertical box centered on the candidate feature exceed the current PSC detection threshold or to have been identified as a PSC at a previous (finer) averaging scale. This revised approach does a better job overall of capturing PSC clusters identified by the naked eye in CALIOP orbital images while continuing to eliminate false PSC identifications stemming from positive noise spikes in the data. Spot checks of the V2 Antarctic PSC database from early May - when no PSCs observations are expected - indicate that the V2 false positive rate is much less than 0.01%.

Note, the tropopause is often difficult to locate in the polar regions and we make no explicit attempt to distinguish tropospheric cloud from stratospheric cloud in the 8.5-30 km altitude range over which we produce the CALIOP PSC cloud mask. However, we do tag each observation in the database with a feature flag that identifies its altitude location relative to the reported MERRA-2 tropopause as one of three possibilities: (1) below the tropopause, (2) between the tropopause and tropopause + 4 km, or (3) above the tropopause + 4 km. This allows data users to perform some crude separation between tropospheric and stratospheric cloud as desired.

PSCs form in the cold, polar winter stratosphere when the temperatures fall below ~195 K. Over the Antarctic, the PSC season extends from May through October. Over the Arctic, the PSC season is more variable, but may extend from December through March. The PSC Mask V2 data files are therefore only produced for the months May-October (Antarctic) and December-March (Arctic).

PSC Composition Classification

CALIOP PSC composition is inferred from the knowledge that enhancements in Β_⊥ are produced by non-spherical PSC particles (NAT or ice), whereas spherical STS PSC droplets produce enhancements in R₅₃₂ but not in Β_⊥. Based on a comparison of Β_⊥ and R₅₃₂ data with temperature-dependent theoretical optical calculations for non-equilibrium mixtures of liquid droplets and prolate NAT or ice spheroids with an aspect (diameter-to-length) ratio of 0.9, CALIOP PSC observations are separated into three major composition classes: STS, liquid-NAT mixtures (“NAT mixtures”), and liquid-ice mixtures (“ice”). There are two additional subclasses: “enhanced NAT mixtures,” which capture higher NAT number density; and “wave ice”, which are high number density ice PSCs likely triggered by mountain waves. As an example, Fig. 1a shows the distribution of CALIOP Antarctic PSC measurements of 10-18 July 2008 in the Β_⊥ vs. R₅₃₂ coordinate system, with PSC composition class/sub-class boundaries. Figure 1b shows optical calculations presented in the same reference frame assuming 50 hPa atmospheric pressure, 5 ppmv H₂O, and 3 ppbv HNO₃, with instrument noise, which is predominantly shot noise. The V2 algorithm includes improvements to correct a number of known deficiencies in the previous V1 composition classification scheme. The V2 algorithm also incorporates a retrieval of 532-nm particulate backscatter, Β_particulate, through which Β′_⊥ and R′₅₃₂ are later corrected for attenuation due to overlying particulate layers (i.e. the “primes” are removed), allowing for a more robust comparison with the theoretical results. These improvements are discussed below in context of Fig. 1b, where the theoretical optical results are plotted in the coordinate system Β_⊥ vs. R₅₃₂, surrogates for the measured attenuated CALIOP quantities Β′_⊥ and R′₅₃₂ used for PSC detection. The following points are to be noted in our revised V2 algorithm:

Assessments of the Quality of CALIOP V2 PSC Data Products

Generally favorable comparisons between CALIOP PSC results and measurements from the MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) instrument on the Envisat spacecraft MIPAS and earlier (V1) CALIOP PSC composition results were found by Höpfner et al. (2009) and more recently by Spang et al. (2016) using a new Bayesian classifier approach. Most recently, Höpfner et al. (2018) found that for coincident PSC scenes classified as predominantly (> 50%) STS by CALIOP, there was good agreement between the magnitudes and vertical profile shapes of CALIOP and MIPAS particle VD. Recent comparisons of CALIOP V2 and MIPAS Bayesian PSC composition classifications for the 2006-2011 overlap period found a high degree of consistency between the datasets.

The near-simultaneous and collocated measurements of gas phase HNO₃ and H₂O by Aura MLS have provided additional validation of the PSC composition inferred from CALIOP data. The basic approach was introduced by Lambert et al. (2012) and involves comparing the observed temperature-dependent uptake of HNO₃ by CALIOP-detected PSCs of different compositions with modeled uptake of HNO₃ for equilibrium STS and NAT. The technique was refined by Pitts et al. (2013), who restricted the CALIOP-MLS comparisons to homogeneous scenes: those with > 75% PSC coverage over the effective MLS measurement volume, with 2/3 of those PSCs identified as a single CALIOP PSC composition. More recent analysis of CALIOP V2 PSC observations and MLS HNO₃ and H₂O data from 2006-2018 (see Fig. 2) show that both STS and ice PSCs are near thermodynamic equilibrium with the gas phase, as expected from theoretical considerations. The ice and STS histogram mode peaks occurring below T_eq are consistent with a small cold bias in the MERRA-2 temperature analyses as noted by Lambert et al. (2012) and Lambert and Santee (2018). The NAT mixture distributions are broader and roughly bimodal with one mode slightly below the NAT equilibrium temperature and a second more populous mode at 3-4 K below NAT equilibrium, which corresponds approximately to the STS equilibrium temperature. As discussed in Pitts et al. (2013), this bimodality is likely a consequence of different exposure times of air parcels to temperatures below T_NAT. The mode near the STS equilibrium temperature represents air parcels with relatively brief exposure to temperatures below T_NAT. These parcels contain non-equilibrium liquid-NAT mixtures with a detectable enhancement in Β_⊥, but the uptake of HNO₃ is dominated by the much more numerous liquid droplets at the lower temperatures. The NAT mixture mode near the NAT equilibrium temperature corresponds to parcels that have been exposed to temperatures below T_NAT for extended periods of time, allowing a larger fraction of the gas-phase HNO₃ to condense onto the thermodynamically-favored NAT particles and bringing the mixture closer to NAT equilibrium. These composite histograms, which incorporate over 12 years of CALIOP PSC measurements, demonstrate behavior consistent with theoretical expectations for each composition class, providing confidence that the V2 composition classification scheme is robust.

References

Höpfner, M., Larsen, N., Spang, R., Luo, B. P., Ma, J., Svendsen, S. H., Eckermann, S. D., Knudsen, B., Massoli, P., Cairo, F., Stiller, G., v. Clarmann, T., and Fischer, H., “MIPAS detects Antarctic stratospheric belt of NAT PSCs caused by mountain waves”, Atmos. Chem. Phys., 6, 1221- 1230, https://doi.org/10.5194/acp-6-1221-2006, 2006.

Höpfner, M., Pitts, M. C., Poole, L. R., “Comparison between CALIPSO and MIPAS observations of polar stratospheric clouds”, J. Geophys. Res., 114, D00H05, doi:10.1029/2009JD012114, 2009.

Höpfner, M., T. Deshler, M. Pitts, L. Poole, R. Spang, G. Stiller, and T. von Clarmann, “The MIPAS/Envisat climatology (2002-2012) of polar stratospheric cloud volume density profiles”, Atmos. Meas. Tech., 11 (10), 5901-5923, doi:10.5194/amt-11-5901-2018, 2018.

Lambert, A., and M. L. Santee, “Accuracy and precision of polar lower stratospheric temperatures from reanalyses evaluated from A-Train CALIOP and MLS, COSMIC GPS RO, and the equilibrium thermodynamics of supercooled ternary solutions and ice clouds”, Atmos. Chem. Phys., 18 (3), 1945-1975, doi:10.5194/acp-18-1945-2018, 2018.

Lambert, A., M. L. Santee, D. L. Wu, and J. H. Chae, “A-train CALIOP and MLS observations of early winter Antarctic polar stratospheric clouds and nitric acid in 2008”, Atmos. Chem. Phys., 12 (6), 2899-2931, doi:10.5194/acp-12-2899-2012, 2012.

Pitts, M. C., Poole, L. R., Lambert, A., Thomason, L. W., “An assessment of CALIOP polar stratospheric cloud composition classification”, Atmos. Chem. Phys., 13, 2975-2988, doi:10.5194/acp-13-2975-2013, 2013.

Pitts, M. C., L. R. Poole, and R. Gonzalez, “Polar stratospheric cloud climatology based on CALIPSO spaceborne lidar measurements from 2006 to 2017”, Atmos. Chem. Phys., 18 (15), 10,881-10,913, doi:10.5194/acp-18-10881-2018, 2018.

Spang, R., Hoffmann, L., Höpfner, M., Griessbach, S., Müller, R., Pitts, M. C., Orr, A. M. W., and Riese, M.: “A multi-wavelength classification method for polar stratospheric cloud types using infrared limb spectra”, Atmos. Meas. Tech., 9, 3619-3639, https://doi.org/10.5194/amt-9-3619-2016, 2016.

Figure 1. (a) 2-D histogram of CALIOP Antarctic PSC data for 10-18 July 2008 at latitudes 65-75° S and potential temperatures (θ) 475-525 K. (b) Theoretical optical calculations for non-equilibrium liquid–NAT and liquid–ice mixtures. The dashed and grey boxes at the lower left represent points that fall below both CALIOP V2 PSC detection thresholds and are classified as non-PSCs, i.e. supercooled binary solution (SBS) background aerosol.

Figure 2. Histograms of CALIOP V2 PSC observations by composition at 21 km altitude from 13 Antarctic and 12 Arctic winters as a function of (a, c) T - T_ice and (b, d) T - T_eq, where T is the ambient temperature at CALIOP/MLS observation points interpolated from MERRA-2 gridded analyses and T_eq is equal to T_ice, T_NAT or T_STS (depending on the respective composition class) calculated using MLS gas-phase H₂O and HNO₃ data. Green = STS; red = NAT mixtures + enhanced NAT mixtures; and dark blue = ice + wave ice. Updated from Pitts et al. (2018).